R for Data Analysis

An introduction to R for non-programmers using the Gapminder data and the tidyverse.

This course is based on the Software Carpentry course, and has been abridged and modified. The most substantive changes are:

• The Tidyverse is taught (the differences between, e.g. data.frames and tibbles is included in the notes - though this is mainly for reference).
• Lists are not taught
• The episode on tidyr has been removed, in the interests of time.
• Git isn't covered (although version control is mentioned, and the learners are signposted to the Research IT's git course)

A rendered version of the course notes can be found at https://UoMResearchIT.github.io/r-tidyverse-intro/.

A rendered version of the original Software Carpentry lesson can be found at: https://swcarpentry.github.io/r-novice-gapminder, the lesson template documentation for instructions on formatting, building, and submitting material, or run make in this directory for a list of helpful commands.

By default the Makefile will build the .md images using R in a Dockerised container, which contains the same packages and versions as the machines which are used for teaching. The lessons should be compatible with newer versions of the packages, but the output (most notably the tidyverse loading messages) will be different in places. To use your local installation of R, rather than the dockerised version, modify the Makefile where indicated in its comments

The remainder of this README is from the original Software Carpentry version of the lesson:

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 focuses on the fundamentals of the programming language R, and not on 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.

Maintainers:

• Tom Wright
• Naupaka Zimmerman