Computer Science, Information Science, Ecology, Measurement and Data
Language(s)
English
Cluster
Transparency and Reproducibility in Computation and Analysis, FAIR Data and Materials
Last updated
29 October 2026
Module
Data Analysis and Visualization in R for Ecologists
By Markus Ankenbrand, Arindam Basu, Jaime Ashander, Christie Bahlai, Alistair Bailey, Erin Alison Becker, Ellen Bledsoe, Fred Boehm, Ben Bolker, Daina Bouquin, Olivia Rata Burge, Marie-Helene Burle, Nick Carchedi, Kyriakos Chatzidimitriou, Marco Chiapello, Ana Costa Conrado, Sandra Cortijo, Karen Cranston, Sergio Martínez Cuesta, Michael Culshaw-Maurer, Max Czapanskiy, Daijiang Li, Harriet Dashnow, Gergana Daskalova, Lachlan Deer, Kenan Direk, Jillian Dunic, Robin Elahi, Dmytro Fishman, Anne Fouilloux, Auriel Fournier, Emilia Gan, Shubhang Goswami, Stéphane Guillou, Stacey Hancock, Achaz Von Hardenberg, Paul Harrison, Ted Hart, Joshua R. Herr, Kate Hertweck, Toby Hodges, Catherine Hulshof, Peter Humburg, Martin Jean, Carolina Johnson, Kayla Johnson, Myfanwy Johnston, Kari L Jordan, K. A. S. Mislan, Jake Kaupp, Jonathan Keane, Dan Kerchner, David Klinges, Michael Koontz, Katrin Leinweber, Mauro Luciano Lepore, Philip Lijnzaad, Ye Li, Katie Lotterhos, Sara Mannheimer, Ben Marwick, François Michonneau, Justin Millar, Melissa Moreno, Najko Jahn, Adam Obeng, Gabriel J. Odom, Richard Pauloo, Aleksandra Natalia Pawlik, Will Pearse, Kayla Peck, Steve Pederson, Ryan Peek, Alex Pletzer, Danielle Quinn, Gede Primahadi Wijaya Rajeg, Taylor Reiter, Francisco Rodriguez-Sanchez, Thomas Sandmann, Brian Seok, Shiklomanov Sfn_brt, Shivshankar Umashankar Alexey, Joseph Stachelek, Eli Strauss, Switzer Sumedh, Tarkowski Callin, Tavares Leszek, Teal Hugo, Theobold Tracy, Tirok Allison, Tylén Katrin, Vanichkina Kristian, Voter Darya, Webster Carolyn, Weisner Tara, White Michael, Ethan P, Earle Wilson, Kara Woo, April Wright, Scott Yanco, Hao Ye
Level Graduate / Professional · For student, teacher · Language English
Abstract
Data Carpentry lesson from Ecology curriculum to learn how to analyse and visualise ecological data in R. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with ecology data in R. This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import CSV files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from R.
