Welcome to the R intro WorkshopBefore we begin, please download the “SwissNotes.csv” and “cardiac.txt” files from the ISCC website, under the R workshop (more info).

www.iub.edu/~iscc

Introduction to RWorkshop in Methods from the Indiana Statistical Consulting Center

Thomas A. JacksonFebruary 15, 2013

OverviewThe R Project for Statistical Computinghttp://cran.r-project.org

“R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and Colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.”

- Description from CRAN Website

BenefitsR …• is free• is interactive: we can type something in and work

with it▫How we analyze data can be broken into small steps

• is interpretative: we give it commands and it translates them into mathematical procedures or data management steps

• can be used in a batch: nice because it is documented

• is a calculator: it is unlike other calculators though because you can create variables and objects

Let’s Get R Started

•How to open R → Start Menu → Programs → Departmentally Supported → Stat/Math → R

Graphical User Interface (GUI)

Three Environments

•Command Window (aka Console)

•Script Window

•Plot Window

Command Window BasicsTo quit: type q()

Save workspace image? Moves from memory to hard-drive

Storing variable in memory• <- , -> , or =• a<- 5 stores the number 5 in the object “a”• pi -> b stores the number π= 3.141593 in “b”• x = 1 + 2 stores the result of the calculation (3) in “x”• “=“ requires left-hand assignment

Try not to overwrite reserved names such as t, c, and pi!

Command Window BasicsPrinting to output• Calculations that are not stored print to output

> 3 + 5[1] 8

• Type name to view stored object> a[1] 5

• Use print()> print(a)[1] 5

View objects in workspace• objects() or ls()

Command Window BasicsClearing the console (command window)• Mac: Edit → Clear Console• Windows: Edit → Clear Console

or• Mac: Alt + Command + L• Windows: Ctrl + L

Removing variables from memory• rm() or remove()

> x <- 4> rm(x)

• rm(list = ls()) remove all variables

Script Window Basics

Saving syntax (code)•Mac: File → New•Windows: File → New Script

Documenting code: # Comments out everything on line behind

Running code from Script Window•Mac: Apple + Enter•Windows: F5 or Ctrl + r

Working Directory

Obtaining working directory•getwd()•Mac: Misc → Get Working Directory•Windows: File → Change dir...

Changing working directory•setwd()•Mac: Misc → Change Working Directory•Windows: File → Change dir...

Path Names

Specify with forward slashes or double backslashes

Enclose in single or double quotation marks

Examples•setwd(“C:/Program Files/R/R-2.6.1”)•setwd(‘C:\\Program Files\\R\\R-2.6.1’)

R Help

Helpful commands•If you know the function name: help() or ?

> help(log)> ?exp

•If you do not know the function name: help.search() or ??

> help.search(“anova”)> ??regression

Documentation

Elements of a documentation file•Function{Package}•Description•Usage: What your code should look like,

“=“ gives default•Arguments: Inputs to the function•Details•Value: What the function will return•See Also: Related functions•Examples

Online Resources•CRAN Website: http://cran.r-project.org/•R Seek: http://www.rseek.org/•Quick-R tutorial: http://www.statmethods.net/•R Tutor: http://www.r-tutor.com/•UCLA: http://www.ats.ucla.edu/stat/r/•R listservs•Google

Google tip: include “[R]” (instead of just “R”) with search topic to help filter out non-R websites

Additional PackagesOver 2,500 listed on the CRAN website!• Use with caution• Initial download of R: base, graphics, stats, utils

1) Installing a package:• Mac: Packages & Data → Package Installer

Use Package Search to locate and press ‘Install Selected’• Windows: Packages → Install Packages

Locate desired package and press ‘OK’• install.packages(“MASS”)

2) Using an installed package:You MUST call it into active memory with library()> library(MASS)

Data StructuresR has several basic types (or “classes”) of data:• Numeric - Numbers• Character – Strings (letters, words, etc.)• Logical – TRUE or FALSE• Vector• Matrix• Array• Data Frame• List

NOTE: There are other classes, but these are most common. Understanding differences will save you some

headache.

Data Structures• Find class of data• Unknown class: class()• Check particular class: is.“classname”()

> a <- 5> class(a)[1] “numeric”> is.character(a)[1] FALSE

Change class: as.classname()> as.character(a)[1] “5”

Vectors

Combine items into vector: c()> c(1,2,3,4,5,6)[1] 1 2 3 4 5 6

Repeat number of sequence of numbers: rep()

> rep(1,5)[1] 1 1 1 1 1> rep (c(2,5,7), times = 3)[1] 2 5 7 2 5 7 2 5 7

Vectors

Sequence generation: seq()> seq(1,5)[1] 1 2 3 4 5> seq(1,5, by = .5)[1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

5.0

Try 1:10 or 10:1

Matrices

Create matrix: matrix()

•6 x 1 matrix: matrix(1:6, ncol = 1)•2 x 3 matrix: matrix(1:6, nrow =2, ncol =3)•2 x 3 matrix filling across rows first:

matrix(1:6, nrow = 2, ncol = 3, byrow = TRUE)

Create matrix of more than two dimensions (array): array()

ListsCreate a list: list()• Holds vectors, matrices, arrays, etc. of varying lengths• Objects in the list can be named or unnamed

> list(matrix(0, 2, 2), y = rep(c(“A”, “B”), each = 2))[[1]]

[,1] [,2][1,] 0 0[2,] 0 0

$y[1] “A” “A” “B” “B”

Data Frame: specialized list that holds variables of same length

Data FramesCreate a data frame: data.frame()• Like a matrix, holds specified number of rows and columns

> x <- 1:4> y <- rep(c(“A”, ”B”), each = 2)> data.frame(x,y) x y1 1 A2 2 A3 3 B4 4 B

• Unnamed variables get assigned names> data.frame(1:2, c(“A”, “B”)) X1.2 c..A….B..1 1 A2 2 B

Basic Operations• Arithmetic: +, -, *, /• Order of operations: ()• Exponentiaition: ^, exp()• Other: log(), sqrt• Evaluate standard Normal density curve,

at x = 3

> x <- 3> 1/sqrt(2*pi)*exp(-(x^2)/2)[1] 0.004431848

VectorizationR is great at vectorizing operations•Feed a matrix or vector into an expression•Receive an object of similar dimension as

output

For example, evaluate at x = 0,1,2,3

> x <- c(0,1,2,3)> 1/sqrt(2*pi)*exp(-(x^2)/2)

[1] 0.39842280 0.241970725 0.053990967 0.004431848

Logical Operations•Compare: ==, >, <, >=, <=, !=

> a <- c(1,1,2,4,3,1)> a == 2[1] FALSE FALSE TRUE FALSE FALSE

FALSE•And: & or &&•Or: | or ||•Find location of TRUEs: which()

> which(a == 1)[1] 1 2 6

Subsetting

> a <- 1:5> b <- matrix(1:12,nrow = 3)

Use Square brackets []•Pick range of elements: a[1:3]•Pick particular elements: a[c(1,3,5)]•Do not include elements: a[-c(1,4)]

Subsetting (cont.)

Use commas in more than on dimension (matrices & data frames)

•Pick particular elements: B[1:2,2:4]•Give all rows and specified columns:

B[,1:2]•Give all columns and specified rows:

B[1:2,]•Note: B[2] coerces into a vector then

gives specified element

Reading External Data Files

SwissNotes.csv Data set•Complied by Bernard Flury•Contains measurements on 200 Swiss

Bank Notes•100 genuine and 100 counterfeit notes

Reading External Data Files (cont.)Most general function: read.table()

read.table(file,header=FALSE,sep = “”,…)

• Creates a data frame• File name must be in quotes, single or double• File name is case sensitive• Include file name extension if data not in working directory

> read.table(“C:/Users/jacksota/Desktop/SwissNotes.csv”, T,“,”)

Don’t know the file extension? Try: file.choose()> read.table(file.choose(), header = TRUE, sep = ”,”)

• sep defines the separator, e.g. “,” or “\t” or “”• header indicates variable names should be read from first row

Reading External Data FilesFor comma delimited files: read.csv()

For tab delimited files: read.delim()

For Minitab, SPSS, SAS, STATA, etc. data: foreign package

•Contains functions to read variety of file formats

•Functions operate like read.data()•Contains functions for writing data into these

file formats

Data Frame Hints• Identify variable names in data frame: names()

> data1 <- read.table(“SwissNotes.csv”, sep=“,”, header =TRUE)> names(data1)[1] “Length” “LeftHeight” “RightHeight” “LowerInner.Frame”[5] “UpperInner.Frame” “Diagonal” “Type”

Assign name to data frame variables

> names(data1) <- c(“Length”, “LeftHeight”, “RightHeight”, “LowerInner..Frame”, “UpperInner.Frame”, “Diagonal”, “Type”)

Note: names are strings and MUST be contained in quotes

Data Frame Hints (cont.)

Create objects out of each data frame variable: attach()

In the Swiss Note data, to refer to Type as its own object

> attach(data1)> Type[1] Genuine Genuine Genuine ….

Data Frame Hints (cont.)

Remove attached objects from workspace: detach()

> detach(data1)> TypeError: object “Type” not found

Note: Type is still part of original data frame, but is no longer a separate object.

plot() function

plot() is the primary plotting function

Calling plot will open a new plotting window

Documentation: ?plot

For complete list of graphical parameters to manipulate: ?par

plot() functionLet’s visualize the SwissNotes.csv data.After loading the data into R, attach the data

frame using attach(data).Let’s try a scatter plot of LeftHeight by

RightHeight.>plot(LeftHeight, RightHeight)

plot() functionChange symbols: Option pch=.See ?par for details.

>plot(LeftHeight,RightHeight,pch=2)

plot() FunctionChange symbol color: Option col=Specify by number or by name: col=2 or col=“red”

Hint: Type palette() to see colors associated with numberType colors() to see all possible colors

> plot(LeftHeight, RightHeight, col=“red”)

What types of points can we get?

plot() FunctionChange plot type: Option type =

“p” for points“l” for lines“b” for both“c” for lines part alone of “b”“o” for both overplotted“h” for histogram like (or high-density) vertical lines“s” for stair steps“S” for other steps, see Details below“n” for no plotting

Plot() FunctionPoints with lines…works better on sorted list of

points>plot(LeftHeight,RightHeight,type=“o”)

Scatterplots for Multiple GroupsUse plot() with points() to plot different groups in same plotGenuine notes vs. Counterfeit notes

>plot(LeftHeight[Type==“Genuine”],Rightheight[Type==“Genuine”],

col=“red”)>points(LeftHeight[Type==“Counterfeit”],RightHeight[Type==“Co

unterfeit”] ,col=“blue”)

Axis Labels and Plot Titles

The plot() command call has options to

•Specify x-axis label: xlab = “X Label”•Specify y-axis label: ylab = “Y Label”•Specify plot title: main = “Main Title”•Specify subtitle: sub = “Subtitle”

Axis Labels and Plot Titles>plot(LeftHeight[Type==”Genuine”],RightHeight[Type==“

Genuine”],col=“red”,main=“Plot of Bank Note Heights”,sub=“Measurements are in mm”,xlab=“Height of Left Side”,ylab=“Height of Right Side”)

>points(LeftHeight[Type==“Counterfeit”],RightHeight[Type=“Counterfeit”],col=“blue”)

Legends legend(“topleft”,c(“Genuine Notes”,

”Counterfeit Notes”),pch=c(21,21),col=c(“red”,”blue”))

Adding Lines

To add straight lines to plot: abline()

abline() refers to standard equation for a line:

y = bx + a

•Horizontal line: abline(h= )•Vertical Line: abline(v= )•Otherwise: abline(a= , b= ) or

abline(coef=c(a,b))

Adding Lines> abline(coef=c(21.7104,0.8319))

HistogramsHistograms are another popular plotting option.> hist(Length)

pairs() FunctionUsing the SwissNote Data> pairs(swiss)

BoxplotsTo create boxplots: boxplot()

Specify one or more variables to plot.> boxplot(swiss$Length)> boxplot(swiss[,2:3])

BoxplotsUse a formula specification for side-by-side

boxplots.Note: boxplot() has many options, e.g. notches.

See ?boxplot.> boxplot(Length~Type,notch=TRUE,data=swiss)

Mean or Average

•Mean()> mean(swiss[,”Length”])> mean(swiss)

•rowMeans()> rowMeans(swiss[,1:6])

•colMeans> colMeans(swiss[,7])

Variability

•Variance: var()> var(swiss[,”Length”])> var(swiss)

•Covariance()> cov(swiss)

•Correlation()> cor(swiss[,1:6])

Five-number Summary

>summary(swiss[1:3]) Length LeftHeight

RightHeight Min. :213.8 Min. :129.0

Min. :129.0 1st Qu.:214.6 1st Qu.:129.9 1st

Qu.:129.7 Median :214.9 Median :130.2 Median :130.0 Mean :214.9 Mean :130.1 Mean :130.0 3rd Qu.:215.1 3rd Qu.:130.4 3rd Qu.:130.2 Max. :216.3 Max. :131.0

Max. :131.1

Creating Tablestable() produces crosstabs of factors or categorical variablesUsing the cardiac data:> table(cardiac[,7:9])

, , newMI = 0

chestpaingender 0 1 F 6 10 M 4 8

, , newMI = 1

chestpaingender 0 1 F 100 222 M 62 146

Univariate t-testst.test() produces 1- and 2-sample (paired or independent) t-

tests.• 1-sample t-test

> t.test(x,alternative=“two.sided”,mu=0,conf.level=0.95)

• 2 independent samples t-test>

t.test(x,y,alternative=“two.sided”,mu=0,paired=FALSE,conf.level=0.95)

• paired t-test>

t.test(x,y,alternative=“two.sided”,mu=0,paired=TRUE,var.equal=TRUE,conf.level=0.95)

2 Independent Samples t-test

x: diagonal measurements for Genuine bank notes

y: diagonal measurements for Counterfeit bank notes

> x = swiss[Type==“Genuine”,”Diagonal”]> y =

swiss[Type==“Counterfeit”,”Diagonal”]> t.test(x,y,alternative=“greater”,mu=0,

paired=FALSE,var.equal=TRUE)

2 Independent Samples t-test> t.test(x,y,alternative=“greater”,mu=0,

paired=FALSE,var.equal=TRUE)

Two Sample t-test

data: x and yT = 28.9149, df = 198, p-value < 2.2e-16alternative hypothesis: true difference in means is greater than

095 percent confidence interval:

1.948864 Infsample estimates:mean of x mean of y

141.517 139.450

Generating Random NumbersR contains functions for generating random numbers from many

well-known distributions.

Random number from standard normal distribution:

> rnorm(1,mean=0,sd=1)[1] 0.5308293

Vector of random numbers from uniform distribution:

> runif(3, min=0, max=1)[1] 0.6578880 0.3261863 0.3093383

To reproduce results: set.seed()

Function Basicsif() statement

> n = rnorm(1)> if(n < 0){

n = abs(n)}

if() statement with else()

> n = rnorm(1)>if (n < 0){

n = abs(n)} else{n = 0}

Function Basics

for() loop

> temp = rep(0,10)> for (i in 1:10){

temp[i] = i+1}

> temp[1] 2 3 4 5 6 7 8 9 10 11

Function Basics

while() loop

> n = 1> while (n < 10 ){

n = n+1}

Creating Functions

test.function = function(input arguments){commands to execute

}

Creating Functions

For example, let’s define a new function average to find the average of a set of numbers.

average = function(x){n = length(x)average = sum(x)/n

print(average)}

Sourcing

After writing a function in a script file, bring it into working memory using source().

Source(“pathname/test.function.R”)