r-ecology-lesson

Data Analysis and Visualization in R for Ecologists

https://github.com/datacarpentry/r-ecology-lesson

Science Score: 77.0%

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    Found 5 DOI reference(s) in README
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Keywords

carpentries data-carpentry data-visualisation data-visualization data-wrangling ecology english lesson open-educational-resources r stable

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bioinformatics tidyverse small-mammal-trapping community-ecology dna open-science drake makefile ropensci osm-data
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Data Analysis and Visualization in R for Ecologists

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carpentries data-carpentry data-visualisation data-visualization data-wrangling ecology english lesson open-educational-resources r stable
Created almost 11 years ago · Last pushed 5 months ago
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README.md

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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

Owner

  • Name: Data Carpentry
  • Login: datacarpentry
  • Kind: organization
  • Email: team@carpentries.org

Workshops teaching scientists basic skills for retrieving, viewing, managing, and manipulating data in an open and reproducible way.

Citation (CITATION)

## Data

Data is from the paper S. K. Morgan Ernest, Thomas J. Valone, and James
H. Brown. 2009. Long-term monitoring and experimental manipulation of a
Chihuahuan Desert ecosystem near Portal, Arizona, USA. Ecology 90:1708.

[https://esapubs.org/archive/ecol/E090/118/](https://esapubs.org/archive/ecol/E090/118/)

A simplified version of this data, suitable for teaching is available on
[figshare](https://doi.org/10.6084/m9.figshare.1314459.v5).

## Lessons

The first workshop was run at NESCent on May 8-9, 2014 with the development and
instruction of lessons by Karen Cranston, Hilmar Lapp, Tracy Teal, and Ethan
White and contributions from Deb Paul and Mike Smorul.

Original materials adapted from SWC Python lessons by Sarah Supp. John Blischak
led the continued development of materials with contributions from Gavin
Simpson, Tracy Teal, Greg Wilson, Diego Barneche, Stephen Turner, and Karthik
Ram. This original material has been modified and expanded by François
Michonneau.

The **`dplyr`** lesson was created by Kara Woo, who copied and modified and
modified from Jeff
Hollister's [materials](https://usepa.github.io/introR/2015/01/14/03-Clean/).

The **`ggplot2`** lesson was initially created by Mateusz Kuzak, Diana Marek,
and Hedi Peterson, during a Hackathon in Espoo, Finland on March 16-17, 2015,
sponsored by the [ELIXIR project](https://elixir-europe.org/).

You can cite this Data Carpentry lesson as follow:

Michonneau F, Teal T, Fournier A, Seok B, Obeng A, Pawlik AN, Conrado AC, Woo K, Lijnzaad
P, Hart T, White EP, Marwick B, Bolker B, Jordan KL, Ashander J, Dashnow H, Hertweck K,
Cuesta SM, Becker EA, Guillou S, Shiklomanov A, Klinges D, Odom GJ, Jean M, Mislan KAS,
Johnson K, Jahn N, Mannheimer S, Pederson S, Pletzer A, Fouilloux A, Switzer C, Bahlai C,
Li D, Kerchner D, Rodriguez-Sanchez F, Rajeg GPW, Ye H, Tavares H, Leinweber K, Peck K,
Lepore ML, Hancock S, Sandmann T, Hodges T, Tirok K, Jean M, Bailey A, von Hardenberg A,
Theobold A, Wright A, Basu A, Johnson C, Voter C, Hulshof C, Bouquin D, Quinn D,
Vanichkina D, Wilson E, Strauss E, Bledsoe E, Gan E, Fishman D, Boehm F, Daskalova G,
Tavares H, Kaupp J, Dunic J, Keane J, Stachelek J, Herr JR, Millar J, Lotterhos K,
Cranston K, Direk K, Tylén K, Chatzidimitriou K, Deer L, Tarkowski L, Chiapello M, Burle
M, Ankenbrand M, Czapanskiy M, Moreno M, Culshaw-Maurer M, Koontz M, Weisner M, Johnston
M, Carchedi N, Burge OR, Harrison P, Humburg P, Pauloo R, Peek R, Elahi R, Cortijo S,
sfn_brt, Umashankar S, Goswami S, Sumedh, Yanco S, Webster T, Reiter T, Pearse W, Li Y
(2025). “datacarpentry/R-ecology-lesson: Data Carpentry: Data Analysis and Visualization
in R for Ecologists, June 2019.” doi:10.5281/zenodo.3264888
<https://doi.org/10.5281/zenodo.3264888>, <https://datacarpentry.org/R-ecology-lesson/>.

or as a BibTeX entry:

@Misc{,
  author = {François Michonneau and Tracy Teal and Auriel Fournier and Brian Seok and Adam Obeng and Aleksandra Natalia Pawlik and Ana Costa Conrado and Kara Woo and Philip Lijnzaad and Ted Hart and Ethan P White and Ben Marwick and Ben Bolker and Kari L Jordan and Jaime Ashander and Harriet Dashnow and Kate Hertweck and Sergio Martínez Cuesta and Erin Alison Becker and Stéphane Guillou and Alexey Shiklomanov and David Klinges and Gabriel J. Odom and Martin Jean and K. A. S. Mislan and Kayla Johnson and Najko Jahn and Sara Mannheimer and Steve Pederson and Alex Pletzer and Anne Fouilloux and Callin Switzer and Christie Bahlai and Daijiang Li and Dan Kerchner and Francisco Rodriguez-Sanchez and Gede Primahadi Wijaya Rajeg and Hao Ye and Hugo Tavares and Katrin Leinweber and Kayla Peck and Mauro Luciano Lepore and Stacey Hancock and Thomas Sandmann and Toby Hodges and Katrin Tirok and Martin Jean and Alistair Bailey and Achaz {von Hardenberg} and Allison Theobold and April Wright and Arindam Basu and Carolina Johnson and Carolyn Voter and Catherine Hulshof and Daina Bouquin and Danielle Quinn and Darya Vanichkina and Earle Wilson and Eli Strauss and Ellen Bledsoe and Emilia Gan and Dmytro Fishman and Fred Boehm and Gergana Daskalova and Hugo Tavares and Jake Kaupp and Jillian Dunic and Jonathan Keane and Joseph Stachelek and Joshua R. Herr and Justin Millar and Katie Lotterhos and Karen Cranston and Kenan Direk and Kristian Tylén and Kyriakos Chatzidimitriou and Lachlan Deer and Leszek Tarkowski and Marco Chiapello and Marie-Helene Burle and Markus Ankenbrand and Max Czapanskiy and Melissa Moreno and Michael Culshaw-Maurer and Michael Koontz and Michael Weisner and Myfanwy Johnston and Nick Carchedi and Olivia Rata Burge and Paul Harrison and Peter Humburg and Richard Pauloo and Ryan Peek and Robin Elahi and Sandra Cortijo and {sfn_brt} and Shivshankar Umashankar and Shubhang Goswami and {Sumedh} and Scott Yanco and Tara Webster and Taylor Reiter and Will Pearse and Ye Li},
  title = {datacarpentry/R-ecology-lesson: Data Carpentry: Data Analysis and Visualization in R for Ecologists, June 2019},
  editor = {Ana Costa Conrado and Auriel M.V. Fournier and Brian Seok and Francois Michonneau},
  month = {May},
  year = {2025},
  url = {https://datacarpentry.org/R-ecology-lesson/},
  doi = {10.5281/zenodo.3264888},
}

GitHub Events

Total
  • Issues event: 7
  • Watch event: 11
  • Delete event: 23
  • Issue comment event: 40
  • Push event: 118
  • Pull request review comment event: 11
  • Pull request event: 34
  • Pull request review event: 14
  • Fork event: 20
  • Create event: 25
Last Year
  • Issues event: 7
  • Watch event: 11
  • Delete event: 23
  • Issue comment event: 40
  • Push event: 118
  • Pull request review comment event: 11
  • Pull request event: 34
  • Pull request review event: 14
  • Fork event: 20
  • Create event: 25

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 1,021
  • Total Committers: 217
  • Avg Commits per committer: 4.705
  • Development Distribution Score (DDS): 0.635
Past Year
  • Commits: 25
  • Committers: 5
  • Avg Commits per committer: 5.0
  • Development Distribution Score (DDS): 0.6
Top Committers
Name Email Commits
Francois Michonneau f****u@g****m 373
Tobias Busch t****h@g****m 46
Toby Hodges t****s@g****m 31
Katrin Leinweber K****r@t****u 27
Brian Seok s****k@c****u 27
Adam Obeng g****b@b****m 25
zkamvar z****r 20
Mateusz Kuzak m****k@g****m 15
Katrin Leinweber 9****r 14
Tracy Teal t****l@g****m 14
Edmund Hart e****t@g****m 14
Ana Costa 2****t 13
Philip Lijnzaad p****d@g****m 12
Kara Woo w****a@g****m 12
Ben Marwick b****k@h****m 10
maneesha sane 8****a 10
Aleksandra Pawlik a****k@g****m 9
Auriel M.V. Fournier a****r@g****m 9
Matthias Grenié m****e@h****m 8
kathy0305 k****5@h****m 8
Erin Becker e****r@c****g 8
Ben Bolker b****r@g****m 8
Ethan White e****n@w****g 7
chriseshleman e****s@g****m 7
Kari L. Jordan k****n@m****m 7
Doug Joubert d****C 5
Mark Robinson m****n@i****h 5
David k****d@s****u 5
Sergio Martínez Cuesta s****e@g****m 5
Ruud Steltenpool g****m@s****m 5
and 187 more...

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 100
  • Total pull requests: 113
  • Average time to close issues: over 2 years
  • Average time to close pull requests: 4 months
  • Total issue authors: 76
  • Total pull request authors: 38
  • Average comments per issue: 2.2
  • Average comments per pull request: 1.56
  • Merged pull requests: 74
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 6
  • Pull requests: 41
  • Average time to close issues: 13 days
  • Average time to close pull requests: 10 days
  • Issue authors: 3
  • Pull request authors: 8
  • Average comments per issue: 0.67
  • Average comments per pull request: 1.12
  • Merged pull requests: 31
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • tobyhodges (7)
  • maneesha (5)
  • zkamvar (4)
  • CarolynKoehn (3)
  • ErinBecker (2)
  • mikemahoney218 (2)
  • Bisaloo (2)
  • edbennett (2)
  • murraycadzow (2)
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  • rgaiacs (2)
  • klbarnes20 (2)
  • karenword (1)
  • GOnormandie (1)
Pull Request Authors
  • carpentries-bot (34)
  • tobyhodges (17)
  • zkamvar (8)
  • doujouDC (6)
  • CarolynKoehn (4)
  • Bisaloo (4)
  • unode (2)
  • ErinBecker (2)
  • edbennett (2)
  • sgichuki (2)
  • BenjaminJPerry (2)
  • josenino95 (2)
  • steltenpower (2)
  • maneesha (2)
  • tefer0 (1)
Top Labels
Issue Labels
type:clarification (17) type:enhancement (15) good first issue (12) help wanted (10) type:discussion (5) type:instructor guide (4) status:waiting for response (4) status:wait (4) type:template and tools (3) status:duplicate (2) status:refer to cac (2) type:formatting (2) high priority (1) type:teaching example (1) type:accessibility (1) type: package cache (1)
Pull Request Labels
type: package cache (23) type: template and tools (11) status:in progress (2) status:waiting for response (1) type:clarification (1) type:template and tools (1) type:enhancement (1) status:wait (1)

Dependencies

DESCRIPTION cran
  • RSQLite * imports
  • dbplyr * imports
  • gridExtra * imports
  • hexbin * imports
  • hunspell * imports
  • knitr * imports
  • patchwork * imports
  • remotes * imports
  • rmarkdown * imports
  • tidyverse * imports