stats 330: lecture 4

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STATS 330: Lecture 4

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HousekeepingMy contact details….

Plus much else on course web page

www.stat.auckland.ac.nz/~lee/330/

Or via Cecil

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Today’s lecture: R for graphics

Aim of the lecture:

To show you how to use R to produce the plots shown in the last few lectures

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Getting data into R In 330, as in many cases, data comes in 2 main

forms• As a text file• As an Excel spreadsheet

Need to convert from these formats to R Data in R is organized in data frames

• Row by column arrangement of data (as in Excel)

• Variables are columns• Rows are cases (individuals)

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Text files to R Suppose we have the data in the form of a text

file Edit the text file (use Notepad or similar) so that

• The first row consists of the variable names• Each row of data (i.e. data on a complete case)

corresponds to one line of the file Suppose data fields are separated by spaces

and/or tabs Then, to create a data frame containing the

data, we use the R function read.table

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Example: the cherry tree data

Suppose we have a text file called cherry.txt (probably created using Notepad or maybe Word, but saved as a text file)

First line: variable names

Data for each tree on a separate line, separated

by “white space” (spaces or tabs)

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Creating the data frame

In R, type

cherry.df = read.table(file.choose(),

header=TRUE)

and press the return key

This brings up the dialog to select the file cherry.txt

containing the data.

Click here to select file

Click here to load data

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Check all is OK!

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Getting data from a spreadsheet (1)

Create the spreadsheet in Excel

Save it as Comma Delimited Text (CSV)

This is a text file with all cells separated by commas

File is called cherry.csv

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Getting data from a spreadsheet (2)

In R, type

cherry.df = read.table(file.choose(),

header=TRUE, sep=“,”)

and proceed as before

Getting data from the R330 package

The package R330 contains several data sets used in the course, including the cherry tree data

To access the data frame:• Install the R330 package (see Appendix A.10 of the

coursebook)• In R, type

> library(R330)

> data(cherry.df)

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Data frames and variables

Suppose we have read in data and made a data frame

At this point R doesn’t know about the variables in the data frame, so we can’t use e.g. the variable diameter in R commands

We need to say attach(cherry.df)

to make the variables in cherry.df visible to R.

Alternatively, say cherry.df$diameter (better)

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Scatterplots

In R, there are 2 distinct sets of functions for graphics, one for ordinary graphics, one for trellis.

Eg for scatterplots, we use either plot (ordinary R) or xyplot (Trellis)

In the next few slides, we discuss plot.

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Simple plottingplot(cherry.df$height,

cherry.df$volume,

xlab=“Height (feet)”,

ylab=“Volume (cubic feet)”,

main = “Volume versus height for 31 black cherry trees”)

i.e. label axes (give units if possible), give a title

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Volume versus height for 31 black cherry trees

Height (feet)

Vo

lum

e (

cub

ic fe

et)

Alternative form of plotplot(volume ~ height,

xlab=“Height (feet)”,

ylab=“Volume (cubic feet)”,

main = “Volume versus height for 31 black cherry trees”,

data = cherry.df)

Don’t need use the $ notation with this form, note reversal of x,y

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Colours, points, etcpar(bg="darkblue")plot(cherry.df$height, cherry.df$volume, xlab="Height (feet)", ylab="Volume (cubic feet)", main = "Volume versus height for 31 black cherry trees", pch=19,fg="white", col.axis=“lightblue",col.main="white", col.lab=“white",col="white",cex=1.3)

Type

?par

for more info

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Volume versus height for 31 black cherry trees

Height (feet)

Vo

lum

e (

cub

ic fe

et)

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Lines Suppose we want to join up the rats on the

rats plot. (see data next slide) We could try

plot(rats.df$day, rats.df$growth, type=“l”)

but this won’t work Points are plotted in order they appear in

the data frame and each point is joined to the next

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Rats: the data> rats.df growth group rat change day1 240 1 1 1 12 250 1 1 1 83 255 1 1 1 154 260 1 1 1 225 262 1 1 1 296 258 1 1 1 367 266 1 1 2 438 266 1 1 2 449 265 1 1 2 5010 272 1 1 2 5711 278 1 1 2 6412 225 1 2 1 112 230 1 2 1 8

... More data

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wth

Don’t want this!

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SolutionVarious solutions, but one is to plot each line

separately, using subsetting

plot(day,growth,type="n")lines (day[rat==1],growth[rat==1])lines (day[rat==2],growth[rat==2])

and so on …. (boring!), or (better)

for(j in 1:16){lines (day[rat==j],growth[rat==j])}

Draw axes, labels only

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Indicating groupsWant to plot the litters with different colours, add a legend:

Rats 1-8 are litter 1, 9-12 litter 2, 13-16 litter 3

plot(day,growth,type="n")

for(j in 1:8)lines(day[rat==j],growth[rat==j],col="white") # litter 1

for(j in 9:12)lines (day[rat==j], growth[rat==j],col="yellow") # litter 2

for(j in 13:16)lines (day[rat==j], growth[rat==j],col="purple") # litter 3

Set colour of line

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legendlegend(13,380,legend = c(“Litter 1”, “Litter 2”,

“Litter 3”), col = c("white","yellow","purple"),lwd = c(2,2,2),horiz = TRUE,cex = 0.7)

(Type ?legend for a full explanation of these parameters)

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Litter 1 Litter 2 Litter 3

Points and text

x=1:25

y=1:25

plot(x,y, type="n")

points(x,y,pch=1:25, col="red",

cex=1.2)

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Points and text (3)

x=1:26

y=1:26

plot(x,y, type="n")

text(x,y, letters, col="blue", cex=1.2)

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x

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ab

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Use of pos

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x = 1:10y = 1:10plot(x,y)

position = rep(c(2,4), 5)mytext = rep(c(“Left",“Right"), 5)text(x,y,mytext, pos=position)

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Trellis Must load trellis library first

library(lattice)

General form of trellis plots

xyplot(y~x|W*Z, data=some.df)

Don’t need to use the $ form, , trellis functions can pick out the variables, given the data frame

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Main trellis functions

dotplot for dotplots, use when X is categorical, Y is continuous

bwplot for boxplots, use when X is categorical, Y is continuous

xyplot for scatter plots, use when both x and y are continuous

equal.count use to turn continuous conditioning variable into groups

Changing background colour

To change trellis background to white

trellis.par.set(background = list(col="white"))

To change plotting symbols

trellis.par.set(plot.symbol = list(pch=16, col="red", cex=1))

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Equal.countxyplot(volume~height|diameter, data=cherry.df)

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diameter diameter

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diameter diameter

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diameter diameter

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Equal.count (2)diam.gp<-equal.count(diameter,number=4,overlap=0) xyplot(volume~height|diam.gp, data=cherry.df)

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Changing plotting symbols

To change plotting symbols

trellis.par.set(plot.symbol = list(pch=16, col="red", cex=1))

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height

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diam.gp diam.gp

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Non-trellis version

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Given : diameter

coplot(volume~height|diameter, data=cherry.df)

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Non-trellis version (2)

coplot(volume~height|diameter,data=cherry.df,number=4,overlap=0)

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Given : diameter

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Other useful functions

Regular R• scatterplot3d (3d scatter plot, load library

scatterplot3d)• contour, persp (draws contour plots, surfaces)• pairs

Trellis• cloud (3d scatter plot)

Rotating plots You need to install the R330 package

Create a data frame e.g. called data.df with the response in the first column

Then, type

reg3d(data.df)

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