Biostatistical Computing, PHC 6068

R graphics

Zhiguang Huo (Caleb)

Wednesday September 12, 2018

Plotting in R

R is powerful to visualize your data.

plot(): a generic plotting function
- points(): add points to the plot.
- lines(), abline(): add lines
- text(): add text
- legend(): add legend
hist(): histogram
image(), heatmap(): heatmaps
boxplot(): boxplot
curve(): any function curve
contour(): contour plot

scattered plot

n <- 80
set.seed(32611)
x <- sort(runif(n, min = 0, max=2*pi))
y <- sin(x) + rnorm(n, mean = 0, sd = 0.2)
plot(x,y)

Type

“p”: for points
“l”: for lines
“b” for both,
“o” for both ‘overplotted’,
“n” for no plotting.

plot(x,y, type="p") ## default is "p"

plot(x,y, type="l") ## # default xlab and ylab are the variable

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

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

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

Labels

main: title
xlab: x labels
ylab: y labels

plot(x,y, main="sin function with Gaussian noise", 
     xlab="x axis", ylab="y axis")

Text and Symbol Size

cex: number indicating the amount by which plotting text and symbols should be scaled relative to the default. 1=default, 1.5 is 50% larger, 0.5 is 50% smaller, etc.
cex.axis magnification of axis annotation relative to cex
cex.lab magnification of x and y labels relative to cex
cex.main magnification of titles relative to cex
cex.sub magnification of subtitles relative to cex

plot(x,y, main="sin function with Gaussian noise", 
     xlab="x axis", ylab="y axis", 
     cex=2, cex.axis=2, cex.lab=2, cex.main=2, cex.sub=2)

Text

After create a plot, you can add text to it by text.

letters[1:10] ## letters contain a-z

##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"

plot(x=1:10, y=1:10, type="n", main = "some texts") 
text(x = 1:10, y=1:10, labels = letters[1:10])

Math expression

plot(1:10, type="n", xlab="X", ylab="Y")
text(5.5, 9, "expression(y==alpha[1]*x+alpha[2]*x^2)", cex=1.5)
text(5.5, 8, expression(y==alpha[1]*x+alpha[2]*x^2), cex=1.5)
theta = 3
text(5.5, 6, "theta=3; bquote(hat(theta)==.(theta))", cex=1.5)
text(5.5, 5, bquote(hat(theta)==.(theta)), cex=1.5)

Point type

pch is an argument for point typs

plot(1:25,1:25,pch=1:25)

use pch = 19

plot(x,y, pch=19)

Multiple plots

Use par() with mfcol or mfrow settings to create a nxm grid of figures.
- mfcol=c(nr, nc) adds figures by column
- mfrow=c(nr, nc) adds figures by row

n <- 80
set.seed(32611)
x <- sort(runif(n, min = 0, max=2*pi))
y1 <- sin(x) + rnorm(n, mean = 0, sd = 0.2)
y2 <- cos(x) + rnorm(n, mean = 0, sd = 0.2)

par(mfrow=c(1,2))
plot(x,y1, main="sin plot")
plot(x,y2, main="cos plot")

Lines

lty: line type. see the chart below.

lwd line width relative to the default (default=1). 2 is twice as wide.

par(mfrow=c(2,2))
plot(x,y, type="l",lwd=1) 
plot(x,y, type="l",lwd=2) 
plot(x,y, type="l",lwd=3) 
plot(x,y, type="l",lwd=4)

Color

col argument to control the color
col = 1:8 for eight basic colors
a string for 657 available colors (check out colors() )
RGB: “#rrggbb”.
- rr: red intensity, 00-FF (Hexadecimal)
- gg: green intensity, 00-FF (Hexadecimal)
- bb: blue intensity, 00-FF (Hexadecimal)
- e.g. #FF0000 represent red color.

plot(1:8,1:8, col=1:8)

par(mfrow=c(2,2))
plot(x,y, main = "black color") ## default is black
plot(x,y, main = "blue color", col=4) ## basic color option 1:8
plot(x,y, main = "green color", col='green') ## find colors from colors()
plot(x,y, main = "red color", col='#FF0000') ## RGB mode

More on colors

More on color argument for plot
- col plotting color.
- col.axis color for axis annotation
- col.lab color for x and y labels
- col.main color for titles
- col.sub color for subtitles
- fg plot foreground color (axes, boxes - also sets col= to same)
- bg plot background color
You can also create a vector of n contiguous colors using the functions rainbow(n), heat.colors(n), terrain.colors(n), topo.colors(n), and cm.colors(n).

n <- 10
par(mfrow=c(2,3))
plot(1:n,1:n, col=rainbow(n), main = "rainbow") 
plot(1:n,1:n, col=heat.colors(n), main = "heat.colors") 
plot(1:n,1:n, col=terrain.colors(n), main = "terrain.colors") 
plot(1:n,1:n, col=topo.colors(n), main = "topo.colors") 
plot(1:n,1:n, col=cm.colors(n), main = "cm.colors")

Curve

curve() allows you to plot any function

curve(sin, from = 0, to = 2*pi)

chippy <- function(x) sin(cos(x)*exp(-x/2))
curve(chippy, -8, 7, n = 2001) # n specify number of points used to draw the curve, default n = 101

Legend

?legend for more options

curve(sin, 0, 2*pi, col=2)
curve(cos, 0, 2*pi, col=4, add=T) ## add=T to overlay a new curve on top of the original figure
legend("bottomleft", legend = c("sin", "cos"), col = c(2,4),lty = 1)

margin

Margin order: bottom, left, top, right

to change margins, use the par() function, with the argument mar.

par(mar=c(1,2,3,4)) # bottem, left, top, right order, inner margin
plot(x, y, main="Red sin", pch=20, col="red")

par(oma = c(4,3,2,1), mar=c(1,2,3,4)) # bottem, left, top, right order, inner margin and outer margin
plot(x, y, main="Red sin", pch=20, col="red")

save plot

Figures can be saved as .pdf, .png, .jpeg, .bmp, .tiff by using pdf(), png(), jpeg(), bmp(), tiff().
PDF format is recommended since it is vector format. resize the figure woun’t affect the quality.

pdf("sinFunction.pdf")
n <- 80
set.seed(32611)
x <- sort(runif(n, min = 0, max=2*pi))
y <- sin(x) + rnorm(n, mean = 0, sd = 0.2)
plot(x,y)
dev.off()

## quartz_off_screen 
##                 2

IRIS data example

data(iris)
head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

boxplot

boxplot(Petal.Length ~ Species, data = iris)

names(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"

par(mfrow = c(2,2))
for(i in 1:4){
  aname <- names(iris)[i]
  boxplot(iris[[i]] ~ iris$Species, xlab="Species", col=i, ylab=aname, main=aname)  
}

histogram

default
set nclass
density

par(mfrow=c(2,2))
hist(iris$Petal.Length) ## default
hist(iris$Petal.Length, nclass=50) ## set number of bins
hist(iris$Petal.Length,  prob=TRUE, col="grey") ## grey color
lines(density(iris$Petal.Length), col="blue") ## and put the density function on top of the histogram

par(mfrow = c(2,2))
xlims <- range(iris$Petal.Length) ## set common x limits
uniqueSpecies <- levels(iris$Species)
for(i in 1:3){
  aspecies <- uniqueSpecies[i]
  sampleSelection <- iris$Species==aspecies
  adata <- iris$Petal.Length[sampleSelection]
  hist(adata, col=i, xlim=xlims, xlab="petal length",main=aspecies)
}

contour plot

x <- y <- seq(-1, 1, len=25)
z <- outer(x, y, FUN=function(x,y) -x*y*exp(-x^2-y^2))
# Contour plots
contour(x,y,z, main="Contour Plot")

filled.contour(x,y,z, main="Filled Contour Plot")

filled.contour(x,y,z, color.palette = heat.colors)

filled.contour(x,y,z, color.palette = colorRampPalette(c("red", "white", "blue")))

image

matrix <- as.matrix(iris[,1:4])
image(matrix)

heatmap (1)

heatmap(matrix)

heatmap (2)

do not reorder rows and columns

heatmap(matrix, Rowv = NA, Colv = NA)

heatmap (3)

red and green color
- red: hot, high
- green: cold, low

B=16
greenRed <- rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1))
heatmap(matrix, col= greenRed, Rowv = NA, Colv = NA)

heatmap (4)

specify side color bar
- rows
- columns

B=16
greenRed <- rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1))

species <- iris$Species
levels(species) <- 1:3
rowColor <- palette()[species]

heatmap(matrix, col= greenRed, 
        Rowv = NA, Colv = NA, RowSideColors = rowColor)

heatmap (5)

add legend

B=16
greenRed <- rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1))

species <- iris$Species
levels(species) <- 1:3
rowColor <- palette()[species]

heatmap(matrix, col= greenRed, margin=c(7,7), 
        Rowv = NA, Colv = NA, RowSideColors = rowColor)
legend("topright",legend = levels(iris$Species),fill=levels(species))

heatmap (6)

column names and row names
title

B=16
greenRed <- rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1))

species <- iris$Species
levels(species) <- 1:3
rowColor <- palette()[species]

heatmap(matrix, col= greenRed,  margin=c(7,7), 
        Rowv = NA, Colv = NA, 
        RowSideColors = rowColor, 
        labRow = "", labCol = "", main="heatmap of iris data")
legend("topright",legend = levels(iris$Species),fill=levels(species))

Advanced heatmap

heatmap.2
- Check out the tutorial https://sebastianraschka.com/Articles/heatmaps_in_r.html