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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.
=c(population="H011001",households="H013001")
variables
<- c(2000,2010) %>%
meta lapply(function(year){
<- variables %>% setNames(paste0(names(.),"_",year))
v meta_for_additive_variables(paste0("dec",year),v)
%>%
}) bind_rows()
meta#> # A tibble: 4 × 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.
<- get_tongfen_us_census(regions = list(state="CA"), meta=meta, level="tract") %>%
census_data mutate(change=population_2010/households_2010-population_2000/households_2000)
%>% names()
census_data #> [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))
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.