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prisonbrief: An R package that returns tidy data from the World Prison Brief website

The goal of prisonbrief is to download, clean and return data from the World Prison Brief website. The World Prison Brief is an online database compiled by the Institute for Criminal Policy Research with information on prison systems around the world. Data currently cover 223 jurisdictions and have been collected from public sources. The prisonbrief package provides easy-to-use functions to convert WPB data into a format convenient for statistical analysis.


The stable version of prisonbrief is available on CRAN. To install it, just type:


You can install the most recent development version of prisonbrief using the remotes package. First, you need to install the remotes package with the following code:

if(!require(remotes)) install.packages("remotes")

Then you can install prisonbrief from its GitHub repository:


If you are using Linux, you may need to type the following command before installing prisonbrief:

sudo apt-get install libudunits2-dev

prisonbrief relies on some spatial-data packages in order to return data as simple features dataframes. You may need to install packages such as rgeos if you do not already have them installed.


prisonbrief is quite simple to use. The package contains only three functions, all of them starting with wpb, a mnemonic for World Prison Brief.

The first is a convenience function named wpb_list(). It prints a list of available countries to the console.

#> Warning: package 'prisonbrief' was built under R version 4.0.2

#> # A tibble: 226 x 2
#>    country_name              country_url            
#>    <chr>                     <chr>                  
#>  1 Afghanistan               afghanistan            
#>  2 Albania                   albania                
#>  3 Algeria                   algeria                
#>  4 American Samoa  (USA)     american-samoa-usa     
#>  5 Andorra                   andorra                
#>  6 Angola                    angola                 
#>  7 Anguilla (United Kingdom) anguilla-united-kingdom
#>  8 Antigua and Barbuda       antigua-and-barbuda    
#>  9 Argentina                 argentina              
#> 10 Armenia                   armenia                
#> # … with 216 more rows

The second function is wpb_table(). This function returns a series of variables about the prison systems of the world, of a particular region, or of a specific country. For instance, the code below downloads prison data for Africa:

africa <- wpb_table(region = "Africa")
#> Warning: Prefixing `UQ()` with the rlang namespace is deprecated as of rlang 0.3.0.
#> Please use the non-prefixed form or `!!` instead.
#>   # Bad:
#>   rlang::expr(mean(rlang::UQ(var) * 100))
#>   # Ok:
#>   rlang::expr(mean(UQ(var) * 100))
#>   # Good:
#>   rlang::expr(mean(!!var * 100))
#> This warning is displayed once per session.
#>  [1] "country"                 "prison_population_rate" 
#>  [3] "prison-population-total" "female-prisoners"       
#>  [5] "pre-trial-detainees"     "foreign-prisoners"      
#>  [7] "occupancy-level"         "iso_a2"                 
#>  [9] "name"                    "geometry"

The region choices are “Africa”, “Asia”, “Caribbean”, “Central America”, “Europe”, “Middle East”, “North America”, “Oceania”, “South America” and “All”.

wpb_table() also provides geometric shapes for maps. For instance, you can download and plot the prison population rate in South America with only a few lines of code:

south_america <- wpb_table(region = "South America")

ggplot(south_america, aes(fill = prison_population_rate)) +
        geom_sf() +
        scale_fill_distiller(palette = "YlOrRd", trans = "reverse") +

The function can also be used to retrieve data for a single country. The data returned are parsed from the single country tables, however, and are not ready for quantitative analysis without further cleaning (removing parentheses etc.). Since some of this information may be relevant, we have chosen to leave it in. Data from regions instead of a single country are fully prepared for automated analysis.

Finally, we have added the wpb_series() function to the package. The function downloads and tidies the tables describing the trends in the prison population total and the prison population rate for every jurisdiction included in the project. Below is an example taken from Germany’s country profile:

You can retrieve the same information with the following code:

germany <- wpb_series(country = "Germany")
#>    Country Year Prison population total Prison population rate
#> 1  germany 2000                   70252                     85
#> 2  germany 2002                   70977                     86
#> 3  germany 2004                   79452                     96
#> 4  germany 2006                   76629                     93
#> 5  germany 2008                   72259                     88
#> 6  germany 2010                   69385                     85
#> 7  germany 2012                   65889                     82
#> 8  germany 2014                   61872                     76
#> 9  germany 2016                   62865                     76
#> 10 germany 2018                   63643                     77

wpb_series() can also be combined with wpb_list() to make interesting time series graphs. The code below downloads data for all countries then plots the prison population rate for Brazil, Germany, Russia and the United States:


x <- list()
countries <- wpb_list()
for(i in 1:nrow(countries)){
  y <- try(wpb_series(country = countries$country_url[i]), silent = FALSE)
  if(class(y) != 'try-error'){
    x[[i]] <- y
  } else{
X <- data.table::rbindlist(x, fill = TRUE) %>%
  dplyr::full_join(countries, by = c("Country" = "country_url"))

X %>% dplyr::filter(country_name %in% c("Brazil",
                                        "Russian Federation",
                                        "United States of America")) %>%
        ggplot(aes(x = Year, y = `Prison population rate`,
                   group = country_name, colour = country_name)) +
        geom_line() +


prisonbrief was written by Danilo Freire and Robert Myles McDonnell. Feedback and comments are most welcome. If you have any suggestions on how to improve this package feel free to open an issue on GitHub.


You can cite the prisonbrief package with:

#> To cite 'prisonbrief' in publications, please use:
#>   Danilo Freire and Robert Myles McDonnell (2017). prisonbrief:
#>   Downloads and Parses World Prison Brief Data. R package version
#>   0.1.0. URL:
#> A BibTeX entry for LaTeX users is
#>   @Manual{,
#>     title = {{prisonbrief}: Downloads and Parses World Prison Brief Data},
#>     author = {Danilo Freire and Robert Myles McDonnell},
#>     note = {R package version 0.1.0},
#>     year = {2017},
#>     url = {},
#>   }

Please also cite the source as World Prison Brief, Institute for Criminal Policy Research.