Computing for Research I Spring 2014

Computing for Research ISpring 2014 Introduction to R March 17 Primary Instructor: Elizabeth Garrett-Mayer

Check out online resources http://people.musc.edu/~elg26/teaching/methods2.2010/R-intro.pdf http://www.ats.ucla.edu/stat/r/ http://www.statmethods.net/about/learningcurve.html http://www.mayin.org/ajayshah/KB/R/index.html http://chartsgraphs.wordpress.com/2011/01/09/learnr-toolkit-to-help-excel-users-move-up-to-r/

R. Kabacoff on learning R after SPSS and SAS (http://www.statmethods.net/about/learningcurve.html) Why R has A Steep Learning Curve : A long answer to a simple question... • I have been a hardcore SAS and SPSS programmer for more than 25 years, a Systat programmer for 15 years and a Stata programmer for 2 years. But when I started learning R recently, I found it frustratingly difficult. Why? I think that there are two reasons why R can be challenging to learn quickly. • First, while there are many introductory tutorials (covering data types, basic commands, the interface), none alone are comprehensive. In part, this is because much of the advanced functionality of R comes from hundreds of user contributed packages. Hunting for what you want can be time consuming, and it can be hard to get a clear overview of what procedures are available. • The second reason is more ephemeral. As users of statistical packages, we tend to run one proscribed procedure for each type of analysis. Think of PROC GLM in SAS. We can carefully set up the run with all the parameters and options that we need. When we run the procedure, the resulting output may be a hundred pages long. We then sift through this output pulling out what we need and discarding the rest. The paradigm in R is different. • Rather than setting up a complete analysis at once, the process is highly interactive. You run a command (say fit a model), take the results and process it through another command (say a set of diagnostic plots), take those results and process it through another command (say cross-validation), etc. The cycle may include transforming the data, and looping back through the whole process again. You stop when you feel that you have fully analyzed the data. It may sound trite, but this reminds me of the paradigm shift from top-down procedural programming to object oriented programming we saw a few years ago. It is not an easy mental shift for many of us to make. • In that in the end, however, I believe that you will feel much more intimately in touch with your data and in control of your work. And it's fun!

Installing R • http://cran.r-project.org/ • Choose appropriate interface • windows • Mac • Linux • Follow install instructions • Rstudio: https://www.rstudio.com/

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]) or ?function • help.search(“linear model”) or ??”linear model” • help.start()

Choosing your default • setwd(“[pathname for directory]”) • getwd() • need “\\” instead of “\” when giving paths • Alternatively, you can use a ‘/’ to give path names. • .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

Data 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$AGE<50,1,0) • for equality must use “==“ • “or” is indicated by `|’ e.g., young.or.old <- ifelse(data$AGE<30 | data$AGE>65,1,0) • cut(x, breaks) • agegrp <- cut(data$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 • attributes

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, stddev s • qnorm(p,m,s): quantile associated with cumulative probability of p for normal with mean m, stddev s • pnorm(q,m,s): cumulative probability at quantile q for normal with mean m, stddev s • Binomial • rbinom • etc.

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

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

Computing for Research I Spring 2014