R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing...
Transcript of R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing...
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R: Graphics
140.776 Statistical Computing
August 21, 2011
140.776 Statistical Computing R: Graphics
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Run R program from a file
Create a file ex1.R that contains two lines:
x<-rnorm(1000)hist(x)
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Run R program from a file
>source("ex1.R")
140.776 Statistical Computing R: Graphics
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Basic graphics
Plotting in R is easy. There are many functions for plotting yourdata:
plot(): 2-D graphics
boxplot(): box plot
hist(): histogram
qqplot(): QQ plot
. . .
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Histogram
> x<-rnorm(1000)> hist(x)
Histogram of x
x
Fre
quen
cy
−3 −2 −1 0 1 2 3
050
100
150
200
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Box plot
> boxplot(x)
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Normal QQ plot
You can use qqnorm() to check whether data are collected from anormal, a long tail, or a short tail distribution
−4 −2 0 2 4
0.0
0.1
0.2
0.3
0.4
x
y
normallong tailshort tail
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Normal QQ plot
> y<-rnorm(2000, mean=2, sd=3)> qqnorm(y)
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−3 −2 −1 0 1 2 3
−5
05
10
Normal Q−Q Plot
Theoretical Quantiles
Sam
ple
Qua
ntile
s
140.776 Statistical Computing R: Graphics
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Exercise
Which one of the following is long tail? Short tail? Normal?
Histogram of w
w
Fre
quen
cy
−2 −1 0 1 2
050
100
150
200
Histogram of w
w
Fre
quen
cy
−10 −5 0 5 10
010
020
030
040
0
Histogram of w
w
Fre
quen
cy
−2 0 2 4
050
100
150
200
140.776 Statistical Computing R: Graphics
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Exercise
Find the corresponding normal QQ plot for each histogram:
Histogram of w
w
Fre
quen
cy
−2 −1 0 1 2
050
100
150
200
Histogram of w
w
Fre
quen
cy−10 −5 0 5 10
010
020
030
040
0
Histogram of w
w
Fre
quen
cy
−2 0 2 4
050
100
150
200
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−3 −2 −1 0 1 2 3
−10
−5
05
10
Normal Q−Q Plot
Theoretical Quantiles
Sam
ple
Qua
ntile
s
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−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
Normal Q−Q Plot
Theoretical Quantiles
Sam
ple
Qua
ntile
s
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−3 −2 −1 0 1 2 3
−2
−1
01
2
Normal Q−Q Plot
Theoretical Quantiles
Sam
ple
Qua
ntile
s
140.776 Statistical Computing R: Graphics
![Page 11: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/11.jpg)
Normal QQ plot
If you want to find out, you can try t-distribution (a long taildistribution)
> w<-rt(1000,df=3)> hist(w)> qqnorm(w)
140.776 Statistical Computing R: Graphics
![Page 12: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/12.jpg)
QQ plot
qqplot() allow you compare two distributions:
> x<-rnorm(1000)> y<-rnorm(2000, mean=2, sd=3)> z<-rt(1000,df=3)> qqplot(x,y)> qqplot(x,z)
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140.776 Statistical Computing R: Graphics
![Page 13: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/13.jpg)
Plot
plot() is perhaps the most frequently used plotting function in R.Let us study Y = X + ε, where X ∼ N(10, 2.52) andε ∼ N(0, 0.252).
> x<-rnorm(1000,mean=10,sd=2.5)> y<-x+rnorm(1000,mean=0,sd=0.25)> plot(x,y)
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![Page 14: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/14.jpg)
Plot
Now let us rotate the plot 45◦ and plot (y-x) vs. (y+x)/2. This is the socalled “M-A plot”.
> M<-y-x> A<-(y+x)/2> plot(A,M)
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![Page 15: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/15.jpg)
Setting graphical parameters
The MA plot looks quite different from the original plot. Why?
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M
140.776 Statistical Computing R: Graphics
![Page 16: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/16.jpg)
Plot
If you want to have the two figures on a similar scale, you can usethe xlim and ylim options of the plot() function:
> plot(A,M, xlim=c(0,20), ylim=c(-10,10))
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A
M
140.776 Statistical Computing R: Graphics
![Page 17: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/17.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot")
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4
M−A plot
A
M
140.776 Statistical Computing R: Graphics
![Page 18: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/18.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot",+ sub="A simulation")
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0 5 10 15 20
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02
4
M−A plot
A simulationA
M
140.776 Statistical Computing R: Graphics
![Page 19: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/19.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot",+ sub="A simulation",+ xlab="Intensity", ylab="log2 Fold Change")
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0 5 10 15 20
−4
−2
02
4
M−A plot
A simulationIntensity
log2
Fol
d C
hang
e
140.776 Statistical Computing R: Graphics
![Page 20: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/20.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot",+ sub="A simulation",+ xlab="Intensity", ylab="log2 Fold Change"),+ pch=20)
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0 5 10 15 20
−4
−2
02
4M−A plot
A simulationIntensity
log2
Fol
d C
hang
e
140.776 Statistical Computing R: Graphics
![Page 21: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/21.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot",+ sub="A simulation",+ xlab="Intensity", ylab="log2 Fold Change"),+ pch=20, col="blue")
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0 5 10 15 20
−4
−2
02
4M−A plot
A simulationIntensity
log2
Fol
d C
hang
e
140.776 Statistical Computing R: Graphics
![Page 22: R: Graphicshji/courses/statcomputing/Graphics1.pdf · R: Graphics 140.776 Statistical Computing August 21, 2011 140.776 Statistical Computing R: Graphics](https://reader035.fdocuments.in/reader035/viewer/2022063018/5fdcf7c93606e41c7a4a3ccc/html5/thumbnails/22.jpg)
Plot
Indeed, there are a lot of parameters you can adjust.
> plot(A,M, xlim=c(0,20), ylim=c(-5,5), main="M-A plot",+ sub="A simulation",+ xlab="Intensity", ylab="log2 Fold Change"),+ pch=20, col="blue",+ cex=1.2, cex.lab=1.2, cex.main=3, cex.sub=2)
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0 5 10 15 20
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4M−A plot
A simulationIntensity
log2
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hang
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Plot
Another example (draw lines instead of points):
> x<-seq(0, 2*pi, by=0.01)> y<-sin(x)> plot(x,y,type="l")
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par()
You can also access and modify the list of graphics parameters for thecurrent graphics device using the function par():
par() returns a list of all graphics parameters.
par(c(“col”,“lty”)) returns only the named graphics parameters.
par(col=4,lty=2) sets the value of the named parameters, returnsthe old values as a list.
For example:
> par(c("col","lty"))$col[1] "black"$lty[1] "solid"> oldpar<-par(col=4,lty=2)> par(oldpar) ## restores the original setting
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par()
Differences between par() and setting parameters in plot() (andother high-level plotting functions):
Setting parameters using par() result in permanent changes ofthe values for the current graphics device.
Parameter values set in plot() etc. are only effective whenexecuting that particular command.
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par()
For example:
> x<-seq(0, 2*pi, by=0.01)> y<-sin(x)
> oldpar<-par(col="blue")> plot(x,y,type="l")> plot(x,y^2,type="l")> par(oldpar)
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par()
> plot(x,y,type="l",col="blue")> plot(x,y^2,type="l")
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Types of plotting commands
plot(), hist(), etc. are high-level plotting functions. Sometimes,you want to add points or lines to an existing plot. To do this, youneed low-level plotting functions.
In general, there are three types of plotting commands:
High-level: create a new plot on the graphics device, withaxes, labels, titles etc.
Low-level: add information to an existing plot.
Interactive: interactively add or extract information to orfrom an existing plot using a pointing device (e.g. mouse)
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Low-level plotting functions
Examples are:
points(): add points
lines(): add connected lines
text(): add texts
abline(): add straight lines
legend(): add legend
title(): add titles
. . .
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Low-level plotting functions
> x<-seq(0,2*pi,by=0.5)> y<-sin(x)> z<-cos(x)> plot(x,y,type="o",col="blue",lwd=2,pch="s")
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Low-level plotting functions
> lines(x,z,type="o",col="red",lty=2,lwd=2,pch="c")
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Low-level plotting functions
> abline(h=0, lty=2)
s
s
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s
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Low-level plotting functions
> text(3,0.5,"sin(x)=0")
s
s
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ss
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c
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cc
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sin(x)=0
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Low-level plotting functions
> legend("bottomleft", cex=1.25,+legend = c("sin(x)", "cos(x)"), pch = c("s", "c"),+ col=c("blue","red"))
s
s
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ss
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y
c
c
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c
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cc
c
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sin(x)=0
sc
sin(x)cos(x)
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Interactive plotting functions
Try the locator() function:
> plot(x,y)> text(locator(1),"y=sin(x)")
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Exercise
−2 −1 0 1 2
−2
−1
01
2
Panda
140.776
Exe
rcis
e
Hello, my name is panda!
140.776 Statistical Computing R: Graphics