Seamless Access to World Bank International Debt Statistics (IDS).

wbids

wbids is an R package to access and analyze the World Bank’s International Debt Statistics (IDS). IDS provides creditor-debtor relationships between countries, regions, and institutions. ‘wbids’ enables users to download, process and work with IDS series across multiple geographies, counterparts, and time periods.

The wbids package relies on a redefinition of the original World Bank data: ‘geographies’ contain both countries and regions, while ‘counterparts’ include both counterpart areas and institutions. wbids provides a consistent mapping of identifiers and names across these different types. The corresponding package vignette provides more details on the data model.

This package is a product of Teal Insights and not sponsored by or affiliated with the World Bank in any way, except for the use of the World Bank IDS API.

Installation

You can install wbids from CRAN via:

install.packages("wbids")

You can also install the development version of wbids like this:

# install.packages("pak")
pak::pak("teal-insights/r-wbids")

Usage

The main function ids_get() provides an interface to download multiple IDS series for multiple geographies and counterparts and specific date ranges.

library(wbids)

ids_get(
  geographies = c("ZMB", "ZAF"),
  series = c("DT.DOD.DPPG.CD", "BM.GSR.TOTL.CD"),
  counterparts = c("216", "231"),
  start_date = 2015,
  end_date = 2020
)

The package comes with prepared metadata about available series, geographies, counterparts, and topics. Please consult the package vignette for details.

ids_list_series()
ids_list_geographies()
ids_list_counterparts()
ids_list_series_topics()

This data can be used to enrich the IDS series or facilitate data discovery. For further applications, please consult Teal Insights’ Guide to Working with the World Bank International Debt Statistics.

The interface and column names are fully consistent with World Development Indicators (WDI) data provided through the wbwdi package. You can find details on github.com/tidy-intelligence/r-wbwdi.