The document introduces R as a free software environment for statistical computing and advocates for its learning by highlighting twelve reasons, including its rigor in data analysis, community support, and cost-effectiveness. It outlines initial steps for downloading and using R, along with basic programming concepts such as variables, functions, vectors, matrices, and data frames. Additionally, it covers operations on data, data visualization, and importing data from CSV files.

1. Getting started with RJacob van Etten

2. What is R?“R is a free software environment for statistical computing and graphics.”www.r-project.org

3. Why R?R takes time to learn and use.So why should I bother?There are more user-friendly programmes, right?

4. 12 reasons to learn R1. Rigour and strategy in data analysis – not “thinking after clicking”.2. Automatizing repeated calculations can save time in the end.3. A lot of stuff is simply not feasible in menu driven software.

5. 12 reasons to learn R4. R is not only about software, it is also an online community of user support and collaboration.5. Scripts make it easy to communicate about your problem. Important for collaborative research!6. Research becomes replicable when scripts are published.

6. 12 reasons to learn R7. R packages represent state-of-the-art in many academic fields. 8. Graphics in R are very good.9. R stimulates learning – graduation from user to developer.

7. 12 reasons to learn R10. R is free, which saves you money. Or R may be the only option when budgets are restricted.11. R encourages to freely explore new methods and learn about them.12. Knowing to work with R is a valuable and transferable skill.

8. It’s the long way but it’s worth it...

9. Resources to learn R My two picks for youhttp://pj.freefaculty.org/R/Rtips.html

10. Some R packages of interestRNCEP – Reanalysis dataclim.pact– Downscaling climate dataGhcnDaily – dailyweather dataweatherData(R-Forge) – Daily weather data and derived bioclimatic variables relevant to plant growthraster – gives access to WorldClim data

11. And a lot more here...http://cran.r-project.org/other-docs.html

12. Downloading R

13. Choose a mirror nearby and then...Binaries“When downloading, a completely functional program without any installer is also often called program binary, or binaries (as opposed to the source code).”(Wikipedia)

14. And finally,you can download R...When downloading has finished, run the installer

15. The bare R interface

16. RStudio makes life easierRstudio.org

17. Four partsCreate a new R script: File – New – R ScriptScripts, documentationWorkspace/historyFiles, plots, packages and helpConsole

18. Our first code...Type1 + 1into the script area.Then click “Run”.What happens?

19. Exercises: running codeType a second line with another calculation (use “-”, “/”, or “*”) and click “Run” again.Select only one line with the mouse or Shift + arrows. Then click “Run”.Save your first code to a separate folder “Rexercises”.

20. Following exercisesIn the next exercises, we will develop a script.Just copy every new line and add it to your script, without erasing the previous part.If you want to make a comment in your script, put a # before that line. Like this:#important to remember: use # to comment

21. If the exercises are a bit silly......that’s because you are learning.

22. VectorType a new line with the expression1:10in the script and run this line.A concatenation of values is called a vector.

23. Making a new variableIf we send 1:10 into the console it will only print the outcome. To “store” this vector, we need to do the following.a <- 1:10new variable “a” assign vector values 1 to 10

24. Operations with vectorsTry the following and see what happens.aa * 2a * ab <- a * abprint(b)

25. Other ways of making vectorsd <- c(1, 6, 9)dclass(d)f <- LETTERSfclass(f)What is the difference between d and f?

26. FunctionsActually, we have already seen functions!Functions consist of a name followed by one or more arguments. Commas and brackets complete the expression.class(f)c(d,f)name argument

27. Cheat sheetWhen you use R, you will become familiar with the most common functions.If you need a less common function, there are ways to discover the right one.For now, use the cheat sheet to look up the functions you need.

28. Getting help on functionsThis will open help pages for the functions in your browser.?c?classEspecially the examples are often helpful. Just copy and paste the code into the console and see with your own eyes what happens!

29. MatricesWe have already met the vector.If we put two or more vector together as columns, we get a matrix.X <- c(1,2,3)Y <- c(8,9,7)Z <- c(4,2,8)M <- cbind(X, Y, Z)How many columns and rows does M have?

30. Data framesMatrices must consist of values of the same class. But often datasets consist of a mix of different types of variables (real numbers and groups). This is the job of data frames.L <- c(“a”, “b”, “c”)Df <- data.frame(X,Y,Z,L)Visualize Df like this: str(Df)What would happen if you tried to make a matrix out of these same vectors instead? Try and see.

31. Getting data into R?read.csvCSV files are a relatively trouble-free way of getting data into R.It is a fairly common format.You can make a CSV file in any spreadsheet software.

32. Create a CSV fileAdd your own favorite actor, too.Open the file with Notepad. Make sure the values are separated by commas.

33. Now use R to read itNow read it into R.actors <- read.csv(yourfile.csv)str(actors)

34. SubsettingThere are many ways of selecting only part of a data frame. Observe carefully what happens.actors[1:2,]actors[,1:2]actors[“Age”]actors[c(“Name”, “Age”)]subset(actors, Age> 40)Now create a new data frame with the actors younger than 45.

35. GraphicsThe plot function makes graphs.plot(actors[c(“sex”, “Age”)])

36. SummaryYou now know about:VariablesFunctionsVectorsMatricesData framesGetting tabular data into RSubsettingSimple plotting

37. Time for your first fight...