1. Introduction to R A. Di Bucchianico

2. Introduction to R2 Types of statistical software command-line software –requires knowledge of syntax of commands –reproducible results through scripts –detailed analyses possible GUI-based software –does not require knowledge of commands –not reproducible actions hybrid types (both command-line and GUI)

3. Introduction to R3 Well-known statistical software SAS SPSS Minitab Statgraphics S-Plus R …

4. Introduction to R4 R free language almost the same as S maintained by top quality experts available on all platforms continuous improvement Available through www.r-project.org

5. Introduction to R5 Contents Basic operations Data creation + I/O Component extraction Plots Basic statistics Libraries Regression analysis Survival analysis

6. Introduction to R6 Basic operations assignment operation: a <- 2+sqrt(5) help function: –help(pnorm) –help.search(“normal distribution”) probability functions: –d (density): dgamma(x,n,  ) –p (probability=cdf): pweibull(x,3,2) –q (quantile): qnorm(0.95) –r (random numbers): rexp(10,  )

7. Introduction to R7 Data creation + I/O create –vectors: c(1,2,3) –matrices: matrix(c(1,2,3,4,5,6),2,3,byrow=T) (2=#rows) –list patterns: –“:” (1,2,3) = 1:3 –seq (1,2,3) = seq(1,3,by=1) working directories and files: –setwd –getwd –attach read data –from file: read.table(“file.txt”,header=TRUE) –from web: read.data.url

8. Introduction to R8 Component extraction d[r,]: rth row of object d d[,c]: cth column of object d d[r,c]: entry in row r and column c of object d length(d): length of d d[d<20]: extract all elements of d that are smaller than 20 d[“age”]: extract column “age” from object d

9. Introduction to R9 Plots plot: both 1D and 2D plots hist: histogram qqnorm: normal probability plot (“quantile- quantile” plot) Save graphics by choosing File -> Save as

10. Introduction to R10 Basic statistics summary mean stdev t.test boxplot

11. Introduction to R11 Packages specialized functions available through packages and libraries in Windows interface choose Packages -> Load Packages examples of packages: –qcc (quality control) –survival

12. Introduction to R12 Functions Analyses that have to be performed often can be put in the form of functions Example: simple <- function(data,mean=0,alpha=0.05) {hist(data),t.test(data,conf.level=alpha,mu= mean,alternative=“two-sided”)} simple(data,4) uses the default value 0.05 and test the null hypothesis mu=4.

13. Introduction to R13 Regression analysis general command: lm (linear model) requires data to be available in the form of a data frame –more general than matrix because columns need not have same length ) –use command data.frame for conversion other types of regression also possible (see also dedicated packages)

14. Introduction to R14 Survival analysis through library Surv of survival Cox proportional hazards: coxph

15. Introduction to R15 Useful web sites www.r-project.org http://cran.r-project.org/doc/contrib/Short-refcard.pdf http://www.uni- muenster.de/ZIV/Mitarbeiter/BennoSueselbeck/s- html/shelp.html http://www.maths.lth.se/help/R/ http://www.mas.ncl.ac.uk/~ndjw1/teaching/sim/R- intro.html http://stats.math.uni-augsburg.de/JGR/ http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/index.html