| title | author | date | number-sections |
|---|---|---|---|
Coding for research |
Hugo Tavares, Alexia Cardona, Martin van Rongen |
today |
false |
These sessions provide an introduction to coding in R and Python. The aim is to get you comfortable with coding techniques commonly used in scientific research.
::: {.callout-tip}
- Get familiar with the R or Python programming language
- Learn to visualise data
- Be able to manipulate and transform data :::
This course is aimed at people without any prior programming experience. It does however, allow people with some experience to further enhance their knowledge through different level exercises.
No prerequisites.
Exercises in these materials are labelled according to their level of difficulty:
| Level | Description |
|---|---|
| {{< fa solid star >}} {{< fa regular star >}} {{< fa regular star >}} | Exercises in level 1 are simpler and designed to get you familiar with the concepts and syntax covered in the course. |
| {{< fa solid star >}} {{< fa solid star >}} {{< fa regular star >}} | Exercises in level 2 combine different concepts together and apply it to a given task. |
| {{< fa solid star >}} {{< fa solid star >}} {{< fa solid star >}} | Exercises in level 3 require going beyond the concepts and syntax introduced to solve new problems. |
Please cite these materials if:
- You adapted or used any of them in your own teaching.
- These materials were useful for your research work. For example, you can cite us in the methods section of your paper: "We carried our analyses based on the recommendations in YourReferenceHere".
{{< citation CITATION.cff >}}
These materials are based on the original course contents of the "Data Carpentry lesson in Ecology".
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 (2019). “datacarpentry/R-ecology-lesson: Data Carpentry: Data Analysis and Visualization in R for Ecologists, June 2019.” doi: 10.5281/zenodo.3264888, "http://datacarpentry.org/R-ecology-lesson/".