R package to analyse Q methodology data
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This R package performs the analysis of Q methodology data. See a visual demo here, and more details in the wiki.

You can install the stable version from CRAN:


Q is a methodology to study the distinct perspectives existing within a group on a topic of interest. It is used across social, health, and environmental studies.

Contributions to developing the package are most welcome, either in the form of new function development or as suggestions. See the current projects and suggestions in the list of issues, an please report any bugs or comments for improvement by adding new ones.

Some friendly suggestions for contributing:

  1. Log issues, so that we know what everyone is up to and interested in
  • assign backlog milestone if it's not happening anytime soon
  • assign oneself as an assignee if one is actively working on it (so as to avoid duplicate efforts)
  1. Collaborators: Create forks
  • such that other collaborators can work (and mess up) in their own sandbox
  • but remember to pull in upstream changes from aiorazabala/qmethod/master frequently, so as to stay up to date and avoid merge conflicts.
  1. Create "feature-branches", keep work on features separate from bugfixes etc.
  • create a branch off of (forked) masters for some feature to be added, say rotation-visualization (hehe, I wish)
  • creating "feature-branches" can seem cumbersome, but it pays off with transparent pull requests (see below)
  1. Once work is done, put up a pull-request (see #36), which @aiorazabala as the maintainer and creator then accepts after review.
  • Crucially pull requests should be put up only once R CMD check passes.
  • by accepting, the pull request, some feature-branch is then merged into @aiorazabala's master
  1. Periodically, whenever significant work has been done, @aiorazabala drafts a release from master, essentially just marking some point in the history of the package as x.x.x, and sends it off to CRAN.

Or, you know, just hack away. Please, though, be kind enough to squash your own R CMD check issues as they arise. It's very hard to fix very many of them once they pile up, and CRAN must be obeyed 😏