Download Data from the World Inequality Database

Download data from the online World Inequality Database directly into R. Data are retrieved from WID.world's online data service. The World Inequality Database is an extensive source on the historical evolution of the distribution of income and wealth both within and between countries. It relies on the combined effort of an international network of over a hundred researchers covering more than seventy countries from all continents.


R package to download data from the World Inequality Database (WID.world)

This package downloads data from the online World Inequality Database (WID.world) directly into R. The World Inequality Database is an extensive source on the historical evolution of the distribution of income and wealth both within and between countries. It relies on the combined effort of an international network of over a hundred researchers covering more than seventy countries from all continents.

Installation

Install the CRAN release with:

install.packages("wid")

To install the development version from GitHub:

install.packages("devtools")
devtools::install_github("world-inequality-database/wid-r-tool")

Data source

Data are retrieved from the WID.world online data service. The main website is https://wid.world.

Usage

The package exports a single function download_wid(...). See ?download_wid for help.

Demo

See the PDF demonstration for a detailed presentation of the package.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("wid")

0.0.3 by Ignacio Flores, 2 months ago


https://github.com/world-inequality-database/wid-r-tool, https://wid.world


Report a bug at https://github.com/world-inequality-database/wid-r-tool/issues


Browse source code at https://github.com/cran/wid


Authors: Thomas Blanchet [aut] , Ignacio Flores [cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports httr, base64enc, plyr, jsonlite

Suggests testthat, knitr, rmarkdown, dplyr, ggplot2, scales, tidyverse


See at CRAN