Date & time: Thursday, June 22 – Friday, June 23 (2 full days)
Location: Philipps-Universität Marburg MARA F|05, Deutschhausstraße 11+13 Seminar room 01.0010
Teachers: Alexander Zizka & Steffen Ehrmann, University of Leipzig and German Centre for Integrative Biodiversity Research
Day 1 - 9:00 - 17:00 (room 01.0010)
Morning
- Hi from Alex and & Steffen
- Lectures “Main components of an R-package“
- Student project design
- “How to build an R package – Tools and workflow“
Afternoon
- Demo + exercise 1 “Package skeleton and data”
- Demo + exercise 2&3 “Data and Functions”
- Demo + exercise 4: “Building and checks”
Day 2 - 9:00 - 17:00 (room 01.0010)
Morning
- Project work
- Lecture & exercise “package testing” (Steffen)
Afternoon
- Demo Code documentation (Steffen)
- Project work
- Demo package release (Alex)
- Student presentations
- Course evaluation and Wrap up
Please:
-
Make sure to have the following software installed an running on your computer before the course:
- R (4.3, https://cran.r-project.org/)
- Rstudio (https://www.rstudio.com/products/rstudio/download/#download)
- Rtools (https://cran.rstudio.com/bin/windows/Rtools/ for windows, see https://www.rstudio.com/products/rpackages/devtools/ for Mac and linux)
- Git (https://git-scm.com/downloads)
- If you do not yet have a github account, please open one at https://github.com/
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Read chapters 2, 6-10 and 16 of this free online book: https://r-pkgs.org/.
Please do not hesitate to contact us if you have any questions.
After this course, you will be able to
- understand the structure of R-packages and their main elements
- use up-to-date methodology to provide R code and data as R-package
- use common tools for packaging, including roxygen2, devtools, and GitHub
R is a widely used tool for data analyses in ecology. Part of its success is due to the active community and the wide array of add-on packages contributed by scientists from across disciplines, via the CRAN network. R packages are an excellent and standardize way to distribute code and data to a huge community, and a great tool to ensure reproducible research. The course guides through the process of building and R package starting from conceptualization to testing. The course consists of introductory demonstrations coupled with hands-on exercises on the different stages of package building, plus time to work on a small example.
During the course you will write a small data package based on your own data with guidance from the teachers.
Grades are pass/fail. Successful participants should participate in all course days and present a project on the last day. A certification will be issued for all participants.
https://r-pkgs.org/, chapters 2, 6-10 and 16.
This is a course for graduate students and researchers that routinely use R for analysis and want to learn how to distribute data and code as R package.
max. 10
English