Full documentation for COINr is available here: https://bluefoxr.github.io/COINrDoc/

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:

• Flexible and fast development of composite indicators with no limits on aggregation levels, numbers of indicators, highly flexible set of methodological choices.
• Denomination by other indicators (including built in world denominators data set)
• Screening units by data requirements
• Imputation of missing data, by a variety of methods
• Data treatment using Winsorisation and nonlinear transformations
• Normalisation by more than ten methods, either for all indicators or for each individually
• Weighting using either manual weighting, PCA weights or correlation optimised weights. COINr also includes a reweighting app which explores the effects of weights on correlations.
• Aggregation of indicators using a variety of methods which can be different for each aggregation level.

Analysis features:

• Detailed indicator statistics, and data availability within aggregation groups
• Multivariate analysis, including quick functions for PCA, and a detailed correlation analysis and visualisation
• Easy “what if” analysis - very quickly checking the effects of adding and removing indicators, changing weights, methodological variations
• Full global uncertainty and sensitivity analysis which can check the impacts of uncertainties in weighting and many methodological choices

Visualisation and presentation:

• Statistical plots of indicators - histograms, violin plots, dot plots, scatter plots and more, including interactive html plots and an app for exploring indicator data
• Bar charts, stacked bar charts, maps, tables and radar charts for presenting indicator data and making comparisons between units
• Static and interactive correlation plots for visualising correlations between indicators and between aggregation levels
• An interactive app for visualising and presenting initial results
• Automatic generation of unit reports (e.g. country reports) using customisable R markdown templates

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.

Installation

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.