I am trying to create a data table whose cells are different colors based on the value in the cell. I can achieve this with the function addtable2plot from the plotrix package. The addtable2plot function lays a table on an already existing plot. The problem with that solution is that I don't want a plot, just the table.
I've also looked at the heatmap functions. The problem there is that some of the values in my table are character, and the heatmap functions, from what I can tell, only accept numeric matrices. Also, I want my column names to be at the top of the table, not the bottom, and that doesn't seem to be an option.
Here's the example code for addtable2plot. If I could get just the table, filling the whole screen, that would be great.
library(plotrix)
testdf<-data.frame(Before=c(10,7,5,9),During=c(8,6,2,5),After=c(5,3,4,3))
rownames(testdf)<-c("Red","Green","Blue","Lightblue")
barp(testdf,main="Test addtable2plot",ylab="Value",
names.arg=colnames(testdf),col=2:5)
# show most of the options including the christmas tree colors
abg<-matrix(c(2,3,5,6,7,8),nrow=4,ncol=3)
addtable2plot(2,8,testdf,bty="o",display.rownames=TRUE,hlines=TRUE,
vlines=TRUE,title="The table",bg=abg)
Any help would be greatly appreciated.
A heatmap alternative:
library(gplots)
# need data as matrix
mm <- as.matrix(testdf, ncol = 3)
heatmap.2(x = mm, Rowv = FALSE, Colv = FALSE, dendrogram = "none",
cellnote = mm, notecol = "black", notecex = 2,
trace = "none", key = FALSE, margins = c(7, 11))
In heatmap.2 the side of the plot the axis is to be drawn on is hard-coded. But if you type "heatmap.2" at the console and copy the output to an editor, you can search for axis(1, where the 1 is the side argument (two hits). You can then change from a 1 (axis below plot) to a 3 (axis above the plot). Assign the updated function to a new name, e.g. heatmap.3, and run it as above.
An addtable2plot alternative
library(plotrix)
# while plotrix is loaded anyway:
# set colors with color.scale
# need data as matrix*
mm <- as.matrix(testdf, ncol = 3)
cols <- color.scale(mm, extremes = c("red", "yellow"))
par(mar = c(0.5, 1, 2, 0.5))
# create empty plot
plot(1:10, axes = FALSE, xlab = "", ylab = "", type = "n")
# add table
addtable2plot(x = 1, y = 1, table = testdf,
bty = "o", display.rownames = TRUE,
hlines = TRUE, vlines = TRUE,
bg = cols,
xjust = 2, yjust = 1, cex = 3)
# *According to `?color.scale`, `x` can be a data frame.
# However, when I tried with `testdf`, I got "Error in `[.data.frame`(x, segindex) : undefined columns selected".
A color2D.matplot alternative
library(plotrix)
par(mar = c(0.5, 8, 3.5, 0.5))
color2D.matplot(testdf,
show.values = TRUE,
axes = FALSE,
xlab = "",
ylab = "",
vcex = 2,
vcol = "black",
extremes = c("red", "yellow"))
axis(3, at = seq_len(ncol(testdf)) - 0.5,
labels = names(testdf), tick = FALSE, cex.axis = 2)
axis(2, at = seq_len(nrow(testdf)) -0.5,
labels = rev(rownames(testdf)), tick = FALSE, las = 1, cex.axis = 2)
After this little exercise, I tend to agree with #Drew Steen that LaTeX alternatives may be investigated as well. For example, check here and here.
You can hack something with grid and gtable,
palette(c(RColorBrewer::brewer.pal(8, "Pastel1"),
RColorBrewer::brewer.pal(8, "Pastel2")))
library(gtable)
gtable_add_grobs <- gtable_add_grob # alias
d <- head(iris, 3)
nc <- ncol(d)
nr <- nrow(d)
extended_matrix <- cbind(c("", rownames(d)), rbind(colnames(d), as.matrix(d)))
## text for each cell
all_grobs <- matrix(lapply(extended_matrix, textGrob), ncol=ncol(d) + 1)
## define the fill background of cells
fill <- lapply(seq_len(nc*nr), function(ii)
rectGrob(gp=gpar(fill=ii)))
## some calculations of cell sizes
row_heights <- function(m){
do.call(unit.c, apply(m, 1, function(l)
max(do.call(unit.c, lapply(l, grobHeight)))))
}
col_widths <- function(m){
do.call(unit.c, apply(m, 2, function(l)
max(do.call(unit.c, lapply(l, grobWidth)))))
}
## place labels in a gtable
g <- gtable_matrix("table", grobs=all_grobs,
widths=col_widths(all_grobs) + unit(4,"mm"),
heights=row_heights(all_grobs) + unit(4,"mm"))
## add the background
g <- gtable_add_grobs(g, fill, t=rep(seq(2, nr+1), each=nc),
l=rep(seq(2, nc+1), nr), z=0,name="fill")
## draw
grid.newpage()
grid.draw(g)
Sort of a hacky solution based on ggplot2. I don't totally understand how you actually want to map your colors, since in your example the colors in the table are not mapped to the rownames of testdf, but here I've mapped the colors to the value (converted to a factor).
testdf$color <- rownames(testdf)
dfm <- melt(testdf, id.vars="color")
p <- ggplot(dfm, aes(x=variable, y=color, label=value, fill=as.factor(value))) +
geom_text(colour="black") +
geom_tile(alpha=0.2)
p
You can change what variable the values are mapped to using fill=, and you can change the mapping using scale_fill_manual(values=[a vector of values].
That said, I'd be curious to see a solution that produces an actual table, rather than a plot masquerading as a table. Possibly using Sweave and LaTeX tables?
Related
I have drawn heatmap in biclust package using the following code, but I couldn't find any option for adding row and column names.
library(biclust)
set.seed(1234)
data(BicatYeast)
resplaid <- biclust(BicatYeast, BCBimax(), verbose = FALSE)
heatmapBC(x = BicatYeast, bicResult = resplaid)
How can I draw them?
Here a solution. Looking at the heatmapBC function you see that axes as set as FALSE by default!
You will be able to put your labels both in the rows and columns of your heatmap by using the axis command.
I've used a subsetted version of BicatYeast data for making plots clearer
library(biclust)
set.seed(1234)
data(BicatYeast)
d <- as.matrix(BicatYeast)[1:30, 1:20]; d
resplaid <- biclust(d, BCBimax())
par(mar=c(10, 6, 2, 2) + 0.1)
heatmapBC(x = d, bicResult = resplaid, axes = F, xlab = "", ylab = "")
axis(1, at=1:dim(d)[2], labels = colnames(d), las=2)
axis(2, at=1:dim(d)[1], labels = rownames(d), las=2)
I am trying to create a data table whose cells are different colors based on the value in the cell. I can achieve this with the function addtable2plot from the plotrix package. The addtable2plot function lays a table on an already existing plot. The problem with that solution is that I don't want a plot, just the table.
I've also looked at the heatmap functions. The problem there is that some of the values in my table are character, and the heatmap functions, from what I can tell, only accept numeric matrices. Also, I want my column names to be at the top of the table, not the bottom, and that doesn't seem to be an option.
Here's the example code for addtable2plot. If I could get just the table, filling the whole screen, that would be great.
library(plotrix)
testdf<-data.frame(Before=c(10,7,5,9),During=c(8,6,2,5),After=c(5,3,4,3))
rownames(testdf)<-c("Red","Green","Blue","Lightblue")
barp(testdf,main="Test addtable2plot",ylab="Value",
names.arg=colnames(testdf),col=2:5)
# show most of the options including the christmas tree colors
abg<-matrix(c(2,3,5,6,7,8),nrow=4,ncol=3)
addtable2plot(2,8,testdf,bty="o",display.rownames=TRUE,hlines=TRUE,
vlines=TRUE,title="The table",bg=abg)
Any help would be greatly appreciated.
A heatmap alternative:
library(gplots)
# need data as matrix
mm <- as.matrix(testdf, ncol = 3)
heatmap.2(x = mm, Rowv = FALSE, Colv = FALSE, dendrogram = "none",
cellnote = mm, notecol = "black", notecex = 2,
trace = "none", key = FALSE, margins = c(7, 11))
In heatmap.2 the side of the plot the axis is to be drawn on is hard-coded. But if you type "heatmap.2" at the console and copy the output to an editor, you can search for axis(1, where the 1 is the side argument (two hits). You can then change from a 1 (axis below plot) to a 3 (axis above the plot). Assign the updated function to a new name, e.g. heatmap.3, and run it as above.
An addtable2plot alternative
library(plotrix)
# while plotrix is loaded anyway:
# set colors with color.scale
# need data as matrix*
mm <- as.matrix(testdf, ncol = 3)
cols <- color.scale(mm, extremes = c("red", "yellow"))
par(mar = c(0.5, 1, 2, 0.5))
# create empty plot
plot(1:10, axes = FALSE, xlab = "", ylab = "", type = "n")
# add table
addtable2plot(x = 1, y = 1, table = testdf,
bty = "o", display.rownames = TRUE,
hlines = TRUE, vlines = TRUE,
bg = cols,
xjust = 2, yjust = 1, cex = 3)
# *According to `?color.scale`, `x` can be a data frame.
# However, when I tried with `testdf`, I got "Error in `[.data.frame`(x, segindex) : undefined columns selected".
A color2D.matplot alternative
library(plotrix)
par(mar = c(0.5, 8, 3.5, 0.5))
color2D.matplot(testdf,
show.values = TRUE,
axes = FALSE,
xlab = "",
ylab = "",
vcex = 2,
vcol = "black",
extremes = c("red", "yellow"))
axis(3, at = seq_len(ncol(testdf)) - 0.5,
labels = names(testdf), tick = FALSE, cex.axis = 2)
axis(2, at = seq_len(nrow(testdf)) -0.5,
labels = rev(rownames(testdf)), tick = FALSE, las = 1, cex.axis = 2)
After this little exercise, I tend to agree with #Drew Steen that LaTeX alternatives may be investigated as well. For example, check here and here.
You can hack something with grid and gtable,
palette(c(RColorBrewer::brewer.pal(8, "Pastel1"),
RColorBrewer::brewer.pal(8, "Pastel2")))
library(gtable)
gtable_add_grobs <- gtable_add_grob # alias
d <- head(iris, 3)
nc <- ncol(d)
nr <- nrow(d)
extended_matrix <- cbind(c("", rownames(d)), rbind(colnames(d), as.matrix(d)))
## text for each cell
all_grobs <- matrix(lapply(extended_matrix, textGrob), ncol=ncol(d) + 1)
## define the fill background of cells
fill <- lapply(seq_len(nc*nr), function(ii)
rectGrob(gp=gpar(fill=ii)))
## some calculations of cell sizes
row_heights <- function(m){
do.call(unit.c, apply(m, 1, function(l)
max(do.call(unit.c, lapply(l, grobHeight)))))
}
col_widths <- function(m){
do.call(unit.c, apply(m, 2, function(l)
max(do.call(unit.c, lapply(l, grobWidth)))))
}
## place labels in a gtable
g <- gtable_matrix("table", grobs=all_grobs,
widths=col_widths(all_grobs) + unit(4,"mm"),
heights=row_heights(all_grobs) + unit(4,"mm"))
## add the background
g <- gtable_add_grobs(g, fill, t=rep(seq(2, nr+1), each=nc),
l=rep(seq(2, nc+1), nr), z=0,name="fill")
## draw
grid.newpage()
grid.draw(g)
Sort of a hacky solution based on ggplot2. I don't totally understand how you actually want to map your colors, since in your example the colors in the table are not mapped to the rownames of testdf, but here I've mapped the colors to the value (converted to a factor).
testdf$color <- rownames(testdf)
dfm <- melt(testdf, id.vars="color")
p <- ggplot(dfm, aes(x=variable, y=color, label=value, fill=as.factor(value))) +
geom_text(colour="black") +
geom_tile(alpha=0.2)
p
You can change what variable the values are mapped to using fill=, and you can change the mapping using scale_fill_manual(values=[a vector of values].
That said, I'd be curious to see a solution that produces an actual table, rather than a plot masquerading as a table. Possibly using Sweave and LaTeX tables?
I found many resources on how to draw Venn diagrams in R. Stack Overflow has a lot of them. However, I still can't draw my diagrams the way I want. Take the following code as an example:
library("VennDiagram")
A <- 1:4
B <- 3:6
d <- list(A, B)
vp <- venn.diagram(d, fill = c("white", "white"), alpha = 1, filename = NULL,
category.names=c("A", "B"))
grid.draw(vp)
I want the intersection between the sets to be red. However, if I change any of the white colors to red, I get the following:
vp_red <- venn.diagram(d, fill = c("red", "white"), alpha = 1, filename = NULL,
category.names=c("A", "B"))
grid.draw(vp_red)
That's not quite what I want. I want only the intersection to be red. If I change the alpha, this is what I get:
vp_alpha <- venn.diagram(d, fill = c("red", "white"), alpha = 0.5, filename = NULL,
category.names=c("A", "B"))
grid.draw(vp_alpha)
Now I have pink in my intersection. This is not what I want as well. What I want is something like this image from Wikipedia:
How can I do this? Maybe VennDiagram package can't do it and I need some other package, but I've been testing different ways to do it, and I'm not being able to find a solution.
I will show two different possibilities. In the first example, polyclip::polyclip is used to get the intersection. In the second example, circles are converted to sp::SpatialPolygons and we get the intersection using rgeos::gIntersection. Then we re-plot the circles and fill the intersecting area.
The resulting object when using venn.diagram is
"of class gList containing the grid objects that make up the diagram"
Thus, in both cases we can grab relevant data from "vp". First, check the structure and list the grobs of the object:
str(vp)
grid.ls()
# GRID.polygon.234
# GRID.polygon.235
# GRID.polygon.236 <~~ these are the empty circles
# GRID.polygon.237 <~~ $ col : chr "black"; $ fill: chr "transparent"
# GRID.text.238 <~~ labels
# GRID.text.239
# GRID.text.240
# GRID.text.241
# GRID.text.242
1. polyclip
Grab x- and y-values, and put them in the format required for polyclip:
A <- list(list(x = as.vector(vp[[3]][[1]]), y = as.vector(vp[[3]][[2]])))
B <- list(list(x = as.vector(vp[[4]][[1]]), y = as.vector(vp[[4]][[2]])))
Find intersection:
library(polyclip)
AintB <- polyclip(A, B)
Grab labels:
ix <- sapply(vp, function(x) grepl("text", x$name, fixed = TRUE))
labs <- do.call(rbind.data.frame, lapply(vp[ix], `[`, c("x", "y", "label")))
Plot it!
plot(c(0, 1), c(0, 1), type = "n", axes = FALSE, xlab = "", ylab = "")
polygon(A[[1]])
polygon(B[[1]])
polygon(AintB[[1]], col = "red")
text(x = labs$x, y = labs$y, labels = labs$label)
2. SpatialPolygons and gIntersection
Grab the coordinates of the circles:
# grab x- and y-values from first circle
x1 <- vp[[3]][["x"]]
y1 <- vp[[3]][["y"]]
# grab x- and y-values from second circle
x2 <- vp[[4]][["x"]]
y2 <- vp[[4]][["y"]]
Convert points to SpatialPolygons and find their intersection:
library(sp)
library(rgeos)
p1 <- SpatialPolygons(list(Polygons(list(Polygon(cbind(x1, y1))), ID = 1)))
p2 <- SpatialPolygons(list(Polygons(list(Polygon(cbind(x2, y2))), ID = 2)))
ip <- gIntersection(p1, p2)
Plot it!
# plot circles
plot(p1, xlim = range(c(x1, x2)), ylim = range(c(y1, y2)))
plot(p2, add = TRUE)
# plot intersection
plot(ip, add = TRUE, col = "red")
# add labels (see above)
text(x = labs$x, y = labs$y, labels = labs$label)
I'm quite sure you could work directly on the grobs using clipping functions in grid or gridSVG package.
It's very easy in eulerr R package
library(eulerr)
plot(euler(c("A"=5,"B"=4,"A&B"=2)),quantities = TRUE,fills=c("white","white","red"))
euler set colours
is there an comfortable way to set the legend/key label inside the rectanlge in latice plots: (although overplot/overlayer lines, points, rectangles in keys would be nice)
library(lattice)
barchart(yield ~ variety | site, data = barley,
groups = year, layout = c(1,6), stack = TRUE,
auto.key = list(space = "right"),
ylab = "Barley Yield (bushels/acre)",
scales = list(x = list(rot = 45)))
Well, there's no really automatic way, but it can be done. Here are a couple of options I came up with. Both construct a legend 'grob' and pass it in via the barchart()'s legend= argument. The first solution uses the nifty gtable package to construct a table grob. The second is a bit more programmatic, and uses grid's own frameGrob() and packGrob() functions to construct a similar legend.
Option 1: Construct legend using gtable()
library(lattice)
library(grid)
library(gtable)
## Extract group labels and their colors for use in gtable
ll <- levels(barley[["year"]])
cc <- trellis.par.get("superpose.polygon")[["col"]][seq_along(ll)]
## Prepare a grob for passing in to legend.
## Set up a two cell gtable , and 'paint' then annotate both cells
## (Note: this could be further "vectorized", as, e.g., at
## http://stackoverflow.com/a/18033613/980833)
gt <- gtable(widths = unit(1.5,"cm"), heights = unit(rep(.7,2), "cm"))
gt <- gtable_add_grob(gt, rectGrob(gp=gpar(fill=cc[1])), 1, 1, name=1)
gt <- gtable_add_grob(gt, textGrob(ll[1]), 1, 1, name=2)
gt <- gtable_add_grob(gt, rectGrob(gp=gpar(fill=cc[2])), 2, 1, name=1)
gt <- gtable_add_grob(gt, textGrob(ll[2]), 2, 1, name=2)
## Plot barchart with legend
barchart(yield ~ variety | site, data = barley,
groups = year, layout = c(1,6), stack = TRUE,
legend = list(right=list(fun=gt)),
ylab = "Barley Yield (bushels/acre)",
scales = list(x = list(rot = 45)))
Option 2: Construct legend by packing a frameGrob()
library(lattice)
library(grid)
## A function for making grobs with text on a colored background
labeledRect <- function(text, color) {
rg <- rectGrob(gp=gpar(fill=color))
tg <- textGrob(text)
gTree(children=gList(rg, tg), cl="boxedTextGrob")
}
## A function for constructing a legend consisting of several
## labeled rectangles
legendGrob <- function(labels, colors) {
gf <- frameGrob()
border <- unit(c(0,0.5,0,0.5), "cm")
for (i in seq_along(labels)) {
gf <- packGrob(gf, labeledRect(labels[i], colors[i]),
width = 1.1*stringWidth(labels[i]),
height = 1.5*stringHeight(labels[i]),
col = 1, row = i, border = border)
}
gf
}
## Use legendGrob() to prepare the legend
ll <- levels(barley[["year"]])
cc <- trellis.par.get("superpose.polygon")[["col"]][seq_along(ll)]
gf <- legendGrob(labels=ll, colors=cc)
## Put it all together
barchart(yield ~ variety | site, data = barley,
groups = year, layout = c(1,6), stack = TRUE,
legend = list(right=list(fun=gf)),
ylab = "Barley Yield (bushels/acre)",
scales = list(x = list(rot = 45)))
In an R session, given:
foo <- matrix(1:25, 5, 5)
image(foo)
What is the best way for me to add a legend or key for the color gradient used on this image() plot of foo?
This is for a large dataset of global precipitation values so hacking something together with legend() doesn't seem to be a viable option. filled.contour() has a number of side effects that I'm not happy with. I'm using image() because it is the simplest plotting method to layer or add onto.
For the moment, my biggest issue with filled.contour() is that I'm trying to add contours from a different dataset via contour() to the plot. With filled.contour(), the contours would need to be adjusted to account for the default gradient key on the side of the plot, though I suppose this would also be the case if I added a key to the image() plot.
Thank you kindly for your time.
For future reference:
When using filled.contour(), you can call contour() and/or map() in addition to any other function you like by assigning it to filled.contour()'s plot.axes argument. It might be helpful to remember that you can stack multiple lines of code with braces.
Here's some code adapted from the zernike package. You can use it all, or just pull out the piece which creates a gradient key.
# written 13 April 2011 by CGWitthoft. Watch for updates from the
# owner of the zernike package.
pupilplot <- function (wf, cp = NULL, col = topo.colors(256), addContours = FALSE,
cscale = TRUE, ...)
{
if (cscale) {
mar.orig <- (par.orig <- par(c("mar", "las", "mfrow")))$mar
on.exit(par(par.orig))
w <- (3 + mar.orig[2]) * par("csi") * 2.54
layout(matrix(c(2, 1), ncol = 2), widths = c(1, lcm(w)))
par(las = 1)
mar <- mar.orig
mar[4] <- mar[2]
mar[2] <- 1
par(mar = mar)
thelist <- list(...)
findz <- which(names(thelist) == 'zlim')
if (length(findz) > 0 ) {
zlim <- thelist$zlim
}else{
zlim <- range(wf, finite = TRUE) #the original line
}
# end of my hack
levels <- seq(zlim[1], zlim[2], length = length(col))
plot.new()
plot.window(xlim = c(0, 1), ylim = range(levels), xaxs = "i", yaxs = "i")
rect(0, levels[-length(levels)], 1, levels[-1], col = col, density = NA)
axis(4)
box()
mar <- mar.orig
mar[4] <- 0
par(mar = mar)
}
if (is.null(cp)) {
axis1 <- 1:nrow(wf)
axis2 <- 1:ncol(wf)
}
else {
axis1 <- ((1:nrow(wf)) - cp$xc)/cp$rx
axis2 <- ((1:ncol(wf)) - cp$yc)/cp$ry
}
image(axis1, axis2, wf, col = col, asp = 1, xlab = "X", ylab = "Y", ...)
if (addContours)
contour(axis1, axis2, wf, add = TRUE)
}