R tutorial

http://people.musc.edu/~elg26/teaching/methods2.2010/R-intro.pdf

Installing R

http://cran.r-project.org/ 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