Installation

# CRANinstall.packages("tidycensuskr")# R-universeinstall.packages("tidycensuskr",repos="https://sigmafelix.r-universe.dev")

# Development version from GitHubrlang::check_installed("remotes")remotes::install_github("sigmafelix/tidycensuskr")

About the data

1. Census data

anycensus()

# loading Seoul population datatidycensuskr::anycensus(codes="Seoul",type="population")#> # A tibble: 25 × 17#>     year adm1  adm1_code adm2          adm2_code type     all households_total…¹#>    <dbl> <chr>     <dbl> <chr>             <dbl> <chr>                     <dbl>#>  1  2020 Seoul        11 Dobong-gu         11100 populat…                 312878#>  2  2020 Seoul        11 Dongdaemun-gu     11060 populat…                 332796#>  3  2020 Seoul        11 Dongjak-gu        11200 populat…                 378749#>  4  2020 Seoul        11 Eunpyeong-gu      11120 populat…                 458777#>  5  2020 Seoul        11 Gangbuk-gu        11090 populat…                 295304#>  6  2020 Seoul        11 Gangdong-gu       11250 populat…                 440022#>  7  2020 Seoul        11 Gangnam-gu        11230 populat…                 509899#>  8  2020 Seoul        11 Gangseo-gu        11160 populat…                 564114#>  9  2020 Seoul        11 Geumcheon-gu      11180 populat…                 225594#> 10  2020 Seoul        11 Guro-gu           11170 populat…                 394733#> # ℹ 15 more rows#> # ℹ abbreviated name: ¹​`all households_total_prs`#> # ℹ 10 more variables: `all households_male_prs` <dbl>,#> #   `all households_female_prs` <dbl>, fertility_total_brt <dbl>,#> #   `fertility_15-19 (simulated)_bp1` <dbl>, `fertility_20-24_bp1` <dbl>,#> #   `fertility_25-29_bp1` <dbl>, `fertility_30-34_bp1` <dbl>,#> #   `fertility_35-39_bp1` <dbl>, `fertility_40-44_bp1` <dbl>, …

censuskor

2. Administrative boundaries

load_district()

# loading boundary sf file: 2020 boundaries are included in this packagedata(adm2_sf_2020)# tidycensuskr.sf::load_districts(year = 2020)

Examples

Simple map making

library(tidycensuskr)#> tidycensuskr 0.2.6 (2025-12-09)#> Please install the companion data package tidycensuskr.sf to use the district boundaries.#> install.packages('tidycensuskr.sf', repos = 'https://sigmafelix.r-universe.dev')library(ggplot2)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, unionlibrary(tidyr)library(sf)#> Linking to GEOS 3.12.2, GDAL 3.11.4, PROJ 9.4.1; sf_use_s2() is TRUElibrary(biscale)library(cowplot)sf_use_s2(FALSE)#> Spherical geometry (s2) switched offoptions(scipen=100)# load census datacensus_pop_2020<- anycensus(year=2020,codes=NULL,type="population")#> Using character codes that are convertible to integers. Automatically converting to integers...census_pop_2020<-census_pop_2020|>  rename(population_total=`all households_total_prs`)# load boundariesdata(adm2_sf_2020)adm2_2020<-adm2_sf_2020# merge boundaries and census datacensus_2020_sf<-adm2_2020|>  left_join(census_pop_2020,by= c("adm2_code"="adm2_code"))# plot population datacensus_2020_pop<-  ggplot(census_2020_sf)+  geom_sf(aes(fill=population_total),color="white",size=0.1)+  theme_minimal()+  labs(title="Population (2020)",fill="Population"  )+  theme(plot.title= element_text(size=12),axis.text= element_text(size=7),legend.text= element_text(size=7),legend.title= element_text(size=8)  )census_2020_pop

census_pop_2020_sma<-  anycensus(year=2020,codes= c("Seoul","Incheon","Gyeonggi"),type="population"  )|>  rename(population_total=`all households_total_prs`)census_2020_sf_sma<-adm2_2020|>  inner_join(census_pop_2020_sma,by= c("year","adm2_code"))# plot population datacensus_2020_pop_sma<-  ggplot(census_2020_sf_sma)+  geom_sf(aes(fill=population_total),color="white",size=0.1)+  theme_minimal()+  labs(title="Population in Seoul Metropolitan Area (2020)",fill="Population"  )+  theme(plot.title= element_text(size=12),axis.text= element_text(size=7),legend.text= element_text(size=7),legend.title= element_text(size=8)  )census_2020_pop_sma

Bivariate map

census_housing_2020<- anycensus(year=2020,codes=NULL,type="housing")#> Using character codes that are convertible to integers. Automatically converting to integers...census_housing_2020<-census_housing_2020|>  rename(housing_total_units=`housing types_total_cnt`)census_pop_housing_2020<-census_pop_2020|>  left_join(census_housing_2020|>              select(adm2_code,housing_total_units),by="adm2_code")|>  transmute(adm2_code=adm2_code,persons_per_housing=population_total/housing_total_units  )census_mort_2020<- anycensus(year=2020,codes=NULL,type="mortality")#> Using character codes that are convertible to integers. Automatically converting to integers...census_mort_2020<-census_mort_2020|>  rename(mortality_total=`all causes_total_p1p`)census_pph_mort_2020<-census_pop_housing_2020|>  left_join(census_mort_2020|>              select(adm2_code,mortality_total),by="adm2_code")# merge boundaries and census datacensus_2020_sf<-adm2_2020|>  left_join(census_pph_mort_2020,by= c("adm2_code"="adm2_code"))census_2020_mapbase<-biscale::bi_class(census_2020_sf,x=persons_per_housing,y=mortality_total,style="quantile",dim=3  )# draw a bivariate legendlegend<- bi_legend(pal="DkCyan",dim=3,xlab="More Persons per Housing",ylab="All-Cause Mortality",size=6)# plot population datacensus_2020_bmap<-  ggplot(census_2020_mapbase)+  geom_sf(    aes(fill=bi_class),color="white",size=0.1,show.legend=FALSE  )+  bi_scale_fill(pal="DkCyan",dim=3)+  theme_minimal()+  labs(title="Persons per housing unit and all-cause mortality rate (2020)")+  bi_theme(base_size=10)+  theme(plot.title= element_text(size=10))# combine map with legendcensus_2020_bimap<-cowplot::ggdraw()+cowplot::draw_plot(census_2020_bmap,0,0,1,1)+cowplot::draw_plot(legend,0.7,0.02,0.3,0.3)census_2020_bimap