An Introduction to R graphics
Cody Chiuzan
Division of Biostatistics and Epidemiology
Computing for Research I, 2012
R graphics – Nice and Simple
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R has powerful graphics facilities for the production of
publication-quality diagrams and plots.
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Can produce traditional plots as well as grid graphics.
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Great reference: Murrell P., R Graphics
Topics for today
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Histograms
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Plot, points, lines, legend, xlab, ylab, main, xlim, ylim, pch, lty,
lwd.
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Scatterplot matrix
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Individual profiles
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3D graphs
Data Puromycin – Before and After
R code
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Data available in R; for a full description: help(Puromycin).
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We will start with the basic command plot() and tackle each
parameter.
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Generate multiple graphs in the same window using: par(mfrow).
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For a better understanding use help().
Change parameters using par()
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A list of graphical parameters that define the default behavior of
all plot functions.
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Just like other R objects, par elements are similarly modifiable,
with slightly different syntax.
 e.g. par(“bg”=“lightcyan”)
 This would change the background color of all subsequent plots
to light cyan
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When par elements are modified directly (as above, this changes
all subsequent plotting behavior.
Par examples modifiable from within plotting
functions
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bg – plot background color
lty – line type (e.g. dot, dash, solid)
lwd – line width
col – color
cex – text size inside plot
xlab, ylab – axes labels
main – title
pch – plotting symbol
… and many more (learn as you need them)
Plotting symbols for pch
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Great website for choosing colors:
http://research.stowersinstitute.org/efg/R/Color/Chart/Color
Chart.pdf
Multiple plots
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The number of plots on a page, and their placement on the page,
can be controlled using par() or layout().
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The number of figure regions can be controlled using mfrow and
mfcol.
e.g. par(mfrow=c(3,2))
# Creates 6 figures arranged in
3 rows and 2 columns
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Layout() allows the creation of multiple figure regions of unequal
sizes.
e.g. layout(matrix(c(1,2)), heights=c(2,1))
Graph using statistical function output
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Many statistical functions (regression, cluster analysis) create
special objects. These arguments will automatically format
graphical output in a specific way.
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e.g. Produce diagnostic plots from a linear model analysis (see R
code)
# Reg = lm()
# plot(Reg)
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hclust()
agnes() # hierarchical cluster analysis
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Save the output
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Specify destination of graphics output or simply right click and
copy
Could be files
 Not Scalable
 JPG
# not recommended, introduces blurry artifacts
around the lines
 BMP
 PNG
 Scalable:
 Postscript # preferred in LaTex
 Pdf
# great for posters
Save the output
setwd("")
# this is where the plot will be saved
pdf(file="Puromycin.pdf“, width = , height = , res = )
dev.off()
Next - 3D graphs