1. R tutorial Stat 140 Linjuan Qian lqian@fas.harvard.edu

2. Accessing R “Windows”: free version of R can be downloaded from http://www.r-project.org; Also every machine in FAS lab and stat lab preinstalled R. Just open “R console”.http://www.r-project.org “Unix”: login into nice.harvard.edu or stat.harvard.edu, type “R” at the Unix prompt. “Web-based”: copy and paste http://rweb.stat.umn.edu/Rweb/Rweb.general.html Using q() to quit from R Get help: help(mean) or help->Html help

3. Basic Stuff: Assignment operators: x<-c(1,2); y=c(3,4 ); a<-matrix(c(1,2,3,4), nrow=2, ncol=2); z<-cbind(x,y); w<-rbind(x,y); “matrix based”: everything is done as vectors. try x+y;x*y;x%*%y;x%*%t(y);solve(a) Arithmetic Operators: +,-,*,/,^ Comparison Operators : ==, !=, >, =, <= Logical Operators: &&, || Comment : #

4. Input data From command line: x <- c(1,2,3,4) Y = 1:100; Y=seq(1,100,1) new.vector <- rep(0, n) z <- matrix(Y, nrow=20, ncol=5) From data file: data<-read.table(“dataset.txt”, header=T)

5. Basic Statistics: Summmary: summary(vector),mean, var, sd Tests: t.test(vector), var.test(vector1, vector2) Sorting: max, min, sort Graph : hist(vector), boxplot, qqplot, plot Regression : model<- lm(z~x+y) Sampling: sample(x, 1000, replace=T, prob=NULL) Or next slide

6. Control Structure Condition: if(a==5){ b=1 } else if(a<=3){ b=2 } else{ b=3 } Loop : be aware, R is notorious for loops, try to avoid using loops as much as you can! while (a<3) {a=a+1} for (i in 1:10){x=x+i} break next

7. Functions about Distributions The names for various distributions are as follows: norm : normal;beta : beta chisq : chi squared;pois : Poisson gamma : gamma;binom : binomial nbinom: negative binomial;t : t;f : F unif: uniform; Then you can add one of four letters to each distribution to create a command: r: random draws d: density functions (pdf or pmf) q: quantiles p: cdf (cumulative probabilities) Example: rnorm(10, mean=0, sd=1); dnorm(0, mean=0, sd=1)

8. Enhance your productivity Save a series of command as a text file and run it as a program. Within R: source(“infile.txt”) Writing your own functions: my.function <- function(arguments) { content return(output) }

9. Other packages: Functions are grouped as packages Install packages: Windows menu: packages -> install.. Unix&Windows:install.packages("MASS") Load packages before use: library(MASS) help(mvrnorm)

10. Relevant to HW 1 Install a package called “gregmisc”, which offers the permutation and combination function. Input data values and compute basic statistics such as sum and mean For matrix, compute row/column mean “Filtering” out important values Plots and test commands can be easily found