Untitled.Rmd

---
title: "census"
output: html_document
date: "2024-02-07"
editor_options: 
  chunk_output_type: console
---
```{r relevant packages}

library(tidycensus)
library(ggplot2)
library(tidyverse)
library(usmap)
library(maps)
library(dplyr)
library(viridis)
library(wesanderson)
library(RColorBrewer)


```



```{r }
dat<-read.csv("~/Dropbox/EDS_2024/eds_summit_comm_access/NRI_Table_CensusTracts_Mississippi/NRI_Table_CensusTracts_Mississippi.csv")

names(dat)[names(dat) == 'STCOFIPS'] <- 'fips'
pal <- wes_palette("Zissou1", 100, type = "continuous")
plot_usmap(data = dat, values = "RISK_SCORE", include=c("MS")) + 
  scale_fill_gradientn(colours = pal,name = "Risk", label = scales::comma) + 
  labs(title = "FEMA RISK") +
  theme(legend.position = "right")

hist(dat$RISK_SCORE)

nation<-read.csv("~/Dropbox/EDS_2024/NRI_Table_CensusTracts/NRI_Table_CensusTracts.csv")
names(nation)[names(nation) == 'STCOFIPS'] <- 'fips'

plot_usmap(data = nation, values = "RISK_SCORE") + 
  scale_fill_gradientn(colours = pal,name = "Risk", label = scales::comma) + 
  labs(title = "FEMA RISK") +
  theme(legend.position = "right")

gtrends<-read.csv("~/Dropbox/EDS_2024/eds_summit_comm_access/data/gTrends/gTrendMS.csv")

ggplot(gtrends, 
       aes(x = FEMA, y = Toolkit))+
  geom_point()+theme_classic()


```