Data Carpentry: R for data analysis and visualization of Ecological Data
This is an introduction to R designed for participants with no programming
experience. It can be taught in 3/4 of a day (approximately 6 hours).
It is a redesigned version of the original Data Carpentry lesson.
The initial effort towards this redesign was done by Michael Culshaw-Maurer in another repository in The Carpentries Incubator: https://github.com/carpentries-incubator/R-ecology-lesson (now archived). See Michael’s notes while preparing the redesign in the update_plans.md file of that repository.
The lesson starts with information about the R programming language and the RStudio interface. It then moves to loading in data and exploring how to visualise it with ggplot2. The next episode takes learners through an exploration of data frames and some common data cleaning operations, before discussing vectors and factors. The final episode introduces the flow of data in R, and how to combine operations to select, filter, and mutate a data frame.
Illustrations from the previous version of this lesson are now available in the
carpentries/commons repository.
Providing feedback on this lesson
If you teach this redesigned lesson, please open an issue on this repository to share your experience.
Prerequisites
The lesson assumes no prior knowledge of R or RStudio.
Learners should have R and RStudio installed on their computers. They will also
need to be able to install R packages from CRAN, create directories, and
download files.
See the lesson website
for instructions on installing R, RStudio, and the required R packages.
Contributing
Contributions to the content and development of these lesson are very welcome!
If you would like to contribute, we encourage you to review our contributing guide.
Questions
If you have any questions or feedback, please open an issue, contact the
maintainers, or come chat with us on the
Slack Channel for this lesson.
If you don’t already have a Slack account with the Carpentries, you can
create one.
Maintainers
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