1 / 12

R tutorial

R tutorial. Stat 221, Section 1 Xiaodan Fan (Dan) xfan@fas.harvard.edu. Introduction. S-Plus is an implementation of S language, which was originated in Bell Lab. R is ‘GNU S’, an open-source implementation. Matrix-based programming language with rich statistical features. Accessing R.

glynn
Download Presentation

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 Stat 221, Section 1 Xiaodan Fan (Dan) xfan@fas.harvard.edu

  2. Introduction • S-Plus is an implementation of S language, which was originated in Bell Lab. R is ‘GNU S’, an open-source implementation. • Matrix-based programming language with rich statistical features.

  3. Accessing R • “Windows”: free version of R can be downloaded from http://www.r-project.org; Also every machine in FAS lab and stat lab preinstalled R. Just open “R console”. • “Unix”: login into nice.harvard.edu or stat.harvard.edu, type “R” at the Unix prompt. • “Web-based”: copy and paste http://rweb.stat.umn.edu/Rweb/Rweb.general.html • Using q() to quit from R • Get help: help(mean) or help->Html help

  4. Basic Stuff: • Assignment operators: x=c(1,2); y=c(3,4); a=matrix(c(1,2,3,4), nrow=2, ncol=2, byrow=T) • “matrix based”: everything is done as vectors. try x+y;x*y;x%*%y;x%*%t(y);solve(a) • Arithmetic Operators: +,-,*,/,^,%/%,%% • Comparison Operators: ==, !=, >, <, >=, <= • Logical Operators: &&, ||, ! • Comment: #

  5. Input data • From command line: • x = c(1,2,3,4) • Y = 1:100; Y=seq(1,100,1) • new.vector = rep(0, n) • z = matrix(Y, nrow=20, ncol=5) • From data file: • data<-read.table(“dataset.txt”, header=T)

  6. Control Structure • Condition: if(a==5){ b=1 } if(a<=3){ b=2 } else{ b=3 } • Loop: be aware, R is notorious for loops, try to avoid using loops as much as you can! • while (a<3) {a=a+1} • for (i in 1:10){x=x+i} break next

  7. Write your own function SmallerValue <-function(x,y){ if(x<y){ return(x) }else{ return(y) } }

  8. Basic Statistics: • Summmary: summary(vector),mean, var, sd • Tests: t.test(vector), var.test(vector1, vector2) • Sorting: max, min, sort • Graph: hist(vector), boxplot, qqplot, plot • Regression: model<- lm(z~x+y) • Sampling: sample(x, 1000, replace=T, prob=NULL) Ornext slide

  9. Functions about Distributions • The names for various distributions are as follows: norm : normal; beta : beta chisq : chi squared; pois : Poisson gamma : gamma; binom : binomial nbinom: negative binomial; t : t; f : F • Then you can add one of four letters to each distribution to create a command: r: random draws d: density functions (pdf or pmf) q: quantiles p: cdf (cumulative probabilities) • Example: rnorm(10, mean=0, sd=1); dnorm(0, mean=0, sd=1)

  10. Enhance your productivity • Save a series of command as a text file and run it as a program. • Within R: source(“infile.txt”) • Outside R: R CMD BATCH infile.txt outfile.txt • Writing your own functions: • my.function <- function(arguments) { content return(output) }

  11. Other packages: • Functions are grouped as packages • Install packages: Windows menu: packages -> install.. Unix&Windows:install.packages("MASS") • Load packages before use: library(MASS) help(mvrnorm)

  12. Real world examples: • example1: y = seq(-7,10,.02) dens = 0.5*dnorm(y,1,2) + 0.5*dnorm(y,2,2) plot (y, dens, type="l", xlab="y", ylab="") • example2: lambda0 = c(.9,.1) mu0 = c(0,15) tau0 = c(10,25) y = c(28.39, 7.94, -2.75, 6.82, -0.64, 0.63, 18.01, 12.16) sigma = c(14.9, 10.2, 16.3, 11.0, 9.4, 11.4, 10.4, 17.6) J = length(y) M = length(lambda0) lambda = array (NA, c(J,M)) mu = array (NA, c(J,M)) tau = array (NA, c(J,M)) for (m in 1:M){ lambda[,m] = lambda0[m]* dnorm(y,mu0[m],sqrt(sigma^2+tau0[m])) tau[,m] = sqrt(1/(1/tau0[m]^2 + 1/sigma^2)) mu[,m] = (mu0[m]/tau0[m]^2 + y/sigma^2)*tau[,m]^2} for (j in 1:J) lambda[j,] = lambda[j,]/sum(lambda[j,]) print (cbind(lambda[,1],mu[,1],tau[,1], lambda[,2],mu[,2],tau[,2])) possible = c(0,25,50,100) for (i in 1:length(possible)){ y[8] = possible[i] for (m in 1:M){ lambda[,m] = lambda0[m]* dnorm(y,mu0[m],sqrt(sigma^2+tau0[m])) tau[,m] = sqrt(1/(1/tau0[m]^2 + 1/sigma^2)) mu[,m] = (mu0[m]/tau0[m]^2 + y/sigma^2)*tau[,m]^2} for (j in 1:J) lambda[j,] = lambda[j,]/sum(lambda[j,]) theta = seq(-40,120,.1) dens = lambda[8,1]*dnorm(theta,mu[8,1],tau[8,1]) + lambda[8,2]*dnorm(theta,mu[8,2],tau[8,2]) plot (theta, dens, ylim=c(0,1.1*max(dens)), type="l", xlab="theta=8", ylab="", xaxs="i", yaxs="i", yaxt="n", bty="n", cex=2) mtext (paste("Post density of theta=8 when", "y=8 =", possible[i]), cex=2, 3)}

More Related