ipynb/clarman_cruz-quanteconwordbankedstats.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# A Data Science Example In Julia Using Education Statistics From The Work Bank\n",
                "By Clarman Cruz\n",
                "\n",
                "February 2020\n",
                "\n",
                "## Introduction\n",
                "Queryverse along other Julia packages are great for a data science project. We are able to efficiently load data stored in many different file formats. Clean the data and prepare it for analysis using the Query package. Improve data quality with the easiness of the Query package along other Julia packages. Then, we graph all our data with just a single DataVoyager command. We can quickly store both our data files and graphs in a couple of file formats. Depending on the type of data, we can just continue with Julia machine learning packages to perform predictions. \n",
                "\n",
                "This JupyterLab is meant as a tutorial. It showcases [Queryverse](https://www.queryverse.org), \n",
                "[ARCHModels](https://s-broda.github.io/ARCHModels.jl/stable/) package, and some basic [Julia](https://julialang.org) programming. It is an end to end example using a real-world dataset from The World Bank website. We start the journey reading the statistics files, and then cleaning the data. We prepare further the data for deeper analysis. Afterwards, we graph the prepared data and create an interactive data visualizer. Then, we save our prepared data and graphs for publication, or team sharing. Lastly, we predict future time series values with a machine learning model created with the ARCHModels package."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Reading In The Education Statitics Files\n",
                "Queryverse supports reading from different file formats including:\n",
                "* CSVFiles.jl can read and write CSV files. Under the hood it uses the extremly fast TextParse.jl package.\n",
                "* FeatherFiles.jl can read and write Feather files.\n",
                "* ExcelFiles.jl can read and write Excel Files.\n",
                "* StatFiles.jl can read SPSS, STATA and SAS files.\n",
                "* ParquetFiles.jl can read Parquet files.\n",
                "Just ensure the file name contains for correct format extension.  For example, CSV file names end with .csv while feather names files end with .feather.  Queryverse uses the file extension to determine the file format. \n",
                "\n",
                "Our data set is composed of 5 CSV files obtained from the The World Bank data catalog, https://datacatalog.worldbank.org/dataset/education-statistics.  The data set is statistics about education around the world.  The data \"covers over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education.\"\n",
                "\n",
                "* EdStatsCountry.csv is static data such as ISO Country Code, Country Long Name, Region, Income Group, and External debt Reporting status\n",
                "* EdStatsCountry-Series.csv is a map between one Country Code and its Series Codes\n",
                "* EdStatsData.csv is the main file in the data set contain Country Code, Indicator values from 1970 to 2100.  \"Adjusted net enrolment rate, lower secondary, both sexes (%)\", \"Adjusted net enrolment rate, primary, female (%)\", \"Adjusted net enrolment rate, primary, gender parity index (GPI)\", and \"Annual statutory teacher salaries in public institutions in USD\" are examples of Indicators.  The indicator values after the last reported measurement are estimates, if found.\n",
                "* EdStatsFootNote.csv is static data of Country Code, Series Code, Year, and Description\n",
                "* EdStatsSeries.csv is static data of Series Code, Topic, Indicator Name, Long definition, Unit of measure, Periodicity, and Statistical concept and methodology.  The indicators are time series\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "using Queryverse, IndexedTables, DataFrames, FeatherFiles, ARCHModels\n",
                "cd(\"C:\\\\Users\\\\drcxcruz\\\\Documents\\\\TheWorkBank\\\\EdStats\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Table with 241 rows, 32 columns:\n",
                            "Columns:\n",
                            "\u001b[1m#   \u001b[22m\u001b[1mcolname                                            \u001b[22m\u001b[1mtype\u001b[22m\n",
                            "──────────────────────────────────────────────────────────────\n",
                            "1   Country_Code                                       String\n",
                            "2   Short_Name                                         String\n",
                            "3   Table_Name                                         String\n",
                            "4   Long_Name                                          String\n",
                            "5   2-alpha_code                                       String\n",
                            "6   Currency_Unit                                      String\n",
                            "7   Special_Notes                                      String\n",
                            "8   Region                                             String\n",
                            "9   Income_Group                                       String\n",
                            "10  WB-2_code                                          String\n",
                            "11  National_accounts_base_year                        String\n",
                            "12  National_accounts_reference_year                   String\n",
                            "13  SNA_price_valuation                                String\n",
                            "14  Lending_category                                   String\n",
                            "15  Other_groups                                       String\n",
                            "16  System_of_National_Accounts                        String\n",
                            "17  Alternative_conversion_factor                      String\n",
                            "18  PPP_survey_year                                    String\n",
                            "19  Balance_of_Payments_Manual_in_use                  String\n",
                            "20  External_debt_Reporting_status                     String\n",
                            "21  System_of_trade                                    String\n",
                            "22  Government_Accounting_concept                      String\n",
                            "23  IMF_data_dissemination_standard                    String\n",
                            "24  Latest_population_census                           String\n",
                            "25  Latest_household_survey                            String\n",
                            "26  Source_of_most_recent_Income_and_expenditure_data  String\n",
                            "27  Vital_registration_complete                        String\n",
                            "28  Latest_agricultural_census                         String\n",
                            "29  Latest_industrial_data                             String\n",
                            "30  Latest_trade_data                                  String\n",
                            "31  Latest_water_withdrawal_data                       String\n",
                            "32                                                     Missing"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fileName = \"EdStatsCountry.csv\"\n",
                "dfCountry = load(fileName) |> DataFrame;  # the ; indicates not the read from disk into Julia the entire file\n",
                "newColNames = [replace(String(n), \" \" => \"_\") for n in names(dfCountry)] # remove spaces in the column names\n",
                "itCountry = load(fileName, colnames = newColNames) \n",
                "table(itCountry, pkey = [:Country_Code])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Table with 613 rows, 4 columns:\n",
                            "Columns:\n",
                            "\u001b[1m#  \u001b[22m\u001b[1mcolname       \u001b[22m\u001b[1mtype\u001b[22m\n",
                            "────────────────────────\n",
                            "1  Country_Code  String\n",
                            "2  Series_Code   String\n",
                            "3  Description   String\n",
                            "4                Missing"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fileName = \"EdStatsCountry-Series.csv\"\n",
                "itCountrySeries = load(fileName, colnames = [\"Country_Code\", \"Series_Code\", \"Description\"]) \n",
                "table(itCountrySeries, pkey = [:Country_Code, :Series_Code])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Table with 69634 rows, 70 columns:\n",
                            "Columns:\n",
                            "\u001b[1m#   \u001b[22m\u001b[1mcolname         \u001b[22m\u001b[1mtype\u001b[22m\n",
                            "───────────────────────────\n",
                            "1   Country_Name    String\n",
                            "2   Country_Code    String\n",
                            "3   Indicator_Name  String\n",
                            "4   Indicator_Code  String\n",
                            "5   1970            String\n",
                            "6   1971            String\n",
                            "7   1972            String\n",
                            "8   1973            String\n",
                            "9   1974            String\n",
                            "10  1975            String\n",
                            "11  1976            String\n",
                            "12  1977            String\n",
                            "13  1978            String\n",
                            "14  1979            String\n",
                            "15  1980            String\n",
                            "16  1981            String\n",
                            "17  1982            String\n",
                            "18  1983            String\n",
                            "19  1984            String\n",
                            "20  1985            String\n",
                            "21  1986            String\n",
                            "22  1987            String\n",
                            "23  1988            String\n",
                            "24  1989            String\n",
                            "25  1990            String\n",
                            "26  1991            String\n",
                            "27  1992            String\n",
                            "28  1993            String\n",
                            "29  1994            String\n",
                            "30  1995            String\n",
                            "31  1996            String\n",
                            "32  1997            String\n",
                            "33  1998            String\n",
                            "34  1999            String\n",
                            "35  2000            String\n",
                            "36  2001            String\n",
                            "37  2002            String\n",
                            "38  2003            String\n",
                            "39  2004            String\n",
                            "40  2005            String\n",
                            "41  2006            String\n",
                            "42  2007            String\n",
                            "43  2008            String\n",
                            "44  2009            String\n",
                            "45  2010            String\n",
                            "46  2011            String\n",
                            "47  2012            String\n",
                            "48  2013            String\n",
                            "49  2014            String\n",
                            "50  2015            String\n",
                            "51  2016            String\n",
                            "52  2017            String\n",
                            "53  2018            String\n",
                            "54  2019            String\n",
                            "55  2020            String\n",
                            "56  2021            String\n",
                            "57  2022            String\n",
                            "58  2023            String\n",
                            "59  2024            String\n",
                            "60  2025            String\n",
                            "61  2026            String\n",
                            "62  2027            String\n",
                            "63  2028            String\n",
                            "64  2029            String\n",
                            "65  2030            String\n",
                            "66  2031            String\n",
                            "67  2032            String\n",
                            "68  2033            String\n",
                            "69  2034            String\n",
                            "70  2035            Missing"
                        ]
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fileName = \"EdStatsData.csv\"  \n",
                "colNames = [\"Country_Name\", \"Country_Code\", \"Indicator_Name\", \"Indicator_Code\"]\n",
                "years = [string(y) for y in 1970:2100]\n",
                "append!(colNames, years)\n",
                "\n",
                "# reading from Turkey to Zimbabwe including United Kingdom and United States\n",
                "itData = load(fileName, colnames = colNames, skiplines_begin = 817297, header_exists=false); \n",
                "table(itData, pkey = [:Country_Code, :Indicator_Code])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Table with 643638 rows, 5 columns:\n",
                            "Columns:\n",
                            "\u001b[1m#  \u001b[22m\u001b[1mcolname       \u001b[22m\u001b[1mtype\u001b[22m\n",
                            "────────────────────────\n",
                            "1  Country_Code  String\n",
                            "2  Series_Code   String\n",
                            "3  Year          String\n",
                            "4  Description   String\n",
                            "5                Missing"
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fileName = \"EdStatsFootNote.csv\"\n",
                "itFootNote = load(fileName, colnames = [\"Country_Code\", \"Series_Code\", \"Year\", \"Description\"]) \n",
                "table(itFootNote, pkey = [:Country_Code, :Series_Code])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Table with 3665 rows, 21 columns:\n",
                            "Columns:\n",
                            "\u001b[1m#   \u001b[22m\u001b[1mcolname                              \u001b[22m\u001b[1mtype\u001b[22m\n",
                            "────────────────────────────────────────────────\n",
                            "1   Series_Code                          String\n",
                            "2   Topic                                String\n",
                            "3   Indicator_Name                       String\n",
                            "4   Short_definition                     String\n",
                            "5   Long_definition                      String\n",
                            "6   Unit_of_measure                      String\n",
                            "7   Periodicity                          String\n",
                            "8   Base_Period                          String\n",
                            "9   Other_notes                          String\n",
                            "10  Aggregation_method                   String\n",
                            "11  Limitations_and_exceptions           String\n",
                            "12  Notes_from_original_source           String\n",
                            "13  General_comments                     String\n",
                            "14  Source                               String\n",
                            "15  Statistical_concept_and_methodology  String\n",
                            "16  Development_relevance                String\n",
                            "17  Related_source_links                 String\n",
                            "18  Other_web_links                      String\n",
                            "19  Related_indicators                   String\n",
                            "20  License_Type                         String\n",
                            "21                                       Missing"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "fileName =\"EdStatsSeries.csv\"\n",
                "dfSeries = load(fileName) |> DataFrame;\n",
                "newColNames = [replace(String(n), \" \" => \"_\") for n in names(dfSeries)]  # remove spaces from the column names\n",
                "itSeries = load(fileName, colnames = newColNames) \n",
                "table(itSeries, pkey = [:Series_Code])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let's see a few charactericts of our data by joining a couple of files. \n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
                                    "Country_Code": "TUR",
                                    "Description": "Data sources : United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "Upper middle income",
                                    "Lending_category": "IBRD",
                                    "Long_Name": "Republic of Turkey",
                                    "Region": "Europe & Central Asia",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "TUR",
                                    "Description": "Data sources : United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "Upper middle income",
                                    "Lending_category": "IBRD",
                                    "Long_Name": "Republic of Turkey",
                                    "Region": "Europe & Central Asia",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "GBR",
                                    "Description": "Data sources: Eurostat",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: OECD",
                                    "Lending_category": "",
                                    "Long_Name": "United Kingdom of Great Britain and Northern Ireland",
                                    "Region": "Europe & Central Asia",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "GBR",
                                    "Description": "Data sources : Eurostat",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: OECD",
                                    "Lending_category": "",
                                    "Long_Name": "United Kingdom of Great Britain and Northern Ireland",
                                    "Region": "Europe & Central Asia",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "URY",
                                    "Description": "Data sources: United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "IBRD",
                                    "Long_Name": "Oriental Republic of Uruguay",
                                    "Region": "Latin America & Caribbean",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "URY",
                                    "Description": "Data sources : United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "IBRD",
                                    "Long_Name": "Oriental Republic of Uruguay",
                                    "Region": "Latin America & Caribbean",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "VIR",
                                    "Description": "Data sources : U.S. Census Bureau, United Nations Population and Vital Statistics Report",
                                    "Government_Accounting_concept": "",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "",
                                    "Long_Name": "Virgin Islands of the United States",
                                    "Region": "Latin America & Caribbean",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "VIR",
                                    "Description": "Data sources: U.S. Census Bureau, United Nations Population and Vital Statistics Report",
                                    "Government_Accounting_concept": "",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "",
                                    "Long_Name": "Virgin Islands of the United States",
                                    "Region": "Latin America & Caribbean",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "ARE",
                                    "Description": "Data sources: United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "",
                                    "Long_Name": "United Arab Emirates",
                                    "Region": "Middle East & North Africa",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "ARE",
                                    "Description": "Data sources : United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: nonOECD",
                                    "Lending_category": "",
                                    "Long_Name": "United Arab Emirates",
                                    "Region": "Middle East & North Africa",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "USA",
                                    "Description": "Data sources : U.S. Census Bureau",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: OECD",
                                    "Lending_category": "",
                                    "Long_Name": "United States of America",
                                    "Region": "North America",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "USA",
                                    "Description": "Data sources: U.S. Census Bureau",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "High income: OECD",
                                    "Lending_category": "",
                                    "Long_Name": "United States of America",
                                    "Region": "North America",
                                    "Series_Code": "SP.POP.GROW"
                                },
                                {
                                    "Country_Code": "ZWE",
                                    "Description": "Data sources : United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "Low income",
                                    "Lending_category": "Blend",
                                    "Long_Name": "Republic of Zimbabwe",
                                    "Region": "Sub-Saharan Africa",
                                    "Series_Code": "SP.POP.TOTL"
                                },
                                {
                                    "Country_Code": "ZWE",
                                    "Description": "Data sources: United Nations World Population Prospects",
                                    "Government_Accounting_concept": "Consolidated central government",
                                    "Income_Group": "Low income",
                                    "Lending_category": "Blend",
                                    "Long_Name": "Republic of Zimbabwe",
                                    "Region": "Sub-Saharan Africa",
                                    "Series_Code": "SP.POP.GROW"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country_Code",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Region",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Long_Name",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Income_Group",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Lending_category",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Government_Accounting_concept",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Series_Code",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Description",
                                        "type": "string"
                                    }
                                ]
                            }
                        },
                        "text/html": [
                            "<table><thead><tr><th>Country_Code</th><th>Region</th><th>Long_Name</th><th>Income_Group</th><th>Lending_category</th><th>Government_Accounting_concept</th><th>Series_Code</th><th>Description</th></tr></thead><tbody><tr><td>&quot;TUR&quot;</td><td>&quot;Europe &amp; Central Asia&quot;</td><td>&quot;Republic of Turkey&quot;</td><td>&quot;Upper middle income&quot;</td><td>&quot;IBRD&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.TOTL&quot;</td><td>&quot;Data sources : United Nations World Population Prospects&quot;</td></tr><tr><td>&quot;TUR&quot;</td><td>&quot;Europe &amp; Central Asia&quot;</td><td>&quot;Republic of Turkey&quot;</td><td>&quot;Upper middle income&quot;</td><td>&quot;IBRD&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.GROW&quot;</td><td>&quot;Data sources : United Nations World Population Prospects&quot;</td></tr><tr><td>&quot;GBR&quot;</td><td>&quot;Europe &amp; Central Asia&quot;</td><td>&quot;United Kingdom of Great Britain and Northern Ireland&quot;</td><td>&quot;High income: OECD&quot;</td><td>&quot;&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.GROW&quot;</td><td>&quot;Data sources: Eurostat&quot;</td></tr><tr><td>&quot;GBR&quot;</td><td>&quot;Europe &amp; Central Asia&quot;</td><td>&quot;United Kingdom of Great Britain and Northern Ireland&quot;</td><td>&quot;High income: OECD&quot;</td><td>&quot;&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.TOTL&quot;</td><td>&quot;Data sources : Eurostat&quot;</td></tr><tr><td>&quot;URY&quot;</td><td>&quot;Latin America &amp; Caribbean&quot;</td><td>&quot;Oriental Republic of Uruguay&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;IBRD&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.GROW&quot;</td><td>&quot;Data sources: United Nations World Population Prospects&quot;</td></tr><tr><td>&quot;URY&quot;</td><td>&quot;Latin America &amp; Caribbean&quot;</td><td>&quot;Oriental Republic of Uruguay&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;IBRD&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.TOTL&quot;</td><td>&quot;Data sources : United Nations World Population Prospects&quot;</td></tr><tr><td>&quot;VIR&quot;</td><td>&quot;Latin America &amp; Caribbean&quot;</td><td>&quot;Virgin Islands of the United States&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;SP.POP.TOTL&quot;</td><td>&quot;Data sources : U.S. Census Bureau, United Nations Population and Vital Statistics Report&quot;</td></tr><tr><td>&quot;VIR&quot;</td><td>&quot;Latin America &amp; Caribbean&quot;</td><td>&quot;Virgin Islands of the United States&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;SP.POP.GROW&quot;</td><td>&quot;Data sources: U.S. Census Bureau, United Nations Population and Vital Statistics Report&quot;</td></tr><tr><td>&quot;ARE&quot;</td><td>&quot;Middle East &amp; North Africa&quot;</td><td>&quot;United Arab Emirates&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.GROW&quot;</td><td>&quot;Data sources: United Nations World Population Prospects&quot;</td></tr><tr><td>&quot;ARE&quot;</td><td>&quot;Middle East &amp; North Africa&quot;</td><td>&quot;United Arab Emirates&quot;</td><td>&quot;High income: nonOECD&quot;</td><td>&quot;&quot;</td><td>&quot;Consolidated central government&quot;</td><td>&quot;SP.POP.TOTL&quot;</td><td>&quot;Data sources : United Nations World Population Prospects&quot;</td></tr><tr><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table><p>... with more rows.</p>"
                        ],
                        "text/plain": [
                            "?x8 query result\n",
                            "Country_Code │ Region                    \n",
                            "─────────────┼───────────────────────────\n",
                            "TUR          │ Europe & Central Asia     \n",
                            "TUR          │ Europe & Central Asia     \n",
                            "GBR          │ Europe & Central Asia     \n",
                            "GBR          │ Europe & Central Asia     \n",
                            "URY          │ Latin America & Caribbean \n",
                            "URY          │ Latin America & Caribbean \n",
                            "VIR          │ Latin America & Caribbean \n",
                            "VIR          │ Latin America & Caribbean \n",
                            "ARE          │ Middle East & North Africa\n",
                            "ARE          │ Middle East & North Africa\n",
                            "... with more rows, and 6 more columns: Long_Name, Income_Group, Lending_category, Government_Accounting_concept, Series_Code, Description"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "query = @from c in itCountry begin\n",
                "    @join s in itCountrySeries on c.Country_Code equals s.Country_Code\n",
                "    @select {c.Country_Code, c.Region, c.Long_Name, c.Income_Group, c.Lending_category, c.Government_Accounting_concept, s.Series_Code, s.Description}\n",
                "    end\n",
                "table(query, pkey = [:Country_Code])\n",
                "\n",
                "query |> @filter( occursin(r\"ZWE|VIR|URY|USA|GBR|TUR|ARE\", _.Country_Code)) |> @orderby(_.Region) |> @thenby(_.Long_Name)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Data Cleaning and Preparation\n",
                "Let's analyze years from 1990 to 2014.  We are considering a few of the indicators and the few countries. Yet, you can easily include more countries, indicators or years as you wish.  Let's pick the following indicators and their codes.  \n",
                "\n",
                "* \"Pupil-teacher ratio in secondary education (headcount basis)\", \"SE.SEC.ENRL.TC.ZS\"\n",
                "* \"Rate of out-of-school youth of upper secondary school age, both sexes (%)\", \"UIS.ROFST.3.CP\"\n",
                "* \"Rate of out-of-school youth of upper secondary school age, female (%)\", \"UIS.ROFST.3.F.CP\"\n",
                "* \"Rate of out-of-school youth of upper secondary school age, male (%)\", \"UIS.ROFST.3.M.CP\"\n",
                "* \"Government expenditure in secondary institutions education as % of GDP (%)\", \"XGDP.23.FSGOV.FDINSTADM.FFD\"\n",
                "\n",
                "We are going to analyze the relationship between pupil-teacher ratio and out-of-school youth rate in secondary school.  \n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
                                    "1990": "",
                                    "1991": "",
                                    "1992": "",
                                    "1993": "",
                                    "1994": "",
                                    "1995": "",
                                    "1996": "",
                                    "1997": "",
                                    "1998": "1.73608994483948",
                                    "1999": "1.72523999214172",
                                    "2000": "1.7501300573349",
                                    "2001": "1.88098001480103",
                                    "2002": "1.90707004070282",
                                    "2003": "2.03250002861023",
                                    "2004": "1.94648003578186",
                                    "2005": "1.82843005657196",
                                    "2006": "1.92949998378754",
                                    "2007": "1.92969000339508",
                                    "2008": "1.96256005764008",
                                    "2009": "1.97350001335144",
                                    "2010": "1.91955995559692",
                                    "2011": "1.87390005588531",
                                    "2012": "1.76548004150391",
                                    "2013": "",
                                    "2014": "",
                                    "Country_Code": "USA",
                                    "Country_Name": "United States",
                                    "Indicator_Code": "XGDP.23.FSGOV.FDINSTADM.FFD",
                                    "Indicator_Name": "Government expenditure in secondary institutions education as % of GDP (%)"
                                },
                                {
                                    "1990": "",
                                    "1991": "",
                                    "1992": "",
                                    "1993": "15.5334100723267",
                                    "1994": "15.6976499557495",
                                    "1995": "15.4257001876831",
                                    "1996": "15.4034900665283",
                                    "1997": "",
                                    "1998": "15.1882801055908",
                                    "1999": "14.9240102767944",
                                    "2000": "14.6057996749878",
                                    "2001": "15.1627101898193",
                                    "2002": "14.8834495544434",
                                    "2003": "14.9155302047729",
                                    "2004": "14.9754199981689",
                                    "2005": "14.9440803527832",
                                    "2006": "14.6182203292847",
                                    "2007": "14.5639095306396",
                                    "2008": "14.3765897750854",
                                    "2009": "13.9601697921753",
                                    "2010": "13.7594299316406",
                                    "2011": "14.4905595779419",
                                    "2012": "14.6637201309204",
                                    "2013": "14.6957397460938",
                                    "2014": "14.7868299484253",
                                    "Country_Code": "USA",
                                    "Country_Name": "United States",
                                    "Indicator_Code": "SE.SEC.ENRL.TC.ZS",
                                    "Indicator_Name": "Pupil-teacher ratio in secondary education (headcount basis)"
                                },
                                {
                                    "1990": "",
                                    "1991": "",
                                    "1992": "",
                                    "1993": "",
                                    "1994": "",
                                    "1995": "",
                                    "1996": "",
                                    "1997": "",
                                    "1998": "",
                                    "1999": "",
                                    "2000": "15.3355",
                                    "2001": "14.20598",
                                    "2002": "16.60763",
                                    "2003": "11.95517",
                                    "2004": "10.79784",
                                    "2005": "8.91003",
                                    "2006": "8.92473",
                                    "2007": "7.24616",
                                    "2008": "7.10633",
                                    "2009": "6.92348",
                                    "2010": "9.4071",
                                    "2011": "9.25726",
                                    "2012": "10.49112",
                                    "2013": "10.03378",
                                    "2014": "7.50121",
                                    "Country_Code": "USA",
                                    "Country_Name": "United States",
                                    "Indicator_Code": "UIS.ROFST.3.CP",
                                    "Indicator_Name": "Rate of out-of-school youth of upper secondary school age, both sexes (%)"
                                },
                                {
                                    "1990": "",
                                    "1991": "",
                                    "1992": "",
                                    "1993": "",
                                    "1994": "",
                                    "1995": "",
                                    "1996": "",
                                    "1997": "",
                                    "1998": "",
                                    "1999": "",
                                    "2000": "15.97203",
                                    "2001": "13.04092",
                                    "2002": "16.74537",
                                    "2003": "11.60295",
                                    "2004": "10.80064",
                                    "2005": "6.2645",
                                    "2006": "6.21013",
                                    "2007": "6.17753",
                                    "2008": "6.56122",
                                    "2009": "5.58257",
                                    "2010": "9.36999",
                                    "2011": "7.95532",
                                    "2012": "9.75025",
                                    "2013": "9.24792",
                                    "2014": "6.52425",
                                    "Country_Code": "USA",
                                    "Country_Name": "United States",
                                    "Indicator_Code": "UIS.ROFST.3.F.CP",
                                    "Indicator_Name": "Rate of out-of-school youth of upper secondary school age, female (%)"
                                },
                                {
                                    "1990": "",
                                    "1991": "",
                                    "1992": "",
                                    "1993": "",
                                    "1994": "",
                                    "1995": "",
                                    "1996": "",
                                    "1997": "",
                                    "1998": "",
                                    "1999": "",
                                    "2000": "14.7336",
                                    "2001": "15.3063",
                                    "2002": "16.47762",
                                    "2003": "12.28699",
                                    "2004": "10.79521",
                                    "2005": "11.38762",
                                    "2006": "11.46333",
                                    "2007": "8.24965",
                                    "2008": "7.61971",
                                    "2009": "8.18763",
                                    "2010": "9.44207",
                                    "2011": "10.48286",
                                    "2012": "11.18935",
                                    "2013": "10.77544",
                                    "2014": "8.4247",
                                    "Country_Code": "USA",
                                    "Country_Name": "United States",
                                    "Indicator_Code": "UIS.ROFST.3.M.CP",
                                    "Indicator_Name": "Rate of out-of-school youth of upper secondary school age, male (%)"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country_Name",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Country_Code",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Indicator_Name",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Indicator_Code",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1990",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1991",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1992",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1993",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1994",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1995",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1996",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1997",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1998",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1999",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2000",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2001",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2002",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2003",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2004",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2005",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2006",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2007",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2008",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2009",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2010",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2011",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2012",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2013",
                                        "type": "string"
                                    },
                                    {
                                        "name": "2014",
                                        "type": "string"
                                    }
                                ]
                            }
                        },
                        "text/html": [
                            "<table><thead><tr><th>Country_Name</th><th>Country_Code</th><th>Indicator_Name</th><th>Indicator_Code</th><th>1990</th><th>1991</th><th>1992</th><th>1993</th><th>1994</th><th>1995</th><th>1996</th><th>1997</th><th>1998</th><th>1999</th><th>2000</th><th>2001</th><th>2002</th><th>2003</th><th>2004</th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>&quot;United States&quot;</td><td>&quot;USA&quot;</td><td>&quot;Government expenditure in secondary institutions education as &#37; of GDP &#40;&#37;&#41;&quot;</td><td>&quot;XGDP.23.FSGOV.FDINSTADM.FFD&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;1.73608994483948&quot;</td><td>&quot;1.72523999214172&quot;</td><td>&quot;1.7501300573349&quot;</td><td>&quot;1.88098001480103&quot;</td><td>&quot;1.90707004070282&quot;</td><td>&quot;2.03250002861023&quot;</td><td>&quot;1.94648003578186&quot;</td><td>&quot;1.82843005657196&quot;</td><td>&quot;1.92949998378754&quot;</td><td>&quot;1.92969000339508&quot;</td><td>&quot;1.96256005764008&quot;</td><td>&quot;1.97350001335144&quot;</td><td>&quot;1.91955995559692&quot;</td><td>&quot;1.87390005588531&quot;</td><td>&quot;1.76548004150391&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;USA&quot;</td><td>&quot;Pupil-teacher ratio in secondary education &#40;headcount basis&#41;&quot;</td><td>&quot;SE.SEC.ENRL.TC.ZS&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;15.5334100723267&quot;</td><td>&quot;15.6976499557495&quot;</td><td>&quot;15.4257001876831&quot;</td><td>&quot;15.4034900665283&quot;</td><td>&quot;&quot;</td><td>&quot;15.1882801055908&quot;</td><td>&quot;14.9240102767944&quot;</td><td>&quot;14.6057996749878&quot;</td><td>&quot;15.1627101898193&quot;</td><td>&quot;14.8834495544434&quot;</td><td>&quot;14.9155302047729&quot;</td><td>&quot;14.9754199981689&quot;</td><td>&quot;14.9440803527832&quot;</td><td>&quot;14.6182203292847&quot;</td><td>&quot;14.5639095306396&quot;</td><td>&quot;14.3765897750854&quot;</td><td>&quot;13.9601697921753&quot;</td><td>&quot;13.7594299316406&quot;</td><td>&quot;14.4905595779419&quot;</td><td>&quot;14.6637201309204&quot;</td><td>&quot;14.6957397460938&quot;</td><td>&quot;14.7868299484253&quot;</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;USA&quot;</td><td>&quot;Rate of out-of-school youth of upper secondary school age, both sexes &#40;&#37;&#41;&quot;</td><td>&quot;UIS.ROFST.3.CP&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;15.3355&quot;</td><td>&quot;14.20598&quot;</td><td>&quot;16.60763&quot;</td><td>&quot;11.95517&quot;</td><td>&quot;10.79784&quot;</td><td>&quot;8.91003&quot;</td><td>&quot;8.92473&quot;</td><td>&quot;7.24616&quot;</td><td>&quot;7.10633&quot;</td><td>&quot;6.92348&quot;</td><td>&quot;9.4071&quot;</td><td>&quot;9.25726&quot;</td><td>&quot;10.49112&quot;</td><td>&quot;10.03378&quot;</td><td>&quot;7.50121&quot;</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;USA&quot;</td><td>&quot;Rate of out-of-school youth of upper secondary school age, female &#40;&#37;&#41;&quot;</td><td>&quot;UIS.ROFST.3.F.CP&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;15.97203&quot;</td><td>&quot;13.04092&quot;</td><td>&quot;16.74537&quot;</td><td>&quot;11.60295&quot;</td><td>&quot;10.80064&quot;</td><td>&quot;6.2645&quot;</td><td>&quot;6.21013&quot;</td><td>&quot;6.17753&quot;</td><td>&quot;6.56122&quot;</td><td>&quot;5.58257&quot;</td><td>&quot;9.36999&quot;</td><td>&quot;7.95532&quot;</td><td>&quot;9.75025&quot;</td><td>&quot;9.24792&quot;</td><td>&quot;6.52425&quot;</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;USA&quot;</td><td>&quot;Rate of out-of-school youth of upper secondary school age, male &#40;&#37;&#41;&quot;</td><td>&quot;UIS.ROFST.3.M.CP&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;&quot;</td><td>&quot;14.7336&quot;</td><td>&quot;15.3063&quot;</td><td>&quot;16.47762&quot;</td><td>&quot;12.28699&quot;</td><td>&quot;10.79521&quot;</td><td>&quot;11.38762&quot;</td><td>&quot;11.46333&quot;</td><td>&quot;8.24965&quot;</td><td>&quot;7.61971&quot;</td><td>&quot;8.18763&quot;</td><td>&quot;9.44207&quot;</td><td>&quot;10.48286&quot;</td><td>&quot;11.18935&quot;</td><td>&quot;10.77544&quot;</td><td>&quot;8.4247&quot;</td></tr></tbody></table>"
                        ],
                        "text/plain": [
                            "5x29 query result\n",
                            "Country_Name  │ Country_Code\n",
                            "──────────────┼─────────────\n",
                            "United States │ USA         \n",
                            "United States │ USA         \n",
                            "United States │ USA         \n",
                            "United States │ USA         \n",
                            "United States │ USA         \n",
                            "... with 27 more columns: Indicator_Name, Indicator_Code, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# this is where you modify the year range, indicator list, or country list\n",
                "dfClean = itData |>  \n",
                "    @filter( occursin(r\"ZWE|VIR|URY|USA|GBR|TUR|ARE\", _.Country_Code)) |>\n",
                "    @filter( occursin(r\"SE.SEC.ENRL.TC.ZS|UIS.ROFST.3.CP|UIS.ROFST.3.F.CP|UIS.ROFST.3.M.CP|XGDP.23.FSGOV.FDINSTADM.FFD\", _.Indicator_Code)) |> \n",
                "    @select(1:4, 25:49) |>  \n",
                "    DataFrame\n",
                "\n",
                "dfClean |> @filter(_.Country_Code == \"USA\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "A few things before we move forward.  Make the years column float and shorten the indicators name.  The code columns are removed also. The column names are also shortening while keeping readability."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
                                    "1990": null,
                                    "1991": null,
                                    "1992": null,
                                    "1993": null,
                                    "1994": null,
                                    "1995": null,
                                    "1996": null,
                                    "1997": null,
                                    "1998": 1.73608994483948,
                                    "1999": 1.72523999214172,
                                    "2000": 1.7501300573349,
                                    "2001": 1.88098001480103,
                                    "2002": 1.90707004070282,
                                    "2003": 2.03250002861023,
                                    "2004": 1.94648003578186,
                                    "2005": 1.82843005657196,
                                    "2006": 1.92949998378754,
                                    "2007": 1.92969000339508,
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                                    "Country": "United States",
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                                    "2012": 14.6637201309204,
                                    "2013": 14.6957397460938,
                                    "2014": 14.7868299484253,
                                    "Country": "United States",
                                    "Series": "PupilTeacherRatio"
                                },
                                {
                                    "1990": null,
                                    "1991": null,
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                                    "1993": null,
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                                    "2005": 8.91003,
                                    "2006": 8.92473,
                                    "2007": 7.24616,
                                    "2008": 7.10633,
                                    "2009": 6.92348,
                                    "2010": 9.4071,
                                    "2011": 9.25726,
                                    "2012": 10.49112,
                                    "2013": 10.03378,
                                    "2014": 7.50121,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolBothSexes"
                                },
                                {
                                    "1990": null,
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                                    "2012": 9.75025,
                                    "2013": 9.24792,
                                    "2014": 6.52425,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolFemale"
                                },
                                {
                                    "1990": null,
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                                    "2002": 16.47762,
                                    "2003": 12.28699,
                                    "2004": 10.79521,
                                    "2005": 11.38762,
                                    "2006": 11.46333,
                                    "2007": 8.24965,
                                    "2008": 7.61971,
                                    "2009": 8.18763,
                                    "2010": 9.44207,
                                    "2011": 10.48286,
                                    "2012": 11.18935,
                                    "2013": 10.77544,
                                    "2014": 8.4247,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolMale"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Series",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1990",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1991",
                                        "type": "number"
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                                        "name": "1992",
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                                    {
                                        "name": "1993",
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                                        "name": "2013",
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                                        "name": "2014",
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                        },
                        "text/html": [
                            "<table><thead><tr><th>Country</th><th>Series</th><th>1990</th><th>1991</th><th>1992</th><th>1993</th><th>1994</th><th>1995</th><th>1996</th><th>1997</th><th>1998</th><th>1999</th><th>2000</th><th>2001</th><th>2002</th><th>2003</th><th>2004</th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>&quot;United States&quot;</td><td>&quot;GovernmentExpenditure&quot;</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>1.73609</td><td>1.72524</td><td>1.75013</td><td>1.88098</td><td>1.90707</td><td>2.0325</td><td>1.94648</td><td>1.82843</td><td>1.9295</td><td>1.92969</td><td>1.96256</td><td>1.9735</td><td>1.91956</td><td>1.8739</td><td>1.76548</td><td>#NA</td><td>#NA</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;PupilTeacherRatio&quot;</td><td>#NA</td><td>#NA</td><td>#NA</td><td>15.5334</td><td>15.6976</td><td>15.4257</td><td>15.4035</td><td>#NA</td><td>15.1883</td><td>14.924</td><td>14.6058</td><td>15.1627</td><td>14.8834</td><td>14.9155</td><td>14.9754</td><td>14.9441</td><td>14.6182</td><td>14.5639</td><td>14.3766</td><td>13.9602</td><td>13.7594</td><td>14.4906</td><td>14.6637</td><td>14.6957</td><td>14.7868</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolBothSexes&quot;</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>15.3355</td><td>14.206</td><td>16.6076</td><td>11.9552</td><td>10.7978</td><td>8.91003</td><td>8.92473</td><td>7.24616</td><td>7.10633</td><td>6.92348</td><td>9.4071</td><td>9.25726</td><td>10.4911</td><td>10.0338</td><td>7.50121</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolFemale&quot;</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>15.972</td><td>13.0409</td><td>16.7454</td><td>11.6029</td><td>10.8006</td><td>6.2645</td><td>6.21013</td><td>6.17753</td><td>6.56122</td><td>5.58257</td><td>9.36999</td><td>7.95532</td><td>9.75025</td><td>9.24792</td><td>6.52425</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolMale&quot;</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>#NA</td><td>14.7336</td><td>15.3063</td><td>16.4776</td><td>12.287</td><td>10.7952</td><td>11.3876</td><td>11.4633</td><td>8.24965</td><td>7.61971</td><td>8.18763</td><td>9.44207</td><td>10.4829</td><td>11.1893</td><td>10.7754</td><td>8.4247</td></tr></tbody></table>"
                        ],
                        "text/plain": [
                            "5x27 query result\n",
                            "Country       │ Series                   │ 1990 │ 1991 │ 1992 │ 1993   \n",
                            "──────────────┼──────────────────────────┼──────┼──────┼──────┼────────\n",
                            "United States │ GovernmentExpenditure    │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA    \u001b[39m\n",
                            "United States │ PupilTeacherRatio        │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ 15.5334\n",
                            "United States │ RateOutofSchoolBothSexes │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA    \u001b[39m\n",
                            "United States │ RateOutofSchoolFemale    │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA    \u001b[39m\n",
                            "United States │ RateOutofSchoolMale      │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA \u001b[39m │ \u001b[90m#NA    \u001b[39m\n",
                            "... with 21 more columns: 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#convert year columns from string to float \n",
                "dfCleanFloat = copy(dfClean)\n",
                "for c in 5:ncol(dfCleanFloat)\n",
                "  dfCleanFloat[!,c] = map(x -> (v = tryparse(Float64,x); v == nothing ? missing : v),  dfCleanFloat[!,c])\n",
                "end\n",
                "\n",
                "function indicatorRename(name::String)\n",
                "    if name == \"Government expenditure in secondary institutions education as % of GDP (%)\"\n",
                "        return string(\"GovernmentExpenditure\")\n",
                "    elseif name == \"Pupil-teacher ratio in secondary education (headcount basis)\"\n",
                "        return string(\"PupilTeacherRatio\") \n",
                "    elseif name == \"Rate of out-of-school youth of upper secondary school age, both sexes (%)\"\n",
                "        return string(\"RateOutofSchoolBothSexes\") \n",
                "    elseif name == \"Rate of out-of-school youth of upper secondary school age, female (%)\"\n",
                "        return string(\"RateOutofSchoolFemale\") \n",
                "    elseif name == \"Rate of out-of-school youth of upper secondary school age, male (%)\"\n",
                "        return string(\"RateOutofSchoolMale\")\n",
                "    end     \n",
                "end\n",
                "\n",
                "dfCleanFloat |> @select(3) |> @unique(_.Indicator_Name)  #unique Indicator Names\n",
                "\n",
                "dfCleanShort = dfCleanFloat |> \n",
                "    @mutate(Indicator_Name = indicatorRename(_.Indicator_Name)) |>\n",
                "    @select(-:Country_Code, -:Indicator_Code) |> \n",
                "    @rename(:Country_Name => :Country, :Indicator_Name => :Series) |> \n",
                "    @orderby(:Country )  |>\n",
                "    @thenby(:Series) |> DataFrame\n",
                "\n",
                "dfCleanShort |> @filter(_.Country == \"United States\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "No data science project is complete without talking about missing values. Julia supports missing values as part of the language itself. The Julia document says that \"missing values are represented via the missing object, which is the singleton instance of the type Missing. Missing is equivalent to NULL in SQL and NA in R and behaves like them in most situations.\"\n",
                "\n",
                "Here for simplicity, we fill a missing value in a time series with the previous value. Take a look at the forwardBackwardFill function. First, the function fills missing values with a forward loop. Secondly, any values still missing are filled with a backward loop. A little recursion ensures we impute all values. In Python, such loops are slow. In Julia the loops are as fast as C. I implemented the function in C style to get the C speed point across."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Union{Missing, Float64}[1.0, 1.0, 7.0, 7.0]\n",
                        "Union{Missing, Float64}[1.0, 1.0, 4.0, 4.0, 7.0]\n",
                        "Union{Missing, Float64}[2.0, 2.0, 2.0]\n",
                        "Union{Missing, Float64}[2.0, 2.0, 2.0, 2.0]\n",
                        "Missing[missing, missing, missing, missing]\n"
                    ]
                }
            ],
            "source": [
                "function forwardBackwardFill(v)    \n",
                "  if all(ismissing, v)\n",
                "    # nothing we can do without any values    \n",
                "    return v\n",
                "  end\n",
                "    \n",
                "  nEnd = length(v)\n",
                "  r = zeros(nEnd)\n",
                "  r = convert(Vector{Union{Missing,Float64}}, r)  # to allow assinging a missing to a float array item \n",
                "  r[1] = v[1]   \n",
                "       \n",
                "  # forward filling\n",
                "  for i in 2:nEnd\n",
                "    if ismissing(v[i]) \n",
                "        r[i] = v[i-1]\n",
                "    else\n",
                "        r[i] = v[i]\n",
                "    end\n",
                "  end\n",
                "  \n",
                "  # backward filling\n",
                "  for i in reverse(1:nEnd-1)\n",
                "    if ismissing(r[i]) \n",
                "        r[i] = r[i+1]\n",
                "    end\n",
                "  end\n",
                "    \n",
                "  if any(ismissing, r) \n",
                "    forwardBackwardFill(r)     \n",
                "  else \n",
                "    return r\n",
                "  end\n",
                "        \n",
                "end\n",
                "\n",
                "println(forwardBackwardFill([1,missing,missing,7]))\n",
                "println(forwardBackwardFill([1,missing,4,missing,7]))\n",
                "println(forwardBackwardFill([missing,missing,2]))\n",
                "println(forwardBackwardFill([2,missing,missing,missing]))\n",
                "println(forwardBackwardFill([missing,missing,missing,missing]))"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Next are a few helper functions. We imputate and rescale using the helper functions. We keep things simple for supportability. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "getFirst (generic function with 1 method)"
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "function getYearValues( df, r)\n",
                "    v = convert(Vector, df[r,3:end])    #skipping country and series\n",
                "    return v    \n",
                "end\n",
                "\n",
                "function setYearValues!( df, r, v)\n",
                "    df[r,3:end] .= v \n",
                "end\n",
                "\n",
                "function getMax( df, r)\n",
                "   v = getYearValues( df, r)\n",
                "   return partialsort(v,1,rev=true) \n",
                "end\n",
                "\n",
                "function getMin( df, r)\n",
                "   v = getYearValues( df, r)\n",
                "   return partialsort(v,1,rev=false) \n",
                "end\n",
                "\n",
                "function getFirst( df, r)\n",
                "   v = getYearValues( df, r)\n",
                "   return v[1] \n",
                "end"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
                                    "1990": 1.73608994483948,
                                    "1991": 1.73608994483948,
                                    "1992": 1.73608994483948,
                                    "1993": 1.73608994483948,
                                    "1994": 1.73608994483948,
                                    "1995": 1.73608994483948,
                                    "1996": 1.73608994483948,
                                    "1997": 1.73608994483948,
                                    "1998": 1.73608994483948,
                                    "1999": 1.72523999214172,
                                    "2000": 1.7501300573349,
                                    "2001": 1.88098001480103,
                                    "2002": 1.90707004070282,
                                    "2003": 2.03250002861023,
                                    "2004": 1.94648003578186,
                                    "2005": 1.82843005657196,
                                    "2006": 1.92949998378754,
                                    "2007": 1.92969000339508,
                                    "2008": 1.96256005764008,
                                    "2009": 1.97350001335144,
                                    "2010": 1.91955995559692,
                                    "2011": 1.87390005588531,
                                    "2012": 1.76548004150391,
                                    "2013": 1.76548004150391,
                                    "2014": 1.76548004150391,
                                    "Country": "United States",
                                    "Series": "GovernmentExpenditure"
                                },
                                {
                                    "1990": 15.5334100723267,
                                    "1991": 15.5334100723267,
                                    "1992": 15.5334100723267,
                                    "1993": 15.5334100723267,
                                    "1994": 15.6976499557495,
                                    "1995": 15.4257001876831,
                                    "1996": 15.4034900665283,
                                    "1997": 15.4034900665283,
                                    "1998": 15.1882801055908,
                                    "1999": 14.9240102767944,
                                    "2000": 14.6057996749878,
                                    "2001": 15.1627101898193,
                                    "2002": 14.8834495544434,
                                    "2003": 14.9155302047729,
                                    "2004": 14.9754199981689,
                                    "2005": 14.9440803527832,
                                    "2006": 14.6182203292847,
                                    "2007": 14.5639095306396,
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                                    "2009": 13.9601697921753,
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                                    "2012": 14.6637201309204,
                                    "2013": 14.6957397460938,
                                    "2014": 14.7868299484253,
                                    "Country": "United States",
                                    "Series": "PupilTeacherRatio"
                                },
                                {
                                    "1990": 15.3355,
                                    "1991": 15.3355,
                                    "1992": 15.3355,
                                    "1993": 15.3355,
                                    "1994": 15.3355,
                                    "1995": 15.3355,
                                    "1996": 15.3355,
                                    "1997": 15.3355,
                                    "1998": 15.3355,
                                    "1999": 15.3355,
                                    "2000": 15.3355,
                                    "2001": 14.20598,
                                    "2002": 16.60763,
                                    "2003": 11.95517,
                                    "2004": 10.79784,
                                    "2005": 8.91003,
                                    "2006": 8.92473,
                                    "2007": 7.24616,
                                    "2008": 7.10633,
                                    "2009": 6.92348,
                                    "2010": 9.4071,
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                                    "2012": 10.49112,
                                    "2013": 10.03378,
                                    "2014": 7.50121,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolBothSexes"
                                },
                                {
                                    "1990": 15.97203,
                                    "1991": 15.97203,
                                    "1992": 15.97203,
                                    "1993": 15.97203,
                                    "1994": 15.97203,
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                                    "1996": 15.97203,
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                                    "1998": 15.97203,
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                                    "2000": 15.97203,
                                    "2001": 13.04092,
                                    "2002": 16.74537,
                                    "2003": 11.60295,
                                    "2004": 10.80064,
                                    "2005": 6.2645,
                                    "2006": 6.21013,
                                    "2007": 6.17753,
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                                    "2009": 5.58257,
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                                    "2012": 9.75025,
                                    "2013": 9.24792,
                                    "2014": 6.52425,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolFemale"
                                },
                                {
                                    "1990": 14.7336,
                                    "1991": 14.7336,
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                                    "1993": 14.7336,
                                    "1994": 14.7336,
                                    "1995": 14.7336,
                                    "1996": 14.7336,
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                                    "1998": 14.7336,
                                    "1999": 14.7336,
                                    "2000": 14.7336,
                                    "2001": 15.3063,
                                    "2002": 16.47762,
                                    "2003": 12.28699,
                                    "2004": 10.79521,
                                    "2005": 11.38762,
                                    "2006": 11.46333,
                                    "2007": 8.24965,
                                    "2008": 7.61971,
                                    "2009": 8.18763,
                                    "2010": 9.44207,
                                    "2011": 10.48286,
                                    "2012": 11.18935,
                                    "2013": 10.77544,
                                    "2014": 8.4247,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolMale"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Series",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1990",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1991",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1992",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1993",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1994",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1995",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1996",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1997",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1998",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1999",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2000",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2001",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2002",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2003",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2004",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2005",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2006",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2007",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2008",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2009",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2010",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2011",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2012",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2013",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2014",
                                        "type": "number"
                                    }
                                ]
                            }
                        },
                        "text/html": [
                            "<table><thead><tr><th>Country</th><th>Series</th><th>1990</th><th>1991</th><th>1992</th><th>1993</th><th>1994</th><th>1995</th><th>1996</th><th>1997</th><th>1998</th><th>1999</th><th>2000</th><th>2001</th><th>2002</th><th>2003</th><th>2004</th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>&quot;United States&quot;</td><td>&quot;GovernmentExpenditure&quot;</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.73609</td><td>1.72524</td><td>1.75013</td><td>1.88098</td><td>1.90707</td><td>2.0325</td><td>1.94648</td><td>1.82843</td><td>1.9295</td><td>1.92969</td><td>1.96256</td><td>1.9735</td><td>1.91956</td><td>1.8739</td><td>1.76548</td><td>1.76548</td><td>1.76548</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;PupilTeacherRatio&quot;</td><td>15.5334</td><td>15.5334</td><td>15.5334</td><td>15.5334</td><td>15.6976</td><td>15.4257</td><td>15.4035</td><td>15.4035</td><td>15.1883</td><td>14.924</td><td>14.6058</td><td>15.1627</td><td>14.8834</td><td>14.9155</td><td>14.9754</td><td>14.9441</td><td>14.6182</td><td>14.5639</td><td>14.3766</td><td>13.9602</td><td>13.7594</td><td>14.4906</td><td>14.6637</td><td>14.6957</td><td>14.7868</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolBothSexes&quot;</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>15.3355</td><td>14.206</td><td>16.6076</td><td>11.9552</td><td>10.7978</td><td>8.91003</td><td>8.92473</td><td>7.24616</td><td>7.10633</td><td>6.92348</td><td>9.4071</td><td>9.25726</td><td>10.4911</td><td>10.0338</td><td>7.50121</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolFemale&quot;</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>15.972</td><td>13.0409</td><td>16.7454</td><td>11.6029</td><td>10.8006</td><td>6.2645</td><td>6.21013</td><td>6.17753</td><td>6.56122</td><td>5.58257</td><td>9.36999</td><td>7.95532</td><td>9.75025</td><td>9.24792</td><td>6.52425</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolMale&quot;</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>14.7336</td><td>15.3063</td><td>16.4776</td><td>12.287</td><td>10.7952</td><td>11.3876</td><td>11.4633</td><td>8.24965</td><td>7.61971</td><td>8.18763</td><td>9.44207</td><td>10.4829</td><td>11.1893</td><td>10.7754</td><td>8.4247</td></tr></tbody></table>"
                        ],
                        "text/plain": [
                            "5x27 query result\n",
                            "Country       │ Series                   │ 1990    │ 1991    │ 1992   \n",
                            "──────────────┼──────────────────────────┼─────────┼─────────┼────────\n",
                            "United States │ GovernmentExpenditure    │ 1.73609 │ 1.73609 │ 1.73609\n",
                            "United States │ PupilTeacherRatio        │ 15.5334 │ 15.5334 │ 15.5334\n",
                            "United States │ RateOutofSchoolBothSexes │ 15.3355 │ 15.3355 │ 15.3355\n",
                            "United States │ RateOutofSchoolFemale    │ 15.972  │ 15.972  │ 15.972 \n",
                            "United States │ RateOutofSchoolMale      │ 14.7336 │ 14.7336 │ 14.7336\n",
                            "... with 22 more columns: 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014"
                        ]
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfImputation = copy(dfCleanShort)\n",
                "for r in 1:nrow(dfImputation)\n",
                "    v = getYearValues( dfImputation, r)\n",
                "    fb = forwardBackwardFill(v)\n",
                "    setYearValues!( dfImputation, r, fb)\n",
                "end\n",
                "\n",
                "dfImputation |> @filter(_.Country == \"United States\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "   \n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We like to compare different kind of time series and between different countries.  Therefore, we must normalize the time series and start them at zero.  The result of normalization is to scale the original values to a range between 0 and 1.  We make each time series start at zero so we can compare their trend side by side.\n",
                "\n",
                "Many machine learning models require the data to be standardized as well.  You can standardize the data yourself depending on your chosen machine learning model.  "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
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                                    "1999": 0,
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                                    "2014": 0.13096414953499502,
                                    "Country": "United States",
                                    "Series": "GovernmentExpenditure"
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                                {
                                    "1990": 0.9152625185067368,
                                    "1991": 0.9152625185067368,
                                    "1992": 0.9152625185067368,
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                                    "Country": "United States",
                                    "Series": "PupilTeacherRatio"
                                },
                                {
                                    "1990": 0.8686379289870562,
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                                    "Country": "United States",
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                                    "Series": "RateOutofSchoolFemale"
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                                {
                                    "1990": 0.8031115692076346,
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                                    "2011": 0.32323087500324565,
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                                    "2013": 0.3562612399538942,
                                    "2014": 0.09087809652615562,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolMale"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Series",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1990",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1991",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1992",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1993",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1994",
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                                    {
                                        "name": "1995",
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                                    },
                                    {
                                        "name": "1996",
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                                    },
                                    {
                                        "name": "1997",
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                                    },
                                    {
                                        "name": "1998",
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                                    },
                                    {
                                        "name": "1999",
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                                    },
                                    {
                                        "name": "2000",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2001",
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                                    },
                                    {
                                        "name": "2002",
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                                    },
                                    {
                                        "name": "2003",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2004",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2005",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2006",
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                                    },
                                    {
                                        "name": "2007",
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                                    },
                                    {
                                        "name": "2008",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2009",
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                                    },
                                    {
                                        "name": "2010",
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                                    },
                                    {
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                                    },
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                                        "name": "2012",
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                                    },
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                                    {
                                        "name": "2014",
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                                ]
                            }
                        },
                        "text/html": [
                            "<table><thead><tr><th>Country</th><th>Series</th><th>1990</th><th>1991</th><th>1992</th><th>1993</th><th>1994</th><th>1995</th><th>1996</th><th>1997</th><th>1998</th><th>1999</th><th>2000</th><th>2001</th><th>2002</th><th>2003</th><th>2004</th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>&quot;United States&quot;</td><td>&quot;GovernmentExpenditure&quot;</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.035312</td><td>0.0</td><td>0.0810065</td><td>0.506867</td><td>0.591779</td><td>1.0</td><td>0.720042</td><td>0.33584</td><td>0.664779</td><td>0.665397</td><td>0.772375</td><td>0.80798</td><td>0.632428</td><td>0.483825</td><td>0.130964</td><td>0.130964</td><td>0.130964</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;PupilTeacherRatio&quot;</td><td>0.915263</td><td>0.915263</td><td>0.915263</td><td>0.915263</td><td>1.0</td><td>0.859691</td><td>0.848232</td><td>0.848232</td><td>0.737197</td><td>0.60085</td><td>0.436674</td><td>0.724005</td><td>0.579924</td><td>0.596475</td><td>0.627375</td><td>0.611205</td><td>0.443082</td><td>0.415061</td><td>0.318416</td><td>0.103569</td><td>0.0</td><td>0.377217</td><td>0.466557</td><td>0.483077</td><td>0.530074</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolBothSexes&quot;</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.868638</td><td>0.752002</td><td>1.0</td><td>0.51958</td><td>0.400072</td><td>0.205134</td><td>0.206652</td><td>0.0333204</td><td>0.0188814</td><td>0.0</td><td>0.256462</td><td>0.24099</td><td>0.3684</td><td>0.321174</td><td>0.0596573</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolFemale&quot;</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.930722</td><td>0.668143</td><td>1.0</td><td>0.539325</td><td>0.467452</td><td>0.0610895</td><td>0.0562189</td><td>0.0532985</td><td>0.0876707</td><td>0.0</td><td>0.339289</td><td>0.212559</td><td>0.373354</td><td>0.328354</td><td>0.0843588</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolMale&quot;</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.803112</td><td>0.867766</td><td>1.0</td><td>0.526905</td><td>0.358493</td><td>0.425372</td><td>0.43392</td><td>0.0711161</td><td>0.0</td><td>0.0641144</td><td>0.205733</td><td>0.323231</td><td>0.402989</td><td>0.356261</td><td>0.0908781</td></tr></tbody></table>"
                        ],
                        "text/plain": [
                            "5x27 query result\n",
                            "Country       │ Series                   │ 1990     │ 1991     │ 1992    \n",
                            "──────────────┼──────────────────────────┼──────────┼──────────┼─────────\n",
                            "United States │ GovernmentExpenditure    │ 0.035312 │ 0.035312 │ 0.035312\n",
                            "United States │ PupilTeacherRatio        │ 0.915263 │ 0.915263 │ 0.915263\n",
                            "United States │ RateOutofSchoolBothSexes │ 0.868638 │ 0.868638 │ 0.868638\n",
                            "United States │ RateOutofSchoolFemale    │ 0.930722 │ 0.930722 │ 0.930722\n",
                            "United States │ RateOutofSchoolMale      │ 0.803112 │ 0.803112 │ 0.803112\n",
                            "... with 22 more columns: 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014"
                        ]
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfNormalize = copy(dfImputation)\n",
                "for r in 1:nrow(dfNormalize)\n",
                "    v = getYearValues( dfNormalize, r)\n",
                "    fMin = getMin(dfNormalize, r)\n",
                "    fMax = getMax(dfNormalize, r)\n",
                "    if ismissing(fMax - fMin)    \n",
                "        n = Vector{Union{Missing, Float64}}(missing, length(v))\n",
                "    elseif (fMax - fMin) == 0\n",
                "        n = v\n",
                "    else \n",
                "        n = (v .- fMin) ./ (fMax - fMin)\n",
                "    end\n",
                "    setYearValues!( dfNormalize, r, n)\n",
                "end\n",
                "\n",
                "dfNormalize |> @filter(_.Country == \"United States\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.dataresource+json": {
                            "data": [
                                {
                                    "1990": 0,
                                    "1991": 0,
                                    "1992": 0,
                                    "1993": 0,
                                    "1994": 0,
                                    "1995": 0,
                                    "1996": 0,
                                    "1997": 0,
                                    "1998": 0,
                                    "1999": -0.03531195537976147,
                                    "2000": 0.04569456105255278,
                                    "2001": 0.4715552065502649,
                                    "2002": 0.5564670818519,
                                    "2003": 0.9646880446202385,
                                    "2004": 0.6847297597191496,
                                    "2005": 0.3005275687453213,
                                    "2006": 0.629466953044126,
                                    "2007": 0.6300853855930639,
                                    "2008": 0.7370633532545653,
                                    "2009": 0.7726682299482557,
                                    "2010": 0.5971164127497698,
                                    "2011": 0.4485129684606863,
                                    "2012": 0.09565219415523354,
                                    "2013": 0.09565219415523354,
                                    "2014": 0.09565219415523354,
                                    "Country": "United States",
                                    "Series": "GovernmentExpenditure"
                                },
                                {
                                    "1990": 0,
                                    "1991": 0,
                                    "1992": 0,
                                    "1993": 0,
                                    "1994": 0.08473748149326321,
                                    "1995": -0.055571546730417754,
                                    "1996": -0.06703057660243161,
                                    "1997": -0.06703057660243161,
                                    "1998": -0.17806542211046072,
                                    "1999": -0.3144120832269661,
                                    "2000": -0.47858880096204254,
                                    "2001": -0.19125789533509174,
                                    "2002": -0.3353388726762947,
                                    "2003": -0.3187872686630979,
                                    "2004": -0.2878878905475849,
                                    "2005": -0.304057182473102,
                                    "2006": -0.4721805221586026,
                                    "2007": -0.5002014888030211,
                                    "2008": -0.5968467371361259,
                                    "2009": -0.8116933374861297,
                                    "2010": -0.9152625185067368,
                                    "2011": -0.5380454651242457,
                                    "2012": -0.4487054774940433,
                                    "2013": -0.432185363794299,
                                    "2014": -0.3851885310310118,
                                    "Country": "United States",
                                    "Series": "PupilTeacherRatio"
                                },
                                {
                                    "1990": 0,
                                    "1991": 0,
                                    "1992": 0,
                                    "1993": 0,
                                    "1994": 0,
                                    "1995": 0,
                                    "1996": 0,
                                    "1997": 0,
                                    "1998": 0,
                                    "1999": 0,
                                    "2000": 0,
                                    "2001": -0.11663594636596908,
                                    "2002": 0.13136207101294384,
                                    "2003": -0.3490579968298714,
                                    "2004": -0.4685656459265913,
                                    "2005": -0.6635037664637576,
                                    "2006": -0.6619858221939974,
                                    "2007": -0.8353175033430915,
                                    "2008": -0.849756560978506,
                                    "2009": -0.8686379289870562,
                                    "2010": -0.6121755652277174,
                                    "2011": -0.6276482706277783,
                                    "2012": -0.500238017791959,
                                    "2013": -0.5474636390390484,
                                    "2014": -0.808980653955174,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolBothSexes"
                                },
                                {
                                    "1990": 0,
                                    "1991": 0,
                                    "1992": 0,
                                    "1993": 0,
                                    "1994": 0,
                                    "1995": 0,
                                    "1996": 0,
                                    "1997": 0,
                                    "1998": 0,
                                    "1999": 0,
                                    "2000": 0,
                                    "2001": -0.2625783853513455,
                                    "2002": 0.06927831726806899,
                                    "2003": -0.39139642383631346,
                                    "2004": -0.4632699681083599,
                                    "2005": -0.8696321711398574,
                                    "2006": -0.8745028129143224,
                                    "2007": -0.8774232271473106,
                                    "2008": -0.8430510266241444,
                                    "2009": -0.930721682731931,
                                    "2010": -0.5914322571397856,
                                    "2011": -0.7181630056974951,
                                    "2012": -0.5573673271938939,
                                    "2013": -0.6023676855269287,
                                    "2014": -0.8463629196975668,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolFemale"
                                },
                                {
                                    "1990": 0,
                                    "1991": 0,
                                    "1992": 0,
                                    "1993": 0,
                                    "1994": 0,
                                    "1995": 0,
                                    "1996": 0,
                                    "1997": 0,
                                    "1998": 0,
                                    "1999": 0,
                                    "2000": 0,
                                    "2001": 0.0646540775419936,
                                    "2002": 0.1968884307923654,
                                    "2003": -0.2762062382661372,
                                    "2004": -0.4446184257911853,
                                    "2005": -0.37773921839350355,
                                    "2006": -0.36919205546229306,
                                    "2007": -0.731995470714875,
                                    "2008": -0.8031115692076346,
                                    "2009": -0.738997122346016,
                                    "2010": -0.5973790657164049,
                                    "2011": -0.47988069420438895,
                                    "2012": -0.40012260228428603,
                                    "2013": -0.4468503292537404,
                                    "2014": -0.712233472681479,
                                    "Country": "United States",
                                    "Series": "RateOutofSchoolMale"
                                }
                            ],
                            "schema": {
                                "fields": [
                                    {
                                        "name": "Country",
                                        "type": "string"
                                    },
                                    {
                                        "name": "Series",
                                        "type": "string"
                                    },
                                    {
                                        "name": "1990",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1991",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1992",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1993",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1994",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1995",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1996",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1997",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1998",
                                        "type": "number"
                                    },
                                    {
                                        "name": "1999",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2000",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2001",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2002",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2003",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2004",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2005",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2006",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2007",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2008",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2009",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2010",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2011",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2012",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2013",
                                        "type": "number"
                                    },
                                    {
                                        "name": "2014",
                                        "type": "number"
                                    }
                                ]
                            }
                        },
                        "text/html": [
                            "<table><thead><tr><th>Country</th><th>Series</th><th>1990</th><th>1991</th><th>1992</th><th>1993</th><th>1994</th><th>1995</th><th>1996</th><th>1997</th><th>1998</th><th>1999</th><th>2000</th><th>2001</th><th>2002</th><th>2003</th><th>2004</th><th>2005</th><th>2006</th><th>2007</th><th>2008</th><th>2009</th><th>2010</th><th>2011</th><th>2012</th><th>2013</th><th>2014</th></tr></thead><tbody><tr><td>&quot;United States&quot;</td><td>&quot;GovernmentExpenditure&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-0.035312</td><td>0.0456946</td><td>0.471555</td><td>0.556467</td><td>0.964688</td><td>0.68473</td><td>0.300528</td><td>0.629467</td><td>0.630085</td><td>0.737063</td><td>0.772668</td><td>0.597116</td><td>0.448513</td><td>0.0956522</td><td>0.0956522</td><td>0.0956522</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;PupilTeacherRatio&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0847375</td><td>-0.0555715</td><td>-0.0670306</td><td>-0.0670306</td><td>-0.178065</td><td>-0.314412</td><td>-0.478589</td><td>-0.191258</td><td>-0.335339</td><td>-0.318787</td><td>-0.287888</td><td>-0.304057</td><td>-0.472181</td><td>-0.500201</td><td>-0.596847</td><td>-0.811693</td><td>-0.915263</td><td>-0.538045</td><td>-0.448705</td><td>-0.432185</td><td>-0.385189</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolBothSexes&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-0.116636</td><td>0.131362</td><td>-0.349058</td><td>-0.468566</td><td>-0.663504</td><td>-0.661986</td><td>-0.835318</td><td>-0.849757</td><td>-0.868638</td><td>-0.612176</td><td>-0.627648</td><td>-0.500238</td><td>-0.547464</td><td>-0.808981</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolFemale&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-0.262578</td><td>0.0692783</td><td>-0.391396</td><td>-0.46327</td><td>-0.869632</td><td>-0.874503</td><td>-0.877423</td><td>-0.843051</td><td>-0.930722</td><td>-0.591432</td><td>-0.718163</td><td>-0.557367</td><td>-0.602368</td><td>-0.846363</td></tr><tr><td>&quot;United States&quot;</td><td>&quot;RateOutofSchoolMale&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0646541</td><td>0.196888</td><td>-0.276206</td><td>-0.444618</td><td>-0.377739</td><td>-0.369192</td><td>-0.731995</td><td>-0.803112</td><td>-0.738997</td><td>-0.597379</td><td>-0.479881</td><td>-0.400123</td><td>-0.44685</td><td>-0.712233</td></tr></tbody></table>"
                        ],
                        "text/plain": [
                            "5x27 query result\n",
                            "Country       │ Series                   │ 1990 │ 1991 │ 1992 │ 1993\n",
                            "──────────────┼──────────────────────────┼──────┼──────┼──────┼─────\n",
                            "United States │ GovernmentExpenditure    │ 0.0  │ 0.0  │ 0.0  │ 0.0 \n",
                            "United States │ PupilTeacherRatio        │ 0.0  │ 0.0  │ 0.0  │ 0.0 \n",
                            "United States │ RateOutofSchoolBothSexes │ 0.0  │ 0.0  │ 0.0  │ 0.0 \n",
                            "United States │ RateOutofSchoolFemale    │ 0.0  │ 0.0  │ 0.0  │ 0.0 \n",
                            "United States │ RateOutofSchoolMale      │ 0.0  │ 0.0  │ 0.0  │ 0.0 \n",
                            "... with 21 more columns: 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014"
                        ]
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfSeriesFromZero = copy(dfNormalize)\n",
                "for r in 1:nrow(dfSeriesFromZero)\n",
                "    v = getYearValues( dfSeriesFromZero, r)\n",
                "    fFirst = getFirst(dfSeriesFromZero, r)\n",
                "    n = v .- fFirst\n",
                "    setYearValues!( dfSeriesFromZero, r, n)\n",
                "end\n",
                "\n",
                "dfSeriesFromZero |> @filter(_.Country == \"United States\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We filled our missing years and normalized our data. Let's get ready to transform our data to time series style. The years are rows instead of columns. Our dataframe needs transposing. Luckily, Julia data frame package has the stack function to do so. The Julia Timeseries package requires the first column to be both unique and convertible to the time type. Our \"timestamp\" is the year only without any month or day. Thus, we cannot easily use the Julia Timeseries package."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<table class=\"data-frame\"><thead><tr><th></th><th>Timestamp</th><th>Value</th><th>Country</th><th>Series</th></tr><tr><th></th><th>Symbol</th><th>Float64⍰</th><th>String</th><th>String</th></tr></thead><tbody><p>875 rows × 4 columns</p><tr><th>1</th><td>1990</td><td>missing</td><td>Turkey</td><td>GovernmentExpenditure</td></tr><tr><th>2</th><td>1990</td><td>0.0</td><td>Turkey</td><td>PupilTeacherRatio</td></tr><tr><th>3</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>4</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolFemale</td></tr><tr><th>5</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolMale</td></tr><tr><th>6</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>GovernmentExpenditure</td></tr><tr><th>7</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>PupilTeacherRatio</td></tr><tr><th>8</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>9</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolFemale</td></tr><tr><th>10</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolMale</td></tr><tr><th>11</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>GovernmentExpenditure</td></tr><tr><th>12</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>PupilTeacherRatio</td></tr><tr><th>13</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>14</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolFemale</td></tr><tr><th>15</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolMale</td></tr><tr><th>16</th><td>1990</td><td>0.0</td><td>United States</td><td>GovernmentExpenditure</td></tr><tr><th>17</th><td>1990</td><td>0.0</td><td>United States</td><td>PupilTeacherRatio</td></tr><tr><th>18</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>19</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolFemale</td></tr><tr><th>20</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolMale</td></tr><tr><th>21</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>GovernmentExpenditure</td></tr><tr><th>22</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>PupilTeacherRatio</td></tr><tr><th>23</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>24</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolFemale</td></tr><tr><th>25</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolMale</td></tr><tr><th>26</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>GovernmentExpenditure</td></tr><tr><th>27</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>PupilTeacherRatio</td></tr><tr><th>28</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>29</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolFemale</td></tr><tr><th>30</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolMale</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
                        ],
                        "text/latex": [
                            "\\begin{tabular}{r|cccc}\n",
                            "\t& Timestamp & Value & Country & Series\\\\\n",
                            "\t\\hline\n",
                            "\t& Symbol & Float64⍰ & String & String\\\\\n",
                            "\t\\hline\n",
                            "\t1 & 1990 &  & Turkey & GovernmentExpenditure \\\\\n",
                            "\t2 & 1990 & 0.0 & Turkey & PupilTeacherRatio \\\\\n",
                            "\t3 & 1990 & 0.0 & Turkey & RateOutofSchoolBothSexes \\\\\n",
                            "\t4 & 1990 & 0.0 & Turkey & RateOutofSchoolFemale \\\\\n",
                            "\t5 & 1990 & 0.0 & Turkey & RateOutofSchoolMale \\\\\n",
                            "\t6 & 1990 & 0.0 & United Arab Emirates & GovernmentExpenditure \\\\\n",
                            "\t7 & 1990 & 0.0 & United Arab Emirates & PupilTeacherRatio \\\\\n",
                            "\t8 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolBothSexes \\\\\n",
                            "\t9 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolFemale \\\\\n",
                            "\t10 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolMale \\\\\n",
                            "\t11 & 1990 & 0.0 & United Kingdom & GovernmentExpenditure \\\\\n",
                            "\t12 & 1990 & 0.0 & United Kingdom & PupilTeacherRatio \\\\\n",
                            "\t13 & 1990 & 0.0 & United Kingdom & RateOutofSchoolBothSexes \\\\\n",
                            "\t14 & 1990 & 0.0 & United Kingdom & RateOutofSchoolFemale \\\\\n",
                            "\t15 & 1990 & 0.0 & United Kingdom & RateOutofSchoolMale \\\\\n",
                            "\t16 & 1990 & 0.0 & United States & GovernmentExpenditure \\\\\n",
                            "\t17 & 1990 & 0.0 & United States & PupilTeacherRatio \\\\\n",
                            "\t18 & 1990 & 0.0 & United States & RateOutofSchoolBothSexes \\\\\n",
                            "\t19 & 1990 & 0.0 & United States & RateOutofSchoolFemale \\\\\n",
                            "\t20 & 1990 & 0.0 & United States & RateOutofSchoolMale \\\\\n",
                            "\t21 & 1990 & 0.0 & Uruguay & GovernmentExpenditure \\\\\n",
                            "\t22 & 1990 & 0.0 & Uruguay & PupilTeacherRatio \\\\\n",
                            "\t23 & 1990 & 0.0 & Uruguay & RateOutofSchoolBothSexes \\\\\n",
                            "\t24 & 1990 & 0.0 & Uruguay & RateOutofSchoolFemale \\\\\n",
                            "\t25 & 1990 & 0.0 & Uruguay & RateOutofSchoolMale \\\\\n",
                            "\t26 & 1990 &  & Virgin Islands (U.S.) & GovernmentExpenditure \\\\\n",
                            "\t27 & 1990 &  & Virgin Islands (U.S.) & PupilTeacherRatio \\\\\n",
                            "\t28 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolBothSexes \\\\\n",
                            "\t29 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolFemale \\\\\n",
                            "\t30 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolMale \\\\\n",
                            "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
                            "\\end{tabular}\n"
                        ],
                        "text/plain": [
                            "875×4 DataFrame. Omitted printing of 1 columns\n",
                            "│ Row │ Timestamp │ Value      │ Country               │\n",
                            "│     │ \u001b[90mSymbol\u001b[39m    │ \u001b[90mFloat64⍰\u001b[39m   │ \u001b[90mString\u001b[39m                │\n",
                            "├─────┼───────────┼────────────┼───────────────────────┤\n",
                            "│ 1   │ 1990      │ \u001b[90mmissing\u001b[39m    │ Turkey                │\n",
                            "│ 2   │ 1990      │ 0.0        │ Turkey                │\n",
                            "│ 3   │ 1990      │ 0.0        │ Turkey                │\n",
                            "│ 4   │ 1990      │ 0.0        │ Turkey                │\n",
                            "│ 5   │ 1990      │ 0.0        │ Turkey                │\n",
                            "│ 6   │ 1990      │ 0.0        │ United Arab Emirates  │\n",
                            "│ 7   │ 1990      │ 0.0        │ United Arab Emirates  │\n",
                            "│ 8   │ 1990      │ 0.0        │ United Arab Emirates  │\n",
                            "│ 9   │ 1990      │ 0.0        │ United Arab Emirates  │\n",
                            "│ 10  │ 1990      │ 0.0        │ United Arab Emirates  │\n",
                            "⋮\n",
                            "│ 865 │ 2014      │ -0.513991  │ Uruguay               │\n",
                            "│ 866 │ 2014      │ \u001b[90mmissing\u001b[39m    │ Virgin Islands (U.S.) │\n",
                            "│ 867 │ 2014      │ \u001b[90mmissing\u001b[39m    │ Virgin Islands (U.S.) │\n",
                            "│ 868 │ 2014      │ \u001b[90mmissing\u001b[39m    │ Virgin Islands (U.S.) │\n",
                            "│ 869 │ 2014      │ \u001b[90mmissing\u001b[39m    │ Virgin Islands (U.S.) │\n",
                            "│ 870 │ 2014      │ \u001b[90mmissing\u001b[39m    │ Virgin Islands (U.S.) │\n",
                            "│ 871 │ 2014      │ 0.0        │ Zimbabwe              │\n",
                            "│ 872 │ 2014      │ -0.752613  │ Zimbabwe              │\n",
                            "│ 873 │ 2014      │ -0.28204   │ Zimbabwe              │\n",
                            "│ 874 │ 2014      │ -0.468758  │ Zimbabwe              │\n",
                            "│ 875 │ 2014      │ -0.0239847 │ Zimbabwe              │"
                        ]
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfStack = DataFrames.stack(dfSeriesFromZero, 3:ncol(dfSeriesFromZero)) \n",
                "dfTranspose = dfStack |> @rename(:variable => :Timestamp, :value => :Value) |> DataFrame"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Data Exploring and Graphing \n",
                "\n",
                "It is time to review our data. We have time series that start at zero, imputed, and scaled by simple Julia code. Let's ask one question: what is relationship between pupil-teacher ratio and out-of-school youth rate in secondary school? We expect that higher pupil-teacher ratio correlates to lower out-of-school youth rate. A higher pupil-teacher ratio allows teachers to more personal time with pupils. Teachers would have extra time to resolve pupils' needs. Pupils with more issues seem more likely to drop out of school. The pupil-teacher ratio depends on school budget, and that is the reason \"Government Expenditure\" series is included in our cleaned data.\n",
                "\n",
                "We must always remember that correlation does not imply causation. In addition, we easily add years and indicators to our data processing to ask many other questions.\n",
                "\n",
                "First, we ought to show some statistics of our data. We use data before imputation to show that Julia can easily handle missing values. The DataFrame package has a describe function that compute some basic numbers."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<table class=\"data-frame\"><thead><tr><th></th><th>variable</th><th>mean</th><th>min</th><th>median</th><th>max</th><th>nunique</th></tr><tr><th></th><th>Symbol</th><th>Union…</th><th>Any</th><th>Union…</th><th>Any</th><th>Union…</th></tr></thead><tbody><p>27 rows × 8 columns (omitted printing of 2 columns)</p><tr><th>1</th><td>Country</td><td></td><td>Turkey</td><td></td><td>Zimbabwe</td><td>7</td></tr><tr><th>2</th><td>Series</td><td></td><td>GovernmentExpenditure</td><td></td><td>RateOutofSchoolMale</td><td>5</td></tr><tr><th>3</th><td>1990</td><td>17.9134</td><td>12.5649</td><td>17.9134</td><td>23.262</td><td></td></tr><tr><th>4</th><td>1991</td><td>21.5524</td><td>12.5956</td><td>23.8903</td><td>28.1712</td><td></td></tr><tr><th>5</th><td>1992</td><td>21.3931</td><td>12.5144</td><td>23.3714</td><td>28.2936</td><td></td></tr><tr><th>6</th><td>1993</td><td>18.8025</td><td>12.242</td><td>15.5334</td><td>29.8818</td><td></td></tr><tr><th>7</th><td>1994</td><td>17.9684</td><td>12.2141</td><td>15.6976</td><td>25.7173</td><td></td></tr><tr><th>8</th><td>1995</td><td>17.6755</td><td>11.021</td><td>15.4257</td><td>25.8975</td><td></td></tr><tr><th>9</th><td>1996</td><td>17.498</td><td>10.4978</td><td>15.4035</td><td>26.5927</td><td></td></tr><tr><th>10</th><td>1997</td><td>20.0675</td><td>10.7195</td><td>21.7562</td><td>27.7267</td><td></td></tr><tr><th>11</th><td>1998</td><td>15.4092</td><td>0.55201</td><td>10.7648</td><td>40.401</td><td></td></tr><tr><th>12</th><td>1999</td><td>27.2157</td><td>0.89598</td><td>25.975</td><td>59.9952</td><td></td></tr><tr><th>13</th><td>2000</td><td>25.0277</td><td>0.91436</td><td>15.0517</td><td>61.3123</td><td></td></tr><tr><th>14</th><td>2001</td><td>24.0803</td><td>0.90915</td><td>14.8696</td><td>60.5851</td><td></td></tr><tr><th>15</th><td>2002</td><td>25.2492</td><td>0.79844</td><td>16.5426</td><td>62.6981</td><td></td></tr><tr><th>16</th><td>2003</td><td>23.8189</td><td>0.67068</td><td>14.8155</td><td>66.0035</td><td></td></tr><tr><th>17</th><td>2004</td><td>14.8144</td><td>0.8618</td><td>10.7965</td><td>48.917</td><td></td></tr><tr><th>18</th><td>2005</td><td>13.8225</td><td>0.96365</td><td>8.91003</td><td>45.2854</td><td></td></tr><tr><th>19</th><td>2006</td><td>14.0505</td><td>1.03576</td><td>11.4633</td><td>42.4161</td><td></td></tr><tr><th>20</th><td>2007</td><td>16.1464</td><td>1.64455</td><td>13.7981</td><td>40.3969</td><td></td></tr><tr><th>21</th><td>2008</td><td>16.1446</td><td>1.64223</td><td>12.619</td><td>39.2483</td><td></td></tr><tr><th>22</th><td>2009</td><td>15.4673</td><td>1.78182</td><td>11.3147</td><td>36.7681</td><td></td></tr><tr><th>23</th><td>2010</td><td>11.2928</td><td>0.50527</td><td>9.4071</td><td>29.3719</td><td></td></tr><tr><th>24</th><td>2011</td><td>9.8244</td><td>0.6848</td><td>10.1009</td><td>23.2453</td><td></td></tr><tr><th>25</th><td>2012</td><td>20.8544</td><td>0.7117</td><td>14.6637</td><td>55.7197</td><td></td></tr><tr><th>26</th><td>2013</td><td>18.4723</td><td>0.467</td><td>14.8868</td><td>54.6936</td><td></td></tr><tr><th>27</th><td>2014</td><td>10.481</td><td>0.81929</td><td>8.4247</td><td>19.4811</td><td></td></tr></tbody></table>"
                        ],
                        "text/latex": [
                            "\\begin{tabular}{r|ccccccc}\n",
                            "\t& variable & mean & min & median & max & nunique & \\\\\n",
                            "\t\\hline\n",
                            "\t& Symbol & Union… & Any & Union… & Any & Union… & \\\\\n",
                            "\t\\hline\n",
                            "\t1 & Country &  & Turkey &  & Zimbabwe & 7 & $\\dots$ \\\\\n",
                            "\t2 & Series &  & GovernmentExpenditure &  & RateOutofSchoolMale & 5 & $\\dots$ \\\\\n",
                            "\t3 & 1990 & 17.9134 & 12.5649 & 17.9134 & 23.262 &  & $\\dots$ \\\\\n",
                            "\t4 & 1991 & 21.5524 & 12.5956 & 23.8903 & 28.1712 &  & $\\dots$ \\\\\n",
                            "\t5 & 1992 & 21.3931 & 12.5144 & 23.3714 & 28.2936 &  & $\\dots$ \\\\\n",
                            "\t6 & 1993 & 18.8025 & 12.242 & 15.5334 & 29.8818 &  & $\\dots$ \\\\\n",
                            "\t7 & 1994 & 17.9684 & 12.2141 & 15.6976 & 25.7173 &  & $\\dots$ \\\\\n",
                            "\t8 & 1995 & 17.6755 & 11.021 & 15.4257 & 25.8975 &  & $\\dots$ \\\\\n",
                            "\t9 & 1996 & 17.498 & 10.4978 & 15.4035 & 26.5927 &  & $\\dots$ \\\\\n",
                            "\t10 & 1997 & 20.0675 & 10.7195 & 21.7562 & 27.7267 &  & $\\dots$ \\\\\n",
                            "\t11 & 1998 & 15.4092 & 0.55201 & 10.7648 & 40.401 &  & $\\dots$ \\\\\n",
                            "\t12 & 1999 & 27.2157 & 0.89598 & 25.975 & 59.9952 &  & $\\dots$ \\\\\n",
                            "\t13 & 2000 & 25.0277 & 0.91436 & 15.0517 & 61.3123 &  & $\\dots$ \\\\\n",
                            "\t14 & 2001 & 24.0803 & 0.90915 & 14.8696 & 60.5851 &  & $\\dots$ \\\\\n",
                            "\t15 & 2002 & 25.2492 & 0.79844 & 16.5426 & 62.6981 &  & $\\dots$ \\\\\n",
                            "\t16 & 2003 & 23.8189 & 0.67068 & 14.8155 & 66.0035 &  & $\\dots$ \\\\\n",
                            "\t17 & 2004 & 14.8144 & 0.8618 & 10.7965 & 48.917 &  & $\\dots$ \\\\\n",
                            "\t18 & 2005 & 13.8225 & 0.96365 & 8.91003 & 45.2854 &  & $\\dots$ \\\\\n",
                            "\t19 & 2006 & 14.0505 & 1.03576 & 11.4633 & 42.4161 &  & $\\dots$ \\\\\n",
                            "\t20 & 2007 & 16.1464 & 1.64455 & 13.7981 & 40.3969 &  & $\\dots$ \\\\\n",
                            "\t21 & 2008 & 16.1446 & 1.64223 & 12.619 & 39.2483 &  & $\\dots$ \\\\\n",
                            "\t22 & 2009 & 15.4673 & 1.78182 & 11.3147 & 36.7681 &  & $\\dots$ \\\\\n",
                            "\t23 & 2010 & 11.2928 & 0.50527 & 9.4071 & 29.3719 &  & $\\dots$ \\\\\n",
                            "\t24 & 2011 & 9.8244 & 0.6848 & 10.1009 & 23.2453 &  & $\\dots$ \\\\\n",
                            "\t25 & 2012 & 20.8544 & 0.7117 & 14.6637 & 55.7197 &  & $\\dots$ \\\\\n",
                            "\t26 & 2013 & 18.4723 & 0.467 & 14.8868 & 54.6936 &  & $\\dots$ \\\\\n",
                            "\t27 & 2014 & 10.481 & 0.81929 & 8.4247 & 19.4811 &  & $\\dots$ \\\\\n",
                            "\\end{tabular}\n"
                        ],
                        "text/plain": [
                            "27×8 DataFrame. Omitted printing of 4 columns\n",
                            "│ Row │ variable │ mean    │ min                   │ median  │\n",
                            "│     │ \u001b[90mSymbol\u001b[39m   │ \u001b[90mUnion…\u001b[39m  │ \u001b[90mAny\u001b[39m                   │ \u001b[90mUnion…\u001b[39m  │\n",
                            "├─────┼──────────┼─────────┼───────────────────────┼─────────┤\n",
                            "│ 1   │ Country  │         │ Turkey                │         │\n",
                            "│ 2   │ Series   │         │ GovernmentExpenditure │         │\n",
                            "│ 3   │ 1990     │ 17.9134 │ 12.5649               │ 17.9134 │\n",
                            "│ 4   │ 1991     │ 21.5524 │ 12.5956               │ 23.8903 │\n",
                            "│ 5   │ 1992     │ 21.3931 │ 12.5144               │ 23.3714 │\n",
                            "│ 6   │ 1993     │ 18.8025 │ 12.242                │ 15.5334 │\n",
                            "│ 7   │ 1994     │ 17.9684 │ 12.2141               │ 15.6976 │\n",
                            "│ 8   │ 1995     │ 17.6755 │ 11.021                │ 15.4257 │\n",
                            "│ 9   │ 1996     │ 17.498  │ 10.4978               │ 15.4035 │\n",
                            "│ 10  │ 1997     │ 20.0675 │ 10.7195               │ 21.7562 │\n",
                            "⋮\n",
                            "│ 17  │ 2004     │ 14.8144 │ 0.8618                │ 10.7965 │\n",
                            "│ 18  │ 2005     │ 13.8225 │ 0.96365               │ 8.91003 │\n",
                            "│ 19  │ 2006     │ 14.0505 │ 1.03576               │ 11.4633 │\n",
                            "│ 20  │ 2007     │ 16.1464 │ 1.64455               │ 13.7981 │\n",
                            "│ 21  │ 2008     │ 16.1446 │ 1.64223               │ 12.619  │\n",
                            "│ 22  │ 2009     │ 15.4673 │ 1.78182               │ 11.3147 │\n",
                            "│ 23  │ 2010     │ 11.2928 │ 0.50527               │ 9.4071  │\n",
                            "│ 24  │ 2011     │ 9.8244  │ 0.6848                │ 10.1009 │\n",
                            "│ 25  │ 2012     │ 20.8544 │ 0.7117                │ 14.6637 │\n",
                            "│ 26  │ 2013     │ 18.4723 │ 0.467                 │ 14.8868 │\n",
                            "│ 27  │ 2014     │ 10.481  │ 0.81929               │ 8.4247  │"
                        ]
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "describe(dfCleanShort)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Second, statistics are great but a picture says a thousand words. Let's graph each of the five time series for the United States. We use cool VigaLite package. The graphs show that our intuition was not correct for the United States, surprise! Higher pupil-teacher ratio is positively correlated to out of school rate. Both rates are going down. It is interesting that United States schools are keeping pupils while reducing the pupil-teacher ratio. The budget is increasing implying there are other factors keeping pupils in school. Better teach training programs, computer aid learning, no student left behind law, and better after school programs are some examples contributing to lower pupil-teacher ratio in the USA."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.vegalite.v3+json": {
                            "data": {
                                "values": [
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "RateOutofSchoolBothSexes",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1991",
                                        "Value": 0
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                                        "Country": "United States",
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                                        "Timestamp": "1991",
                                        "Value": 0
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                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1991",
                                        "Value": 0
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                                        "Country": "United States",
                                        "Series": "RateOutofSchoolMale",
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                                        "Series": "GovernmentExpenditure",
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                                        "Timestamp": "1992",
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                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1992",
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                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1993",
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                                        "Country": "United States",
                                        "Series": "PupilTeacherRatio",
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                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1993",
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                                        "Country": "United States",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1993",
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                                        "Country": "United States",
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fill: none;\"></path><g><g class=\"mark-group role-scope\"><g transform=\"translate(0,0)\"><path class=\"background\" d=\"M0,0h138v11h-138Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #4c78a8; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">GovernmentExpenditure</text></g></g></g><g transform=\"translate(0,13)\"><path class=\"background\" d=\"M0,0h138v11h-138Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #f58518; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">PupilTeacherRatio</text></g></g></g><g transform=\"translate(0,26)\"><path class=\"background\" d=\"M0,0h138v11h-138Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #e45756; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">RateOutofSchoolBothSexes</text></g></g></g><g transform=\"translate(0,39)\"><path class=\"background\" d=\"M0,0h138v11h-138Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #72b7b2; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">RateOutofSchoolFemale</text></g></g></g><g transform=\"translate(0,52)\"><path class=\"background\" d=\"M0,0h138v11h-138Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #54a24b; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">RateOutofSchoolMale</text></g></g></g></g></g></g></g><g class=\"mark-text role-legend-title\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(0,9)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Series</text></g></g></g></g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,200.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; 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stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2006</text><text text-anchor=\"end\" transform=\"translate(350,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2007</text><text text-anchor=\"end\" transform=\"translate(370,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2008</text><text text-anchor=\"end\" transform=\"translate(390,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2009</text><text text-anchor=\"end\" transform=\"translate(410,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2010</text><text text-anchor=\"end\" transform=\"translate(430,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2011</text><text text-anchor=\"end\" transform=\"translate(450,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(250,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,150)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,100)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,50)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,0)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(-7,203)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">-1.0</text><text text-anchor=\"end\" transform=\"translate(-7,153)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">-0.5</text><text text-anchor=\"end\" transform=\"translate(-7,103)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.0</text><text text-anchor=\"end\" transform=\"translate(-7,53)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.5</text><text text-anchor=\"end\" transform=\"translate(-7,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1.0</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"0\" y2=\"-200\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(-28,100) rotate(-90) translate(0,-2)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Value</text></g></g></g></g></g></g></g></g></svg>"
                        ],
                        "text/plain": [
                            "VegaLite.VLSpec{:plot}"
                        ]
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "graph = dfTranspose |>\n",
                "    @filter(_.Country == \"United States\") |>\n",
                "    @vlplot(:line, x=:\"Timestamp:o\", y=:Value, color=:Series)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The story is different for the United Kingdom.  From 2005, pupil-teacher ratio is going up but also the out of school rate is going up.  The budget goes down around 2005.  Perhaps, programs such as after school project were cut off.  Other factors probably caused the out of school rate to increase even with an increasing pupil-teacher ratio.  Apparently, the UK government noticed such undesired trend increasing the 2008 budget for school programs.  Afterwards, out of school rate and pupil-teacher ratio drop similar to the USA case. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.vegalite.v3+json": {
                            "data": {
                                "values": [
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1994",
                                        "Value": -0.005183783437159251
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
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fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(130,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(150,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(170,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(190,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(210,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(230,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(250,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(270,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; 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font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(250,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,144)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,89)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,33)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(-7,203)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">-1.0</text><text text-anchor=\"end\" transform=\"translate(-7,147.44444444444443)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">-0.5</text><text text-anchor=\"end\" transform=\"translate(-7,91.88888888888889)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.0</text><text text-anchor=\"end\" transform=\"translate(-7,36.33333333333335)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.5</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"0\" y2=\"-200\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(-28,100) rotate(-90) translate(0,-2)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Value</text></g></g></g></g></g></g></g></g></svg>"
                        ],
                        "text/plain": [
                            "VegaLite.VLSpec{:plot}"
                        ]
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "graph = dfTranspose |>\n",
                "    @filter(_.Country == \"United Kingdom\") |>\n",
                "    @filter(_.Series != \"RateOutofSchoolBothSexes\") |> \n",
                "    @vlplot(:line, x=:\"Timestamp:o\", y=:Value, color=:Series)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Let's see Turkey and Zimbabwe out of school rate and pupil tearch ratio.  Zimbabwe out of school rate for males is higher than famales.  Turkey does not show a gender differnce.  "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.vegalite.v3+json": {
                            "data": {
                                "values": [
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1991",
                                        "Value": 0.10297303092494958
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1992",
                                        "Value": 0.01793770750672774
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1992",
                                        "Value": 0.016187044458600197
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1993",
                                        "Value": 0.12879341504720565
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1993",
                                        "Value": 0.2263425118640513
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1994",
                                        "Value": 0.07836228867995143
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1994",
                                        "Value": 0
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1994",
                                        "Value": -0.32471030475386314
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1995",
                                        "Value": 0.01019631206135252
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1995",
                                        "Value": -0.3008630852553165
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1996",
                                        "Value": 0.01019631206135252
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1996",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1996",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1996",
                                        "Value": -0.20887990730356953
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1996",
                                        "Value": 0
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1996",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1997",
                                        "Value": -0.24675662818108468
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1997",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1997",
                                        "Value": 0
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1997",
                                        "Value": -0.05882038200676787
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1997",
                                        "Value": 0
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1997",
                                        "Value": 0
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                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1998",
                                        "Value": -0.24675662818108468
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1998",
                                        "Value": 0
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1998",
                                        "Value": 0
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1998",
                                        "Value": -0.05882038200676787
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1998",
                                        "Value": 0
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1998",
                                        "Value": 0
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                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1999",
                                        "Value": -0.8712065849527943
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1999",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1999",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "1999",
                                        "Value": -0.11413814765149644
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "1999",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "1999",
                                        "Value": 0
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                                    {
                                        "Country": "Turkey",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "2000",
                                        "Value": -0.8712065849527943
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "2000",
                                        "Value": 9.375086664342724e-05
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                                    {
                                        "Country": "Turkey",
                                        "Series": "RateOutofSchoolMale",
                                        "Timestamp": "2000",
                                        "Value": 0.05729175921337426
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "PupilTeacherRatio",
                                        "Timestamp": "2000",
                                        "Value": -0.45793995781616015
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                                    {
                                        "Country": "Zimbabwe",
                                        "Series": "RateOutofSchoolFemale",
                                        "Timestamp": "2000",
                                        "Value": 0.11645705716751342
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                                    {
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fill: none;\"></path><g><g class=\"mark-text role-title-text\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(0,8)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">Turkey</text></g></g></g></g></g></g><g transform=\"translate(522,-1)\"><path class=\"background\" d=\"M0,0h500v0h-500Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-title\"><g transform=\"translate(250,-20)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-text role-title-text\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(0,8)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">Zimbabwe</text></g></g></g></g></g></g></g><g class=\"mark-group role-column-footer column_footer\"><g transform=\"translate(0,203)\"><path class=\"background\" d=\"M0,0h500v0h-500Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(130,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(150,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(170,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(190,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(210,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(230,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(250,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(270,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2006</text><text text-anchor=\"end\" transform=\"translate(350,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2007</text><text text-anchor=\"end\" transform=\"translate(370,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2008</text><text text-anchor=\"end\" transform=\"translate(390,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2009</text><text text-anchor=\"end\" transform=\"translate(410,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2010</text><text text-anchor=\"end\" transform=\"translate(430,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2011</text><text text-anchor=\"end\" transform=\"translate(450,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(250,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g></g></g><g transform=\"translate(522,203)\"><path class=\"background\" d=\"M0,0h500v0h-500Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(130,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(150,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(170,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(190,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(210,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(230,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(250,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(270,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2006</text><text text-anchor=\"end\" transform=\"translate(350,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2007</text><text text-anchor=\"end\" transform=\"translate(370,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2008</text><text text-anchor=\"end\" transform=\"translate(390,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2009</text><text text-anchor=\"end\" transform=\"translate(410,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2010</text><text text-anchor=\"end\" transform=\"translate(430,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2011</text><text text-anchor=\"end\" transform=\"translate(450,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(250,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g></g></g></g><g class=\"mark-group role-scope cell\"><g transform=\"translate(0,0)\"><path class=\"background\" d=\"M0.5,0.5h500v200h-500Z\" style=\"fill: none; stroke: #ddd;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-grid\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #ddd; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,150)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #ddd; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,100)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #ddd; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,50)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #ddd; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,0)\" x2=\"500\" y2=\"0\" style=\"fill: none; stroke: #ddd; stroke-width: 1; opacity: 1;\"></line></g></g></g></g><g class=\"mark-group role-scope child_pathgroup\"><g transform=\"translate(0,0)\"><path class=\"background\" d=\"M0,0h500v200h-500Z\" style=\"fill: none;\"></path><g><g class=\"mark-line role-mark child_marks\"><path d=\"M10,100L30,89.70269690750503L50,98.20622924932722L70,87.12065849527943L90,92.16377113200487L110,98.98036879386476L130,98.98036879386476L150,124.67566281810846L170,124.67566281810846L190,187.12065849527943L210,187.12065849527943L230,187.12065849527943L250,187.12065849527943L270,187.12065849527943L290,187.12065849527943L310,187.12065849527943L330,187.12065849527943L350,187.12065849527943L370,187.12065849527943L390,187.12065849527943L410,187.12065849527943L430,187.12065849527943L450,187.12065849527943L470,151.28422083121947L490,161.9602637331851\" style=\"fill: none; 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fill: none;\"></path><g><g class=\"mark-group role-scope\"><g transform=\"translate(0,0)\"><path class=\"background\" d=\"M0,0h123v11h-123Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #4c78a8; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">PupilTeacherRatio</text></g></g></g><g transform=\"translate(0,13)\"><path class=\"background\" d=\"M0,0h123v11h-123Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #f58518; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">RateOutofSchoolFemale</text></g></g></g><g transform=\"translate(0,26)\"><path class=\"background\" d=\"M0,0h123v11h-123Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M-5,0L5,0\" style=\"fill: none; stroke: #e45756; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">RateOutofSchoolMale</text></g></g></g></g></g></g></g><g class=\"mark-text role-legend-title\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(0,9)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Series</text></g></g></g></g></g></g></g></g></svg>"
                        ],
                        "text/plain": [
                            "VegaLite.VLSpec{:plot}"
                        ]
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "graph = dfTranspose |>\n",
                "    @filter(_.Country == \"Turkey\" || _.Country == \"Zimbabwe\") |>\n",
                "    @filter(_.Series == \"PupilTeacherRatio\" || _.Series == \"RateOutofSchoolFemale\" || _.Series == \"RateOutofSchoolMale\") |> \n",
                "    @vlplot(:line, x=:\"Timestamp:o\", y=:Value, color=:Series, column=:Country)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Third, the previous graphs are very informative but we can go one step further. We create an interactive data visualizer for our data. We can slice and dice different parts of our data with a few mouse clicks. We can easily gain deeper insight into our data. Incredibly, we can create an interactive application with just one Queryverse command! And, we can easily save UI for sharing."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "Voyager(Electron.Window(Application(Base.PipeEndpoint(Base.Libc.WindowsRawSocket(0x0000000000000530) open, 0 bytes waiting), Process(`\u001b[4m'C:\\Users\\drcxcruz\\.julia\\artifacts\\3cac82ef60c809398a5dd6870b5e97d42dbe2df1\\electron.exe'\u001b[24m \u001b[4m'C:\\Users\\drcxcruz\\.julia\\packages\\Electron\\MNI24\\src\\main.js'\u001b[24m \u001b[4m'\\\\.\\pipe\\jlel-642b471050d711ea15cb49255953af32'\u001b[24m \u001b[4m'\\\\.\\pipe\\jlel-sn-642db81050d711ea0521c910d4efc841'\u001b[24m \u001b[4m3aT6GK25V1ZmZ9DldpL6Y4vzVkLO/I4md1fBcl1dZ3pzPpG2g2XypVlkPB6y/uwGGmQOoTbsoSiC/sGtshup8uHrz8E2k6f7RkmU9A38H9d9PUfE4/TBROJn5nC6vGk7gwTCuJdgv8M40i8VMVgEizpO+2KeLwEgjl/83SWIOHA=\u001b[24m`, ProcessRunning), [1 window]), 1, true, Channel{Any}(sz_max:128,sz_curr:0)))"
                        ]
                    },
                    "execution_count": 20,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "UI = dfTranspose |> Voyager"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "655×852 Array{RGBA{N0f8},2} with eltype RGBA{Normed{UInt8,8}}:\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)        …  RGBA{N0f8}(0.173,0.149,0.157,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.176,0.149,0.157,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.176,0.149,0.157,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.176,0.149,0.161,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.176,0.149,0.161,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)        …  RGBA{N0f8}(0.176,0.149,0.161,1.0)\n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)        …  RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.153,0.161,1.0) \n",
                            " RGBA{N0f8}(1.0,1.0,1.0,1.0)           RGBA{N0f8}(0.18,0.149,0.161,1.0) \n",
                            " ⋮                                  ⋱                                   \n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.157,0.157,0.165,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.157,0.161,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)  …  RGBA{N0f8}(0.153,0.153,0.161,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.161,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.161,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.149,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)  …  RGBA{N0f8}(0.153,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.157,1.0)\n",
                            " RGBA{N0f8}(0.204,0.259,0.365,1.0)     RGBA{N0f8}(0.153,0.153,0.157,1.0)"
                        ]
                    },
                    "execution_count": 1,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "using Images, ImageMagick\n",
                "img = load(\"DataVoyagerUI.png\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "\n",
                "\n",
                "Here, we save our prepared data in Feather file format for further analysis.  We also stored our graphs and interactive visualizer for future use.\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": [
                "dfSeriesFromZero |> save(\"SeriesFromZero.feather\")\n",
                "graph |> save(\"USASeries.vegalite\")\n",
                "UI[] |> save(\"CountrySeries.vegalite\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Machine Learning Time\n",
                "As stated before, we can take our time series and feed them to a machine learning model.  Let use the ARCHModels package to predict future values of As stated before, we can take our time series and feed them to a machine learning model. Let use the ARCHModels package to predict future values of time series. Our time series end in 2014. The goal is to predict from 2015 and 2017. The reason for only three values into the future is that our time series are short. The model could easily predict more values into the future if we had larger time series. \n",
                "\n",
                "Remember the topics behind ARCH modeling. Part 1 is the exponential smoothing model. Exponential smoothing considers all observations in the time series. The user is able to define the level of importance of the most recent values. Part 2 is to incorporate trends over time, cyclical variations, and randomness. \n",
                "\n",
                "ARIMA is a generalization or expansion of exponential smoothing model. ARIMA has three components.\n",
                "1. Take the difference between consequence time series values instead of the values themselves. Ths is the d order\n",
                "2. Autoregression predicts time series values using the previous p time periods\n",
                "3. Moving average predicts linear errors using the previous q time periods\n",
                "\n",
                "GARCH estimates the variance, not the values, of a time series. GARCH does not use any difference of variances. The variance shows the range of error in the value estimate."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We predict using the Government Expenditure for USA and UK. The machine learning model and Julia code can easily used any other of the time series in our education statistics. Notice we are predicting using the time series values already normalized and starting from zero. This makes it easy to graph our prediction along the previous graphs. One needs to non-normalize and translate the predictions to see values in the original value range."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "25-element Array{Float64,1}:\n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            "  0.0                \n",
                            " -0.03531195537976147\n",
                            "  0.04569456105255278\n",
                            "  0.4715552065502649 \n",
                            "  0.5564670818519    \n",
                            "  0.9646880446202385 \n",
                            "  0.6847297597191496 \n",
                            "  0.3005275687453213 \n",
                            "  0.629466953044126  \n",
                            "  0.6300853855930639 \n",
                            "  0.7370633532545653 \n",
                            "  0.7726682299482557 \n",
                            "  0.5971164127497698 \n",
                            "  0.4485129684606863 \n",
                            "  0.09565219415523354\n",
                            "  0.09565219415523354\n",
                            "  0.09565219415523354"
                        ]
                    },
                    "execution_count": 23,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfTimeValuesUK = dfTranspose |>\n",
                "    @filter(_.Country == \"United Kingdom\") |>\n",
                "    @filter(_.Series == \"GovernmentExpenditure\") |>\n",
                "    DataFrame\n",
                "\n",
                "vUKGovernmentExpenditure = replace(dfTimeValuesUK[!, 2], missing=>NaN)  # convert to float vector\n",
                "\n",
                "dfTimeValuesUSA = dfTranspose |>\n",
                "    @filter(_.Country == \"United States\") |>\n",
                "    @filter(_.Series == \"GovernmentExpenditure\") |>\n",
                "    DataFrame\n",
                "\n",
                "vUSAGovernmentExpenditure = replace(dfTimeValuesUSA[!, 2], missing=>NaN)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The ARCHModels package finds optimal coeffiencents for the given time series. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Model USA\n",
                        "TGARCH{0,0,0} model with Gaussian errors, T=25.\n",
                        "\n",
                        "Mean equation parameters:\n",
                        "────────────────────────────────────────────\n",
                        "     Estimate  Std.Error   z value  Pr(>|z|)\n",
                        "────────────────────────────────────────────\n",
                        "c   0.0766742  0.0456966  1.6779      0.0934\n",
                        "φ₁  0.755663   0.166285   4.5444      <1e-5 \n",
                        "θ₁  0.169521   0.236156   0.717834    0.4729\n",
                        "────────────────────────────────────────────\n",
                        "\n",
                        "Volatility parameters:\n",
                        "─────────────────────────────────────────\n",
                        "   Estimate  Std.Error  z value  Pr(>|z|)\n",
                        "─────────────────────────────────────────\n",
                        "ω  0.034447  0.0107551  3.20286    0.0014\n",
                        "─────────────────────────────────────────\n",
                        "\n",
                        "Model UK\n",
                        "TGARCH{0,0,0} model with Gaussian errors, T=25.\n",
                        "\n",
                        "Mean equation parameters:\n",
                        "──────────────────────────────────────────────\n",
                        "      Estimate  Std.Error    z value  Pr(>|z|)\n",
                        "──────────────────────────────────────────────\n",
                        "c   -0.0908406  0.0128988  -7.04255     <1e-11\n",
                        "φ₁   0.376258   0.272877    1.37886     0.1679\n",
                        "φ₂   0.169518   0.353733    0.479226    0.6318\n",
                        "φ₃  -0.538235   0.184911   -2.91077     0.0036\n",
                        "θ₁   1.0        0.178747    5.59449     <1e-7 \n",
                        "θ₂   0.221982   0.214621    1.0343      0.3010\n",
                        "θ₃   0.890425   0.207606    4.28901     <1e-4 \n",
                        "──────────────────────────────────────────────\n",
                        "\n",
                        "Volatility parameters:\n",
                        "───────────────────────────────────────────\n",
                        "    Estimate   Std.Error  z value  Pr(>|z|)\n",
                        "───────────────────────────────────────────\n",
                        "ω  0.0118368  0.00504084  2.34817    0.0189\n",
                        "───────────────────────────────────────────\n",
                        "\n"
                    ]
                }
            ],
            "source": [
                "modelUSA = selectmodel(ARCH{0}, vUSAGovernmentExpenditure;  meanspec=ARMA)\n",
                "println(\"Model USA\", modelUSA)\n",
                "modelUK = selectmodel(ARCH{0}, vUKGovernmentExpenditure;  meanspec=ARMA)\n",
                "println(\"Model UK\", modelUK)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, we are ready to time series predict. We create a function to predict many values in the future. Again, only three values into the future because our time series are very short in time. "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "USA [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.03531195537976147, 0.04569456105255278, 0.4715552065502649, 0.5564670818519, 0.9646880446202385, 0.6847297597191496, 0.3005275687453213, 0.629466953044126, 0.6300853855930639, 0.7370633532545653, 0.7726682299482557, 0.5971164127497698, 0.4485129684606863, 0.09565219415523354, 0.09565219415523354, 0.09565219415523354, 0.13994636330740723, 0.18238750370821127, 0.21442658590980135]\n",
                        "UK [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.08701043295048905, 0.04774100840553319, 0.0622568753061955, 0.3207623665481527, 0.47715314794959995, 0.34829896021261364, 0.3121314764560086, -0.5002882127419274, -0.5200025781302012, -0.5228468520504, -0.3517072366536428, -0.19263177986860147, 0.06230598254822861, 0.06230598254822861, -0.05491329648730353, -0.05491329648730353, -0.13250846231113753, -0.10259002029188521, -0.08424483319422896]\n"
                    ]
                }
            ],
            "source": [
                "function predictTimeValue(v, window)\n",
                "    l=length(v)\n",
                "    predictions = deepcopy(v) \n",
                "    for i = 1:futureWindow \n",
                "      push!(predictions, 0.0)\n",
                "    end\n",
                "\n",
                "    for t = l:l+window-1\n",
                "      m = selectmodel(ARCH{0}, predictions[1:t];  meanspec=ARMA)  \n",
                "      predictions[t+1] = predict(m, :return; level=0.05);\n",
                "    end\n",
                "    return predictions\n",
                "end\n",
                "\n",
                "futureWindow = 3\n",
                "\n",
                "predictionsUSA = predictTimeValue( vUSAGovernmentExpenditure, futureWindow)\n",
                "println(\"USA \", predictionsUSA)\n",
                "\n",
                "predictionsUK = predictTimeValue( vUKGovernmentExpenditure, futureWindow)\n",
                "println(\"UK \", predictionsUK)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, we add our predictions to original time series.  We will soon see visually the quality of our predictions."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<table class=\"data-frame\"><thead><tr><th></th><th>Timestamp</th><th>Value</th><th>Country</th><th>Series</th></tr><tr><th></th><th>Symbol</th><th>Float64⍰</th><th>String</th><th>String</th></tr></thead><tbody><p>881 rows × 4 columns</p><tr><th>1</th><td>1990</td><td>missing</td><td>Turkey</td><td>GovernmentExpenditure</td></tr><tr><th>2</th><td>1990</td><td>0.0</td><td>Turkey</td><td>PupilTeacherRatio</td></tr><tr><th>3</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>4</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolFemale</td></tr><tr><th>5</th><td>1990</td><td>0.0</td><td>Turkey</td><td>RateOutofSchoolMale</td></tr><tr><th>6</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>GovernmentExpenditure</td></tr><tr><th>7</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>PupilTeacherRatio</td></tr><tr><th>8</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>9</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolFemale</td></tr><tr><th>10</th><td>1990</td><td>0.0</td><td>United Arab Emirates</td><td>RateOutofSchoolMale</td></tr><tr><th>11</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>GovernmentExpenditure</td></tr><tr><th>12</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>PupilTeacherRatio</td></tr><tr><th>13</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>14</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolFemale</td></tr><tr><th>15</th><td>1990</td><td>0.0</td><td>United Kingdom</td><td>RateOutofSchoolMale</td></tr><tr><th>16</th><td>1990</td><td>0.0</td><td>United States</td><td>GovernmentExpenditure</td></tr><tr><th>17</th><td>1990</td><td>0.0</td><td>United States</td><td>PupilTeacherRatio</td></tr><tr><th>18</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>19</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolFemale</td></tr><tr><th>20</th><td>1990</td><td>0.0</td><td>United States</td><td>RateOutofSchoolMale</td></tr><tr><th>21</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>GovernmentExpenditure</td></tr><tr><th>22</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>PupilTeacherRatio</td></tr><tr><th>23</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>24</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolFemale</td></tr><tr><th>25</th><td>1990</td><td>0.0</td><td>Uruguay</td><td>RateOutofSchoolMale</td></tr><tr><th>26</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>GovernmentExpenditure</td></tr><tr><th>27</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>PupilTeacherRatio</td></tr><tr><th>28</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolBothSexes</td></tr><tr><th>29</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolFemale</td></tr><tr><th>30</th><td>1990</td><td>missing</td><td>Virgin Islands (U.S.)</td><td>RateOutofSchoolMale</td></tr><tr><th>&vellip;</th><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td><td>&vellip;</td></tr></tbody></table>"
                        ],
                        "text/latex": [
                            "\\begin{tabular}{r|cccc}\n",
                            "\t& Timestamp & Value & Country & Series\\\\\n",
                            "\t\\hline\n",
                            "\t& Symbol & Float64⍰ & String & String\\\\\n",
                            "\t\\hline\n",
                            "\t1 & 1990 &  & Turkey & GovernmentExpenditure \\\\\n",
                            "\t2 & 1990 & 0.0 & Turkey & PupilTeacherRatio \\\\\n",
                            "\t3 & 1990 & 0.0 & Turkey & RateOutofSchoolBothSexes \\\\\n",
                            "\t4 & 1990 & 0.0 & Turkey & RateOutofSchoolFemale \\\\\n",
                            "\t5 & 1990 & 0.0 & Turkey & RateOutofSchoolMale \\\\\n",
                            "\t6 & 1990 & 0.0 & United Arab Emirates & GovernmentExpenditure \\\\\n",
                            "\t7 & 1990 & 0.0 & United Arab Emirates & PupilTeacherRatio \\\\\n",
                            "\t8 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolBothSexes \\\\\n",
                            "\t9 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolFemale \\\\\n",
                            "\t10 & 1990 & 0.0 & United Arab Emirates & RateOutofSchoolMale \\\\\n",
                            "\t11 & 1990 & 0.0 & United Kingdom & GovernmentExpenditure \\\\\n",
                            "\t12 & 1990 & 0.0 & United Kingdom & PupilTeacherRatio \\\\\n",
                            "\t13 & 1990 & 0.0 & United Kingdom & RateOutofSchoolBothSexes \\\\\n",
                            "\t14 & 1990 & 0.0 & United Kingdom & RateOutofSchoolFemale \\\\\n",
                            "\t15 & 1990 & 0.0 & United Kingdom & RateOutofSchoolMale \\\\\n",
                            "\t16 & 1990 & 0.0 & United States & GovernmentExpenditure \\\\\n",
                            "\t17 & 1990 & 0.0 & United States & PupilTeacherRatio \\\\\n",
                            "\t18 & 1990 & 0.0 & United States & RateOutofSchoolBothSexes \\\\\n",
                            "\t19 & 1990 & 0.0 & United States & RateOutofSchoolFemale \\\\\n",
                            "\t20 & 1990 & 0.0 & United States & RateOutofSchoolMale \\\\\n",
                            "\t21 & 1990 & 0.0 & Uruguay & GovernmentExpenditure \\\\\n",
                            "\t22 & 1990 & 0.0 & Uruguay & PupilTeacherRatio \\\\\n",
                            "\t23 & 1990 & 0.0 & Uruguay & RateOutofSchoolBothSexes \\\\\n",
                            "\t24 & 1990 & 0.0 & Uruguay & RateOutofSchoolFemale \\\\\n",
                            "\t25 & 1990 & 0.0 & Uruguay & RateOutofSchoolMale \\\\\n",
                            "\t26 & 1990 &  & Virgin Islands (U.S.) & GovernmentExpenditure \\\\\n",
                            "\t27 & 1990 &  & Virgin Islands (U.S.) & PupilTeacherRatio \\\\\n",
                            "\t28 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolBothSexes \\\\\n",
                            "\t29 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolFemale \\\\\n",
                            "\t30 & 1990 &  & Virgin Islands (U.S.) & RateOutofSchoolMale \\\\\n",
                            "\t$\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ & $\\dots$ \\\\\n",
                            "\\end{tabular}\n"
                        ],
                        "text/plain": [
                            "881×4 DataFrame. Omitted printing of 1 columns\n",
                            "│ Row │ Timestamp │ Value      │ Country              │\n",
                            "│     │ \u001b[90mSymbol\u001b[39m    │ \u001b[90mFloat64⍰\u001b[39m   │ \u001b[90mString\u001b[39m               │\n",
                            "├─────┼───────────┼────────────┼──────────────────────┤\n",
                            "│ 1   │ 1990      │ \u001b[90mmissing\u001b[39m    │ Turkey               │\n",
                            "│ 2   │ 1990      │ 0.0        │ Turkey               │\n",
                            "│ 3   │ 1990      │ 0.0        │ Turkey               │\n",
                            "│ 4   │ 1990      │ 0.0        │ Turkey               │\n",
                            "│ 5   │ 1990      │ 0.0        │ Turkey               │\n",
                            "│ 6   │ 1990      │ 0.0        │ United Arab Emirates │\n",
                            "│ 7   │ 1990      │ 0.0        │ United Arab Emirates │\n",
                            "│ 8   │ 1990      │ 0.0        │ United Arab Emirates │\n",
                            "│ 9   │ 1990      │ 0.0        │ United Arab Emirates │\n",
                            "│ 10  │ 1990      │ 0.0        │ United Arab Emirates │\n",
                            "⋮\n",
                            "│ 871 │ 2014      │ 0.0        │ Zimbabwe             │\n",
                            "│ 872 │ 2014      │ -0.752613  │ Zimbabwe             │\n",
                            "│ 873 │ 2014      │ -0.28204   │ Zimbabwe             │\n",
                            "│ 874 │ 2014      │ -0.468758  │ Zimbabwe             │\n",
                            "│ 875 │ 2014      │ -0.0239847 │ Zimbabwe             │\n",
                            "│ 876 │ 2015      │ 0.139946   │ United States        │\n",
                            "│ 877 │ 2016      │ 0.182388   │ United States        │\n",
                            "│ 878 │ 2017      │ 0.214427   │ United States        │\n",
                            "│ 879 │ 2015      │ -0.132508  │ United Kingdom       │\n",
                            "│ 880 │ 2016      │ -0.10259   │ United Kingdom       │\n",
                            "│ 881 │ 2017      │ -0.0842448 │ United Kingdom       │"
                        ]
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "dfPredictions = copy(dfTranspose) \n",
                "\n",
                "# you can create a loop to define the 3 rows per country, if you like\n",
                "l=length(predictionsUSA)\n",
                "push!(dfPredictions,[Symbol(\"2015\"), predictionsUSA[l-2], \"United States\", \"GovernmentExpenditurePredicted\"])\n",
                "push!(dfPredictions,[Symbol(\"2016\"), predictionsUSA[l-1], \"United States\", \"GovernmentExpenditurePredicted\"])\n",
                "push!(dfPredictions,[Symbol(\"2017\"), predictionsUSA[l],   \"United States\", \"GovernmentExpenditurePredicted\"])\n",
                "\n",
                "l=length(predictionsUK)\n",
                "push!(dfPredictions,[Symbol(\"2015\"), predictionsUK[l-2], \"United Kingdom\", \"GovernmentExpenditurePredicted\"])\n",
                "push!(dfPredictions,[Symbol(\"2016\"), predictionsUK[l-1], \"United Kingdom\", \"GovernmentExpenditurePredicted\"])\n",
                "push!(dfPredictions,[Symbol(\"2017\"), predictionsUK[l],   \"United Kingdom\", \"GovernmentExpenditurePredicted\"])"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "The graphs of the original time series and our predictions are next.  I say our predictions are pretty good, what do you think?"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "application/vnd.vegalite.v3+json": {
                            "data": {
                                "values": [
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1990",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1991",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1992",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1993",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1994",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1995",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1996",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1996",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1997",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1997",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1998",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1998",
                                        "Value": 0
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1999",
                                        "Value": -0.08701043295048905
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "1999",
                                        "Value": -0.03531195537976147
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2000",
                                        "Value": 0.04774100840553319
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2000",
                                        "Value": 0.04569456105255278
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2001",
                                        "Value": 0.0622568753061955
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2001",
                                        "Value": 0.4715552065502649
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2002",
                                        "Value": 0.3207623665481527
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2002",
                                        "Value": 0.5564670818519
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2003",
                                        "Value": 0.47715314794959995
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2003",
                                        "Value": 0.9646880446202385
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2004",
                                        "Value": 0.34829896021261364
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2004",
                                        "Value": 0.6847297597191496
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2005",
                                        "Value": 0.3121314764560086
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2005",
                                        "Value": 0.3005275687453213
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2006",
                                        "Value": -0.5002882127419274
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2006",
                                        "Value": 0.629466953044126
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2007",
                                        "Value": -0.5200025781302012
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2007",
                                        "Value": 0.6300853855930639
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2008",
                                        "Value": -0.5228468520504
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2008",
                                        "Value": 0.7370633532545653
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2009",
                                        "Value": -0.3517072366536428
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2009",
                                        "Value": 0.7726682299482557
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                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2010",
                                        "Value": -0.19263177986860147
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2010",
                                        "Value": 0.5971164127497698
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                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2011",
                                        "Value": 0.06230598254822861
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2011",
                                        "Value": 0.4485129684606863
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                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2012",
                                        "Value": 0.06230598254822861
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2012",
                                        "Value": 0.09565219415523354
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2013",
                                        "Value": -0.05491329648730353
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2013",
                                        "Value": 0.09565219415523354
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2014",
                                        "Value": -0.05491329648730353
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditure",
                                        "Timestamp": "2014",
                                        "Value": 0.09565219415523354
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2015",
                                        "Value": 0.13994636330740723
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2016",
                                        "Value": 0.18238750370821127
                                    },
                                    {
                                        "Country": "United States",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2017",
                                        "Value": 0.21442658590980135
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2015",
                                        "Value": -0.13250846231113753
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2016",
                                        "Value": -0.10259002029188521
                                    },
                                    {
                                        "Country": "United Kingdom",
                                        "Series": "GovernmentExpenditurePredicted",
                                        "Timestamp": "2017",
                                        "Value": -0.08424483319422896
                                    }
                                ]
                            },
                            "encoding": {
                                "color": {
                                    "field": "Series",
                                    "type": "nominal"
                                },
                                "column": {
                                    "field": "Country",
                                    "type": "nominal"
                                },
                                "x": {
                                    "field": "Timestamp",
                                    "type": "ordinal"
                                },
                                "y": {
                                    "field": "Value",
                                    "type": "quantitative"
                                }
                            },
                            "mark": {
                                "point": true,
                                "type": "line"
                            }
                        },
                        "image/png": 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fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(0,188)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,125)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,63)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(0,0)\" x2=\"-5\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(-7,190.5)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">-0.5</text><text text-anchor=\"end\" transform=\"translate(-7,128)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.0</text><text text-anchor=\"end\" transform=\"translate(-7,65.5)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">0.5</text><text text-anchor=\"end\" transform=\"translate(-7,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1.0</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,200)\" x2=\"0\" y2=\"-200\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(-28,100) rotate(-90) translate(0,-2)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Value</text></g></g></g></g></g></g></g><g class=\"mark-group role-column-header column_header\"><g transform=\"translate(0,-1)\"><path class=\"background\" d=\"M0,0h560v0h-560Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-title\"><g transform=\"translate(280,-20)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-text role-title-text\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(0,8)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">United Kingdom</text></g></g></g></g></g></g><g transform=\"translate(582,-1)\"><path class=\"background\" d=\"M0,0h560v0h-560Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-title\"><g transform=\"translate(280,-20)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-text role-title-text\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(0,8)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">United States</text></g></g></g></g></g></g></g><g class=\"mark-group role-column-footer column_footer\"><g transform=\"translate(0,201)\"><path class=\"background\" d=\"M0,0h560v0h-560Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(130,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(150,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(170,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(190,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(210,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(230,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(250,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(270,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(510,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(530,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(550,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2006</text><text text-anchor=\"end\" transform=\"translate(350,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2007</text><text text-anchor=\"end\" transform=\"translate(370,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2008</text><text text-anchor=\"end\" transform=\"translate(390,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2009</text><text text-anchor=\"end\" transform=\"translate(410,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2010</text><text text-anchor=\"end\" transform=\"translate(430,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2011</text><text text-anchor=\"end\" transform=\"translate(450,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text><text text-anchor=\"end\" transform=\"translate(510,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2015</text><text text-anchor=\"end\" transform=\"translate(530,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2016</text><text text-anchor=\"end\" transform=\"translate(550,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2017</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"560\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(280,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g></g></g><g transform=\"translate(582,201)\"><path class=\"background\" d=\"M0,0h560v0h-560Z\" style=\"fill: none;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-tick\" style=\"pointer-events: none;\"><line transform=\"translate(10,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(30,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(50,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(70,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(90,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(110,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(130,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(150,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(170,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(190,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(210,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(230,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(250,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(270,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(290,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(310,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(330,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(350,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(370,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(390,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(410,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(430,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(450,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(470,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(490,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(510,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(530,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line><line transform=\"translate(550,0)\" x2=\"0\" y2=\"5\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-label\" style=\"pointer-events: none;\"><text text-anchor=\"end\" transform=\"translate(10,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1990</text><text text-anchor=\"end\" transform=\"translate(30,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1991</text><text text-anchor=\"end\" transform=\"translate(50,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1992</text><text text-anchor=\"end\" transform=\"translate(70,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1993</text><text text-anchor=\"end\" transform=\"translate(90,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1994</text><text text-anchor=\"end\" transform=\"translate(110,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1995</text><text text-anchor=\"end\" transform=\"translate(130,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1996</text><text text-anchor=\"end\" transform=\"translate(150,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1997</text><text text-anchor=\"end\" transform=\"translate(170,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1998</text><text text-anchor=\"end\" transform=\"translate(190,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">1999</text><text text-anchor=\"end\" transform=\"translate(210,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2000</text><text text-anchor=\"end\" transform=\"translate(230,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2001</text><text text-anchor=\"end\" transform=\"translate(250,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2002</text><text text-anchor=\"end\" transform=\"translate(270,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2003</text><text text-anchor=\"end\" transform=\"translate(290,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2004</text><text text-anchor=\"end\" transform=\"translate(310,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2005</text><text text-anchor=\"end\" transform=\"translate(330,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2006</text><text text-anchor=\"end\" transform=\"translate(350,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2007</text><text text-anchor=\"end\" transform=\"translate(370,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2008</text><text text-anchor=\"end\" transform=\"translate(390,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2009</text><text text-anchor=\"end\" transform=\"translate(410,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2010</text><text text-anchor=\"end\" transform=\"translate(430,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2011</text><text text-anchor=\"end\" transform=\"translate(450,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2012</text><text text-anchor=\"end\" transform=\"translate(470,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2013</text><text text-anchor=\"end\" transform=\"translate(490,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2014</text><text text-anchor=\"end\" transform=\"translate(510,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2015</text><text text-anchor=\"end\" transform=\"translate(530,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2016</text><text text-anchor=\"end\" transform=\"translate(550,7) rotate(270) translate(0,3)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">2017</text></g><g class=\"mark-rule role-axis-domain\" style=\"pointer-events: none;\"><line transform=\"translate(0,0)\" x2=\"560\" y2=\"0\" style=\"fill: none; stroke: #888; stroke-width: 1; opacity: 1;\"></line></g><g class=\"mark-text role-axis-title\" style=\"pointer-events: none;\"><text text-anchor=\"middle\" transform=\"translate(280,40)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Timestamp</text></g></g></g></g></g></g></g><g class=\"mark-group role-scope cell\"><g transform=\"translate(0,0)\"><path class=\"background\" d=\"M0.5,0.5h560v200h-560Z\" style=\"fill: none; stroke: #ddd;\"></path><g><g class=\"mark-group role-axis\"><g transform=\"translate(0.5,0.5)\"><path class=\"background\" d=\"M0,0h0v0h0Z\" style=\"pointer-events: none; fill: none;\"></path><g><g class=\"mark-rule role-axis-grid\" style=\"pointer-events: none;\"><line transform=\"translate(0,188)\" x2=\"560\" y2=\"0\" style=\"fill: none; 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fill: #000; opacity: 1;\">GovernmentExpenditure</text></g></g></g><g transform=\"translate(0,16)\"><path class=\"background\" d=\"M0,0h168v12.5h-168Z\" style=\"pointer-events: none; fill: none; opacity: 1;\"></path><g><g class=\"mark-symbol role-legend-symbol\" style=\"pointer-events: none;\"><path transform=\"translate(6,6)\" d=\"M5,0A5,5,0,1,1,-5,0A5,5,0,1,1,5,0\" style=\"fill: #f58518; stroke: #f58518; stroke-width: 1.5; opacity: 1;\"></path></g><g class=\"mark-text role-legend-label\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(16,9)\" style=\"font-family: sans-serif; font-size: 10px; fill: #000; opacity: 1;\">GovernmentExpenditurePredicted</text></g></g></g></g></g></g></g><g class=\"mark-text role-legend-title\" style=\"pointer-events: none;\"><text text-anchor=\"start\" transform=\"translate(0,9)\" style=\"font-family: sans-serif; font-size: 11px; font-weight: bold; fill: #000; opacity: 1;\">Series</text></g></g></g></g></g></g></g></g></svg>"
                        ],
                        "text/plain": [
                            "VegaLite.VLSpec{:plot}"
                        ]
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "graph = dfPredictions |>\n",
                "    @filter(_.Country == \"United States\" || _.Country == \"United Kingdom\") |>\n",
                "    @filter(_.Series == \"GovernmentExpenditure\" || _.Series == \"GovernmentExpenditurePredicted\") |> \n",
                "    @vlplot(\n",
                "        mark={\n",
                "        :line,\n",
                "        point=true\n",
                "        },\n",
                "        x=:\"Timestamp:o\", y=:Value, color=:Series, column=:Country)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Conclusion\n",
                "This JupyterLab shows the power and convenience of Queryverse, ARCHModels package, and Julia 1.31. We read real world statistics. Then, we cleaned, imputed, and graph our data. We also created an interactive data visualizer for our data. Lastly, we predicted future values with ARCH modeling. All with simple and fast Julia code.\n",
                "\n",
                "As most real world data science projects, the data cleaning and preparation is the most time consuming sections. Each data cleaning and preparation step is just a few simple lines of Julia code when using Queryverse. Queryverse allows us to use Dataframes, and IndexTables whenever and however it was convenient. That is amazing. Queryverse works with any Julia package that implements IterableTable package which is a named tuple interface. We could easily use the TimeSeries package if our time period was smaller than 1 year.\n",
                "\n",
                "VegaLite and Voyager are easy to use and are superior to graphing in R Studio. It literally takes one line of code to graph or create an interactive visualizer of our data. In addition, the ARCHModels package is well documented and easy to use."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## About the Author\n",
                "\n",
                "Clarman Cruz completed his BS/MS in Computer Science from State University of New York at Stony Brook/Albany. He has more than 15 years technology experience in the banking industry. He is completing on May 2020 his edX MicroMasters in Analytics from Georgia Tech."
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Julia 1.3.1",
            "language": "julia",
            "name": "julia-1.3"
        },
        "language_info": {
            "file_extension": ".jl",
            "mimetype": "application/julia",
            "name": "julia",
            "version": "1.3.1"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}