Package: tidycensuskr 0.3.0

Insang Song

tidycensuskr: Easy Access for South Korea Census Data and Boundaries

Census and administrative data in South Korea are a basic source of quantitative and mixed-methods research for social and urban scientists. This package provides a 'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) based standardized workflow based on direct open API access to the major census and administrative data sources and pre-generated files in South Korea.

Authors:Insang Song [aut, cre], Sohyun Park [aut, ctb], Hyesop Shin [aut, ctb]

tidycensuskr_0.3.0.tar.gz
tidycensuskr_0.3.0.zip(r-4.7-any)
tidycensuskr_0.3.0.tar.gz(r-4.7-any)tidycensuskr_0.3.0.tar.gz(r-4.6-any)
tidycensuskr_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidycensuskr/json (API)

# Install 'tidycensuskr' in R:
install.packages('tidycensuskr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sigmafelix/tidycensuskr/issues

Pkgdown/docs site:https://sigmafelix.github.io

Datasets:

On CRAN:

Conda:

4.95 score 1 packages 20 scripts 196 downloads 4 exports 41 dependencies

Last updated from:b2ef1120f2. Checks:5 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK188
source / vignettesOK268
linux-release-x86_64OK177
windows-devel-x86_64OK121
wasm-releaseOK179

Exports:anycensusdetect_adm2_typeload_districtsset_kosis_key

Dependencies:askpassclassclassIntclicpp11curldata.tableDBIdplyre1071genericsgluehttrjsonliteKernSmoothkosislifecyclemagrittrMASSmimeopensslpillarpkgconfigproxypurrrR6Rcpprlangs2sfstringistringrsystibbletidyrtidyselectunitsutf8vctrswithrwk

Regional analysis with tidycensuskr
Example 1: Population change in the Gyeongsangnam-do region | Design idea | Data prep | A choropleth map with an inset map | Example 2: Bivariate mapping of population vs. tax | A bivariate choropleth map | Example 3: Sex ratio in Seoul Metropolitan Area | Histograms | A choropleth map | Example 4: Socioeconomic profiling | Context | data prep | PCA results | Biplot
Rendered fromv02_regional_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2025-11-10

Working with tidycensuskr
Getting Started with tidycensuskr | 1. Understanding Korean Geographic Hierarchies | Comparison of Administrative Divisions | 2. Available census data | Data types | Query data using anycensus() | Built-in dataset censuskor | Quick Visualization
Rendered fromv01_intro.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2025-11-10

Analytics Gallery with tidycensuskr
Example 1: Spatial autocorrelation of economic activity in South Korea | Design idea | Data prep | Global Moran's I | Local Moran's I and LISA map | Example 2: Population change by sex and districts | Small multiples
Rendered fromv03_gallery.Rmdusingknitr::rmarkdown

Last update: 2026-01-08
Started: 2025-11-10

Using KOSIS Interface for Contributors
Objectives | Navigating KOSIS Interface | Setting Download Options | Notes on Size Restriction | Downloading Data | Post-processing Downloaded Data | Assigning Proper adm2_code | Example Code for Post-processing
Rendered fromv05_contributors_guide.Rmdusingknitr::rmarkdown

Last update: 2026-01-08
Started: 2025-11-22

Data Cleaning: From KOSIS Raw Data to Tidy Format
Introduction | The Challenge | Raw Data Sources | API call parameters | Data variables | API Key | Example URL | Data Retrieval | Setting up API Access | Downloading Raw Data | Data Cleaning Workflow | 1. Administrative Code Mapping | 2. Tax Data Processing | 3. Population Data Processing | Transforming to Tidy Format | 1. Convert Each Dataset to Long Format | 2. Combine into Single Tidy Dataset | The Result: A Tidy Dataset | Example Usage | Notes on adm2_code changes
Rendered fromv04_data_cleaning_kosis.Rmdusingknitr::rmarkdown

Last update: 2025-11-10
Started: 2025-11-10