World Bank Data in Julia

The World Bank provides access to global development data at data.worldbank.org.

The primary collection of development indicators is called World Development Indicators (WDI).

This module provides two functions to access and download the data: search_wdi() and wdi(). These functions return DataFrames.

It follows roughly the R WDI package.

Build Status

Installation

using Pkg
Pkg.add("WorldBankData")

Basic Examples

Get a DataFrame of the U.S. population:

julia> using WorldBankData
julia> df = wdi("SP.POP.TOTL", "US")
60×4 DataFrame
│ Row │ iso2c  │ country       │ year    │ SP_POP_TOTL │
│     │ String │ String        │ Float64 │ Float64?    │
├─────┼────────┼───────────────┼─────────┼─────────────┤
│ 1   │ US     │ United States │ 1960.0  │ 1.80671e8   │
│ 2   │ US     │ United States │ 1961.0  │ 1.83691e8   │
⋮
│ 59  │ US     │ United States │ 2018.0  │ 3.26688e8   │
│ 60  │ US     │ United States │ 2019.0  │ 3.2824e8    │

The WDI indicator SP.POP.TOTL becomes the column SP_POP_TOTL in the DataFrame, i.e. . gets replaced by _.

Get a DataFrame of the U.S. population from 1980 until 2012 including region data:

julia> df = wdi("SP.POP.TOTL", "US", 1980, 2012, extra=true)
33×12 DataFrame. Omitted printing of 5 columns
│ Row │ iso2c  │ country       │ SP_POP_TOTL │ year    │ iso3c  │ name          │ region        │
│     │ String │ String        │ Float64?    │ Float64 │ String │ String        │ String        │
├─────┼────────┼───────────────┼─────────────┼─────────┼────────┼───────────────┼───────────────┤
│ 1   │ US     │ United States │ 2.27225e8   │ 1980.0  │ USA    │ United States │ North America │
│ 2   │ US     │ United States │ 2.29466e8   │ 1981.0  │ USA    │ United States │ North America │
⋮
│ 32  │ US     │ United States │ 3.11557e8   │ 2011.0  │ USA    │ United States │ North America │
│ 33  │ US     │ United States │ 3.13831e8   │ 2012.0  │ USA    │ United States │ North America │

ISO 3 letter country codes are also supported:

df = wdi("SP.POP.TOTL", "USA", 1980, 2012)

Multiple indicators and countries can be requested:

julia> df = wdi(["SP.POP.TOTL", "NY.GDP.MKTP.CD"], ["US","BR"], 1980, 2012)
66×5 DataFrame
│ Row │ iso2c  │ country       │ year    │ NY_GDP_MKTP_CD │ SP_POP_TOTL │
│     │ String │ String        │ Float64 │ Float64?       │ Float64?    │
├─────┼────────┼───────────────┼─────────┼────────────────┼─────────────┤
│ 1   │ BR     │ Brazil        │ 1980.0  │ 2.35025e11     │ 1.20694e8   │
│ 2   │ BR     │ Brazil        │ 1981.0  │ 2.63561e11     │ 1.2357e8    │
⋮
│ 65  │ US     │ United States │ 2011.0  │ 1.55426e13     │ 3.11557e8   │
│ 66  │ US     │ United States │ 2012.0  │ 1.6197e13      │ 3.13831e8   │

By default a wide DataFrame is returned (indicators are columns). The data can also be returned in long format which might be more useful if many indicators are requested:

julia> df = wdi(["SP.POP.TOTL", "NY.GDP.MKTP.CD"], ["US","BR"], 1980, 2012, dflong=true)
132×5 DataFrame
│ Row │ iso2c  │ country       │ year    │ indicator      │ value      │
│     │ String │ String        │ Float64 │ String         │ Float64?   │
├─────┼────────┼───────────────┼─────────┼────────────────┼────────────┤
│ 1   │ BR     │ Brazil        │ 1980.0  │ SP.POP.TOTL    │ 1.20694e8  │
│ 2   │ BR     │ Brazil        │ 1980.0  │ NY.GDP.MKTP.CD │ 2.35025e11 │
⋮
│ 131 │ US     │ United States │ 2012.0  │ SP.POP.TOTL    │ 3.13831e8  │
│ 132 │ US     │ United States │ 2012.0  │ NY.GDP.MKTP.CD │ 1.6197e13  │

Get a DataFrame of the total population for all countries from 1980 to 2012:

using WorldBankData
df = wdi("SP.POP.TOTL", "all", 1980, 2012)

Arguments

The wdi function has the following arguments:

function wdi(indicators::Union{String,Array{String,1}}, countries::Union{String,Array{String,1}},
             startyear::Integer=-1, endyear::Integer=-1;
             extra::Bool=false, sourceid::Integer=2, dflong::Bool=false, verbose::Bool=false)::DataFrame

It needs a minimum of two arguments: the indicators (from the WDI database) and the countries (ISO two or three letter country codes or "all" for all countries). The rest are optional arguments.

startyear: First year to include.

endyear: Last year to include.

extra: If extra=true, wdi() attaches extra country data to the returned DataFrame.

sourceid: Database source number, see https://api.worldbank.org/v2/sources

dflong: If dflong=true return long DataFrame format. Default is wide. If many indicators are requested long might be preferable.

verbose: If verbose=true, wdi() will print URL download information.

Searching

The most convenient way to explore the database is probably through a web browser at data.worldbank.org.

However, the module does provide a search function: search_wdi().

One can search for "countries" or "indicators" data.

Example for country search by name

julia> using WorldBankData
julia> res=search_wdi("countries","name",r"united"i)
julia> res[!, :name]
3-element DataArray{UTF8String,1}:
 "United Arab Emirates"
 "United Kingdom"
 "United States"
julia> res[!, :iso2c]
3-element DataArray{ASCIIString,1}:
 "AE"
 "GB"
 "US"

Example for indicator search by description

julia> using WorldBankData
julia> res=search_wdi("indicators","description",r"gross national expenditure"i)
6x5 DataFrame
...
julia> res[!, :name]
6-element DataArray{UTF8String,1}:
 "Gross national expenditure deflator (base year varies by country)"
 "Gross national expenditure (current US\$)"
 "Gross national expenditure (current LCU)"
 "Gross national expenditure (constant 2005 US\$)"
 "Gross national expenditure (constant LCU)"
 "Gross national expenditure (% of GDP)"

julia> res[!, :indicator]
6-element DataArray{UTF8String,1}:
 "NE.DAB.DEFL.ZS"
 "NE.DAB.TOTL.CD"
 "NE.DAB.TOTL.CN"
 "NE.DAB.TOTL.KD"
 "NE.DAB.TOTL.KN"
 "NE.DAB.TOTL.ZS"

The search_wdi() function

The search_wdi() function has the following arguments

search_wdi(data::String, entry::String, regx::Regex)::DataFrame

data: Either countries or indicators.

entry: One of the attributes (like name).

regex: Regular expression to search for.

"countries" can be searched for "name", "region", "capital", "iso2c", "iso3c", "income", and "lending".

"indicators" can be searched for "name", "description", "topics", "source_database", and "source_organization".

The search function uses two DataFrames country_cache and indicator_cache and searches through these. On the first search it will download the data from the World Bank website. This takes much longer for the larger indicators data. This only happens once per session. After the first use the data is cached.

Note that the last argument to search_wdi() is a regular expression denoted by r"..." and an i at the end means that it is case insensitive.

Examples of country searches

julia> search_wdi("countries","iso2c",r"TZ"i)
1×9 DataFrame. Omitted printing of 2 columns
│ Row │ iso3c  │ iso2c  │ name     │ region              │ capital │ longitude │ latitude │
│     │ String │ String │ String   │ String              │ String  │ Float64?  │ Float64? │
├─────┼────────┼────────┼──────────┼─────────────────────┼─────────┼───────────┼──────────┤
│ 1   │ TZA    │ TZ     │ Tanzania │ Sub-Saharan Africa  │ Dodoma  │ 35.7382   │ -6.17486 │

julia> search_wdi("countries","income",r"upper middle"i)
...

julia> search_wdi("countries","region",r"Latin America"i)
...

julia> search_wdi("countries","capital",r"^Ka"i)
3×9 DataFrame. Omitted printing of 2 columns
│ Row │ iso3c  │ iso2c  │ name        │ region              │ capital   │ longitude │ latitude │
│     │ String │ String │ String      │ String              │ String    │ Float64?  │ Float64? │
├─────┼────────┼────────┼─────────────┼─────────────────────┼───────────┼───────────┼──────────┤
│ 1   │ AFG    │ AF     │ Afghanistan │ South Asia          │ Kabul     │ 69.1761   │ 34.5228  │
│ 2   │ NPL    │ NP     │ Nepal       │ South Asia          │ Kathmandu │ 85.3157   │ 27.6939  │
│ 3   │ UGA    │ UG     │ Uganda      │ Sub-Saharan Africa  │ Kampala   │ 32.5729   │ 0.314269 │

julia> search_wdi("countries","lending",r"IBRD"i)
...

Examples of indicator searches

julia> search_wdi("indicators","name",r"gross national expenditure"i)
...
julia> search_wdi("indicators","description",r"gross national expenditure"i)
...
julia> search_wdi("indicators","source_database",r"Sustainable"i)
...
julia> search_wdi("indicators","source_organization",r"Global Partnership"i)

Tips and Tricks

Extracting country data from results

df = wdi("SP.POP.TOTL", ["US","BR"], 1980, 2012, extra=true)
us_pop = df[df[!, :iso2c] .== "US", :]

Year format

For similarity with the R WDI package the :year column is in Float64 format. WDI data is yearly.

You can easily convert this to a Date series:

using WorldBankData
using Dates

df = wdi("AG.LND.ARBL.HA.PC", "US", 1900, 2011)
df[!, :year] = map(Date, df[!, :year])

Plotting

Install the StatsPlots.jl package with import Pkg; Pkg.add("StatsPlots").

using WorldBankData
using StatsPlots

df = wdi("SP.POP.TOTL", "US", 1980, 2010)

@df df scatter(:year, :SP_POP_TOTL)

Empty/Missing results

wdi will return a DataFrame with missing values if there is no data:

julia> df = wdi("EN.ATM.CO2E.KT", "AS")
60×4 DataFrames.DataFrame
│ Row │ iso2c  │ country        │ EN_ATM_CO2E_KT │ year    │
│     │ String │ String         │ Missing        │ Float64 │
├─────┼────────┼────────────────┼────────────────┼─────────┤
│ 1   │ AS     │ American Samoa │ missing        │ 1960.0  │
│ 2   │ AS     │ American Samoa │ missing        │ 1961.0  │
│ 3   │ AS     │ American Samoa │ missing        │ 1962.0  │
...

julia> df = wdi("EN.ATM.CO2E.KT", ["AS","US"])
120×4 DataFrames.DataFrame
│ Row │ iso2c  │ country        │ EN_ATM_CO2E_KT │ year    │
│     │ String │ String         │ Float64⍰       │ Float64 │
├─────┼────────┼────────────────┼────────────────┼─────────┤
│ 1   │ AS     │ American Samoa │ missing        │ 1960.0  │
│ 2   │ AS     │ American Samoa │ missing        │ 1961.0  │
...
│ 114 │ US     │ United States  │ 5.15916e6      │ 2013.0  │
│ 115 │ US     │ United States  │ 5.25428e6      │ 2014.0  │
│ 116 │ US     │ United States  │ missing        │ 2015.0  │
│ 120 │ US     │ United States  │ missing        │ 2019.0  │
...

Cache

The data in the World Bank database changes infrequently. Therefore it makes little sense to download it every time a script is run.

Metadata

The search_wdi() function internally caches the country and indicator metadata and therefore downloads the country and indicator data only once per session. Even that is usually not necessary. This data can easily be stored on local disk.

Download and store the country and indicator information in csv files:

using WorldBankData
using DataFrames
using CSV
CSV.write("country_cache.csv",WorldBankData.get_countries())
CSV.write("indicator_cache.csv", WorldBankData.get_indicators())

These can be used in the script to set the WorldBankData cache variables WorldBankData.country_cache and WorldBankData.indicator_cache (which are initialized to false) using the WorldBankData.set_country_cache() and WorldBankData.set_indicator_cache() functions:

using WorldBankData
using DataFrames
WorldBankData.set_country_cache(CSV.read("country_cache.csv"))
WorldBankData.set_indicator_cache(CSV.read("indicator_cache.csv"))

From then on the search_wdi() function will use the data read from disk.

The caches can be reset with WorldBankData.reset_country_cache() and WorldBankData.reset_indicator_cache().

Indicator data

In a similar way the indicator data itself can be cached.

using WorldBankData
using DataFrames
using CSV

function update_us_pop_totl()
    df = wdi("SP.POP.TOTL", "US")
    CSV.write("us_pop_totl.csv",df)
end
df = CSV.read("us_pop_totl.csv")

Occasionally update the data by running the update_us_pop_totl() function.