/ubigeosperu

Primary LanguageROtherNOASSERTION

ubigeosperu

Project Status CRAN status

The goal of ubigeosperu is to have an easy way to get the peruvian ubigeos into R. The data has been collected from CONCYTEC’s GitHub repository.

Installation

You can install the released version of ubigeosperu from CRAN with:

## This will work when the package is published into CRAN
install.packages("ubigeosperu")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("calderonsamuel/ubigeosperu")

Example

This is a basic example which shows you how to solve a common problem:

library(ubigeosperu)
library(dplyr)

ubigeosperu contains a single dataframe object containing the peruvian ubigeos codes.

dim(ubigeos)
#> [1] 1876   18

The ubigeos dataset is a tibble.

ubigeos
#> # A tibble: 1,876 x 18
#>    cod_dep_inei desc_dep_inei cod_prov_inei desc_prov_inei cod_ubigeo_inei
#>    <chr>        <chr>         <chr>         <chr>          <chr>          
#>  1 01           AMAZONAS      0101          CHACHAPOYAS    010101         
#>  2 01           AMAZONAS      0101          CHACHAPOYAS    010102         
#>  3 01           AMAZONAS      0101          CHACHAPOYAS    010103         
#>  4 01           AMAZONAS      0101          CHACHAPOYAS    010104         
#>  5 01           AMAZONAS      0101          CHACHAPOYAS    010105         
#>  6 01           AMAZONAS      0101          CHACHAPOYAS    010106         
#>  7 01           AMAZONAS      0101          CHACHAPOYAS    010107         
#>  8 01           AMAZONAS      0101          CHACHAPOYAS    010108         
#>  9 01           AMAZONAS      0101          CHACHAPOYAS    010109         
#> 10 01           AMAZONAS      0101          CHACHAPOYAS    010110         
#> # … with 1,866 more rows, and 13 more variables: desc_ubigeo_inei <chr>,
#> #   cod_dep_reniec <chr>, desc_dep_reniec <chr>, cod_prov_reniec <chr>,
#> #   desc_prov_reniec <chr>, cod_ubigeo_reniec <chr>, desc_ubigeo_reniec <chr>,
#> #   cod_dep_sunat <chr>, desc_dep_sunat <chr>, cod_prov_sunat <chr>,
#> #   desc_prov_sunat <chr>, cod_ubigeo_sunat <chr>, desc_ubigeo_sunat <chr>

You can access the tidy version and pipe it!

ubigeos_tidy %>%
    filter(lugar == "CHORRILLOS", nivel == "Distrito")
#> # A tibble: 3 x 4
#>   lugar      nivel    entidad ubigeo
#>   <chr>      <chr>    <chr>   <chr> 
#> 1 CHORRILLOS Distrito INEI    150108
#> 2 CHORRILLOS Distrito RENIEC  140108
#> 3 CHORRILLOS Distrito SUNAT   150108