Full documentation for COINr is available here:

About COINr

COINr is a high-level R package which is the first fully-flexible development and analysis environment for composite indicators and scoreboards. The main features can be summarised as features for building, features for analysis and features for visualisation and presentation.

Building features:

Analysis features:

Visualisation and presentation:

COINr also allows fast import from the COIN Tool and fast export to Excel.

In short, COINr aims to allow composite indicators to be developed and prototyped very quickly and in a structured fashion, with the results immediately available and able to be explored interactively. Although it is built in R, it is a high-level package that aims to make command simple and intuitive, with the hard work performed behind the scenes, therefore it is also accessible to less experienced R users.


COINr is on CRAN and can be installed by running:

{r InstallCOINrC, eval=FALSE} install.packages("COINr")

Or simply browsing for the package in R Studio. The CRAN version will be updated every 1-2 months or so. If you want the very latest version in the meantime (I am usually adding features and fixing bugs as I find them), you can install the development version from GitHub. First, install the ‘devtools’ package if you don’t already have it, then run:

{r InstallCOINr, eval=FALSE} devtools::install_github("bluefoxr/COINr")

This should directly install the package from Github, without any other steps. You may be asked to update packages. This might not be strictly necessary, so you can also try skipping this step.

Getting started

COINr needs a little reading and learning to understand properly. But once you have done that, it can be very powerful for developing composite indicators.

A good place to get started is COINr’s “Overview” vignette. Try vignette(package = "COINr").

The most thorough documentation is available at COINr’s online documentation, which is a little long but quite comprehensive. If you want to dive straight in, I would recommend looking in particular at:

The Example on building a composite indicator.

The Example on analysing a composite indicator.