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+Coding for research
+Overview
+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.
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+Learning Objectives
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- Get familiar with the R or Python programming language +
- Learn to visualise data +
- Be able to manipulate and transform data +
Target audience
+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.
+Prerequisites
+No prerequisites.
+ +Exercises
+Exercises in these materials are labelled according to their level of difficulty:
+| Level | +Description | +
|---|---|
| + | Exercises in level 1 are simpler and designed to get you familiar with the concepts and syntax covered in the course. | +
| + | Exercises in level 2 combine different concepts together and apply it to a given task. | +
| + | Exercises in level 3 require going beyond the concepts and syntax introduced to solve new problems. | +
Acknowledgements
+ +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/”.
+ + +
3.3.2 Comments in code
+It’s always a good idea to add explanations to your code. We can do that with the hash tag
+#symbol, for example:It’s always a good idea to add lots of comments to your code. What makes sense to you in that moment, might not a week later. Similarly, when sharing code with colleagues and collaborators, it’s always good to be as clear as possible.
+