Base Graphics | Manual

Overview

Important high-level plotting functions
- plot: generic x-y plotting
- barplot: bar plots
- boxplot: box-and-whisker plot
- hist: histograms
- pie: pie charts
- dotchart: cleveland dot plots
- image, heatmap, contour, persp: functions to generate image-like plots
- qqnorm, qqline, qqplot: distribution comparison plots
- pairs, coplot: display of multivariant data
Help on these functions
- ?myfct
- ?plot
- ?par

Preferred Input Data Objects

Matrices and data frames
Vectors
Named vectors

Scatter Plots

Basic scatter plots

Sample data set for subsequent plots

set.seed(1410)
y <- matrix(runif(30), ncol=3, dimnames=list(letters[1:10], LETTERS[1:3]))
plot(y[,1], y[,2])

All pairs

pairs(y)

Plot labels

plot(y[,1], y[,2], pch=20, col="red", main="Symbols and Labels")
text(y[,1]+0.03, y[,2], rownames(y))

More examples

Print instead of symbols the row names

plot(y[,1], y[,2], type="n", main="Plot of Labels")
text(y[,1], y[,2], rownames(y))

Usage of important plotting parameters

grid(5, 5, lwd = 2) 
op <- par(mar=c(8,8,8,8), bg="lightblue")
plot(y[,1], y[,2], type="p", col="red", cex.lab=1.2, cex.axis=1.2, 
     cex.main=1.2, cex.sub=1, lwd=4, pch=20, xlab="x label", 
     ylab="y label", main="My Main", sub="My Sub")
par(op)

Important arguments} - mar: specifies the margin sizes around the plotting area in order: c(bottom, left, top, right) - col: color of symbols - pch: type of symbols, samples: example(points) - lwd: size of symbols - cex.*: control font sizes - For details see ?par

Add a regression line to a plot

plot(y[,1], y[,2])
myline <- lm(y[,2]~y[,1]); abline(myline, lwd=2)

summary(myline)

## 
## Call:
## lm(formula = y[, 2] ~ y[, 1])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.40357 -0.17912 -0.04299  0.22147  0.46623 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.5764     0.2110   2.732   0.0258 *
## y[, 1]       -0.3647     0.3959  -0.921   0.3839  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3095 on 8 degrees of freedom
## Multiple R-squared:  0.09589,	Adjusted R-squared:  -0.01712 
## F-statistic: 0.8485 on 1 and 8 DF,  p-value: 0.3839

Same plot as above, but on log scale

plot(y[,1], y[,2], log="xy")

Add a mathematical expression to a plot

plot(y[,1], y[,2]); text(y[1,1], y[1,2], 
     expression(sum(frac(1,sqrt(x^2*pi)))), cex=1.3)

Exercise 1

Task 1: Generate scatter plot for first two columns in iris data frame and color dots by its Species column.
Task 2: Use the xlim/ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.

Structure of iris data set:

class(iris)

## [1] "data.frame"

iris[1:4,]

##   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

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Line Plots

Single Data Set

plot(y[,1], type="l", lwd=2, col="blue")

Many Data Sets

Plots line graph for all columns in data frame y. The split.screen function is used in this example in a for loop to overlay several line graphs in the same plot.

split.screen(c(1,1))

## [1] 1

plot(y[,1], ylim=c(0,1), xlab="Measurement", ylab="Intensity", type="l", lwd=2, col=1)
for(i in 2:length(y[1,])) { 
	screen(1, new=FALSE)
	plot(y[,i], ylim=c(0,1), type="l", lwd=2, col=i, xaxt="n", yaxt="n", ylab="", 
             xlab="", main="", bty="n") 
}

close.screen(all=TRUE)

Bar Plots

Basics

barplot(y[1:4,], ylim=c(0, max(y[1:4,])+0.3), beside=TRUE, 
        legend=letters[1:4]) 
text(labels=round(as.vector(as.matrix(y[1:4,])),2), x=seq(1.5, 13, by=1)
     +sort(rep(c(0,1,2), 4)), y=as.vector(as.matrix(y[1:4,]))+0.04)

Error bars

bar <- barplot(m <- rowMeans(y) * 10, ylim=c(0, 10))
stdev <- sd(t(y))
arrows(bar, m, bar, m + stdev, length=0.15, angle = 90)

Mirrored bar plot

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
plot(c(0,12),range(df$y),type = "n")
barplot(height = df$y[df$group == "Above"], add = TRUE,axes = FALSE)
barplot(height = df$y[df$group == "Below"], add = TRUE,axes = FALSE)

Histograms

hist(y, freq=TRUE, breaks=10)

Density Plots}

plot(density(y), col="red")

Pie Charts

pie(y[,1], col=rainbow(length(y[,1]), start=0.1, end=0.8), clockwise=TRUE)
legend("topright", legend=row.names(y), cex=1.3, bty="n", pch=15, pt.cex=1.8, 
col=rainbow(length(y[,1]), start=0.1, end=0.8), ncol=1)

Color Selection Utilities

Default color palette and how to change it

palette()

## [1] "black"   "red"     "green3"  "blue"    "cyan"    "magenta" "yellow"  "gray"

palette(rainbow(5, start=0.1, end=0.2))
palette()

## [1] "#FF9900" "#FFBF00" "#FFE600" "#F2FF00" "#CCFF00"

palette("default")

The gray function allows to select any type of gray shades by providing values from 0 to 1

gray(seq(0.1, 1, by= 0.2))

## [1] "#1A1A1A" "#4D4D4D" "#808080" "#B3B3B3" "#E6E6E6"

Color gradients with colorpanel function from gplots library

library(gplots)
colorpanel(5, "darkblue", "yellow", "white")

Much more on colors in R see Earl Glynn’s color chart

Arranging Several Plots on Single Page

With par(mfrow=c(nrow,ncol)) one can define how several plots are arranged next to each other.

par(mfrow=c(2,3)); for(i in 1:6) { plot(1:10) }

Arranging Plots with Variable Width

The layout function allows to divide the plotting device into variable numbers of rows and columns with the column-widths and the row-heights specified in the respective arguments.

nf <- layout(matrix(c(1,2,3,3), 2, 2, byrow=TRUE), c(3,7), c(5,5), 
             respect=TRUE)
# layout.show(nf)
for(i in 1:3) { barplot(1:10) }

Saving Graphics to Files

After the pdf() command all graphs are redirected to file test.pdf. Works for all common formats similarly: jpeg, png, ps, tiff, …

pdf("test.pdf"); plot(1:10, 1:10); dev.off()

Generates Scalable Vector Graphics (SVG) files that can be edited in vector graphics programs, such as InkScape.

svg("test.svg"); plot(1:10, 1:10); dev.off()

Exercise 2

Bar plots

Task 1: Calculate the mean values for the \Rfunction{Species} components of the first four columns in the \Rfunction{iris} data set. Organize the results in a matrix where the row names are the unique values from the \Rfunction{iris Species} column and the column names are the same as in the first four \Rfunction{iris} columns.
Task 2: Generate two bar plots: one with stacked bars and one with horizontally arranged bars.

Structure of iris data set:

class(iris)

## [1] "data.frame"

iris[1:4,]

##   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

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50