1 / 25

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

jorn
Télécharger la présentation

R tutorial

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related