r2d3.R

# Packages ----------------------------------------------------------------

library(tidyverse)
library(janitor)
library(albersusa)
library(sf)
library(tigris)
library(r2d3maps)
library(rmapshaper)

# County geometries -------------------------------------------------------

cty_sf <- counties_sf("longlat") # from albersusa package 
plot(st_geometry(cty_sf)) # checking geometries, looks good
cty_sf <- ms_simplify(cty_sf) # make the geometries simpler for faster rendering 


# Data ---------------------------------------------------------------------

# CDC's social vulnerability index variables: https://svi.cdc.gov/Documents/Data/2018_SVI_Data/SVI2018Documentation.pdf

svi <- read_csv("svi_county.csv") %>% 
  clean_names() %>% # make everything snake_case
  filter(across(where(is.numeric), ~. >= 0)) # because NA's are coded as -Inf

# join data to geometries
cty_sf_joined <- cty_sf %>% geo_join(svi, by_sp = "fips", by_df = "fips")

# make d3 map
d3_map(shape = cty_sf_joined, projection = "Albers") %>%
  add_labs(caption = "Viz: Asmae Toumi | Data: CDC") %>% 
  add_continuous_breaks(var = "rpl_theme1", palette = "Reds") %>%
  add_legend(title = "Socioeconomic Vulnerability (percentile)") %>%
  add_tooltip("<b>{location}</b>: {rpl_theme1}")