wbwdi is an R package to access and analyze the World Bank’s World Development Indicators (WDI) using the corresponding API. WDI provides more than 24,000 country or region-level indicators for various contexts. wbwdi enables users to download, process and work with WDI series across multiple entities and time periods.
The package is designed to work seamlessly with International Debt Statistics (IDS) provided through the wbids package and shares its syntax with its sibling Python library wbwdi. It follows the principles of the econdataverse.
This package is a product of Christoph Scheuch and not sponsored by or affiliated with the World Bank in any way, except for the use of the World Bank WDI API.
Installation
You can install wbwdi from CRAN via:
install.packages("wbwdi")You can install the development version of wbwdi like this:
pak::pak("tidy-intelligence/r-wbwdi")Usage
The main function wdi_get() provides an interface to download multiple WDI series for multiple entities and specific date ranges.
library(wbwdi)
wdi_get(
entities = c("MEX", "CAN", "USA"),
indicators = c("NY.GDP.PCAP.KD", "SP.POP.TOTL"),
start_year = 2020,
end_year = 2024
)
#> # A tibble: 24 × 4
#> entity_id indicator_id year value
#> <chr> <chr> <int> <dbl>
#> 1 CAN NY.GDP.PCAP.KD 2020 42366.
#> 2 MEX NY.GDP.PCAP.KD 2020 9235.
#> 3 USA NY.GDP.PCAP.KD 2020 59493.
#> 4 CAN NY.GDP.PCAP.KD 2021 44360.
#> 5 MEX NY.GDP.PCAP.KD 2021 9728.
#> 6 USA NY.GDP.PCAP.KD 2021 62996.
#> 7 CAN NY.GDP.PCAP.KD 2022 45227.
#> 8 MEX NY.GDP.PCAP.KD 2022 10011.
#> 9 USA NY.GDP.PCAP.KD 2022 64342.
#> 10 CAN NY.GDP.PCAP.KD 2023 44469.
#> # ℹ 14 more rowsYou can also download these indicators for all entities and available dates:
wdi_get(
entities = "all",
indicators = c("NY.GDP.PCAP.KD", "SP.POP.TOTL")
)
#> # A tibble: 34,048 × 4
#> entity_id indicator_id year value
#> <chr> <chr> <int> <dbl>
#> 1 AFE NY.GDP.PCAP.KD 1960 1172.
#> 2 AFW NY.GDP.PCAP.KD 1960 1111.
#> 3 ARB NY.GDP.PCAP.KD 1960 NA
#> 4 CSS NY.GDP.PCAP.KD 1960 4422.
#> 5 CEB NY.GDP.PCAP.KD 1960 NA
#> 6 EAR NY.GDP.PCAP.KD 1960 1062.
#> 7 EAS NY.GDP.PCAP.KD 1960 1144.
#> 8 EAP NY.GDP.PCAP.KD 1960 323.
#> 9 TEA NY.GDP.PCAP.KD 1960 327.
#> 10 EMU NY.GDP.PCAP.KD 1960 9943.
#> # ℹ 34,038 more rowsSome indicators are also available on a monthly basis, e.g.:
wdi_get(
entities = "AUT",
indicators = "DPANUSSPB",
start_year = 2012,
end_year = 2015,
frequency = "month"
)
#> # A tibble: 48 × 5
#> entity_id indicator_id year month value
#> <chr> <chr> <int> <int> <dbl>
#> 1 AUT DPANUSSPB 2012 1 0.775
#> 2 AUT DPANUSSPB 2012 2 0.755
#> 3 AUT DPANUSSPB 2012 3 0.757
#> 4 AUT DPANUSSPB 2012 4 0.760
#> 5 AUT DPANUSSPB 2012 5 0.782
#> 6 AUT DPANUSSPB 2012 6 0.797
#> 7 AUT DPANUSSPB 2012 7 0.814
#> 8 AUT DPANUSSPB 2012 8 0.806
#> 9 AUT DPANUSSPB 2012 9 0.777
#> 10 AUT DPANUSSPB 2012 10 0.771
#> # ℹ 38 more rowsSimilarly, there are also some indicators available on a quarterly frequency, e.g.:
wdi_get(
entities = "NGA",
indicators = "DT.DOD.DECT.CD.TL.US",
start_year = 2012,
end_year = 2015,
frequency = "quarter"
)
#> # A tibble: 16 × 5
#> entity_id indicator_id year quarter value
#> <chr> <chr> <int> <int> <dbl>
#> 1 NGA DT.DOD.DECT.CD.TL.US 2012 1 8235000000
#> 2 NGA DT.DOD.DECT.CD.TL.US 2012 2 8374010000
#> 3 NGA DT.DOD.DECT.CD.TL.US 2012 3 8587040000
#> 4 NGA DT.DOD.DECT.CD.TL.US 2012 4 8883380000
#> 5 NGA DT.DOD.DECT.CD.TL.US 2013 1 9195080000
#> 6 NGA DT.DOD.DECT.CD.TL.US 2013 2 9477070000
#> 7 NGA DT.DOD.DECT.CD.TL.US 2013 3 10817130000
#> 8 NGA DT.DOD.DECT.CD.TL.US 2013 4 11402000000
#> 9 NGA DT.DOD.DECT.CD.TL.US 2014 1 11746100000
#> 10 NGA DT.DOD.DECT.CD.TL.US 2014 2 11957200000
#> 11 NGA DT.DOD.DECT.CD.TL.US 2014 3 12002828000
#> 12 NGA DT.DOD.DECT.CD.TL.US 2014 4 12138750000
#> 13 NGA DT.DOD.DECT.CD.TL.US 2015 1 11781991990.
#> 14 NGA DT.DOD.DECT.CD.TL.US 2015 2 12667757149.
#> 15 NGA DT.DOD.DECT.CD.TL.US 2015 3 12966561500
#> 16 NGA DT.DOD.DECT.CD.TL.US 2015 4 13040050000You can get a list of all indicators supported by the WDI API via:
wdi_get_indicators()
#> # A tibble: 22,806 × 7
#> indicator_id indicator_name source_id source_name source_note
#> <chr> <chr> <int> <chr> <chr>
#> 1 1.0.HCount.1.90usd Poverty Headcount ($1… 37 LAC Equity… "The pover…
#> 2 1.0.HCount.2.5usd Poverty Headcount ($2… 37 LAC Equity… "The pover…
#> 3 1.0.HCount.Mid10to50 Middle Class ($10-50 … 37 LAC Equity… "The pover…
#> 4 1.0.HCount.Ofcl Official Moderate Pov… 37 LAC Equity… "The pover…
#> 5 1.0.HCount.Poor4uds Poverty Headcount ($4… 37 LAC Equity… "The pover…
#> 6 1.0.HCount.Vul4to10 Vulnerable ($4-10 a d… 37 LAC Equity… "The pover…
#> 7 1.0.PGap.1.90usd Poverty Gap ($1.90 a … 37 LAC Equity… "The pover…
#> 8 1.0.PGap.2.5usd Poverty Gap ($2.50 a … 37 LAC Equity… "The pover…
#> 9 1.0.PGap.Poor4uds Poverty Gap ($4 a day) 37 LAC Equity… "The pover…
#> 10 1.0.PSev.1.90usd Poverty Severity ($1.… 37 LAC Equity… "The pover…
#> # ℹ 22,796 more rows
#> # ℹ 2 more variables: source_organization <chr>, topics <list>You can get a list of all supported entities via:
wdi_get_entities()
#> # A tibble: 296 × 19
#> entity_id entity_iso2code entity_type capital_city entity_name region_id
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABW AW country Oranjestad Aruba LCN
#> 2 AFE ZH aggregates <NA> Africa East… NA
#> 3 AFG AF country Kabul Afghanistan SAS
#> 4 AFR A9 aggregates <NA> Africa NA
#> 5 AFW ZI aggregates <NA> Africa West… NA
#> 6 AGO AO country Luanda Angola SSF
#> 7 ALB AL country Tirane Albania ECS
#> 8 AND AD country Andorra la Vella Andorra ECS
#> 9 ARB 1A aggregates <NA> Arab World NA
#> 10 ARE AE country Abu Dhabi United Arab… MEA
#> # ℹ 286 more rows
#> # ℹ 13 more variables: region_iso2code <chr>, region_name <chr>,
#> # admin_region_id <chr>, admin_region_iso2code <chr>,
#> # admin_region_name <chr>, income_level_id <chr>,
#> # income_level_iso2code <chr>, income_level_name <chr>,
#> # lending_type_id <chr>, lending_type_iso2code <chr>,
#> # lending_type_name <chr>, longitude <dbl>, latitude <dbl>You can also get the list of supported indicators and entities in another language, but note that not everything seems to be translated into other languages:
wdi_get_indicators(language = "es")
wdi_get_entities(language = "zh")Check out the following function for a list of supported languages:
wdi_get_languages()
#> # A tibble: 23 × 3
#> language_code language_name native_form
#> <chr> <chr> <chr>
#> 1 en English English
#> 2 es Spanish Español
#> 3 fr French Français
#> 4 ar Arabic عربي
#> 5 zh Chinese 中文
#> 6 bg Bulgarian Български
#> 7 de German Deutsch
#> 8 hi Hindi हिंदी
#> 9 id Indonesian Bahasa indonesia
#> 10 ja Japanese 日本語
#> # ℹ 13 more rowsIn addition, you can list supported regions, sources, topics and lending types, respectively:
If you want to search for specific keywords among indicators or other data sources, you can use the RStudio or Positron data explorer. Alternatively, this package comes with a helper function:
indicators <- wdi_get_indicators()
wdi_search(
indicators,
keywords = c("inequality", "gender"),
columns = c("indicator_name")
)
#> # A tibble: 157 × 7
#> indicator_id indicator_name source_id source_name source_note
#> <chr> <chr> <int> <chr> <chr>
#> 1 2.3_GIR.GPI "Gender parity index … 34 Global Par… "Ratio of …
#> 2 2.6_PCR.GPI "Gender parity index … 34 Global Par… "Ratio of …
#> 3 BI.EMP.PWRK.PB.FE.ZS "Public sector employ… 64 Worldwide … <NA>
#> 4 BI.EMP.PWRK.PB.MA.ZS "Public sector employ… 64 Worldwide … <NA>
#> 5 BI.EMP.TOTL.PB.FE.ZS "Public sector employ… 64 Worldwide … <NA>
#> 6 BI.EMP.TOTL.PB.MA.ZS "Public sector employ… 64 Worldwide … <NA>
#> 7 BI.WAG.PREM.PB.FE "Public sector wage p… 64 Worldwide … <NA>
#> 8 BI.WAG.PREM.PB.FM "P-Value: Public sect… 64 Worldwide … <NA>
#> 9 BI.WAG.PREM.PB.FM.CA "P-Value: Gender wage… 64 Worldwide … <NA>
#> 10 BI.WAG.PREM.PB.FM.ED "P-Value: Gender wage… 64 Worldwide … <NA>
#> # ℹ 147 more rows
#> # ℹ 2 more variables: source_organization <chr>, topics <list>Relation to Existing R Packages
The most important differences to existing packages are that wbwdi is designed to (i) have a narrow focus on World Bank Development Indicators, (ii) have a consistent interface with other R packages (e.g., wbids), (iii) have an MIT license, and (iv) have a shared interface with Python libraries (e.g., wbwdi). wbwdi also refrains from using cached data because this approach frequently leads to problems for users due to outdated caches and it uses httr2 to manage API requests and parse responses.
More specifically, the differences of existing CRAN releases (apart from interface design) are:
-
WDI: uses cached data by default, does not allow downloading meta data from the WDI API (e.g., languages, sources, topics), has a GPL-3 license, and does neither usehttrnorhttr2for requests. -
worldbank: does not have a narrow focus because it includes the Poverty and Inequality Platform and Finances One API. -
wbstats: uses cached data by default, does not usehttr2.