CrowdingMap

If you’re running this for the first time, make sure to uncomment package installation and API key setting.

#install.packages(c("tidycensus", "sf", "tigris", "ggplot2", "viridis"))

# Load the packages
library(tidycensus)
library(sf)

## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE

library(tigris)

## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.

library(ggplot2)
library(viridis)

## Loading required package: viridisLite

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

library(tidyr)
library(leaflet)


#census_api_key("8d2dec7d58676dfb242321e6386d6b8431cf8899",install=TRUE)

R Markdown

IMPORTANT: Change the Variable

# county-level overcrowding data 
overcrowding_data <- get_acs(
  geography = "county",
  variables = c("B25001_001","B25014_005","B25014_006","B25014_007","B25014_011","B25014_012","B25014_013"), # Example variable for overcrowded households (you may need a different variable)
  year = 2020,
  survey = "acs5"
)

## Getting data from the 2016-2020 5-year ACS

overcrowding_data <- overcrowding_data %>% select(-moe) %>% spread(key = variable,value=estimate)
overcrowding_data <-  overcrowding_data %>% mutate(crowded = rowSums(select(.,4:9),na.rm=TRUE))
overcrowding_data <- overcrowding_data %>% select(1:3,10) 
overcrowding_data <- overcrowding_data %>% mutate(crowding_rate = crowded/B25001_001)

# https://github.com/GeoDaCenter/covid/blob/master/public/csv/covid_wk_pos_cdc.csv
coviddata <- read.csv("C:/Users/MBA/OneDrive - Emory University/MBA Program/Semester_24Fall/EH584 Public Health and Built Environment/Final/covid_wk_pos_cdc-2024-12-04.csv",colClasses = c("fips_code"="character"))

coviddata$fips_code<-stringr::str_pad(coviddata$fips_code,width=5, side = "left", pad="0")
coviddata_selected <- coviddata[,c(1,200:350)]
coviddata_selected <- coviddata_selected %>%
  mutate(positivity_avg = rowMeans(select(.,2:152),na.rm=TRUE))
coviddata_selected <- coviddata_selected[,c(1,153)]

# Load shapefiles for us counties
counties_sf <- counties(cb = TRUE)

## Retrieving data for the year 2022

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# Merge the overcrowding data with the county shapefile by GEOID (FIPS code)
overcrowding_sf1 <- left_join(counties_sf, overcrowding_data, by = c("GEOID" = "GEOID"))
overcrowding_sf <- left_join(overcrowding_sf1, coviddata_selected, by = c("GEOID" = "fips_code"))
# Reproject the shapefile to WGS84 (EPSG:4326)
overcrowding_sf <- st_transform(overcrowding_sf, crs = 4326)

# Create a color palette for the overcrowding rate
pal <- colorNumeric(palette = "YlGnBu", domain = overcrowding_sf$positivity_avg)


# Create the interactive map using leaflet
leaflet(data = overcrowding_sf) %>%
  addTiles() %>%  
  addPolygons(
    fillColor = "white",  
    fillOpacity = .8,
    color = ~pal(positivity_avg),  
    weight = ~crowding_rate/0.005,  
    popup = ~paste(
      "County: ", NAME.x, "<br>",
      "Overcrowding Rate: ", crowding_rate, "<br>","pos ",positivity_avg
    ),opacity=1
  ) %>%
  addLegend(
    pal = pal, 
    values = ~positivity_avg, 
    title = "Rate",
    position = "bottomright"
  )