Mid_Project_Update

---
title: "Mid Project Update: Nov 3, 2022"
output: html_notebook
---

```{r}
mosquito_data<-read.csv("CDC_host_seeking_mosquitoes_and_MClim_data_NERC.csv")
```

Exploratory Analysis
```{r}
# Removing the columns that we are not looking at (includes male abundances, and maximum and minimum temperature and humidity)
clean_mosquito_data<-subset(mosquito_data, select = -c(Agml,Afml,Acml,Acfem, 
                                  OthAn,MaxTemp,MaxHum,MinTemp,MinHum))
head(clean_mosquito_data)

write.csv(clean_mosquito_data, "clean_mosquito_data.csv")
```

```{r}
# Plot of MeanTemp and An. gambiae
ggplot(data=clean_mosquito_data)+theme_classic()+
  geom_point(aes(x=MeanTemp, y=Agfed, colour="Agfed"))+
  geom_point(aes(x=MeanTemp, y=Agunf, colour="Agunf"))+
  geom_point(aes(x=MeanTemp, y=Aggrv, colour="Aggrv"))+
  labs(title="Investigating Mean Temp on the three states of An. gambiae", y="abundance")

# Plot of MeanTemp and An. funestus
ggplot(data=clean_mosquito_data)+theme_classic()+
  geom_point(aes(x=MeanTemp, y=Affed, colour="Affed"))+
  geom_point(aes(x=MeanTemp, y=Affunf, colour="Affunf"))+
  geom_point(aes(x=MeanTemp, y=Afgrv, colour="Afgrv"))+
  labs(title="Investigating Mean Temp on the three states of An. funestus", y="abundance")

# Plot of MeanHum and An. gambiae
ggplot(data=clean_mosquito_data)+theme_classic()+
  geom_point(aes(x=MeanHum, y=Agfed, colour="Agfed"))+
  geom_point(aes(x=MeanHum, y=Agunf, colour="Agunf"))+
  geom_point(aes(x=MeanHum, y=Aggrv, colour="Aggrv"))+
  labs(title="Investigating Mean Humidity on the three states of An. gambiae", y="abundance")

# Plot of MeanHum and An. funestus
ggplot(data=clean_mosquito_data)+theme_classic()+
  geom_point(aes(x=MeanHum, y=Affed, colour="Affed"))+
  geom_point(aes(x=MeanHum, y=Affunf, colour="Affunf"))+
  geom_point(aes(x=MeanHum, y=Afgrv, colour="Afgrv"))+
  labs(title="Investigating Mean Humidity on the three states of An. funestus", y="abundance")

```
```{r}
# Looking at number of observations in each village
clean_mosquito_data %>% 
  group_by(village) %>% 
  tally()
```
```{r}
# Looking at number of observations in each household
clean_mosquito_data %>% 
  group_by(VillageHH) %>% 
  tally()
```

Data Collection Methods

The research was conducted and data were collected in the Kilombero Valley, a global malaria hotspot in Tanzania. Since 2007, entomologists from the University of Glasgow have trapped malaria vectors from four households across four villages within the valley (note: vectors were trapped using MET and CDC trap designs). This dataset ranges from June 2016 to September 2017. The individual trapped mosquitoes were sexed, identified as An. gambiae or An. funestus, and classified as fed, unfed and gravid. A number of climatic factors --including maximum, minimum, and mean humidity and temperature-- were tracked daily at each vector trapping site. Standard CDC traps were left overnight inside the households within each village. Each village was visited at least 5 days per month over the project. 

This dataset was collected by the University of Glasgow, in collaboration with the NERC ENvironmental Information Data Centre (a reliable source; it was not open-source community data). The data was clean to begin with; there were no NAs present. As a part of the data manipulation, we removed the columns that we were not investigating. Agml (number of male An. gambiae), Afml (number of male An. funestus) were removed because we are investigating female mosquitoes since they are the only ones who can transmit malaria. We deleted the columns Acml (number of male An.coustani) and Acfem (number of female An.coustani) since there was no data on their fed, unfed or gravid states. OthAn (number of other Anopheles species) was also removed because we are specifically looking at two distinct species. MaxTemp, MaxHum, MinTemp, and MinHum were all removed because we will be using the MeanTemp and MeanHum to investigate our hypotheses.