datacarpentry/R-ecology-lesson: Data Carpentry: Data Analysis and Visualization in R for Ecologists 2024-07a
Creators
- Analytics Enlightened LLC
- Maria Rivera Araya
- Bridget Armstrong
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Karl Benedict
-
Ed Bennett
- Bill
- Matthew Brousil
-
Tobias Busch
-
Murray Cadzow
- Zacchaeus Compson
- csoneson
-
Michael Culshaw-Maurer
- Tauana Junqueira Cunha
-
Wasila Dahdul
- danielkick
- James Deaton
-
doujouDC
- em-bellis
- Kate Evans
- Kara Feilich
-
Jessica Guo
-
FloHu
- Sarah Forrester
-
Natalie Forsdick
- Susan Gichuki
- Clarke Iakovakis
- Aleksander Jankowski
-
Randy Johnson
- jporton
- katbeescience
- klgallagher
-
David Klinges
-
kurtshowmaker
- Fritjof Lammers
- Brandon Le
-
Katrin Leinweber
- lidefi87
- Terry Loecke
-
Heili Lowman
-
Mike Mahoney
- Juan Jose Garcia Mesa
- Michele Mesiti
-
François Michonneau
-
Anna K. Moeller
-
Paula Nieto
-
njlyon0
-
Paula Pappalardo
- Abhijna Parigi
- Lisa Rosenthal
- Kevin Rue-Albrecht
- Austin Rutherford
- Maneesha Sane
- Birgit Schmidt
-
Brian Seok
-
Justin Shaffer
-
Eunice Soh
-
Sarah LR Stevens
-
Liz Stokes
-
Hugo Tavares
-
Allison Shay Theobold
- Sara Tomiolo
- tvo
- vmzhang
- vratchaudhary
- Jessica Ward
- Susan Washko
- xli677
Contributors
Editors:
Description
A release following adoption of a large-scale redesign of the lesson content.
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.
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
Files
datacarpentry/R-ecology-lesson-v2024.07a.zip
Files
(3.6 MB)
| Name | Size | Download all |
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md5:c73ed6687b7c37f9a3fef2d23249c874
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3.6 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Software: https://github.com/datacarpentry/R-ecology-lesson/tree/v2024.07a (URL)