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The main purpose of the course is to teach participants how to program (in R) and effectively use programming for solving common problems. We would like to show that programming is, in principle, easy and anybody can do it (R is very intuitive). Moreover, we would like to demonstrate that R is not just statistics (the course is not about statistics) but can be used to work with graphics, databases, simulations or GIS.
We intend to make the course comprehensible for all students, there are no restrictions concerning year, degree or programme. However, we assume that the attendants will be biologists with elementary experience with biological data and with simple graphs. The course is especially suitable for all who spend more than ~3 hours a day working with computer. This course is run in English. If you are interested in the Czech version, look at MB162P13 - R pro život Also, if you prefer short and intensive courses, have a look at MB120C15E - Flash R course Last update: Weiser Martin, Mgr., Ph.D. (04.08.2022)
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Grolemund G (2014) Hands-On Programming with R. O'Reilly. (https://rstudio-education.github.io/hopr/) Crawley MJ (2007) The R book. John Wiley & Sons. (second edition exists already) Venables WN & Smith DM (2008) An introduction to R. R development core team. http://www.r-project.org Last update: Weiser Martin, Mgr., Ph.D. (02.10.2020)
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"Zapocet": an open-textbook test. Test topics: R help system, data manipulation, basic programming and graphics. Extra points are available via tests completed throughout the semester. Exam: student makes a simple programme and demonstrates it. Last update: Weiser Martin, Mgr., Ph.D. (04.08.2022)
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An interactive lecture (with computers). We will introduce basics of work with data, graphics and programming in R (all the non-statistical tricks). This part roughly corresponds with chapters 1-5 in Crawley (2007). Topics:
1. Introduction to R. Help and literature. R environment and specifics of R. R-editor, Tinn-R with highlighted syntax; data import and export, basics of syntax, operators, signs and brackets.
2. Basic structures in R. Variables, vectors, matrices, data frames, arrays, strings, characters vs. numbers. Indexes as a crucial concept.
3. Brief "bestiary" of some useful functions. Random number generation. Operations with vectors and matrices (sample, order, sort, diff, max, min, unique, sums, which). Operations with strings. Basic mathematical functions.
4. Scripting and programming (code writing): most important, we will dedicate extra time to make sure anybody understand this. Functions, arguments of functions. Control flow & loops (if, else, for, while, repeat). Functions within/inside function.
5. Good programming practice.
6. Data visualisation and graphics in R. Good practice in data visualization. Plot, lines, points, abline, text, image, par etc. as tools to visualize nearly anything. Lattice (Trellis) graphics. Connection of graphics and programming. Last update: Weiser Martin, Mgr., Ph.D. (04.08.2022)
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