# wbstats: An R package for searching and downloading data from the World Bank API.

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")

# Introduction

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.

### Highlighted features of the `wbstats` R-package:

- Uses version 2 of the World Bank API that provides access to more indicators and metadata than the previous API version
- Access to all annual, quarterly, and monthly data available in the API
- Support for searching and downloading data in multiple languages
- Returns data in either wide (default) or long format
- Support for Most Recent Value queries
- Support for `grep` style searching for data descriptions and names
- Ability to download data not only by country, but by aggregates as well, such as High Income or South Asia

# Getting Started

Unless 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.

## Finding available data with `wb_cachelist`

For 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)

## Accessing updated available data with `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()

## Search available data with `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()

## Downloading data with `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>

### Using `mrv` and `mrnev`

If 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

### Dates

Because the majority of data available from the World Bank is at the annual resolution, by default dates in `wbstats` are returned as `numeric`s. 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`

# Some Sharp Corners

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.

## Searching in other languages

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

# Legal

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.