# Working with tidycensuskr

#### July 23, 2026

## Getting Started with tidycensuskr

The `tidycensuskr` package provides easy access to South Korean census and socioeconomic statistics, along with corresponding geospatial boundary data. With this package, R users can query and visualize population, housing, economy, tax, and mortality data linked to administrative districts.

Load the package:

    library(tidycensuskr)

tidycensuskr will work at its full potential with the companion data package `tidycensuskr.sf`, which contains the district boundaries of South Korea. The package can be installed from R-universe:

    install.packages("tidycensuskr.sf", repos = "https://sigmafelix.r-universe.dev")

After installing the companion package, three RDS files for 2010, 2015, and 2020 will be accessible through the function `system.file()`. For example, the RDS file path of the 2010 district boundaries can be loaded as follows:

    fs10 <- system.file("extdata", "adm2_sf_2010.rds", package = "tidycensuskr.sf")
    adm2_sf_2010 <- readRDS(fs10)

## 1. Understanding Korean Geographic Hierarchies

South Korean census data is organized by three levels of administrative divisions:

- Si-Do: The highest level of administrative division.
  - Metropolitan cities are treated as provinces.
  - Jeju-do, Gangwon-do, and Jeollabuk-do have special self-governing status under Korean law.
- Si-Gun-Gu: The second level, which includes cities and counties.
  - Si: Cities (urban administrative units)
  - Gun: Counties (rural areas, typically <50,000 population)
  - Gu: Districts (urban subdivisions of metropolitan cities or large cities).
    - Gu under metropolitan cities are autonomous districts
    - Gu under 11 large cities, as of 2025 (i.e., Suwon-si, Seongnam-si, Anyang-si, Goyang-si, Ansan-si, Yongin-si, Cheongju-si, Cheonan-si, Pohang-si, Changwon-si, Jeonju-si), are administraitve districts
- Eup-Myeon-Dong: The third level, which includes town and districts. (planned for future releases)
  - Eup: Towns (urban, >20,000 population, within a county)
  - Myeon: Townships (rural, <20,000 population, within a county)
  - Dong: Neighborhoods (smallest units within cities and districts)

### Comparison of Administrative Divisions

The table below provides a rough comparison of administrative divisions across South Korea, the United States, the European Union, and the United Kingdom (England). While the correspondence is not exact, it can be helpful to understand the approximate levels when working with census or regional data.

South Korea | US | EU (NUTS1) | UK (England)
--- | --- | --- | ---
Si/Do | State | NUTS1 | Regions / Combined Authorities
Si/Gun/Gu | County | NUTS2 | County
Eup/Myeon/Dong | Townships / Towns / Census County Division | NUTS3 | Districts / Wards / Boroughs

Because administrative boundaries and coding systems can vary across years and data sources, `tidycensuskr` standardizes administrative codes to allow consistent integration of statistics. Currently, for 2020 data there are 250 Si-Gun-Gu and 17 Si-Do. The package is provided with a built-in dataset of 2020 Si-Gun-Gu boundaries, which can be loaded using the function `data()`.

    data(adm2_sf_2020)
    print(length(unique(adm2_sf_2020$adm2_code)))
    #> [1] 250

## 2. Available census data

The package provides census and survey data through:
- The function `anycensus()` for querying subsets
- The built-in dataset `censuskor` in long format

### Data types

type | class1 | class2 | unit | description | available
--- | --- | --- | --- | --- | ---
population | all households | total | persons | Total population count | 2010, 2015, 2020
population | all households | male | persons | Male population count | 2010, 2015, 2020
population | all households | female | persons | Female population count | 2010, 2015, 2020
tax | income | general | million KRW | General income tax revenue | 2020
tax | income | labor | million KRW | Labor income tax revenue | 2020
mortality | All causes | total | per 100k population | Total mortality rate from all causes | 2020
mortality | All causes | male | per 100k population | Male mortality rate from all causes | 2020
mortality | All causes | female | per 100k population | Female mortality rate from all causes | 2020
economy | company | total | count | Total number of companies | 2010, 2015, 2020
housing | housing types | total | count | Total number of housing units | 2010, 2015, 2020
housing | housing types | detached housing | count | Number of detached/single-family houses | 2010, 2015, 2020
housing | housing types | apartment | count | Number of apartment units | 2010, 2015, 2020
housing | housing types | row house | count | Number of row house units | 2010, 2015, 2020
housing | housing types | multiplex | count | Number of multiplex housing units | 2010, 2015, 2020
housing | housing types | non-residential | count | Number of non-residential buildings used for housing | 2010, 2015, 2020
medicine | doctors | anesthesiology and pain medicine | persons | Number of anesthesiologists | 2010, 2015, 2020
medicine | doctors | clinical laboratory medicine | persons | Number of clinical laboratory physicians | 2010, 2015, 2020
medicine | doctors | dermatology | persons | Number of dermatologists | 2010, 2015, 2020
medicine | doctors | emergency medicine | persons | Number of emergency medicine physicians | 2010, 2015, 2020
medicine | doctors | family medicine | persons | Number of family medicine physicians | 2010, 2015, 2020
medicine | doctors | internal medicine | persons | Number of internal medicine physicians | 2010, 2015, 2020
medicine | doctors | neurology | persons | Number of neurologists | 2010, 2015, 2020
medicine | doctors | neurosurgery | persons | Number of neurosurgeons | 2010, 2015, 2020
medicine | doctors | nuclear medicine | persons | Number of nuclear medicine physicians | 2010, 2015, 2020
medicine | doctors | obstetrics and gynecology | persons | Number of OB/GYN physicians | 2010, 2015, 2020
medicine | doctors | occupational and environmental medicine | persons | Number of occupational/environmental medicine physicians | 2010, 2015, 2020
medicine | doctors | ophthalmology | persons | Number of ophthalmologists | 2010, 2015, 2020
medicine | doctors | orthopedics | persons | Number of orthopedic surgeons | 2010, 2015, 2020
medicine | doctors | otorhinolaryngology | persons | Number of ENT specialists | 2010, 2015, 2020
medicine | doctors | pathology | persons | Number of pathologists | 2010, 2015, 2020
medicine | doctors | pediatrics | persons | Number of pediatricians | 2010, 2015, 2020
medicine | doctors | plastic surgery | persons | Number of plastic surgeons | 2010, 2015, 2020
medicine | doctors | preventive medicine | persons | Number of preventive medicine physicians | 2010, 2015, 2020
medicine | doctors | psychiatry | persons | Number of psychiatrists | 2010, 2015, 2020
medicine | doctors | radiation oncology | persons | Number of radiation oncologists | 2010, 2015, 2020
medicine | doctors | radiology | persons | Number of radiologists | 2010, 2015, 2020
medicine | doctors | rehabilitation medicine | persons | Number of rehabilitation medicine physicians | 2010, 2015, 2020
medicine | doctors | surgery | persons | Number of general surgeons | 2010, 2015, 2020
medicine | doctors | thoracic and cardiovascular surgery | persons | Number of thoracic/cardiovascular surgeons | 2010, 2015, 2020
medicine | doctors | total | persons | Total number of doctors across all specialties | 2010, 2015, 2020
medicine | doctors | tuberculosis | persons | Number of tuberculosis specialists | 2010, 2015, 2020
medicine | doctors | urology | persons | Number of urologists | 2010, 2015, 2020
migration | marital | female | count | Number of female marriage migrants | 2010, 2015, 2020
migration | marital | male | count | Number of male marriage migrants | 2010, 2015, 2020
migration | marital | total | count | Total number of marriage migrants | 2010, 2015, 2020
environment | organic_matter | discharge | kg_day | Daily organic matter discharge | 2010, 2015, 2020
environment | wastewater | generation | m3_day | Daily wastewater generation volume | 2010, 2015, 2020
environment | wastewater | discharge | m3_day | Daily wastewater discharge volume | 2010, 2015, 2020
environment | organic_matter | generation | kg_day | Daily organic matter generation | 2010, 2015, 2020
population | fertility | total | births | Total number of births | 2010, 2015, 2020
population | fertility | 15-19 | births per 1000 | Age-specific fertility rate for ages 15-19 | 2010, 2015, 2020
population | fertility | 20-24 | births per 1000 | Age-specific fertility rate for ages 20-24 | 2010, 2015, 2020
population | fertility | 25-29 | births per 1000 | Age-specific fertility rate for ages 25-29 | 2010, 2015, 2020
population | fertility | 30-34 | births per 1000 | Age-specific fertility rate for ages 30-34 | 2010, 2015, 2020
population | fertility | 35-39 | births per 1000 | Age-specific fertility rate for ages 35-39 | 2010, 2015, 2020
population | fertility | 40-44 | births per 1000 | Age-specific fertility rate for ages 40-44 | 2010, 2015, 2020
population | fertility | 45-49 | births per 1000 | Age-specific fertility rate for ages 45-49 | 2010, 2015, 2020
economy | grdp | gross regional domestic product at market prices | million KRW | Total GRDP at market prices | 2010, 2015, 2020
economy | grdp | net taxes on products | million KRW | Net taxes on products component of GRDP | 2010, 2015, 2020
economy | grdp | total value added at basic prices | million KRW | Total value added at basic prices | 2010, 2015, 2020
economy | grdp | agriculture, forestry and fishing | million KRW | GRDP from agriculture, forestry, and fishing sector | 2010, 2015, 2020
economy | grdp | mining and quarrying | million KRW | GRDP from mining and quarrying sector | 2010, 2015, 2020
economy | grdp | manufacturing | million KRW | GRDP from manufacturing sector | 2010, 2015, 2020
economy | grdp | electricity, gas, steam and air conditioning supply; water supply and waste management | million KRW | GRDP from utilities and waste management sector | 2010, 2015, 2020
economy | grdp | construction | million KRW | GRDP from construction sector | 2010, 2015, 2020
economy | grdp | wholesale and retail trade | million KRW | GRDP from wholesale and retail trade sector | 2010, 2015, 2020
economy | grdp | transportation and storage | million KRW | GRDP from transportation and storage sector | 2010, 2015, 2020
economy | grdp | accommodation and food service activities | million KRW | GRDP from accommodation and food services sector | 2010, 2015, 2020
economy | grdp | information and communication | million KRW | GRDP from information and communication sector | 2010, 2015, 2020
economy | grdp | financial and insurance activities | million KRW | GRDP from financial and insurance sector | 2010, 2015, 2020
economy | grdp | real estate activities; rental and leasing activities | million KRW | GRDP from real estate and rental sector | 2010, 2015, 2020
economy | grdp | professional, scientific and technical activities; business support facilities | million KRW | GRDP from professional/technical services sector | 2010, 2015, 2020
economy | grdp | public administration and defence; compulsory social security | million KRW | GRDP from public administration sector | 2010, 2015, 2020
economy | grdp | education | million KRW | GRDP from education sector | 2010, 2015, 2020
economy | grdp | human health and social work activities | million KRW | GRDP from health and social work sector | 2010, 2015, 2020
economy | grdp | arts, sports and recreation; membership organizations and personal services | million KRW | GRDP from arts, recreation, and personal services sector | 2010, 2015, 2020
social security | basic living security | female | persons | Female recipients of basic living security benefits | 2010, 2015, 2020
social security | basic living security | male | persons | Male recipients of basic living security benefits | 2010, 2015, 2020
social security | basic pension | male | persons | Male recipients of basic pension | 2015, 2020
social security | basic pension | female | persons | Female recipients of basic pension | 2015, 2020
welfare | facilities | residential facility | count | Number of residential welfare facilities | 2015, 2020
welfare | facilities | service facility | count | Number of service-oriented welfare facilities | 2015, 2020
welfare | facilities | other facility | count | Number of other welfare facilities | 2015, 2020
welfare | registered physically mentally challenged | female_0-19 | persons | Registered disabled females aged 0-19 | 2015, 2020
welfare | registered physically mentally challenged | female_20-39 | persons | Registered disabled females aged 20-39 | 2015, 2020
welfare | registered physically mentally challenged | female_40-64 | persons | Registered disabled females aged 40-64 | 2015, 2020
welfare | registered physically mentally challenged | female_65-79 | persons | Registered disabled females aged 65-79 | 2015, 2020
welfare | registered physically mentally challenged | female_80+ | persons | Registered disabled females aged 80 and above | 2015, 2020
welfare | registered physically mentally challenged | male_0-19 | persons | Registered disabled males aged 0-19 | 2015, 2020
welfare | registered physically mentally challenged | male_20-39 | persons | Registered disabled males aged 20-39 | 2015, 2020
welfare | registered physically mentally challenged | male_40-64 | persons | Registered disabled males aged 40-64 | 2015, 2020
welfare | registered physically mentally challenged | male_65-79 | persons | Registered disabled males aged 65-79 | 2015, 2020
welfare | registered physically mentally challenged | male_80+ | persons | Registered disabled males aged 80 and above | 2015, 2020
welfare | registered physically mentally challenged | total_0-19 | persons | Total registered disabled persons aged 0-19 | 2015
welfare | registered physically mentally challenged | total_20-39 | persons | Total registered disabled persons aged 20-39 | 2015
welfare | registered physically mentally challenged | total_40-64 | persons | Total registered disabled persons aged 40-64 | 2015
welfare | registered physically mentally challenged | total_65-79 | persons | Total registered disabled persons aged 65-79 | 2015
welfare | registered physically mentally challenged | total_80+ | persons | Total registered disabled persons aged 80 and above | 2015
welfare | registered physically mentally challenged severity | less severely impaired_0-19 | persons | Less severely impaired persons aged 0-19 | 2020
welfare | registered physically mentally challenged severity | less severely impaired_20-39 | persons | Less severely impaired persons aged 20-39 | 2020
welfare | registered physically mentally challenged severity | less severely impaired_40-64 | persons | Less severely impaired persons aged 40-64 | 2020
welfare | registered physically mentally challenged severity | less severely impaired_65-79 | persons | Less severely impaired persons aged 65-79 | 2020
welfare | registered physically mentally challenged severity | less severely impaired_80+ | persons | Less severely impaired persons aged 80 and above | 2020
welfare | registered physically mentally challenged severity | severely impaired_0-19 | persons | Severely impaired persons aged 0-19 | 2020
welfare | registered physically mentally challenged severity | severely impaired_20-39 | persons | Severely impaired persons aged 20-39 | 2020
welfare | registered physically mentally challenged severity | severely impaired_40-64 | persons | Severely impaired persons aged 40-64 | 2020
welfare | registered physically mentally challenged severity | severely impaired_65-79 | persons | Severely impaired persons aged 65-79 | 2020
welfare | registered physically mentally challenged severity | severely impaired_80+ | persons | Severely impaired persons aged 80 and above | 2020
housing | vacant housing | fraction | percent | Percentage of vacant housing units | 2015, 2020
housing | vacant housing | number of vacant housing | count | Total number of vacant housing units | 2015, 2020
housing | vacant housing | total number of housing | count | Total number of housing units (occupied and vacant) | 2015, 2020
landuse | greenspace | number of greenspace | count | Number of greenspaces | 2010, 2015, 2020
landuse | greenspace | area of greenspace | square meters | Total area of greenspaces | 2010, 2015, 2020
landuse | parks | number of parks | count | Number of parks | 2010, 2015, 2020
landuse | parks | area of parks | square meters | Total area of parks | 2010, 2015, 2020
landuse | road | length of roads | meters | Total length of roads | 2010, 2015, 2020
landuse | road | area of roads | square meters | Total area of roads | 2010, 2015, 2020

- As of version 0.3.0, `tidycensuskr` comes with ADM3 level variables for types `population`, `mortality`, `housing`, and `economy` in 2020 census.

- The data is provided under the Korea Open Government License (KOGL) Type 1 (Attribution) (Note: in Korean). Each raw data table is available for download from the Public Data Portal and Korea Statistical Information System (KOSIS) with no cost. KOGL Type 1 is similar to the Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows for free use, distribution, and adaptation of the data as long as proper attribution is given.

### Query data using `anycensus()`

The function `anycensus()` returns a tidy tibble with columns such as:

- `year`: year of the dataset
- `adm1`, `adm1_code`: Si-Do (province) level administrative unit name and its corresponding code
- `adm2`, `adm2_code`: Si-Gun-Gu (district) level administrative unit name and its corresponding code
- `adm3`, `adm3_code`: Eup-Myeon-Dong (neighborhood) level administrative unit name and its corresponding code

Columns containing the values are added as a wide form. The column `adm2_code` or `adm3_code` links census data directly to boundary files retrieved with `load_districts()`, depending on the spatial level of data collection.

    df_2020 <- anycensus(year = 2020,
                         type = "mortality",
                         level = "adm2")
    head(df_2020)
    #> # A tibble: 6 × 9
    #>    year adm1              adm1_code adm2  adm2_code type  `all causes_total_p1p`
    #>   <dbl> <chr>                 <dbl> <chr>     <dbl> <chr>                  <dbl>
    #> 1  2020 Chungcheongbuk-do        33 Cheo…     33040 mort…                   312.
    #> 2  2020 Chungcheongnam-do        34 Cheo…     34010 mort…                   321.
    #> 3  2020 Gyeongsangbuk-do         37 Poha…     37010 mort…                   318.
    #> 4  2020 Gyeongsangnam-do         38 Chan…     38110 mort…                   323.
    #> 5  2020 Jeollabuk-do             35 Jeon…     35010 mort…                   283.
    #> 6  2020 Gyeongsangbuk-do         37 Ando…     37040 mort…                   353
    #> # ℹ 2 more variables: `all causes_male_p1p` <dbl>,
    #> #   `all causes_female_p1p` <dbl>

The function can also aggregate values to higher administrative units. By specifying `level = "adm1"` and providing an aggregation function, we obtain province-level (`adm1`) results that summarize across all districts.

    df_2020_sido <- anycensus(year = 2020,
                              type = "mortality",
                              level = "adm1",
                              aggregator = mean,
                              na.rm = TRUE)
    head(df_2020_sido)
    #> # A tibble: 6 × 7
    #> # Groups:   year, type, adm1, adm1_code [6]
    #>    year type      adm1    adm1_code `all causes_total_p1p` `all causes_male_p1p`
    #>   <dbl> <chr>     <chr>       <dbl>                  <dbl>                 <dbl>
    #> 1  2020 mortality Busan          21                   335.                  458.
    #> 2  2020 mortality Chungc…        33                   344.                  469.
    #> 3  2020 mortality Chungc…        34                   336.                  450.
    #> 4  2020 mortality Daegu          22                   311.                  426.
    #> 5  2020 mortality Daejeon        25                   306.                  399.
    #> 6  2020 mortality Gangwo…        32                   340.                  466.
    #> # ℹ 1 more variable: `all causes_female_p1p` <dbl>

For rate variables, weighted aggregation is also available. The example below computes a population-weighted mortality rate using the total population column from a separate `type = "population"` query.

    df_2020_sido_weighted <- anycensus(year = 2020,
                                       codes = "Seoul",
                                       type = "mortality",
                                       level = "adm1",
                                       aggregator = stats::weighted.mean,
                                       weight_type = "population",
                                       weight_column = "all households_total_prs",
                                       na.rm = TRUE)
    head(df_2020_sido_weighted)
    #> # A tibble: 1 × 8
    #>    year type      adm1  adm1_code `all causes_total_p1p` `all causes_male_p1p`
    #>   <dbl> <chr>     <chr>     <dbl>                  <dbl>                 <dbl>
    #> 1  2020 mortality Seoul        11                   256.                  347.
    #> # ℹ 2 more variables: `all causes_female_p1p` <dbl>,
    #> #   `all households_total_prs` <dbl>

For provinces that contain both autonomous/basic local government codes and non-autonomous `Gu` codes, use `adm2_type = "atn"` to clean to the autonomous/basic local government level. In weighted aggregation, rate rows for those autonomous units are recalculated from the non-autonomous component districts using the supplied weights. This can be returned at `level = "adm2"` or further aggregated to `level = "adm1"`.

    df_2020_adm2_weighted_atn <- anycensus(year = 2020,
                                           codes = "Gyeonggi-do",
                                           type = "mortality",
                                           level = "adm2",
                                           adm2_type = "atn",
                                           aggregator = stats::weighted.mean,
                                           weight_type = "population",
                                           weight_column = "all households_total_prs",
                                           na.rm = TRUE)
    head(df_2020_adm2_weighted_atn)
    #> # A tibble: 6 × 10
    #>    year adm1        adm1_code adm2        adm2_code type  `all causes_total_p1p`
    #>   <dbl> <chr>           <dbl> <chr>           <dbl> <chr>                  <dbl>
    #> 1  2020 Gyeonggi-do        31 Ansan-si        31090 mort…                   336.
    #> 2  2020 Gyeonggi-do        31 Anseong-si      31220 mort…                   316.
    #> 3  2020 Gyeonggi-do        31 Anyang-si       31040 mort…                   256.
    #> 4  2020 Gyeonggi-do        31 Bucheon-si      31050 mort…                   296.
    #> 5  2020 Gyeonggi-do        31 Dongducheo…     31080 mort…                   368
    #> 6  2020 Gyeonggi-do        31 Gapyeong-g…     31370 mort…                   353.
    #> # ℹ 3 more variables: `all causes_male_p1p` <dbl>,
    #> #   `all causes_female_p1p` <dbl>, `all households_total_prs` <dbl>

    df_2020_sido_weighted_atn <- anycensus(year = 2020,
                                           codes = "Gyeonggi-do",
                                           type = "mortality",
                                           level = "adm1",
                                           adm2_type = "atn",
                                           aggregator = stats::weighted.mean,
                                           weight_type = "population",
                                           weight_column = "all households_total_prs",
                                           na.rm = TRUE)
    head(df_2020_sido_weighted_atn)
    #> # A tibble: 1 × 8
    #>    year type      adm1    adm1_code `all causes_total_p1p` `all causes_male_p1p`
    #>   <dbl> <chr>     <chr>       <dbl>                  <dbl>                 <dbl>
    #> 1  2020 mortality Gyeong…        31                   286.                  379.
    #> # ℹ 2 more variables: `all causes_female_p1p` <dbl>,
    #> #   `all households_total_prs` <dbl>

For ADM3 level data, use `level = "adm3"` and provide the corresponding `adm3_code` to retrieve neighborhood-level data.

    df_2020_adm3 <- anycensus(year = 2020,
                              codes = "Seoul",
                              type = "mortality",
                              level = "adm3")
    head(df_2020_adm3)
    #> # A tibble: 6 × 11
    #>    year adm1  adm1_code adm2      adm2_code type      adm3             adm3_code
    #>   <dbl> <chr>     <dbl> <chr>         <dbl> <chr>     <chr>                <dbl>
    #> 1  2020 Seoul        11 Jongno-gu     11010 mortality Sajik-dong        11010530
    #> 2  2020 Seoul        11 Jongno-gu     11010 mortality Samcheong-dong    11010540
    #> 3  2020 Seoul        11 Jongno-gu     11010 mortality Buam-dong         11010550
    #> 4  2020 Seoul        11 Jongno-gu     11010 mortality Pyeongchang-dong  11010560
    #> 5  2020 Seoul        11 Jongno-gu     11010 mortality Muak-dong         11010570
    #> 6  2020 Seoul        11 Jongno-gu     11010 mortality Gyonam-dong       11010580
    #> # ℹ 3 more variables: `all causes_total_p1p` <dbl>,
    #> #   `all causes_male_p1p` <dbl>, `all causes_female_p1p` <dbl>

### Built-in dataset `censuskor`

You can access the whole dataset directly using the function `data(censuskor)` which returns the built-in dataset in a long form.

- `year`: year of the dataset
- `adm1`, `adm1_code`: Si-Do (province) level administrative unit name and its corresponding code
- `adm2`, `adm2_code`: Si-Gun-Gu (district) level administrative unit name and its corresponding code
- `adm3`, `adm3_code`: Eup-Myeon-Dong (neighborhood) level administrative unit name and its corresponding code
- `type`: Types of census or survey
- `class1`, `class2`: Classification variables providing further breakdowns
- `unit`: Measurement unit for the value
- `value`: The observed census value for the given combination of year, region, and category

    data(censuskor)
    head(censuskor)
    #> # A data frame: 6 × 12
    #>    year adm1    adm1_code adm2  adm2_code type  class1 class2 unit   value adm3
    #> * <dbl> <chr>       <dbl> <chr>     <dbl> <chr> <chr>  <chr>  <chr>  <dbl> <chr>
    #> 1  2010 Chungc…        33 Cheo…     33010 popu… all h… total  pers… 646939 <NA>
    #> 2  2010 Chungc…        33 Cheo…     33010 popu… all h… male   pers… 318355 <NA>
    #> 3  2010 Chungc…        33 Cheo…     33010 popu… all h… female pers… 328584 <NA>
    #> 4  2020 Chungc…        33 Cheo…     33040 tax   income gener… mill… 524478 <NA>
    #> 5  2020 Chungc…        33 Cheo…     33040 tax   income labor  mill… 598560 <NA>
    #> 6  2015 Chungc…        33 Cheo…     33040 popu… all h… total  pers… 797099 <NA>
    #> # ℹ 1 more variable: adm3_code <dbl>

### Quick Visualization

Since `anycensus()` returns tidy data, visualization with `ggplot2` is straightforward.

    ggplot(df_2020, aes(x = `all causes_male_p1p`, y = `all causes_female_p1p`)) +
      geom_point() +
      labs(
        x = "Male mortality (per 100,000 population)",
        y = "Female mortality (per 100,000 population)",
        title = "Male vs. Female Age-standardized Mortality Rates in South Korea (2020)"
      ) +
      theme_minimal(base_size = 10)

1. NUTS: Nomenclature of Territorial Units for Statistics, a geocode standard for referencing the subdivisions of countries for statistical purposes.↩︎