climate data.Rmd

---
title: "313 project temp"
author: "Yara Ghabra 1006336056"
date: "2022-10-31"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
getwd()
library(tidyverse)
```

```{r}
library(tidyverse)
library(lubridate)
temp <- read.csv("/Users/gracewalkermitchell/Desktop/EEB313/GroupB/CALIFORNIA.csv")

temp_annual<- temp %>% 
  select(STATION, NAME, DATE, PRCP, TMAX, TMIN) %>% 
  group_by(DATE) %>%
  filter(!is.na(TMAX), !is.na(TMIN), !is.na(PRCP)) %>% 
  summarise(avg_prcp= mean(PRCP), avg_tmax= mean(TMAX), avg_tmin= mean(TMIN))

temp_annual
```

```{r}
#LOUISIANA

Louisiana <- read.csv("/Users/gracewalkermitchell/Desktop/EEB313/GroupB/Louisiana metric.csv")

lou_temp_monthly<- Louisiana %>% 
  select(STATION, NAME, DATE, PRCP, TMAX, TMIN) %>% 
  group_by(DATE) %>%
  filter(!is.na(TMAX), !is.na(TMIN), !is.na(PRCP)) %>%
  mutate(month= month(DATE), year= year(DATE), day=yday(DATE))

lou_temp_month<- lou_temp_monthly %>% 
  group_by(year, month) %>% 
  filter(!is.na(TMAX), !is.na(TMIN), !is.na(PRCP)) %>% 
  summarise(avg_prcp= mean(PRCP), avg_tmax= mean(TMAX), avg_tmin= mean(TMIN)) %>% 
  mutate(year=as.factor(year), month=as.factor(month))
lou_temp_month

lou_temp_month %>% 
  ggplot(aes(x=month, y=avg_tmax, colour=year))+
  geom_point()+
  geom_line(mapping=aes(group=year))


```
```{r}
#LOUISIANA SUMMER TEMP MAXES
lou_summer_max<-lou_temp_monthly %>% 
  group_by(year, month) %>% 
  mutate(avg_temp=mean(TMAX)) %>% 
  filter(5<=month & month<=10) %>% 
  select(month, year, avg_temp) %>% 
  unique()

lou_summer_max$year<-as.factor(lou_summer_max$year)

ggplot(data=lou_summer_max, aes(month, avg_temp, colour=year))+
         geom_point()+
         geom_line(mapping=aes(group=year))
         


```

```{r}
#CALIFORNIA

library("lubridate")
Cali <- read.csv("CALIFORNIA.csv")

cali_temp_monthly<- Cali %>% 
  select(STATION, NAME, DATE, PRCP, TMAX, TMIN) %>% 
  group_by(DATE) %>%
  filter(!is.na(TMAX), !is.na(TMIN), !is.na(PRCP)) %>%
  mutate(month= month(DATE), year= year(DATE), day=yday(DATE))

cali_temp_month<- cali_temp_monthly %>% 
  group_by(year, month) %>% 
  filter(!is.na(TMAX), !is.na(TMIN), !is.na(PRCP)) %>% 
  summarise(avg_prcp= mean(PRCP), avg_tmax= mean(TMAX), avg_tmin= mean(TMIN)) %>% 
  mutate(year=as.factor(year), month=as.factor(month))
cali_temp_month

cali_temp_month %>% 
  ggplot(aes(x=month, y=avg_tmax, colour=year))+
  geom_point()+
  geom_line(mapping=aes(group=year))

  

```
```{r}
#CALIFORNIA SUMMER TEMP MAXES
cali_summer_max<-cali_temp_monthly %>% 
  group_by(year, month) %>% 
  mutate(avg_temp=mean(TMAX)) %>% 
  filter(5<=month & month<=10) %>% 
  select(month, year, avg_temp) %>% 
  unique()

cali_summer_max$year<-as.factor(cali_summer_max$year)

ggplot(data=cali_summer_max, aes(month, avg_temp, colour=year))+
         geom_point()+
         geom_line(mapping=aes(group=year))
```


###Precipitation


```{r}
#CALIFORNIA
cali_precip_yearly<- cali_temp_monthly %>% 
  group_by(year) %>% 
  filter(!is.na(PRCP)) %>% 
  mutate(year=as.factor(year), avg.precip=mean(PRCP)) %>% 
  select(year, avg.precip) %>% 
  unique()

cali_precip_yearly
```

```{r}
cali_precip_yearly %>% 
  ggplot(aes(x=year, y=avg.precip))+
  geom_col()
```
```{r}
#LOUISIANA
lou_precip_yearly<- lou_temp_monthly%>% 
  group_by(year) %>% 
  filter(!is.na(PRCP)) %>% 
  mutate(year=as.factor(year), avg.precip=mean(PRCP)) %>% 
  select(year, avg.precip) %>% 
  unique()

lou_precip_yearly
```
```{r}
lou_precip_yearly %>% 
  ggplot(aes(x=year, y=avg.precip))+
  geom_col()
#much less variable than california
```



###are summers getting drier? could look at dates of rain throughout the years

```{r}
#CALIFORNIA dates of rain - year round
cali_temp_monthly %>% 
  ggplot(aes(day, PRCP))+
  geom_col()+
  facet_wrap(~year)
```

```{r}
#are summers getting drier? dates of rain between April and September
cali_temp_monthly %>% 
  filter(month>=4 & month<=9) %>% 
  ggplot(aes(day, PRCP))+
  geom_col()+
  facet_wrap(~year)
```

```{r}
#mean yearly summer precipitation in California
cali_temp_monthly %>% 
  filter(month>=4 & month<=9) %>%
  group_by(year) %>% 
  summarise(mean_precip=mean(PRCP)) %>% 
  ggplot(aes(year, mean_precip))+
  geom_point()+
  geom_smooth()+
  theme_classic()
```