R Notebook

Read the map:

rm(list = ls())
##
library(sf)

## Warning: package 'sf' was built under R version 4.3.2

## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE

usaMap=read_sf("gz_2010_us_040_00_500k.json")

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

ggplot(data = usaMap)+ geom_sf() +coord_sf(crs = st_crs(2163)) 

Read in the data

variables=read.csv("data_for_map.csv")
head(variables)

Add Region to map:

head(usaMap)

sort(usaMap$NAME)==sort(variables$State)

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Then:

usaMap=merge(usaMap,variables, by.x='NAME', by.y='State')
usaMap

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

usaMap %>%
  group_by(Region) %>%
  summarise_at(vars(Prop_no_Comp), list(Prop_no_Comp_mean=mean)) ->variablesByRegion

#newMap
variablesByRegion

Plot

ggplot(variablesByRegion) + geom_sf(aes(fill=Prop_no_Comp_mean)) +coord_sf(crs = st_crs(2163))