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R tutorial

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

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R tutorial

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

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

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

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

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

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

  7. Start with data • read.table • read.csv • scan • dget

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

  9. Descriptive statistics • summary • mean, median • var • quantile • range, max, min

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

  11. 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

  12. 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

  13. 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)

  14. Looking at objects • dim • length • sort

  15. 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]

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

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

  18. Table • table(x,y) • tabulate(x)

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

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

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

  22. 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.

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

  24. Keeping things tidy • ls() and objects() • rm() • rm(list=ls())

  25. Future Topics • linear regression • sourcing R code • creating functions • organizing R files

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