Introduction to R for non-programmers using inflammation data.

The goal of this lesson is to teach novice programmers to write modular code to perform a data analysis. R is used to teach these skills because it is a commonly used programming language in many scientific disciplines. However, the emphasis is not on teaching every aspect of R, but instead the focus is on language agnostic principles like automation with loops and encapsulation with functions (see Best Practices for Scientific Computing to learn more). In fact, this lesson is a translation of the Python version, and the lesson is also available in MATLAB.

The example used in this lesson is analyzing a set of 12 data files with inflammation data collected from a trial for a new treatment for arthritis (the data was simulated). Learners are shown how it is better to create a function and apply it to each of the 12 files using a loop instead of using copy-paste to analyze the 12 files individually.

Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon.

General instructions for making contributions are summarised in CONTRIBUTING.md. When editing topic pages for R lessons, you should change the source R Markdown file (*.Rmd). Only changes to R markdown files and other supporting files (e.g. data files) should be committed to Git.

To view how the changes will look, when viewed in a web browser, you can render the html pages by running make preview from the base of the repository. Generating the html file(s) is required for viewing the online version of the lessons (you can learn more about the design of the build process here). Building the rendered page with the Makefile requires installing some dependencies first. In addition to the dependencies listed in the lesson template documentation, you also need to install the R package knitr.

Once you've made your edits and rendered the corresponding html files, you need to add, commit, and push just the source R Markdown file(s) and any supporting files (e.g. data files). Changes generated by the make preview command should not be committed or included in a pull request. These changes will be taken care off by the lesson maintainer when the PR is merged.

Please see https://github.com/swcarpentry/lesson-example for instructions on formatting, building, and submitting lessons, or run make in this directory for a list of helpful commands.

If you have questions or proposals, please send them to the r-discuss mailing list.