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R tutorial http://people.musc.edu/~elg26/teachin g/methods2.2010/R-intro.pdf

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Installing R http://cran.r-project.org/ http://cran.r-project.org/ Choose appropriate interface windows Mac Linux Follow install instructions

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

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

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

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

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Start with data read.table read.csv scan dget

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

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

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

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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

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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

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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)

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Looking at objects dim length sort

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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]

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

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

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Table table(x,y) tabulate(x)

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

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

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

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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.

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

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

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

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