A B C Q R S! Coilín Minto Department of Biology, Dalhousie University

Please note Applied introductory class on use of the S language Procedures for a basic analysis of biological data My perspective

Class outline Introduction to the software Workshop Biological example Questions please!

Historical perspective S language and environment developed by John Chambers and colleagues at Bell Laboratories (formerly AT&T, now Lucent Technologies) S-Plus, a commercial version of S by Insightful Software since 1987, now at version 7 R is a GNU project that started in the mid-1990s Current production version released on June 20th is 2.2.1

What can S do for me? Statistical and graphical capabilities –linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering,... –Scatterplots, histograms, q-q plots, maps, … –A well-developed programming language (S) Highly expandable through hundreds of packages

Why use S? Incredibly flexible Accessible/intuitive language Well supported (especially R) Powerful language capabilities

Calculations in S > 19*5 [1] 95 > log(2) [1] >seq(0,5) [1] > plot(cos(seq(0,10,length=100))) > exp(2) [1]

In the beginning <- 1. Create object (vector, matrix, list) > marsupial.vec <- c(“kangaroo”, “possum”, “koala”) # character > marsupial.vec [1] "kangaroo" "possum" "koala" > fib.vec <- c(0, 1, 1, 2, 3, 5, 8, 13) # numeric > fib.vec [1] > unit.mat <- matrix(c(1,0,0,0,1,0,0,0,1),ncol=3) > unit.mat [,1] [,2] [,3] [1,]1 0 0 [2,]0 1 0 [3,]0 0 1

In the beginning 2. Basic manipulations > length(fib.vec) [1] 8 > max(fib.vec) [1] 13 > dim(unit.mat) [1] 3 3 > diag(unit.mat) [1] 1 1 1

Basic steps in an analysis 2. Plot your data > plot(), boxplot(), histogram() 3. Obtain suitable function a. Function available: call function b. Function not loaded in session: library(function) c. Function not downloaded: install.packages(package) then b. NB. Only in R (open source) Please refer to code on webpage 1. Import your data > read.table() Use drop down # easier File: Load library # S-plus Packages: Load package # R

Basic steps in an analysis 4. Analyse / fit models > model1 <- lm(variable.y~variable.x) > summary(model1) > pca1 <- princomp(x, scores=T, cor = ) > summary(pca1) > dfa1 <- discrim(y~x, data=, family=) # S-plus > dfa1 <- lda(y~x, data=) # R > summary(dfa1) > pca1 <- princomp(x, scores=T, cor = ) > summary(pca1)

Mandlebrot set code written by Martin Maechler

Help ? function # brings up a help page # manualshttp://cran.r-project.org/ This contains information on subscribing to S-news and sending messages to the list. A searchable archive of recent messages is available at: Modern Applied Statistics with S-PLUS Venables and Ripley : “The de facto "bible" of statistical analysis with S-PLUS”. This can be acceModern Applied Statistics with S-PLUS

Workshop steps Vectors –Numeric, character Sequences Matrices Plots –Scatter, box, histogram Marsupial example (don’t worry about finishing this) # file:marsupial.code.txt. Written in R