This document provides an introduction and overview of the R programming language and environment. It discusses various interfaces for using R, including from the command line, RStudio, and RevolutionR. It also covers importing and exploring data, common data structures in R, tips for writing clean and reproducible code, popular packages for manipulation and visualization, and provides a quick example use case in RStudio.

1. R IntroWeek 1 Scott Chamberlain [modified from Haldre Rogers] September 9, 2011

2. Don’t just listen to me! Other Intros to R: http://www.stat.duke.edu/programs/gcc/ResourcesDocuments/RTutorial.pdf http://www.cyclismo.org/tutorial/R/ http://www.r-tutor.com/r-introduction Quick R: http://www.statmethods.net/ http://www.bioconductor.org/help/course-materials/2011/CSAMA/Monday/Morning%20Talks/R_intro.pdf

3. R user frameworks R from command line: OSX and PC Just type “R” into the command line – and have fun! R itself http://www.r-project.org/ RStudio – good choice http://www.rstudio.org/ RevolutionR [free academic version] – this is sort of the SAS-ised version of R http://www.revolutionanalytics.com/downloads/free-academic.php Uses proprietary .xdf file format that speeds up computation times Many other ways to use R, including GUIs, other IDEs, and huge variety of text editors https://github.com/RatRiceEEB/RIntroCode/wiki/R-Resources If you are afraid of the code interface, use Rattle, or R Commander, or Deducer, or Red R You can learn using these interfaces what code does what after pressing buttons

4. R user frameworks, cont. R from Python RPy: http://rpy.sourceforge.net/ C from R: rcpp package: http://cran.r-project.org/web/packages/Rcpp/index.html http://dirk.eddelbuettel.com/code/rcpp.html Can hugely speed up computation times by writing R functions in C language. Then the function calls C to run instead of R. E.g., http://helmingstay.blogspot.com/2011/06/efficient-loops-in-r-complexity-versus.html & http://dirk.eddelbuettel.com/code/rcpp.examples.html Excel from R XLConnect package: http://cran.r-project.org/web/packages/XLConnect/index.html And more….see for yourself

5. R Tips R can crash  Do not use R’s built in text editor or solely write code in the R console. Instead use any text editor that integrates with R. See here for links: https://github.com/RatRiceEEB/RIntroCode/wiki/R-Resources When asking for help on listserves/help websites, use BRIEF and REPRODUCIBLE examples Not doing this makes people not want to help you! R automatically overwrites files with the same file name!!!! Make sure you want to overwrite a file before doing so

6. Style

7. Not this kind of style…

8. This kind of style!!!

9. Style Style is important so YOU and OTHERS can read your code and actually use it Google style guide: http://google-styleguide.googlecode.com/svn/trunk/google-r-style.html#generallayout Henrik Bengtsson style guide: http://www1.maths.lth.se/help/R/RCC/ Hadley Wickham's style guide: https://github.com/hadley/devtools/wiki/Style

10. Preparing your data for R What makes clean data? Correct spelling Identical capitalization (e.g. Premna vspremna) If myvector <- c(3, 4, 5), calling Myvector does not work! No spaces between words (spaces turned into “.”) Generally try to avoid, use underscores instead NA or blank (if using csv) for missing values Find and replace to get rid of spaces after words I generally keep an .xls and a .csv file so you can always recreate work in R with the .csv file and still modify the .xls file

11. Bringing data into R Create csv file One worksheet only No special formatting, filters, comments etc. Copy only columns and rows with your data to the CSV, as R will read in columns without data sometimes Name your variables well self-explanatory, unique, lowercase, short-ish, one-word names In R, set the working directory setwd("/Users/ScottMac/Dropbox/R Group/Week1_R-Intro") What is the working directory? getwd() What is in the working directory? dir() Read in data CSV files: iris.df <- read.csv("iris_df.csv", header=T) Clipboard: read.csv("clipboard")- reads in file like cutting and pasting it From web: read.csv("http://explore.data.gov/download/pwaj-zn2n/CSV") From excel files: (using the XLConnect package) iris.df <- readWorksheetFromFile("/Users/ScottMac/Dropbox/R Group/Week1_R-Intro/iris_df.xlsx", sheet=“Sheet1”) Write data write.csv(dataframe, “dataframename.csv”), OR save(iris, “iris.RData”) [and load(“iris.RData”) to open in R]

12. R data structures Scalar: Object with a single value, either numeric or character Vector: Sequence of any values, including numeric, character, and NA List: Arbitrary collections of variables – very useful R object Character: Text, e.g., “this is some text” Factor: Like character vectors, but only w/ values in predefined “levels” Matrix: Only numeric values allowed Dataframe: Each column can be of a different class Immutable dataframe: special dataframe used in plyr package for faster dataframe manipulation, it references the original dataframe for faster calculations Function Environment

13. Exploring dataframes str(dataframe) gives column formats and dimensions head(dataframe) and tail() give first and last 6 rows names(dataframe) gives column names row.names(dataframe) gives row names attributes(dataframe) gives column and row names and object class summary(dataframe) gives a lot of good information Make sure variables are appropriate form Character/string, Numeric, Factor, Integer, logical Make sure mins, maxs, means, etc. seem right Make sure you don’t have typing errors so Premna and premna are two separate factors Use: unique(iris$species) to see what all unique values of a column are Or use: levels(spider$species) to see different levels

14. To attach or not to attach…that is the question Some like to use ‘attach’ to make dataframe variables accessible by name within the R session Generally, ‘attach’ is frowned upon by R junkies. Use dataframe$y, or data=dataframe, or dataframe[,”y”], or dataframe[, 2] To detach the object, use: detach()  I recommend: do not use attach, but do what you want

15. R Packages 3,262 packages!!!! Packages are extensions written by anyone for any purpose, usually loaded by: install.packages(”packagename”), then require(packagename) or library() Use ?functionname for help on any function in base R or in R packages In RStudio, just press tab when in parentheses after the function name to see function options!!! Explore packages at the CRAN site: http://cran.r-project.org/web/packages/ Inside-R package reference: http://www.inside-r.org/packages

16. Data manipulation Packages: plyr, data.table, doBY, sqldf, reshape2, and more Comparison of packages Modified from code from Recipes, scripts and Genomics blog: https://gist.github.com/878919 data.table is by far the fastest!!! BUT, ease of use and flexibility may be plyr? See for yourself… Also, see examples in the tutorial code for reshape2 package for neat data manipulation tricks

17. Visualizations A few different approaches: Base graphics Lattice graphics Grid graphics ggplot2 graphics Further reading: http://www.slideshare.net/dataspora/a-survey-of-r-graphics An example:

18. more on ggplot2 graphics There are classes taught by Hadley Wickham here at Rice if you want to learn more! Data visualization (Stat645): http://had.co.nz/stat645/ Statistical computing (Stat405): http://had.co.nz/stat405/ Hadley’s website is really helpful: http://had.co.nz/ggplot2/ The ggplot2 google groups site: https://groups.google.com/forum/#!forum/ggplot2

19. QUICK RSTUDIO RUN THROUGH Keyboard shortcuts!! http://www.rstudio.org/docs/using/keyboard_shortcuts

20. USE CASE HERE [see intro_usecase.R file]