The function queries a long format census data frame
(censuskor) for specific administrative codes (if provided)
Usage
anycensus(
year = 2020,
codes = NULL,
type = c("population", "housing", "tax", "mortality", "economy", "medicine",
"migration", "environment", "welfare", "social security", "landuse"),
level = c("adm2", "adm3", "adm1"),
adm2_type = c("all", "atn", "non"),
aggregator = sum,
weight_type = NULL,
weight_column = NULL,
geometry = FALSE,
...
)Arguments
- year
integer(1). One of 2010, 2015, 2020, or 2025. 2025 data is currently available only for ADM3 population and housing.
- codes
integer vector of admin codes (e.g.
c(11, 26)) or character administrative area names (e.g.c("Seoul", "Daejeon")).- type
character vector. One or more of "population", "housing", "tax", "economy", "medicine", "migration", "environment", "mortality", "social security", or "landuse". Defaults to "population".
- level
character(1). "adm1" for province-level, "adm2" for municipal-level, or "adm3" for neighborhood/town-level. ADM3 population and housing data is available for 2015, 2020, and 2025; ADM3 economy and mortality data is available for 2020. Defaults to "adm2".
- adm2_type
character(1). Municipal level to return or aggregate. For a city and its non-autonomous Gu with the same indicator,
"all"removes the source city count when Gu values are available, but retains city and Gu rates; the city count isNAin a mixed wide row."atn"returns city rows, summing available Gu counts and retaining the city rate;"non"returns Gu rows where they are available. Incomplete Gu sums emit a warning. When a city rate is missing,"atn"uses the Gu mean; with weights, it recalculates city rates from Gu values. Rates are identified by units per 100,000 population, births per 1,000, births per woman, and percent; other units are treated as additive counts. Selecting a city code with"atn"or"non"also selects its Gu."all"retains both city and Gu rates, so choose the intended geographic level and a suitable aggregator when aggregating rates to"adm1".- aggregator
function to aggregate values when
level = "adm1"or when weightedadm2_type = "atn"recalculates autonomous/basic local government rows.- weight_type
character(1). Optional data type used to supply weights when aggregating. For example, rate variables in
type = "mortality"can be aggregated with population weights fromweight_type = "population".- weight_column
character(1). Optional column name used as weights when aggregating. If
weight_type = "population"andweight_columnis omitted,"all households_total_prs"is used.- geometry
logical(1). If
TRUE, returns ansfobject with ADM2 geometries attached. Geometry is available for 2010, 2015, and 2020; 2025 census data is tabular only. Defaults toFALSE.- ...
additional arguments passed to the
aggregatorfunction. (e.g.,na.rm = TRUE). Whenweight_typeorweight_columnis supplied,aggregatormust accept awargument such asstats::weighted.mean().
Note
Character names are resolved to their administrative codes before
filtering. The 'wide' table is returned with separate columns for each
class1 and class2 and unit (abbreviated whereof) combination.
Examples
# Query mortality data for adm2_code 21 (Busan)
anycensus(codes = 21, type = "mortality")
#> # A tibble: 16 × 9
#> year adm1 adm1_code adm2 adm2_code type `all causes_total_p1p`
#> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
#> 1 2020 Busan 21 Buk-gu 21080 mortality 319.
#> 2 2020 Busan 21 Busanjin-gu 21050 mortality 332.
#> 3 2020 Busan 21 Dong-gu 21030 mortality 372.
#> 4 2020 Busan 21 Dongnae-gu 21060 mortality 297.
#> 5 2020 Busan 21 Gangseo-gu 21120 mortality 290.
#> 6 2020 Busan 21 Geumjeong-gu 21110 mortality 322.
#> 7 2020 Busan 21 Gijang-gun 21310 mortality 329.
#> 8 2020 Busan 21 Haeundae-gu 21090 mortality 302.
#> 9 2020 Busan 21 Jung-gu 21010 mortality 398.
#> 10 2020 Busan 21 Nam-gu 21070 mortality 311.
#> 11 2020 Busan 21 Saha-gu 21100 mortality 342.
#> 12 2020 Busan 21 Sasang-gu 21150 mortality 363.
#> 13 2020 Busan 21 Seo-gu 21020 mortality 395.
#> 14 2020 Busan 21 Suyeong-gu 21140 mortality 294.
#> 15 2020 Busan 21 Yeongdo-gu 21040 mortality 404.
#> 16 2020 Busan 21 Yeonje-gu 21130 mortality 297.
#> # ℹ 2 more variables: `all causes_male_p1p` <dbl>,
#> # `all causes_female_p1p` <dbl>
# Query population data for adm1 "Seoul" or "Daejeon"
anycensus(codes = c("Seoul", "Daejeon"), type = "housing", year = 2015)
#> # A tibble: 30 × 15
#> year adm1 adm1_code adm2 adm2_code type housing types_total_…¹
#> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
#> 1 2015 Daejeon 25 Daedeok-gu 25050 housi… 58548
#> 2 2015 Seoul 11 Dobong-gu 11100 housi… 100589
#> 3 2015 Daejeon 25 Dong-gu 25010 housi… 73731
#> 4 2015 Seoul 11 Dongdaemun-gu 11060 housi… 94464
#> 5 2015 Seoul 11 Dongjak-gu 11200 housi… 107968
#> 6 2015 Seoul 11 Eunpyeong-gu 11120 housi… 136848
#> 7 2015 Seoul 11 Gangbuk-gu 11090 housi… 89911
#> 8 2015 Seoul 11 Gangdong-gu 11250 housi… 114424
#> 9 2015 Seoul 11 Gangnam-gu 11230 housi… 164864
#> 10 2015 Seoul 11 Gangseo-gu 11160 housi… 173366
#> # ℹ 20 more rows
#> # ℹ abbreviated name: ¹`housing types_total_cnt`
#> # ℹ 8 more variables: `housing types_detached housing_cnt` <dbl>,
#> # `housing types_apartment_cnt` <dbl>, `housing types_row house_cnt` <dbl>,
#> # `housing types_multiplex_cnt` <dbl>,
#> # `housing types_non-residential_cnt` <dbl>,
#> # `vacant housing_fraction_prc` <dbl>, …
# Query adm3 population data within Jongno-gu
anycensus(
codes = 11010,
type = "population",
year = 2020,
level = "adm3"
)
#> # A tibble: 34 × 17
#> 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 population Sajik-dong 11010530
#> 2 2020 Seoul 11 Jongno-gu 11010 population Samcheong-dong 11010540
#> 3 2020 Seoul 11 Jongno-gu 11010 population Buam-dong 11010550
#> 4 2020 Seoul 11 Jongno-gu 11010 population Pyeongchang-d… 11010560
#> 5 2020 Seoul 11 Jongno-gu 11010 population Muak-dong 11010570
#> 6 2020 Seoul 11 Jongno-gu 11010 population Gyonam-dong 11010580
#> 7 2020 Seoul 11 Jongno-gu 11010 population Gahoe-dong 11010600
#> 8 2020 Seoul 11 Jongno-gu 11010 population Jongno 1.2.3.… 11010610
#> 9 2020 Seoul 11 Jongno-gu 11010 population Jongno 5.6(or… 11010630
#> 10 2020 Seoul 11 Jongno-gu 11010 population Ihwa-dong 11010640
#> # ℹ 24 more rows
#> # ℹ 9 more variables: `all households_total_prs` <dbl>,
#> # `all households_male_prs` <dbl>, `all households_female_prs` <dbl>,
#> # `domestic population_total_prs` <dbl>,
#> # `domestic population_male_prs` <dbl>,
#> # `domestic population_female_prs` <dbl>,
#> # `foreign population_total_prs` <dbl>, …
# Query 2025 neighborhood-level housing data (without geometry)
if (FALSE) { # \dontrun{
anycensus(
codes = 11010,
type = "housing",
year = 2025,
level = "adm3"
)
} # }
# Aggregate mortality rates to adm1 using population weights
anycensus(
codes = "Seoul",
type = "mortality",
year = 2020,
level = "adm1",
aggregator = stats::weighted.mean,
weight_type = "population",
weight_column = "all households_total_prs",
na.rm = TRUE
)
#> # 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>
# Aggregate rates to adm1 after cleaning to autonomous/
# basic local governments
if (FALSE) { # \dontrun{
anycensus(
codes = "Gyeonggi-do",
type = "mortality",
year = 2020,
level = "adm1",
adm2_type = "atn",
aggregator = stats::weighted.mean,
weight_type = "population",
weight_column = "all households_total_prs",
na.rm = TRUE
)
} # }
# Recalculate adm2 rates after cleaning to autonomous/basic local governments
anycensus(
codes = "Gyeonggi-do",
type = "mortality",
year = 2020,
level = "adm2",
adm2_type = "atn",
aggregator = stats::weighted.mean,
weight_type = "population",
weight_column = "all households_total_prs",
na.rm = TRUE
)
#> # A tibble: 31 × 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 Dongduche… 31080 mort… 368
#> 6 2020 Gyeonggi-do 31 Gapyeong-… 31370 mort… 353.
#> 7 2020 Gyeonggi-do 31 Gimpo-si 31230 mort… 272
#> 8 2020 Gyeonggi-do 31 Goyang-si 31100 mort… 258.
#> 9 2020 Gyeonggi-do 31 Gunpo-si 31160 mort… 277
#> 10 2020 Gyeonggi-do 31 Guri-si 31120 mort… 291.
#> # ℹ 21 more rows
#> # ℹ 3 more variables: `all causes_male_p1p` <dbl>,
#> # `all causes_female_p1p` <dbl>, `all households_total_prs` <dbl>