Combining multiple complex plots as panels in a single figure - r

Question

Introduction by #backlin
Multiple simple plots can combined as panels in a single figure by using layout or par(mfrow=...). However, more complex plots tend to setup their own panel layout internally disabling them from being used as panels. Is there a way to create a nested layout and encapsulating a complex plot into a single panel?
I have a feeling the grid package can accomplish this, e.g. by ploting the panels in separate viewports, but haven't been able to figure out how. Here is a toy example to demonstrate the problem:
my.plot <- function(){
a <- matrix(rnorm(100), 10, 10)
plot.new()
par(mfrow=c(2,2))
plot(1:10, runif(10))
plot(hclust(dist(a)))
barplot(apply(a, 2, mean))
image(a)
}
layout(matrix(1:4, 2, 2))
for(i in 1:4) my.plot()
# How to avoid reseting the outer layout when calling `my.plot`?
Original question by #alittleboy
I use the heatmap.2 function in the gplots package to generate heatmaps. Here is a sample code for a single heatmap:
library(gplots)
row.scaled.expr <- matrix(sample(1:10000),nrow=1000,ncol=10)
heatmap.2(row.scaled.expr, dendrogram ='row',
Colv=FALSE, col=greenred(800),
key=FALSE, keysize=1.0, symkey=FALSE, density.info='none',
trace='none', colsep=1:10,
sepcolor='white', sepwidth=0.05,
scale="none",cexRow=0.2,cexCol=2,
labCol = colnames(row.scaled.expr),
hclustfun=function(c){hclust(c, method='mcquitty')},
lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(0.25, 4, 0.25 ),
)
However, since I want to compare multiple heatmaps in a single plot, I use par(mfrow=c(2,2)) and then call heatmap.2 four times, i.e.
row.scaled.expr <- matrix(sample(1:10000),nrow=1000,ncol=10)
arr <- array(data=row.scaled.expr, dim=c(dim(row.scaled.expr),4))
par(mfrow=c(2,2))
for (i in 1:4)
heatmap.2(arr[ , ,i], dendrogram ='row',
Colv=FALSE, col=greenred(800),
key=FALSE, keysize=1.0, symkey=FALSE, density.info='none',
trace='none', colsep=1:10,
sepcolor='white', sepwidth=0.05,
scale="none",cexRow=0.2,cexCol=2,
labCol = colnames(arr[ , ,i]),
hclustfun=function(c){hclust(c, method='mcquitty')},
lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(0.25, 4, 0.25 ),
)
However, the result is NOT four heatmaps in a single plot, but four separate heatmaps. In other words, if I use pdf() to output the result, the file is four pages instead of one. Do I need to change any parameters somewhere? Thank you so much!

Answer 1

Okay. I suppose this question has been sitting unanswered for enough time that the long answer should be written up.
The answer to most difficult graphics issues is (as #backlin suggests) the raw use of the 'grid' package. Many prebuilt graphics packages override all current viewports and plot device settings, so if you want something done a very specific way, you have to build it yourself.
I recommend picking up Paul Murrell's book "R Graphics" and going over the chapter on the 'grid' package. It's a crazy useful book, and a copy sits on my desk all the time.
For your heatmap, I've written up a quick primer that will get you started quickly.
Functions to know
grid.newpage() This initializes the plotting device. Use it without parameters.
grid.rect() This draws a rectangle. Your heatmap is basically just a giant set of colored rectangles, so this will be bulk of your graphic. It works like so: grid.rect(x=x_Position, y=y_Position, width=width_Value, height=height_Value, gp=gpar(col=section_Color, fill=section_Color), just=c("left", "bottom"), default.units="native") The 'just' argument specifies which point of the rectangle will sit on your specified (x, y) coordinates.
grid.text() This draws text. It works like so: grid.text("Label Text", x_Value, y_Value, gp=gpar(col=color_Value, cex=font_Size), just=c("right","center"), rot=rot_Degrees, default.units="native")
grid.lines() This draws a line. It works like so: grid.lines(c(x_Start,x_End), c(y_Start, y_End), gp=gpar(col=color_Value), default.units="native")
dataViewport() This defines the attributes of a plotting window, which 'grid' refers to as a "viewport." Use it like so: pushViewport(dataViewport(xData=x_Data, yData=y_Data, xscale=c(x_Min, x_Max), yscale=c(y_Min, y_Max), x=x_Value, y=y_Value, width=width_Value, height=height_Value, just=c("left","center"))) There is some stuff to keep in mind here... see the more detailed explanation of viewports.
pushViewport() This is used to initialize a veiwport. You wrap this around a viewport definition to actually execute the viewport, like so: pushViewport(dataViewport([stuff in here]))
popViewport() This finalizes a viewport and moves you up one level in the hierarchy of viewports. See the more detailed explanation of viewports.
Viewports in a nutshell
Viewports are temporary drawing spaces that define where and how 'grid' objects will be drawn. Everything inside the viewport is drawn relative to the viewport. If the viewport is rotated, everything inside will be rotated. Viewports can be nested, can overlap, and are almost infinitely flexible, with one exception: they are always a rectangle.
Something that messes a lot of people up initially is the coordinate system. Every viewport, including the initial 'grid.newpage()' viewport, goes from 0 to 1 on both the x and y axes. The origin (0,0) is the far lower left corner, and the max (1,1) is the far upper right corner. This is the "npc" unit system, and everything that doesn't have a set of units specified will likely end up being drawn according to this system. This means two things for you:
Use the "npc" system when specifying viewport sizes and locations. Just assume that your viewports have to use the "npc" coordinates, and you'll save yourself a LOT of hassle. This means if I want to draw two plots next to each other, the definitions for the two viewports would look something like:
viewport(x=0, y=0, width=0.5, height=1, just=c("left","lower")) and
viewport(x=0.5, y=0, width=0.5, height=1, just=c("left","lower"))
If your viewport has a different coordinate system (for example a viewport for plotting a graph), then you will need to specify the 'default.units' argument for every 'grid' object you draw. For instance, if you tried to plot a point at (2,4) you would never see the point, because it would be far off-screen. Specifying default.units="native" would tell that point to use the viewport's own coordinate system and would draw the point correctly.
Viewports can be navigated and written to directly, but unless you're doing something very automated, it is easier to specify a viewport, draw inside it, and then "pop" (finalize) the viewport. This returns you to the parent viewport, and you can start on the next viewport. Popping each viewport is a clutter-free approach and will suit most purposes (and make it easier to debug!).
The 'dataViewport' function is all important when plotting a graph. This is a special type of viewport that handles all of the coordinates and scales for you, as long as you tell it what data you are using. This is the one I use for any plotting area. When I first started using the 'grid' package, I adjusted all of my values to fit the "npc" coordinate system, but that was a mistake! The 'dataViewport' function makes is all easy as long as you remember to use the "native" units for each drawing item.
Disclaimer
Data visualization is my forte, and I don't mind spending half a day scripting up a good visual. The 'grid' package allows me to create quite sophisticated visuals faster than anything else I found. I script up my visuals as functions, so I can load various data quickly. I couldn't be happier.
However, if you don't like to script things, 'grid' will be your enemy. Also, if you consider half a day to be too much time for a visual, then 'grid' won't help you too much. The (in)famous 'ggplot2' package is what most people settle on, and I heartily recommend it, even though I don't personally find it useful.
If someone wants help learning 'grid' graphics, I'm more than willing to help teach. It has completely revolutionized my ability to create fast, intelligent, and good-looking data visuals.

Answer 2

The gridGraphics package might help,
library(gridGraphics)
library(grid)
grab_grob <- function(){
grid.echo()
grid.grab()
}
arr <- replicate(4, matrix(sample(1:100),nrow=10,ncol=10), simplify = FALSE)
library(gplots)
gl <- lapply(1:4, function(i){
heatmap.2(arr[[i]], dendrogram ='row',
Colv=FALSE, col=greenred(800),
key=FALSE, keysize=1.0, symkey=FALSE, density.info='none',
trace='none', colsep=1:10,
sepcolor='white', sepwidth=0.05,
scale="none",cexRow=0.2,cexCol=2,
labCol = colnames(arr[[i]]),
hclustfun=function(c){hclust(c, method='mcquitty')},
lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(0.25, 4, 0.25 ),
)
grab_grob()
})
grid.newpage()
library(gridExtra)
grid.arrange(grobs=gl, ncol=2, clip=TRUE)

Answer 3

I struggled with a similar problem and came up with a solution that is very easy but requires imagemagick installed. The idea is to plot the heatmaps to separate files and then combine them with the montage command:
library(gplots)
row.scaled.expr <- matrix(sample(1:10000),nrow=1000,ncol=10)
arr <- array(data=row.scaled.expr, dim=c(dim(row.scaled.expr),4))
par(mfrow=c(2,2))
for (i in 1:4) {
ifile <- paste0(i,'_heatmap.pdf')
pdf(ifile)
heatmap.2(arr[ , ,i], dendrogram ='row',
Colv=FALSE, col=greenred(800),
key=FALSE, keysize=1.0, symkey=FALSE, density.info='none',
trace='none', colsep=1:10,
sepcolor='white', sepwidth=0.05,
scale="none",cexRow=0.2,cexCol=2,
labCol = colnames(arr[ , ,i]),
hclustfun=function(c){hclust(c, method='mcquitty')},
lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(0.25, 4, 0.25 ),
)
dev.off()
}
system('montage -geometry 100% -tile 2x2 ./*_heatmap.pdf outfile.pdf')

Answer 4

Just as Dinre said, the "grid" pacakge can handle all complex plots. For the original question by #alittleboy, I think the package "ComplexHeatmap" (which is also base on grid) from Bionconductor can be a nice solution (http://www.bioconductor.org/packages/release/bioc/vignettes/ComplexHeatmap/inst/doc/ComplexHeatmap.html)