The aim of creating the tidycensuskr package is to make the best use case of South Korean boundaries, population and socioeconomic information for R users.
Installation
You can install the released version of palmerpenguins from CRAN with:
install.packages("tidycensuskr")To install the development version, you will need a GitHub account and generate a personal access token with repo permissions.
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 July 2025, this package contains two datasets: Census and the corresponding geospatial data.
1. Census
- Sigungu dataset of three census years (2010, 2015, 2020)
- The curated dataset contain 250 rows long and 8 variables.
head(census)
#> # A tibble: 253 x 6
#> adm1_code adm1_name adm2_code adm2_name pop2015_total pop2015_men pop2015_women pop2020_total pop2020_men pop2020_women
#> <int> <chr> <int> <chr> <int> <int> <int> <int> <int> <int>
#> 1 11 Seoul 11010 Jongno-gu 161521 79510 82011 151291 73062 78229
#> 2 11 Seoul 11020 Jung-gu 128478 63218 65260 128744 62147 66597
#> 3 11 Seoul 11030 Yongsan-gu 227282 109980 117302 225882 109162 116720
#> 4 11 Seoul 11040 Seongdong-gu 295006 146332 148674 291918 142128 149790
#> 5 11 Seoul 11050 Gwangjin-gu 368199 180647 187552 353967 169925 184042
#> 6 11 Seoul 11060 Dongdaemun-gu 364787 181189 183598 351057 171484 179573if you use the function anycensus(), you will get the information by census years and regions.
# Say if you want to query Seoul's 2020 population
# You can type in Seoul's district code, then the census year
# You can retrieve the data using the simplify() function by changing the options to either a data frame or a list.
seoul <- anycensus(11, 2020, simplify = "df")
# Quick visualisation
plot_census_map(seoul, variable = "population_density")
anycensusk()
- The package loads an attached dataset
censuskorthat contains the census data for 2020. This dataset is automatically loaded upon loading the package - The function
anycensusk()allows you to query census data for specific district or province codes and types of data (population, tax, mortality) for the year 2020.
library(tidycensuskr)
# loading Seoul population data
tidycensuskr::anycensusk(codes = "Seoul", type = "population")Examples
You can find these and more code examples for exploring census in vignette("examples")
library(tidyverse)
library(sf)
head(sgg)
#> Simple feature collection with 251 features and 6 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 746111 ymin: 1458603 xmax: 1387949 ymax: 2068444
#> Projected CRS: Korea 2000 / Unified CS
#> # A tibble: 251 × 7
#> BASE_DATE SIGUNGU_CD SIGUNGU_NM geom adm1_code adm1_name adm2_name
#> <int> <int> <chr> <MULTIPOLYGON [m]> <int> <chr> <chr>
#> 1 20201231 11010 종로구 (((953683.8 1959210, 953… 11 Seoul Jongno-gu
#> 2 20201231 11020 중구 (((957890.4 1952617, 957… 11 Seoul Jung-gu
#> 3 20201231 11030 용산구 (((953114.2 1950747, 953… 11 Seoul Yongsan-gu
#> 4 20201231 11040 성동구 (((959381.8 1952724, 959… 11 Seoul Seongdong-gu
#> 5 20201231 11050 광진구 (((964825.1 1952633, 964… 11 Seoul Gwangjin-gu
#> 6 20201231 11060 동대문구 (((961991.8 1956626, 961… 11 Seoul Dongdaemun-gu