R for Reproducible Scientific Analysis

Introduction to R for non-programmers using gapminder data.

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis.

A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

These lesson materials are adapted from the R-novice-inflammation materials, which were translated from the Python materials, and materials from our R Data Carpentry materials used at the Sydney bootcamp last year.

These lesson materials are designed to be run after both the Shell and Git materials, and are built around the Gapminder dataset.

Contributing

Please see the current list of issues for ideas for contributing to this repository, and the guidelines and instructions for contributing.

When editing topic pages, you should change the source R Markdown file. Afterwards you can render the pages by running make preview from the base of the repository. 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 looked over the rendered html files, you should add, commit, and push only the source R Markdown file(s) to your fork, and then open a pull request. The repository maintainers will run the html generation process once the pull request has been merged. You can learn more about the design of the build process here.

Getting Help

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.