In danilofreire/prisonbrief: Downloads and Parses World Prison Brief Data

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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.

Installation

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

install.packages("prisonbrief")

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:

remotes::install_github("danilofreire/prisonbrief")

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

```{bash linux-dependency, eval = FALSE} 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.  

## Usage

`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.

```r
library(prisonbrief)

wpb_list()

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")
names(africa)

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")

library(ggplot2)
ggplot(south_america, aes(fill = prison_population_rate)) +
        geom_sf() +
        scale_fill_distiller(palette = "YlOrRd", trans = "reverse") +
        theme_minimal()

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")
germany

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:

library(dplyr)

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{
    next
  }
}
X <- data.table::rbindlist(x, fill = TRUE) %>%
  dplyr::full_join(countries, by = c("Country" = "country_url"))

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

Contributions

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.

Citation

You can cite the prisonbrief package with:

citation("prisonbrief")

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

danilofreire/prisonbrief documentation built on Feb. 15, 2021, 12:51 p.m.