• Powerful environment for visualizing scientific data
• Integrated graphics and statistics infrastructure
• Publication quality graphics
• Fully programmable
• Highly reproducible
• Full LaTeX and Markdown support via knitr and R markdown
• Vast number of R packages with graphics utilities

Documentation for R Graphics

General

• Graphics Task Page - URL
• R Graph Gallery - URL
• R Graphical Manual - URL
• Paul Murrell’s book R (Grid) Graphics - URL

Interactive graphics

• rggobi (GGobi) - URL
• iplots - URL
• Open GL (rgl) - URL

Graphics Environments

Viewing and saving graphics in R

• On-screen graphics
• postscript, pdf, svg
• jpeg, png, wmf, tiff, …

Four major graphic environments

(a) Low-level infrastructure

• R Base Graphics (low- and high-level)
• grid: Manual

(b) High-level infrastructure \begin{itemize}

Base Graphics: 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 graphics functions

• ?myfct
• ?plot
• ?par

Preferred Object Types

• Matrices and data frames
• Vectors
• Named vectors

Scatter Plots

Basic Scatter Plot

Sample data set for subsequent plots

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

Plot data

plot(y[,1], y[,2]) All pairs

pairs(y) With 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

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

Log scale

Same plot as above, but on log scale

plot(y[,1], y[,2], log="xy") plot(y[,1], y[,2]); text(y[1,1], y[1,2], expression(sum(frac(1,sqrt(x^2*pi)))), cex=1.3) Homework 3B

Homework 3B: Scatter Plots

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
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) 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()
##  "black"   "red"     "green3"  "blue"    "cyan"    "magenta" "yellow"  "gray"
palette(rainbow(5, start=0.1, end=0.2))
palette()
##  "#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))
##  "#1A1A1A" "#4D4D4D" "#808080" "#B3B3B3" "#E6E6E6"

Color gradients with colorpanel function from gplots library

library(gplots)
colorpanel(5, "darkblue", "yellow", "white")
##  "#00008B" "#808046" "#FFFF00" "#FFFF80" "#FFFFFF"

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

Saving Graphics to File

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.

library("RSvgDevice")
devSVG("test.svg")
plot(1:10, 1:10)
dev.off()

Homework 3C

Homework 3C: Bar Plots Previous Page                     Next Page 