Multiple plot in a single image using ImageMagick

Sometimes you need to add several plots/images either by row or by column to a single page/sheet.
If you generate all your plot with R base graphics you can easily accomplished the task using the par() function, e.g., using par(mfrow=c(2,2)) and then drawing 4 plots of your choice.
However, if you need to create a single image build up from different sources, e.g. external images, plots not compatible with R base graphics, etc. , you can create/retrieve the single images and then merge them together using the tools from the Unix (Linux, Mac OS X, etc.) ImageMagick suite.

## Example
# we generate some random plot
require(seqLog)
## the first plot is taken from the seqLogo help ( ?seqLogo )
## I selected this example on purpose because the seqLogo function is based on the grid graphics
and is coded in such a way that doesn't allow the use of the par() function
mFile <- system.file("Exfiles/pwm1", package="seqLogo")
m <- read.table(mFile)
pwm <- makePWM(m)
png("seqLogo1.png", width=400, height=400)
seqLogo(pwm)
dev.off()
## totally unrelated
png("plot1.png", width=400, height=400)
plot(density(rnorm(1000)))
dev.off()

Then you can type:

system("convert \\( seqLogo1.png plot1.png +append \\) \\( seqLogo1.png plot1.png +append \\) -background none -append final.png")

Remember that in R you have to start escape character with '\' !

Or, alternatively, from the command line:

convert \( seqLogo1.png plot1.png +append \) \( seqLogo1.png plot1.png +append \) -background none -append final.png

See man convert and man ImageMagick for the full story.

Alternative implementations using ggplot2

Here and here, you can find alternative implementations of two plots  (1, 2) I created time ago using R basic graphic. The author recreates the plots taking advantage of the excellent ggplot2 package.

no "Infinities"

Thanks to  Pierre-Yves for the below useful tip!

if you have a dataset from which you want the max or min but they have to be real number and not "Inf" or "-Inf" there is a way to do it:

data <- c(-Inf, 1,2,3,4,5,6,7,8,9,10, Inf)
max(data)
# Return Inf
min(data)
# Return -Inf
# To solve the problem I went to:
range(data, finite=TRUE)
# Then you can do
myMinimum <- range(data, finite=TRUE)[1]
myMaximum <- range(data, finite=TRUE)[2]

Dealing with missing values

Two new quick tips from 'almost regular' contributor Jason:

Handling missing values in R can be tricky. Let's say you have a table
with missing values you'd like to read from disk. Reading in the table
with,

read.table( fileName )

might fail. If your table is properly formatted, then R can determine
what's a missing value by using the "sep" option in read.table:

read.table( fileName, sep="\t" )

This tells R that all my columns will be separated by TABS regardless of
whether there's data there or not. So, make sure that your file on disk
really is fully TAB separated: if there is a missing data point you must
have a TAB to tell R that this datum is missing and to move to the next
field for processing.

Lastly, don't forget the "header=T" option if you have a header line in
your file.

Here's the 2nd tip:

Some algorithms in R don't support missing (NA) values. If you have a
data.frame with missing values and quickly want the ROWS with any
missing data to be removed then try:

myData[rowSums(is.na(myData))==0, ]

To find NA values in your data you have to use the "is.na" function.