You can install:
The latest release version from CRAN with
install.packages("wbstats")
or
The latest development version from github with
devtools::install_github("nset-ornl/wbstats")
The World Bank[http://www.worldbank.org/] is a tremendous source of global socio-economic data; spanning several decades and dozens of topics, it has the potential to shed light on numerous global issues. To help provide access to this rich source of information, The World Bank themselves, provide a well structured RESTful API[http://data.worldbank.org/developers]. While this API is very useful for integration into web services and other high-level applications, it becomes quickly overwhelming for researchers who have neither the time nor the expertise to develop software to interface with the API. This leaves the researcher to rely on manual bulk downloads of spreadsheets of the data they are interested in. This too is can quickly become overwhelming, as the work is manual, time consuming, and not easily reproducible. The goal of the wbstats R-package is to provide a bridge between these alternatives and allow researchers to focus on their research questions and not the question of accessing the data. The wbstats R-package allows researchers to quickly search and download the data of their particular interest in a programmatic and reproducible fashion; this facilitates a seamless integration into their workflow and allows analysis to be quickly rerun on different areas of interest and with realtime access to the latest available data.
wbstats R-package:grep style searching for data descriptions and namesUnless you know the country and indicator codes that you want to download the first step would be searching for the data you are interested in. wb_search() provides grep style searching of all available indicators from the World Bank API and returns the indicator information that matches your query.
To access what countries or regions are available you can use the countries data frame from either wb_cachelist or the saved return from wb_cache(). This data frame contains relevant information regarding each country or region. More information on how to use this for downloading data is covered later.
wb_cachelistFor performance and ease of use, a cached version of useful information is provided with the wbstats R-package. This data is called wb_cachelist and provides a snapshot of available countries, indicators, and other relevant information. wb_cachelist is by default the the source from which wb_search() and wb_data() uses to find matching information. The structure of wb_cachelist is as follows
library(wbstats)
str(wb_cachelist, max.level = 1)
#> List of 8
#> $ countries : tibble [304 x 18] (S3: tbl_df/tbl/data.frame)
#> $ indicators : tibble [16,607 x 8] (S3: tbl_df/tbl/data.frame)
#> $ sources : tibble [61 x 9] (S3: tbl_df/tbl/data.frame)
#> $ topics : tibble [21 x 3] (S3: tbl_df/tbl/data.frame)
#> $ regions : tibble [48 x 4] (S3: tbl_df/tbl/data.frame)
#> $ income_levels: tibble [7 x 3] (S3: tbl_df/tbl/data.frame)
#> $ lending_types: tibble [4 x 3] (S3: tbl_df/tbl/data.frame)
#> $ languages : tibble [23 x 3] (S3: tbl_df/tbl/data.frame)
wb_cache()For the most recent information on available data from the World Bank API wb_cache() downloads an updated version of the information stored in wb_cachelist. wb_cachelist is simply a saved return of wb_cache(lang = "en"). To use this updated information in wb_search() or wb_data(), set the cache parameter to the saved list returned from wb_cache(). It is always a good idea to use this updated information to insure that you have access to the latest available information, such as newly added indicators or data sources. There are also cases in which indicators that were previously available from the API have been removed or deprecated.
library(wbstats)
# default language is english
new_cache <- wb_cache()
wb_search()wb_search() searches through the indicators data frame to find indicators that match a search pattern. An example of the structure of this data frame is below
#> # A tibble: 2 x 8
#> indicator_id indicator unit indicator_desc source_org topics source_id source
#> <chr> <chr> <lgl> <chr> <chr> <list> <dbl> <chr>
#> 1 NY.GDP.MKTP~ GDP (cur~ NA GDP at purcha~ World Ban~ <df[,~ 2 World~
#> 2 SP.POP.TOTL Populati~ NA Total populat~ (1) Unite~ <df[,~ 2 World~
By default the search is done over the indicator_id, indicator, and indicator_desc fields and returns the those 3 columns of the matching rows. The indicator_id values are inputs into wb_data(), the function for downloading the data. To return all columns for the indicators data frame, you can set extra = TRUE.
library(wbstats)
unemploy_inds<- wb_search("unemployment")
head(unemploy_inds)
#> # A tibble: 6 x 3
#> indicator_id indicator indicator_desc
#> <chr> <chr> <chr>
#> 1 fin37.t.a Received government transfers ~ The percentage of respondents wh~
#> 2 fin37.t.a.1 Received government transfers ~ The percentage of respondents wh~
#> 3 fin37.t.a.10 Received government transfers ~ The percentage of respondents wh~
#> 4 fin37.t.a.11 Received government transfers ~ The percentage of respondents wh~
#> 5 fin37.t.a.2 Received government transfers ~ The percentage of respondents wh~
#> 6 fin37.t.a.3 Received government transfers ~ The percentage of respondents wh~
Other fields can be searched by simply changing the fields parameter. For example
library(wbstats)
blmbrg_vars <- wb_search("Bloomberg", fields = "source_org")
head(blmbrg_vars)
#> # A tibble: 2 x 3
#> indicator_id indicator indicator_desc
#> <chr> <chr> <chr>
#> 1 GFDD.OM.02 Stock market return (~ Stock market return is the growth rate of~
#> 2 GFDD.SM.01 Stock price volatility Stock price volatility is the average of ~
Regular expressions are also supported
library(wbstats)
# 'poverty' OR 'unemployment' OR 'employment'
povemply_inds <- wb_search(pattern = "poverty|unemployment|employment")
head(povemply_inds)
#> # A tibble: 6 x 3
#> indicator_id indicator indicator_desc
#> <chr> <chr> <chr>
#> 1 1.0.HCount.1.90~ Poverty Headcount ($~ The poverty headcount index measures t~
#> 2 1.0.HCount.2.5u~ Poverty Headcount ($~ The poverty headcount index measures t~
#> 3 1.0.HCount.Mid1~ Middle Class ($10-50~ The poverty headcount index measures t~
#> 4 1.0.HCount.Ofcl Official Moderate Po~ The poverty headcount index measures t~
#> 5 1.0.HCount.Poor~ Poverty Headcount ($~ The poverty headcount index measures t~
#> 6 1.0.HCount.Vul4~ Vulnerable ($4-10 a ~ The poverty headcount index measures t~
As well as any grep function argument
library(wbstats)
# contains "gdp" and NOT "trade"
gdp_no_trade_inds <- wb_search("^(?=.*gdp)(?!.*trade).*", perl = TRUE)
head(gdp_no_trade_inds)
#> # A tibble: 6 x 3
#> indicator_id indicator indicator_desc
#> <chr> <chr> <chr>
#> 1 5.51.01.10.gdp Per capita GDP growth GDP per capita is the sum of gross v~
#> 2 6.0.GDP_current GDP (current $) GDP is the sum of gross value added ~
#> 3 6.0.GDP_growth GDP growth (annual %) Annual percentage growth rate of GDP~
#> 4 6.0.GDP_usd GDP (constant 2005 $) GDP is the sum of gross value added ~
#> 5 6.0.GDPpc_cons~ GDP per capita, PPP (co~ GDP per capita based on purchasing p~
#> 6 BI.WAG.TOTL.GD~ Wage bill as a percenta~ <NA>
The default cached data in wb_cachelist is in English. To search indicators in a different language, you can download an updated copy of wb_cachelist using wb_cache(), with the lang parameter set to the language of interest and then set this as the cache parameter in wb_search(). Other languages are supported in so far as they are supported by the original data sources. Some sources provide full support for other languages, while some have very limited support. If the data source does not have a translation for a certain field or indicator then the result is NA, this may result in a varying number matches depending upon the language you select. To see a list of availabe languages call wb_languages()
library(wbstats)
wb_langs <- wb_languages()
wb_data()Once you have found the set of indicators that you would like to explore further, the next step is downloading the data with wb_data(). The following examples are meant to highlight the different ways in which wb_data() can be used and demonstrate the major optional parameters.
The default value for the country parameter is a special value of "countries_only", which as you might expect, returns data on the selected indicator for only countries. This is in contrast to country = "all" or country = "regions_only" which would return data for countries and regional aggregates together, or only regional aggregates, respectively
library(wbstats)
# Population, total
pop_data <- wb_data("SP.POP.TOTL", start_date = 2000, end_date = 2002)
head(pop_data)
#> # A tibble: 6 x 9
#> iso2c iso3c country date SP.POP.TOTL unit obs_status footnote last_updated
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <date>
#> 1 AW ABW Aruba 2000 90853 <NA> <NA> <NA> 2020-07-01
#> 2 AW ABW Aruba 2001 92898 <NA> <NA> <NA> 2020-07-01
#> 3 AW ABW Aruba 2002 94992 <NA> <NA> <NA> 2020-07-01
#> 4 AF AFG Afghanis~ 2000 20779953 <NA> <NA> <NA> 2020-07-01
#> 5 AF AFG Afghanis~ 2001 21606988 <NA> <NA> <NA> 2020-07-01
#> 6 AF AFG Afghanis~ 2002 22600770 <NA> <NA> <NA> 2020-07-01
If you are interested in only some subset of countries or regions you can pass along the specific codes to the country parameter. The country and region codes and names that can be passed to the country parameter as well, most prominently the coded values from the iso2c and iso3c from the countries data frame in wb_cachelist or the return of wb_cache(). Any values from the above columns can mixed together and passed to the same call
library(wbstats)
# you can mix different ids and they are case insensitive
# you can even use SpOnGeBoB CaSe if that's the kind of thing you're into
# iso3c, iso2c, country, region_iso3c, admin_region_iso3c, admin_region, income_level
example_geos <- c("ABW","AF", "albania", "SSF", "eca", "South Asia", "HiGh InCoMe")
pop_data <- wb_data("SP.POP.TOTL", country = example_geos,
start_date = 2012, end_date = 2012)
pop_data
#> # A tibble: 7 x 9
#> iso2c iso3c country date SP.POP.TOTL unit obs_status footnote last_updated
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <date>
#> 1 AW ABW Aruba 2012 102560 <NA> <NA> <NA> 2020-07-01
#> 2 AF AFG Afghanis~ 2012 31161376 <NA> <NA> <NA> 2020-07-01
#> 3 AL ALB Albania 2012 2900401 <NA> <NA> <NA> 2020-07-01
#> 4 7E ECA Europe &~ 2012 382509766 <NA> <NA> <NA> 2020-07-01
#> 5 8S SAS South As~ 2012 1683747130 <NA> <NA> <NA> 2020-07-01
#> 6 ZG SSF Sub-Saha~ 2012 917726973 <NA> <NA> <NA> 2020-07-01
#> 7 XD HIC High inc~ 2012 1191504227 <NA> <NA> <NA> 2020-07-01
As of wbstats 1.0 queries are now returned in wide format. This was a request made by multiple users and is in line with the principles of tidy data. If you would like to return the data in a long format, you can set return_wide = FALSE
Now that each indicator is it's own column, we can allow custom names for the indicators
library(wbstats)
my_indicators = c("pop" = "SP.POP.TOTL",
"gdp" = "NY.GDP.MKTP.CD")
pop_gdp <- wb_data(my_indicators, start_date = 2010, end_date = 2012)
head(pop_gdp)
#> # A tibble: 6 x 6
#> iso2c iso3c country date gdp pop
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 AW ABW Aruba 2010 2390502793. 101669
#> 2 AW ABW Aruba 2011 2549720670. 102046
#> 3 AW ABW Aruba 2012 2534636872. 102560
#> 4 AF AFG Afghanistan 2010 15856574731. 29185507
#> 5 AF AFG Afghanistan 2011 17804292964. 30117413
#> 6 AF AFG Afghanistan 2012 20001598506. 31161376
You'll notice that when you query only one indicator, as in the first two examples above, it returns the extra fields unit, obs_status, footnote, and last_updated, but when we queried multiple indicators at once, as in our last example, they are dropped. This is because those extra fields are tied to a specific observation of a single indicator and when we have multiple indciator values in a single row, they are no longer consistent with the tidy data format. If you would like that information for multiple indicators, you can use return_wide = FALSE
library(wbstats)
my_indicators = c("pop" = "SP.POP.TOTL",
"gdp" = "NY.GDP.MKTP.CD")
pop_gdp_long <- wb_data(my_indicators, start_date = 2010, end_date = 2012, return_wide = FALSE)
head(pop_gdp_long)
#> # A tibble: 6 x 11
#> indicator_id indicator iso2c iso3c country date value unit obs_status
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 SP.POP.TOTL Populati~ AF AFG Afghan~ 2012 3.12e7 <NA> <NA>
#> 2 SP.POP.TOTL Populati~ AF AFG Afghan~ 2011 3.01e7 <NA> <NA>
#> 3 SP.POP.TOTL Populati~ AF AFG Afghan~ 2010 2.92e7 <NA> <NA>
#> 4 SP.POP.TOTL Populati~ AL ALB Albania 2012 2.90e6 <NA> <NA>
#> 5 SP.POP.TOTL Populati~ AL ALB Albania 2011 2.91e6 <NA> <NA>
#> 6 SP.POP.TOTL Populati~ AL ALB Albania 2010 2.91e6 <NA> <NA>
#> # ... with 2 more variables: footnote <chr>, last_updated <date>
mrv and mrnevIf you do not know the latest date an indicator you are interested in is available for you country you can use the mrv instead of start_date and end_date. mrv stands for most recent value and takes a integer corresponding to the number of most recent values you wish to return
library(wbstats)
# most recent gdp per captia estimates
gdp_capita <- wb_data("NY.GDP.PCAP.CD", mrv = 1)
head(gdp_capita)
#> # A tibble: 6 x 9
#> iso2c iso3c country date NY.GDP.PCAP.CD unit obs_status footnote
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 AW ABW Aruba 2019 NA <NA> <NA> <NA>
#> 2 AF AFG Afghan~ 2019 502. <NA> <NA> <NA>
#> 3 AO AGO Angola 2019 2974. <NA> <NA> <NA>
#> 4 AL ALB Albania 2019 5353. <NA> <NA> <NA>
#> 5 AD AND Andorra 2019 40886. <NA> <NA> <NA>
#> 6 AE ARE United~ 2019 43103. <NA> <NA> <NA>
#> # ... with 1 more variable: last_updated <date>
Often it is the case that the latest available data is different from country to country. There may be 2020 estimates for one location, while another only has estimates up to 2019. This is especially true for survey data. When you would like to return the latest avialble data for each country regardless of its temporal misalignment, you can use the mrnev instead of mrnev. mrnev stands for most recent non empty value.
library(wbstats)
gdp_capita <- wb_data("NY.GDP.PCAP.CD", mrnev = 1)
head(gdp_capita)
#> # A tibble: 6 x 8
#> iso2c iso3c country date NY.GDP.PCAP.CD obs_status footnote last_updated
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <date>
#> 1 AW ABW Aruba 2017 29008. <NA> <NA> 2020-07-01
#> 2 AF AFG Afghanistan 2019 502. <NA> <NA> 2020-07-01
#> 3 AO AGO Angola 2019 2974. <NA> <NA> 2020-07-01
#> 4 AL ALB Albania 2019 5353. <NA> <NA> 2020-07-01
#> 5 AD AND Andorra 2019 40886. <NA> <NA> 2020-07-01
#> 6 AE ARE United Arab~ 2019 43103. <NA> <NA> 2020-07-01
Because the majority of data available from the World Bank is at the annual resolution, by default dates in wbstats are returned as numerics. This default makes common tasks like filtering easier. If you would like the date field to be of class Date you can set date_as_class_date = TRUE
There are a few behaviors of the World Bank API that being aware of could help explain some potentially unexpected results. These results are known but no special actions are taken to mitigate them as they are the result of the API itself and artifically limiting the inputs or results could potentially causes problems or create unnecessary rescrictions in the future.
Not all data sources support all languages. If an indicator does not have a translation for a particular language, the non-supported fields will return as NA. This could potentially result in a differing number of matching indicators from wb_search()
library(wbstats)
# english
cache_en <- wb_cache()
sum(is.na(cache_en$indicators$indicator))
#> [1] 0
# spanish
cache_es <- wb_cache(lang = "es")
sum(is.na(cache_es$indicators$indicator))
#> [1] 14749
The World Bank Group, or any of its member instutions, do not support or endorse this software and are not libable for any findings or conclusions that come from the use of this software.