1. Piotr Wolski Introduction to R

2. Topics What is R? Sample session How to install R? Minimum you have to know to work in R Data objects in R and how to manipulate them Exercises

3. What Is R? a programming “environment” – in fact a programming language Operated through command line, no point and click Rather relaxed approach to term GUI – R GUI is in fact an interface to the command line object-oriented Freeware Cross-platform (windows, linux, mac) Scriptable - thus good to analyse large datasets, Good with matrices and multidimensional arrays excellent graphics capabilities supported by a large user network (you can always ask for help online, or search through mailing list archives) Contributed packages provide multitude of procedures

4. Where does R come from? R started in the early 1990’s as a project by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, intended to provide a statistical environment in their teaching lab. The lab had Macintosh computers, for which no suitable commercial environment was available. It is based on an earlier statistical programming language called S

5. Installing R download from CRAN (Comprehensive R Archive Network) http://cran.r- project.org/http://cran.r- project.org/ …follow instructions This will load R engine, GUI and base packages Extra packages/libraries can be downloaded and installed from within R (easy), or from CRAN website (not so easy)

6. R GUI and environment R GUI offers some administrative options, but all analyses done through command line or scripts Working directory is where data are stored Working directory depends on where you invoke R from, but can be changed during session R session - when you actually start R Data generated during the session are held in a workspace which can be saved into a file only one workspace per session You can import data from other workspace files into current workspace You cannot see data (objects) unless you command to see them

7. R command line You have to type Basic syntax: >command [enter] Two “types” of commands: >function()[enter] Runs a function >object [enter] Returns the object (prints object contents to the screen) Since a function in R is also an object: >function[enter] will display the function, but won’t execute it! Up and down arrows will invoke previous/next command There is also history - list of all issued commands, accessed from menu

8. Creation of objects By assignment “<-” used to indicate assignment > x<-c(1,2,3,4,5,6,7) > x<-c(1:7) > x<-b > x<- “b” > x<- -2 > x<-read.table(“data.txt”) Special case: empty vector: > x<-c() –

9. Naming Convention Names of objects must start with a letter (A-Z or a-z) can contain letters, digits (0-9) and periods “.” case-sensitive mydata different from MyData Names of objects do not have parentheses > “myData” is a one element vector, and that element is a string > myData is an object, and it can be a vector, array etc. Balance between length and meaning: X or tmin B or tmin.clim Climatological.mean.of.monthly.minimum.temperature or tmin.clim

10. Managing workspace during an R session, all objects are stored in a temporary working memory, or workspace list objects in workspace > ls() remove objects from workspace > rm(object) > rm(list=c(“object1”,”object2”)) > rm(list=ls()) objects that you want to access later must be saved in a workspace file – from the menu bar – from the command line: > save(x,file=“MyData.Rdata”) To save all the objects: > save.image(“myData.Rdata”) Previously saved workspace can be loaded with: > load(“MyData.Rdata”)

11. Managing working directory All interaction with the permanent data storage – reading files and workspace from, saving to – takes place within working directory Unless you specify the path explicitly > load(“/data/projects/MyData.Rdata”) > load(“c:\data\projects\MyData.Rdata”) Working directory can be checked with: > getwd() Can be changed with: > setwd(“/new/working/directory”) > setwd(“c:\new\working\directory”)

12. How to get help? Within R > help.start() Will start manual/help/tutorial in a web browser To display help on given function use: > help(function) or > ?function e.g. help on function mean(): > help(mean) or > ?mean to search help database for a string and return all functions that contain it: > ??string

13. Other sources: CRAN website (http://cran.r-project.org/)http://cran.r-project.org/ – Manuals – FAQ – Contributed documents – a mine! Rseek it: http://rseek.org/

14. R object types Vector Array (with special case: matrix) Data frame List Factor Function

15. Vector A sequence of values (one dimensional) only one mode (numeric, character, complex, or logical) allowed can be created using function concatenate: c() > x<-c(1,2,5,2,1) > y<-c(“may”,”june”,”july”,”august”,“september”) Vector has length: > length(x) Logical vectors: > b<- c(TRUE,TRUE,FALSE, FALSE, TRUE) > b 4

16. Working with vectors select only one element > x[2] select range of elements > x[1:3] select all but one element > x[-3] slicing: including only part of the object using index vector > x[c(1,2,5)] select elements based on logical operator > x[x>3] > x[y==“july”] Inverting a vector > x[10:1] > x[length(x):1]

17. Working with vectors Create sequence of numbers > seq(10) > seq(1, 10) > seq(1, 100,5) Repeating elements of a vector > rep(seq(3), 10) Repeating elements of a vector in a different way > rep(seq(3), each=10)

18. R magic - vector arithmetic Arithmetic operations are performed on EACH value of vector

19. R magic - vector arithmetic Vector operations are performed element by element

20. R magic - vector arithmetic R recycles vector elements

21. Vector functions Basic statistics

22. Exercise 1: Create a sequence from 1 to 100. Create the following sequence: 99, 96, 93, …0 Create a sequence of values 1,2,…12, repeated 10 times Create a vector of 30 number (just use a “random number generator” in your head;-) Calculate its mean, standard deviation, variance, minimum, maximum and sum of all values Calculate median and 5 th percentile Calculate minimum value of the first half of the vector (i.e. of first 15 values), and of the second half (i.e. of the last 15 values) Select every second value from that vector, and calculate their mean value