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Get census data tables

Source: R/get_census.R
get_census.Rd

Retrieves curated data tables from Argentina's official population censuses based on table ID or filtering criteria.

Usage

get_census(year = NULL, id = NULL, topic = NULL, geo_code = NULL)

Arguments

year

Integer. Census year. Valid census years are 1970, 1980, 1991, 2001, 2010, and 2022. Currently, only 1970 and 1980 are available in this package. Used only when id is NULL.

id

Character vector. Specific table IDs to retrieve (e.g., "1970_00_estructura_01"). If supplied, this takes precedence over year, topic, and geo_code.

topic

Character vector. Keywords to filter topics (e.g., "migracion", c("vivienda", "material")). Matching is case-insensitive and accent-insensitive.

geo_code

Character vector. Geographic code(s) (e.g., "02" for CABA).

Value

If one table is found, a data frame. If multiple tables are found, a named list of data frames. If no matches are found, returns NULL.

Examples

# 1. Retrieve a specific table by ID
educacion <- get_census(id = "1970_00_educacion_01")
head(educacion)
#> # A tibble: 6 × 4
#>   sexo  grupo_de_edad alfabetismo poblacion
#>   <chr> <chr>         <chr>       <chr>    
#> 1 Total 10-14         Total       2201150  
#> 2 Total 10-14         Alfabetos   2100600  
#> 3 Total 10-14         Analfabetos 100550   
#> 4 Total 15-19         Total       2098700  
#> 5 Total 15-19         Alfabetos   2012900  
#> 6 Total 15-19         Analfabetos 85800    


# 2. Retrieve tables by topic (may return multiple tables)
housing_data <- get_census(year = 1970, topic = "habitacional")


# Explore the list and extract the first table
housing_data[[1]]
#> # A tibble: 5 × 4
#>   regimen_de_tenencia                 hogares personas cuartos 
#>   <chr>                               <chr>   <chr>    <chr>   
#> 1 Propietario                         3553250 13778700 11197900
#> 2 Inquilino o arrendatario            1380950 4692800  3305350 
#> 3 Ocupante en relación de dependencia 353300  1402500  880050  
#> 4 Ocupante gratuito                   575650  2271150  1196500 
#> 5 En otro carácter                    192950  816350   419800  


# 3. Retrieve a single table using filters (returns a data frame)
poblacion_total <- get_census(
  year = 1970,
  topic = "estructura",
  geo_code = "00"
)
head(poblacion_total)
#> # A tibble: 6 × 3
#>   grupo_de_edad sexo    poblacion
#>   <chr>         <chr>   <chr>    
#> 1 0-4           Total   2355300  
#> 2 0-4           Varones 1196950  
#> 3 0-4           Mujeres 1158350  
#> 4 5-9           Total   2297000  
#> 5 5-9           Varones 1163050  
#> 6 5-9           Mujeres 1133950