R tutorial g/methods2.2010/R-intro.pdf

Installing R   Choose appropriate interface windows Mac Linux  Follow install instructions

R interface  batching file: File -> open script  run commands: Ctrl-R  Save session: sink([filename])….sink()  Quit session: q()

General Syntax  result <- function(object(s), options…)  function(object(s), options…)  Object-oriented programming  Note that ‘result’ is an object

First things first:  help([function])  help.search(“linear model”)  help.start()

Choosing your default  setwd(“[pathname for directory]”)  need “\\” instead of “\” when giving paths .Rdata .Rhistory

Start with data  read.table  read.csv  scan  dget

Extracting variables from data  Use $: data$AGE  note it is case-sensitive!  attach([data]) and detach([data])

Descriptive statistics  summary  mean, median  var  quantile  range, max, min

Missing values  sometimes cause ‘error’ message  na.rm=T  na.option=na.omit

Objects  data.frame, as.data.frame, is.data.frame names([data]) row.names([data])  matrix, as.matrix, is.matrix dimnames([data])  factor, as.factor, is.factor levels([factor])  arrays  lists  functions  vectors  scalars

Creating and manipulating  combine: c  cbind: combine as columns  rbind: combine as rows  list: make a list  rep(x,n): repeat x n times  seq(a,b,i): create a sequence between a and b in increments of i  seq(a,b, length=k): create a sequence between a and b with length k with equally spaced increments

ifelse  ifelse(condition, true, false) agelt50 <- ifelse(data$AGE<50,1,0) note for equality must use “==“  cut(x, breaks) agegrp <- cut(data$AGE, breaks=c(0,50,60,130)) agegrp <- cut(data$AGE, breaks=c(0,50,60,130), labels=c(0,1,2)) agegrp <- cut(data$AGE, breaks=c(0,50,60,130), labels=F)

Looking at objects  dim  length  sort

Subsetting  Use [ ]  Vectors data$AGE[data$REGION==1] data$AGE[data$LOS<10]  Matrices & Dataframes data[data$AGE<50, ] data[, 2:5] data[data$AGE<50, 2:5]

Some math  abs(x)  sqrt(x)  x^k  log(x) (natural log, by default)  choose(n,k)

Matrix Manipulation  Matrix multiplication: A%*%B  transpose: t(X)  diag(X)

Table  table(x,y)  tabulate(x)

Statistical Tests and CI’s  t.test  fisher.test and binom.exact  wilcox.test

Plots  hist  boxplot  plot pch, type, lwd xlab, ylab xlim, ylim xaxt, yaxt  axis

Plot Layout  par(mfrow=c(2,1))  par(mfrow=c(1,1))  par(mfcol=c(2,2))  help(par)

Probability Distributions  Normal: rnorm(N,m,s): generate random normal data dnorm(x,m,s): density at x for normal with mean m, std dev s qnorm(p,m,s): quantile associated with cumulative probability of p for normal with mean m, std dev s pnorm(q,m,s): cumulative probability at quantile q for normal with mean m, std dev s  Binomial rbinom etc.

Libraries  Additional packages that can be loaded  Example: epitools  library  library(help=[libname])

Keeping things tidy  ls() and objects()  rm()  rm(list=ls())

Future Topics  linear regression  sourcing R code  creating functions  organizing R files