TongFen for US census data

library(tongfen)
library(dplyr)
library(ggplot2)
#library(mountainmathHelpers)

As an example we will explore changing household size between the 2000 and 2010 US census. First we need to build the metadata for our variables “H011001” for population and “H013001” for households.

variables=c(population="H011001",households="H013001")

meta <- c(2000,2010) %>%
  lapply(function(year){
      v <- variables %>% setNames(paste0(names(.),"_",year))
      meta_for_additive_variables(paste0("dec",year),v)
    }) %>%
  bind_rows()
meta
#> # A tibble: 4 x 8
#>   variable dataset label           type   aggregation rule    geo_dataset parent
#>   <chr>    <chr>   <chr>           <chr>  <chr>       <chr>   <chr>       <lgl> 
#> 1 H011001  dec2000 population_2000 Manual Additive    Additi… dec2000     NA    
#> 2 H013001  dec2000 households_2000 Manual Additive    Additi… dec2000     NA    
#> 3 H011001  dec2010 population_2010 Manual Additive    Additi… dec2010     NA    
#> 4 H013001  dec2010 households_2010 Manual Additive    Additi… dec2010     NA

Armed with that we can call get_tongfen_us_census to request the data on a common geography based on census tracts and compute the change in household size.

census_data <- get_tongfen_us_census(regions = list(state="CA"), meta=meta, level="tract") %>%
  mutate(change=population_2010/households_2010-population_2000/households_2000) 
census_data %>% names()
#> [1] "TongfenID"       "TongfenUID"      "geometry"        "population_2000"
#> [5] "households_2000" "population_2010" "households_2010" "change"

We bin the data for better plotting and zoom in on the Bay area.

census_data %>%
  mutate(c=cut(change,c(-Inf,-0.5,-0.3,-0.2,-0.1,0,0.1,0.2,0.3,0.5,Inf))) %>%
  ggplot() +
  geom_sf(aes(fill=c), size=0.05) +
  scale_fill_brewer(palette = "RdYlGn") +
  labs(title="Bay area change in average household size 2000-2010", fill=NULL) +
  #geom_water() + geom_roads() +
  coord_sf(datum=NA,xlim=c(-122.6,-121.7),ylim=c(37.2,37.9))