Welcome to a trial run of the new Carpentries Geospatial Curriculum at the Data Intensive Biology Summer Institute (DIBSI) at UC Davis! We'll work through the lessons and report back on how awesome they are.
An introduction to R for non-programmers using the Gapminder data.
Please see https://swcarpentry.github.io/r-novice-gapminder for a rendered
version of this material,
the lesson template documentation
for instructions on formatting, building, and submitting material,
or run make in this directory for a list of helpful commands.
The goal of this lesson is to revise best practices for using R in data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. These materials are designed to provide attendees with a concise introduction in the fundamentals of R, and to introdue best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation, before getting started with working with geospatial data.
Note that this workshop focuses on the fundamentals of the programming language R, and not on statistical analysis.
The lesson contains more material than can be taught in a day. The [instructor notes page]({{ page.root }}/guide) has some suggested lesson plans suitable for a one or half day workshop.
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.
- Leah Wasser
- Joseph Stachelek
- Tyson Swetnam
- Lauren O'Brien
- Janani Selvaraj
- Lachlan Deer
- Chris Prener
- Juan Fung