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Fetch Debt Statistics from the World Bank International Debt Statistics API

Source: R/ids_get.R
ids_get.Rd

This function returns a tibble with debt statistics data fetched from the World Bank International Debt Statistics (IDS) API. The data can be filtered by geographies, series, counterparts, and time periods.

Usage

ids_get(
  geographies,
  series,
  counterparts = "all",
  start_date = NULL,
  end_date = NULL,
  progress = FALSE
)

Arguments

geographies

A character vector representing the geographic codes (e.g., "ZMB" for Zambia). This argument is required and cannot contain NA values.

series

A character vector representing the series codes (e.g., "DT.DOD.DPPG.CD"). This argument is required and cannot contain NA values.

counterparts

A character vector representing counterpart areas (e.g., "all", "001"). This argument is required and cannot contain NA values (default: "all").

start_date

An optional numeric value representing the starting year (e.g., 2015). It must be greater than or equal to 1970. If not provided, the entire time range is used.

end_date

An optional numeric value representing the ending year (e.g., 2020). It must be greater than or equal to 1970 and cannot be earlier than start_date. If not provided, the entire available time range is used.

progress

A logical value indicating whether to display a progress message during the request process (default: FALSE). Must be either TRUE or FALSE.

Value

A tibble containing debt statistics with the following columns:

geography_id

The unique identifier for the geography (e.g., "ZMB").

series_id

The unique identifier for the series (e.g., "DT.DOD.DPPG.CD").

counterpart_id

The unique identifier for the counterpart (e.g., "all").

year

The year corresponding to the data (e.g., 2020).

value

The numeric value representing the statistic for the given geography, series, counterpart, and year.

Examples

# \donttest{
# Fetch data for a series without specifying a time range or counterpart
ids_get(
  geographies = "ZMB",
  series = "DT.DOD.DPPG.CD",
)
#> # A tibble: 18,483 × 5
#>    geography_id series_id      counterpart_id  year      value
#>    <chr>        <chr>          <chr>          <int>      <dbl>
#>  1 ZMB          DT.DOD.DPPG.CD 265             1970  19822458.
#>  2 ZMB          DT.DOD.DPPG.CD 288             1970        NA 
#>  3 ZMB          DT.DOD.DPPG.CD 063             1970        NA 
#>  4 ZMB          DT.DOD.DPPG.CD 580             1970        NA 
#>  5 ZMB          DT.DOD.DPPG.CD 019             1970        NA 
#>  6 ZMB          DT.DOD.DPPG.CD 931             1970        NA 
#>  7 ZMB          DT.DOD.DPPG.CD 902             1970        NA 
#>  8 ZMB          DT.DOD.DPPG.CD 905             1970        NA 
#>  9 ZMB          DT.DOD.DPPG.CD 901             1970  61433000 
#> 10 ZMB          DT.DOD.DPPG.CD WLD             1970 623521836.
#> # ℹ 18,473 more rows

# Fetch specific debt statistics for Zambia from 2015 to 2020
ids_get(
  geographies = "ZMB",
  series = c("DT.DOD.DPPG.CD", "BM.GSR.TOTL.CD"),
  start_date = 2015,
  end_date = 2020
)
#> # A tibble: 3,636 × 5
#>    geography_id series_id      counterpart_id  year       value
#>    <chr>        <chr>          <chr>          <int>       <dbl>
#>  1 ZMB          BM.GSR.TOTL.CD 265             2015         NA 
#>  2 ZMB          BM.GSR.TOTL.CD 288             2015         NA 
#>  3 ZMB          BM.GSR.TOTL.CD 063             2015         NA 
#>  4 ZMB          BM.GSR.TOTL.CD 580             2015         NA 
#>  5 ZMB          BM.GSR.TOTL.CD 019             2015         NA 
#>  6 ZMB          BM.GSR.TOTL.CD 931             2015         NA 
#>  7 ZMB          BM.GSR.TOTL.CD 902             2015         NA 
#>  8 ZMB          BM.GSR.TOTL.CD 905             2015         NA 
#>  9 ZMB          BM.GSR.TOTL.CD 901             2015         NA 
#> 10 ZMB          BM.GSR.TOTL.CD WLD             2015 9225953069.
#> # ℹ 3,626 more rows

# Fetch data for specific counterparts
ids_get(
  geographies = "ZMB",
  series = "DT.DOD.DPPG.CD",
  counterparts = c("216", "231")
)
#> # A tibble: 122 × 5
#>    geography_id series_id      counterpart_id  year    value
#>    <chr>        <chr>          <chr>          <int>    <dbl>
#>  1 ZMB          DT.DOD.DPPG.CD 216             1970      NA 
#>  2 ZMB          DT.DOD.DPPG.CD 216             1971      NA 
#>  3 ZMB          DT.DOD.DPPG.CD 216             1972      NA 
#>  4 ZMB          DT.DOD.DPPG.CD 216             1973 4842338.
#>  5 ZMB          DT.DOD.DPPG.CD 216             1974 4241396.
#>  6 ZMB          DT.DOD.DPPG.CD 216             1975 2616250 
#>  7 ZMB          DT.DOD.DPPG.CD 216             1976 1868750 
#>  8 ZMB          DT.DOD.DPPG.CD 216             1977 1121250 
#>  9 ZMB          DT.DOD.DPPG.CD 216             1978 1121250 
#> 10 ZMB          DT.DOD.DPPG.CD 216             1979 1179165 
#> # ℹ 112 more rows

# Fetch data for multiple geographies and counterparts
ids_get(
  geographies = c("ZMB", "CHN"),
  series = "DT.DOD.DPPG.CD",
  counterparts = c("216", "231"),
  start_date = 2019,
  end_date = 2020
)
#> # A tibble: 8 × 5
#>   geography_id series_id      counterpart_id  year     value
#>   <chr>        <chr>          <chr>          <int>     <dbl>
#> 1 CHN          DT.DOD.DPPG.CD 216             2019        NA
#> 2 CHN          DT.DOD.DPPG.CD 216             2020        NA
#> 3 CHN          DT.DOD.DPPG.CD 231             2019        NA
#> 4 CHN          DT.DOD.DPPG.CD 231             2020        NA
#> 5 ZMB          DT.DOD.DPPG.CD 216             2019 279060000
#> 6 ZMB          DT.DOD.DPPG.CD 216             2020 271878000
#> 7 ZMB          DT.DOD.DPPG.CD 231             2019        NA
#> 8 ZMB          DT.DOD.DPPG.CD 231             2020        NA
# }