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STATS 330: Lecture 4

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STATS 330: Lecture 4

Graphics:

Doing it in R

330 lecture 4

My contact details….

Plus much else on course web page

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

Or via Cecil

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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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- In 330, as in most practice, 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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- 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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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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In R, type

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

header=T)

and press the return key

Click here to select file

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

containing the data.

Click here to load data

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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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In R, type

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

header=T, sep=“,”)

and proceed as before

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- 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

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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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plot(height,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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plot(volume ~ height,

xlab=“Height (feet)”,

ylab=“Volume (cubic feet)”,

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

data = cherry.df)

Don’t need to attach with this form, note reversal of x,y

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Type

?par

for more info

par(bg="darkblue")

plot(height,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)

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- Suppose we want to join up the rats on the rats plot. (see data next slide)
- We could try
plot(day, 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.df
- growth group rat change day
- 1 240 1 1 1 1
- 2 250 1 1 1 8
- 3 255 1 1 1 15
- 4 260 1 1 1 22
- 5 262 1 1 1 29
- 6 258 1 1 1 36
- 7 266 1 1 2 43
- 8 266 1 1 2 44
- 9 265 1 1 2 50
- 10 272 1 1 2 57
- 11 278 1 1 2 64
- 12 225 1 2 1 1
- 12 230 1 2 1 8
- ... More data

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Don’t want this!

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Various 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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Want 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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legend(13,380,

legend = c(“Litter 1”, “Litter 2”,

“Litter 3”),

col = c("white","yellow","purple"),

lwd = c(2,2,2),

horiz = T,

cex = 0.7)

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

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x=1:25

y=1:25

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

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

cex=1.2)

x=1:26

y=1:26

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

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

- Must load trellis library first
library(lattice)

- General form of trellis plots
xyplot(y~x|W*Z, data=some.df)

- Don’t need to attach data frame, trellis functions can pick out the variables, given the data frame

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- dotplotfor dotplots, use when X is categorical, Y is continuous
- bwplotfor boxplots, use when X is categorical, Y is continuous
- xyplotfor scatter plots, use when both x and y are continuous
- equal.countuse to turn continuous conditioning variable into groups

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

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diam.gp<-equal.count(diameter,number=4,overlap=0)

xyplot(volume~height|diam.gp, data=cherry.df)

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To change plotting symbols

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

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

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coplot(volume~height|diameter,

data=cherry.df,number=4,overlap=0)

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- Regular R
- scatterplot3d (3d scatter plot, load library scatterplot3d)
- contour, persp (draws contour plots, surfaces)
- pairs

- Trellis
- cloud (3d scatter plot)

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