Return auxiliary data tables

Return auxiliary data tables

Usage

pip_aux(
  table = NULL,
  release_version = NULL,
  ppp_version = NULL,
  version = NULL
)

Source

https://pip.worldbank.org/api

Arguments

table

(NULL | character(1))
Table to be returned. Default NULL.

release_version

(NULL | character(1))
Version of the data release in YYYYMMDD format. Default NULL.

ppp_version

(NULL | character(1) | numeric(1))
Version of the data. Default NULL.

version

(NULL | character(1))
Version of the data. Default NULL.

Value

A character() with the available tables or a data.frame() containing the table data.

See also

Other poverty and inequality statistics: pip_citation(), pip_data(), pip_group(), pip_health_check(), pip_info(), pip_valid_params(), pip_versions()

Examples

# \donttest{
# get a list of available tables
pip_aux()
#>  [1] "aux_versions"           "countries"              "country_coverage"      
#>  [4] "country_list"           "cpi"                    "decomposition"         
#>  [7] "dictionary"             "framework"              "gdp"                   
#> [10] "incgrp_coverage"        "indicators"             "interpolated_means"    
#> [13] "metaregion"             "missing_data"           "national_poverty_lines"
#> [16] "pce"                    "pg_lnp"                 "pg_svy"                
#> [19] "pop"                    "pop_region"             "poverty_lines"         
#> [22] "ppp"                    "region_coverage"        "regions"               
#> [25] "spr_lnp"                "spr_svy"                "survey_means"          

# get countries
aux <- pip_aux("countries")
head(aux)
#>   country_code         country_name                africa_split
#> 1          AGO               Angola Eastern and Southern Africa
#> 2          ALB              Albania                        <NA>
#> 3          ARE United Arab Emirates                        <NA>
#> 4          ARG            Argentina                        <NA>
#> 5          ARM              Armenia                        <NA>
#> 6          AUS            Australia                        <NA>
#>   africa_split_code                      region region_code world world_code
#> 1               AFE          Sub-Saharan Africa         SSA World        WLD
#> 2              <NA>       Europe & Central Asia         ECA World        WLD
#> 3              <NA> Other High Income Countries         OHI World        WLD
#> 4              <NA>   Latin America & Caribbean         LAC World        WLD
#> 5              <NA>       Europe & Central Asia         ECA World        WLD
#> 6              <NA> Other High Income Countries         OHI World        WLD

# get GDP
aux <- pip_aux("gdp")
head(aux)
#>   country_code data_level year value
#> 1          ABW   national 1977    NA
#> 2          ABW   national 1978    NA
#> 3          ABW   national 1979    NA
#> 4          ABW   national 1980    NA
#> 5          ABW   national 1981    NA
#> 6          ABW   national 1982    NA

# get CPI
aux <- pip_aux("cpi")
head(aux)
#>   country_code data_level year value
#> 1          AGO   national 1977    NA
#> 2          AGO   national 1978    NA
#> 3          AGO   national 1979    NA
#> 4          AGO   national 1980    NA
#> 5          AGO   national 1981    NA
#> 6          AGO   national 1982    NA
# }