US Poverty
November 11, 2020
Last updated: 2021-09-08
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Knit directory: myTidyTuesday/
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| Rmd | d0129ae | opus1993 | 2021-09-08 | suppress package messages |
The data here comes from the US Census American Community Survey 2014-2018 5-year dataset.
Our goal is to recreate this beautiful Tableau graphic, but to represent the population size better.
suppressPackageStartupMessages({
library(tidyverse)
library(hrbrthemes) # shortcuts for fonts and color palettes
extrafont::loadfonts(quiet = TRUE)
library(cartogram)
library(sf)
library(tmap)
library(tigris)
library(tidycensus)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())Registered S3 method overwritten by 'tune':
method from
required_pkgs.model_spec parsnipggplot2::theme_set(theme_jim(base_size = 12))nonx <- c("78", "69", "66", "72", "60", "15", "02")
if (file.exists("data/us_states.rds")) {
us_states <- read_rds("data/us_states.rds")
} else {
us_states <- states(cb = TRUE) %>%
filter(!STATEFP %in% nonx) %>%
st_transform(crs = 2163)
write_rds(us_states, "data/us_states.rds")
}
if (file.exists("data/acs_poverty.rds")) {
poverty <- read_rds("data/acs_poverty.rds")
} else {
poverty <- tidycensus::get_acs(
geography = "county",
variables = c(poverty = "B17013_001"),
geometry = TRUE,
year = 2018
) %>%
select(GEOID, NAME, estimate, moe, geometry)
write_rds(poverty, "data/acs_poverty.rds")
}
population <-
suppressMessages(
tidycensus::get_acs(
geography = "county",
variables = c(population = "B01003_001"),
geometry = FALSE,
year = 2018
) %>%
select(GEOID, estimate)
)The bubbles are generated on top of the shapefile centroids, and then adjusted to fit through iterations with the cartogram package.
counties_sf <- poverty %>%
left_join(population, by = "GEOID", suffix = c(".pov", ".pop")) %>%
mutate(
rate = round(estimate.pov / estimate.pop, 2),
moe_rate = round(moe / estimate.pop, 3)
) %>%
st_transform(crs = 2163)
county_dorling <-
cartogram_dorling(
x = counties_sf,
weight = "estimate.pop",
k = 0.2,
itermax = 100
)A ggplot
county_dorling %>%
filter(!str_detect(NAME, "Alaska|Hawaii|Puerto|Guam")) %>%
ggplot(aes(fill = rate)) +
geom_sf(color = "grey60") +
geom_sf(
data = us_states,
fill = NA,
show.legend = F,
color = "grey60",
lwd = .5
) +
coord_sf(crs = 2163, datum = NA) +
scale_fill_stepsn(colors = c("#eff3ff", "#bdd7e7", "#6baed6", "#3182bd", "#08519c")) +
labs(
title = "2014-2018 Poverty Rate in the United States by County", subtitle = "Bubble size corresponds to County Population",
caption = "US Census American Community Survey | jim_gruman"
)
And the original tweet, as posted to twitter:
tweetrmd::include_tweet("https://twitter.com/jim_gruman/status/1326673815288623105")https://t.co/4Vg1ag28Vq this is the same 5-year ACS poverty figures @drsplace, this time assigned to @yake_84 's R adaption of the famous political cartogram where the size of the circles denotes the relative population of the county pic.twitter.com/FYpTDkjtWb
— Jim Gruman📚🚵♂️⚙ (@jim_gruman) November 11, 2020
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidycensus_1.0 tigris_1.4.1 tmap_3.3-2 sf_1.0-2
[5] cartogram_0.2.2 hrbrthemes_0.8.0 forcats_0.5.1 stringr_1.4.0
[9] dplyr_1.0.7 purrr_0.3.4 readr_2.0.1 tidyr_1.1.3
[13] tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_0.1-4 backports_1.2.1
[4] workflows_0.2.3 systemfonts_1.0.2 lwgeom_0.2-7
[7] plyr_1.8.6 sp_1.4-5 splines_4.1.1
[10] listenv_0.8.0 crosstalk_1.1.1 leaflet_2.0.4.1
[13] digest_0.6.27 yardstick_0.0.8 foreach_1.5.1
[16] htmltools_0.5.2 viridis_0.6.1 parsnip_0.1.7.900
[19] fansi_0.5.0 tune_0.1.6 magrittr_2.0.1
[22] tzdb_0.1.2 globals_0.14.0 recipes_0.1.16
[25] modelr_0.1.8 gower_0.2.2 extrafont_0.17
[28] R.utils_2.10.1 extrafontdb_1.0 hardhat_0.1.6
[31] rsample_0.1.0 dials_0.0.9.9000 colorspace_2.0-2
[34] rvest_1.0.1 rappdirs_0.3.3 textshaping_0.3.5
[37] haven_2.4.3 xfun_0.25 rgdal_1.5-25
[40] leafem_0.1.6 crayon_1.4.1 jsonlite_1.7.2
[43] iterators_1.0.13 survival_3.2-11 glue_1.4.2
[46] stars_0.5-3 gtable_0.3.0 ipred_0.9-11
[49] R.cache_0.15.0 tweetrmd_0.0.9 Rttf2pt1_1.3.9
[52] future.apply_1.8.1 abind_1.4-5 scales_1.1.1
[55] infer_1.0.0 DBI_1.1.1 Rcpp_1.0.7
[58] viridisLite_0.4.0 units_0.7-2 GPfit_1.0-8
[61] foreign_0.8-81 proxy_0.4-26 lava_1.6.10
[64] prodlim_2019.11.13 htmlwidgets_1.5.3 httr_1.4.2
[67] RColorBrewer_1.1-2 ellipsis_0.3.2 farver_2.1.0
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[118] vctrs_0.3.8 tidymodels_0.1.3 furrr_0.2.3
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[124] maptools_1.1-2 raster_3.4-13 httpuv_1.6.2
[127] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20
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