Who is the package for?

The tidycensuskr package is designed for R users who want to work with South Korean census and administrative boundary data. It aims to provide an easy-to-use interface for population, housing, and socioeconomic statistics linked with geospatial boundaries.

Installation

You can install the released version of tidycensuskr from CRAN with:

# CRAN 
install.packages("tidycensuskr")

To install the development version, you will need a GitHub account and generate a personal access token with repo permissions.

# Development version from GitHub
rlang::check_installed("remotes")
remotes::install_github("sigmafelix/tidycensuskr", auth_token = "__YOUR_GITHUB_TOKEN__")

After cloning the repository, you can also install the package using:

devtools::install(quick = TRUE)

About the data

As of September 2025, this package contains two datasets: Census data (censuskor) and the corresponding geospatial data.

1. Census data

anycensus()

# loading Seoul population data
tidycensuskr::anycensus(codes = "Seoul", type = "population")
#> # A tibble: 25 × 9
#>     year adm1  adm1_code adm2          adm2_code type     all households_total…¹
#>    <int> <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 2 more variables: `all households_male_prs` <dbl>,
#> #   `all households_female_prs` <dbl>

censuskor

2. Administrative boundaries

load_district()

# loading boundary sf file
tidycensuskr::load_districts(year = 2020)
#> Simple feature collection with 250 features and 2 fields
#> Geometry type: GEOMETRY
#> Dimension:     XY
#> Bounding box:  xmin: 746111 ymin: 1463977 xmax: 1302469 ymax: 2068441
#> Projected CRS: KGD2002 / Unified CS
#> First 10 features:
#>    year adm2_code                       geometry
#> 1  2020     21140 POLYGON ((1147104 1689056, ...
#> 2  2020     21020 POLYGON ((1137763 1683521, ...
#> 3  2020     21010 POLYGON ((1139121 1678921, ...
#> 4  2020     21040 POLYGON ((1144618 1676795, ...
#> 5  2020     21070 POLYGON ((1142639 1682655, ...
#> 6  2020     21130 POLYGON ((1147030 1688822, ...
#> 7  2020     21050 POLYGON ((1138992 1683338, ...
#> 8  2020     21030 POLYGON ((1142527 1684031, ...
#> 9  2020     21150 POLYGON ((1135430 1690583, ...
#> 10 2020     21100 MULTIPOLYGON (((1132627 167...

Examples

Package vignettes are the first place to look for detailed examples. Below are some quick examples to get you started.

Simple map making

anycensus() will return an analysis-ready data.frame that can be easily merged with the corresponding boundary sf object from load_districts(). Here is a simple example of making maps with population data.

library(tidycensuskr)
#> tidycensuskr 0.1.2 (2025-09-23)
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, union
library(tidyr)
library(sf)
#> Linking to GEOS 3.12.2, GDAL 3.11.3, PROJ 9.4.1; sf_use_s2() is TRUE
library(biscale)
library(cowplot)
sf_use_s2(FALSE)
#> Spherical geometry (s2) switched off
options(scipen = 100)

# load census data
census_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 boundaries
adm2_2020 <- load_districts(year = 2020)

# merge boundaries and census data
census_2020_sf <- adm2_2020 |>
  left_join(census_pop_2020, by = c("adm2_code" = "adm2_code"))

# plot population data
census_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))

census_2020_pop

For Seoul Metropolitan Area (including Seoul, Incheon, and Gyeonggi-do), you can use a character vector in codes argument and merge the retrieved data.frame and sf object with inner_join():

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 data
census_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))

census_2020_pop_sma