Intro

A quick/simple post: using the PL94171 package to access Census 2020 counts. Census data won’t be API-accessible until ~late September; these data are available, however, for redistricting purposes – albeit in a funky format. The PL94171 package can be used to download and re-structure these files for super convenient use. (!) Some quick visualizations of demographic change in the state of New Mexico.

Get data

Using several of the functions included in the PL94171 package, we build a simple wrapper below to extract census counts for multiple census years.

library(tidyverse)

get_pl_data <- function(x, state, level){

  y <- PL94171::pl_read(PL94171::pl_url(state, x))
  pl <- PL94171::pl_subset(y, sumlev = level)
  PL94171::pl_select_standard(pl, clean_names = TRUE)
}

The we apply the function, collecting county-level data for the state of New Mexico for the last three decennial censuses.

yrs <- c(2000, 2010, 2020)

x0 <- lapply(yrs, get_pl_data, state = 'NM', level = '050')
names(x0) <- yrs
x1 <- data.table::rbindlist(x0, idcol = 'year')

County level change by sub-group: 2000-2020

Below we re-structure the data some, and download New Mexico county information via the tigris package.

x2 <- x1 %>%
  select(year, GEOID, pop:pop_two) %>%
  gather(key = 'race', value = 'count', -year:-pop) %>%
  mutate(prop = count/pop)
head(x2) %>% knitr::kable()

nm <- tigris::counties(state = 'NM', cb = T)

A homemade palette:

gen_pal <- c('#ead8c3', 
             '#eeeeee',
             '#437193',
             '#7da6aa',
             '#b0bcc1',
             '#55752f', 
             '#dae2ba',
             '#eb7f6b'
             )

Population change by sub-group for a sample of counties in New Mexico:

set.seed(999)
x2 %>%
  inner_join(nm %>% sample_n(12)) %>%
  
  ggplot(aes(x = as.integer(year), 
             y = prop, 
             fill = race)) +
  geom_area(stat = "identity",
            color = 'white',
            alpha = 0.85) +
  geom_hline(yintercept = .5, 
             linetype = 4,
             color = 'white') +
  scale_fill_manual(values = gen_pal) +
  scale_x_continuous(breaks=seq(2000,2020,10)) +
  xlab('') + ylab('') +
  theme_minimal() +
  theme(legend.position="bottom",
        legend.title = element_blank()) +
  facet_wrap(~NAME, ncol = 4) +
  ggtitle('Population composition by county: 2000-2020')

A quick map

x3 <- x2 %>%
  filter(race == 'pop_hisp' &year != 2000) %>%
  select(year, GEOID, prop) %>%
  spread(year, prop) %>%
  mutate(delta = round(`2020` - `2010`, 3))

head(x3) %>% knitr::kable()

The map below details percent change for the Hispanic population in New Mexico from 2010 to 2020.

nm %>%
  left_join(x3) %>%
  ggplot() + 
  geom_sf(aes(fill = delta),
          color = 'darkgray',
          alpha = .85,
          lwd = .2) +

  scale_fill_distiller(palette = "BrBG",  
                        limit = max(abs(x3$delta)) * c(-1, 1)) +

  theme_minimal() + 
  theme(axis.title.x=element_blank(), 
          axis.text.x=element_blank(),
          axis.title.y=element_blank(),
          axis.text.y=element_blank(),
          legend.title=element_blank(),
          legend.position = 'bottom', 
          complete = F) +
  labs(title = 'Percent Change Hispanic Population in New Mexico', 
       subtitle = 'by County: 2010 to 2020')

Census 2020