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.
using Pkg
Pkg.add("WorldBankData")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)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)::DataFrameIt 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.
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.
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"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 has the following arguments
search_wdi(data::String, entry::String, regx::Regex)::DataFramedata: 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.
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)
...
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)
df = wdi("SP.POP.TOTL", ["US","BR"], 1980, 2012, extra=true)
us_pop = df[df[!, :iso2c] .== "US", :]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])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)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 │
...The data in the World Bank database changes infrequently. Therefore it makes little sense to download it every time a script is run.
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().
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.