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tidycensuskr로 작업하기

January 19, 2026

Source: vignettes/v01_intro-ko.Rmd
v01_intro-ko.Rmd

tidycensuskr 시작

tidycensuskr 패키지로 대한민국의 인구주택총조사와 각종 사회경제, 도시 통계치를 시군구 단위로 쉽게 사용할 수 있습니다. 그리고 통계치와 시군구 경계 도형을 함께 제공하여 사용자들이 인구, 주택, 경제, 조세, 사망률 자료 등을 행정구역 경계와 연결하여 쿼리할 수 있습니다.

패키지를 사용하려면 먼저 README를 따라서 패키지를 설치한 다음 불러오십시오.

tidycensuskr을 최대한 활용하려면 대한민국 시군구 경계 도형을 포함하는 동반 데이터 패키지 tidycensuskr.sf가 필요합니다. 이 패키지는 R-universe에서 설치할 수 있습니다:

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

동반 패키지를 설치하고 나면, system.file() 함수를 이용해 2010년, 2015년, 2020년의 시군구 경계를 각각 포함한 세 개의 RDS 파일을 불러올 수 있습니다. 예를 들어, 2010년 시군구 경계 도형의 RDS 파일 경로는 다음과 같이 불러올 수 있습니다:

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

1. 한국 행정구역 체계

한국의 지역통계 자료는 세 가지 행정구역 단위로 구성됩니다.

  • 시도: 가장 높은 수준의 행정구역 단위입니다.
    • 광역시는 도와 동등한 수준으로 취급됩니다.
    • 제주도, 강원도, 전라북도는 특별자치도의 지위를 가지고 있습니다. 다만 사용하는 영문명은 조금씩 다릅니다.
  • 시군구: 두 번째 수준의 행정구역 단위로, 시(도시)와 군(농촌), 구(대도시 내 구역)를 포함합니다.
    • 시: 도시 지역을 나타내는 행정구역 단위입니다.
    • 군: 농촌 지역을 나타내며, 일반적으로 인구가 5만 명 미만인 지역입니다.
    • 구: 대도시나 큰 도시 내의 하위 구역으로, 광역시 내의 자치구와 11개 대도시 내의 자치구가 아닌 구(일반구)로 나뉩니다.
      • 광역시 내의 구는 자치구입니다.
      • 2025년 기준 수원시, 성남시, 안양시, 고양시, 안산시, 용인시, 청주시, 천안시, 포항시, 창원시, 전주시 내의 구는 “자치구가 아닌 구”입니다.
  • 읍면동: 세 번째 수준의 행정구역 단위로, 읍(도시 지역 내의 작은 단위), 면(농촌 지역 내의 작은 단위), 동(도시 내의 가장 작은 단위)을 포함합니다.
    • 읍: 군 내의 도시 지역으로, 일반적으로 인구가 2만 명 이상인 지역입니다.
    • 면: 군 내의 농촌 지역으로, 일반적으로 인구가 2만 명 미만인 지역입니다.
    • 동: 시와 구 내의 가장 작은 행정구역 단위입니다.
    • 기타 단위로는 리(농촌 지역 내의 더 작은 단위)가 있습니다.

행정구역분류의 국제적 비교

아래 표는 대한민국의 행정구역 단위를 미국, 유럽연합, 영국(잉글랜드)과 대략적으로 비교한 것입니다. 각 단위가 일대일로 대응되지는 않을 수 있지만, 인구조사나 지역 데이터를 다룰 때 자료가 조사, 수집된 수준을 대략적으로 이해하는 데 도움이 될 수 있습니다.

한국 미국 유럽연합 (NUTS1) 영국 (잉글랜드)
시도 State NUTS1 Regions / Combined Authorities
시군구 County NUTS2
읍면동 Townships / Towns / Census County Division NUTS3 Districts / Wards / Boroughs

행정구역 경계와 코드 체계는 조사 연도나 자료원에 따라서 다릅니다. tidycensuskr에서는 일관된 행정구역 코드를 제공하여 통계치를 통합적으로 사용할 수 있도록 합니다. 예를 들어, 2020년 기준으로 대한민국에는 250개의 시군구와 17개의 시도가 있습니다.

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

2. 가용 센서스 데이터

이 패키지는 센서스 및 조사 데이터를 다음과 같은 방식으로 제공합니다:
- 특정 하위 집합을 쿼리하기 위한 함수 anycensus()
- 긴 테이블 (long table) 포맷의 내장 데이터셋 censuskor

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 (simulated) births per 1000 Age-specific fertility rate for ages 15-19 (simulated) 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 severity _0-19 persons Total registered disabled persons aged 0-19 2015
welfare registered physically mentally challenged severity _20-39 persons Total registered disabled persons aged 20-39 2015
welfare registered physically mentally challenged severity _40-64 persons Total registered disabled persons aged 40-64 2015
welfare registered physically mentally challenged severity _65-79 persons Total registered disabled persons aged 65-79 2015
welfare registered physically mentally challenged severity _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

anycensus()로 데이터 쿼리하기

anycensus() 함수는 사용자가 원하는 연도, 행정구역 단위, 통계 유형을 지정하여 시군구 단위의 센서스 데이터를 쉽게 쿼리할 수 있도록 합니다.

  • year: 센서스 데이터의 연도 (예: 2010, 2015, 2020)
  • codes: 시도 또는 시군구 이름 또는 코드를 벡터로 지정 (예: c("Seoul", "Incheon") 또는 c("11000", "28000")). NULL로 설정하면 모든 지역을 반환합니다.
  • type: 조회할 센서스 데이터의 유형 (예: “population”, “mortality”, “housing” 등)
  • level: 반환할 행정구역 수준 (“adm2”: 시군구 수준, “adm1”: 시도 수준). 기본값은 “adm2”입니다.

자료값을 포함한 필드는 가로로 긴 형태 (wide form)로 반환됩니다. adm2_code 열은 load_districts()로 불러온 경계 도형 파일과 센서스 데이터를 직접 연결하는 역할을 하고, 통계데이터처의 각 연도 행정구역 코드를 따릅니다.

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>

상위 행정구역 단위로 자료값이 집계되는 기능도 제공됩니다. level = "adm1"로 설정하고 집계 함수를 지정하면, 시도(adm1) 수준에서 각 구역의 통계치를 요약한 결과를 얻을 수 있습니다.

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>

censuskor 내장 데이터셋

data(censuskor)을 이용하면 패키지에 내장된 전체 센서스 데이터를 긴 형태로 불러올 수 있습니다. 이 데이터셋은 anycensus() 함수로 쿼리할 수 있는 모든 연도, 행정구역, 통계 유형을 포함합니다.

각 열의 의미는 다음과 같습니다:

  • year: 연도
  • adm1: 시도 이름
  • adm1_code: 시도 코드
  • adm2: 시군구 이름
  • adm2_code: 시군구 코드
  • type: 센서스 또는 조사 유형
  • class1: 추가 분류 변수 1
  • class2: 추가 분류 변수 2
  • unit: 측정 단위
  • value: 관측된 자료값
data(censuskor)
head(censuskor)
#> # A data frame: 6 × 10
#>    year adm1          adm1_code adm2  adm2_code type  class1 class2 unit   value
#> * <dbl> <chr>             <dbl> <chr>     <dbl> <chr> <chr>  <chr>  <chr>  <dbl>
#> 1  2010 Chungcheongb…        33 Cheo…     33010 popu… all h… total  pers… 646939
#> 2  2010 Chungcheongb…        33 Cheo…     33010 popu… all h… male   pers… 318355
#> 3  2010 Chungcheongb…        33 Cheo…     33010 popu… all h… female pers… 328584
#> 4  2020 Chungcheongb…        33 Cheo…     33040 tax   income gener… mill… 524478
#> 5  2020 Chungcheongb…        33 Cheo…     33040 tax   income labor  mill… 598560
#> 6  2015 Chungcheongb…        33 Cheo…     33040 popu… all h… total  pers… 797099

시각화 예제

anycensus() 함수는 정돈된 데이터를 반환하므로, ggplot2로 시각화하는 것이 간단합니다.

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)