I would like to place the value for each bar in barchart (lattice) at the top of each bar. However, I cannot find any option with which I can achieve this. I can only find options for the axis.
Create a custom panel function, e.g.
library("lattice")
p <- barchart((1:10)^2~1:10, horiz=FALSE, ylim=c(0,120),
panel=function(...) {
args <- list(...)
panel.text(args$x, args$y, args$y, pos=3, offset=1)
panel.barchart(...)
})
print(p)
I would have suggested using the new directlabels package, which can be used with both lattice and ggplot (and makes life very easy for these labeling problems), but unfortunately it doesn't work with barcharts.
Since I had to do this anyway, here's a close-enough-to-figure it out code sample along the lines of what #Alex Brown suggests (scores is a 2D array of some sort, which'll get turned into a grouped vector):
barchart(scores, horizontal=FALSE, stack=FALSE,
xlab='Sample', ylab='Mean Score (max of 9)',
auto.key=list(rectangles=TRUE, points=FALSE),
panel=function(x, y, box.ratio, groups, errbars, ...) {
# We need to specify groups because it's not actually the 4th
# parameter
panel.barchart(x, y, box.ratio, groups=groups, ...)
x <- as.numeric(x)
nvals <- nlevels(groups)
groups <- as.numeric(groups)
box.width <- box.ratio / (1 + box.ratio)
for(i in unique(x)) {
ok <- x == i
width <- box.width / nvals
locs <- i + width * (groups[ok] - (nvals + 1)/2)
panel.arrows(locs, y[ok] + 0.5, scores.ses[,i], ...)
}
} )
I haven't tested this, but the important bits (the parts determining the locs etc. within the panel function) do work. That's the hard part to figure out. In my case, I was actually using panel.arrows to make errorbars (the horror!). But scores.ses is meant to be an array of the same dimension as scores.
I'll try to clean this up later - but if someone else wants to, I'm happy for it!
If you are using the groups parameter you will find the labels in #rcs's code all land on top of each other. This can be fixed by extending panel.text to work like panel.barchart, which is easy enough if you know R.
I can't post the code of the fix here for licencing reasons, sorry.
Related
I have many ggplot objects where I wish to print some text (varies from plot to plot) in the same relative position on each plot, regardless of scale. What I have come up with to make it simple is to
define a rescale function (call it sx) to take the relative position I want and return that position on the plot's x axis.
sx <- function(pct, range=xr){
position <- range[1] + pct*(range[2]-range[1])
}
make the plot without the text (call it plt)
Use the ggplot_build function to find the x scale's range
xr <- ggplot_build(plt)$layout$panel_params[[1]]$x.range
Then add the text to the plot
plt <- plt + annotate("text", x=sx(0.95), ....)
This works well for me, though I'm sure there are other solutions folks have derived. I like the solution because I only need to add one step (step 3) to each plot. And it's a simple modification to the annotate command (x goes to sx(x)).
If someone has a suggestion for a better method I'd like to hear about it. There is one thing about my solution though that gives me a little trouble and I'm asking for a little help:
My problem is that I need a separate function for log scales, (call it lx). It's a bit of a pain because every time I want to change the scale I need to modify the annotate commands (change sx to lx) and occasionally there are many. This could easily be solved in the sx function if there was a way to tell what the type of scale was. For instance, is there a parameter in ggplot_build objects that describe the log/lin nature of the scale? That seems to be the best place to find it (that's where I'm pulling the scale's range) but I've looked and can not figure it out. If there was, then I could add a command to step 3 above to define the scale type, and add a tag to the sx function in step 1. That would save me some tedious work.
So, just to reiterate: does anyone know how to tell the scaling (type of scale: log or linear) of a ggplot object? such as using the ggplot_build command's object?
Suppose we have a list of pre-build plots:
linear <- ggplot(iris, aes(Sepal.Width, Sepal.Length, colour = Species)) +
geom_point()
log <- linear + scale_y_log10()
linear <- ggplot_build(linear)
log <- ggplot_build(log)
plotlist <- list(a = linear, b = log)
We can grab information about their position scales in the following way:
out <- lapply(names(plotlist), function(i) {
# Grab plot, panel parameters and scales
plot <- plotlist[[i]]
params <- plot$layout$panel_params[[1]]
scales <- plot$plot$scales$scales
# Only keep (continuous) position scales
keep <- vapply(scales, function(x) {
inherits(x, "ScaleContinuousPosition")
}, logical(1))
scales <- scales[keep]
# Grab relevant transformations
out <- lapply(scales, function(scale) {
data.frame(position = scale$aesthetics[1],
# And now for the actual question:
transformation = scale$trans$name,
plot = i)
})
out <- do.call(rbind, out)
# Grab relevant ranges
ranges <- params[paste0(out$position, ".range")]
out$min <- sapply(ranges, `[`, 1)
out$max <- sapply(ranges, `[`, 2)
out
})
out <- do.call(rbind, out)
Which will give us:
out
position transformation plot min max
1 x identity a 1.8800000 4.520000
2 y identity a 4.1200000 8.080000
3 y log-10 b 0.6202605 0.910835
4 x identity b 1.8800000 4.520000
Or if you prefer a straightforward answer:
log$plot$scales$scales[[1]]$trans$name
[1] "log-10"
I found a lot of SO question and answers addressing break and gaps in axis. But most of them are of low quality (in an SO meaning) because of no example code, no picture or to complex codes. This is why I asking.
I try to use library(plotrix). If there is a solution without it and/or another library it would be ok for me, too.
This is a normal R-barplot.
barplot(c(10,20,500))
To break the axis and add gap I tried this.
gap.barplot(c(10,20,500),gap=c(50,400), col=FALSE)
The result is not beautiful.
There is no space between the bars. space parameter from barplot() is not accepted by gap.barplot().
The bars have different widths.
The position of the tics are not in the middle of the bar.
Can I control that parameters with plotrix? I don't see something about it in the documentation.
Is there another library or solution for my problem?
There are so many different answers because of a lot of individual problems. For your problem you can try the following. But there is always a better solution out there. And IMO its always better to show your complete data instead of cropping it.
# Your data with names
library(plotrix)
d <- c(10,20,500)
names(d) <- letters[1:3]
# Specify a cutoff where the y.axis should be splitted.
co <- 200
# Now cut off this area in your data.
d[d > co] <- d[d > co] - co
# Create new axis label using the pretty() function
newy <- pretty(d)
newy[ newy > co] <- newy[ newy > co] + co
# remove values in your cutoff.
gr <- which(newy != co)
newy <- newy[ gr ]
# plot the data
barplot(d, axes=F)
# add the axis
axis(2, at = pretty(d)[gr], labels = newy)
axis.break(2, co, style = "gap")
As an alternative you can try to log your axis using log="y".
Is there a way to get the plot function to generate equal xlimand ylimautomatically?
I do not want to define a fix range beforehand, but I want the plot function to decide about the range itself. However, I expect it to pick the same range for x and y.
A possible solution is to define a wrapper to the plot function:
plot.Custom <- function(x, y, ...) {
.limits <- range(x, y)
plot(x, y, xlim = .limits, ylim = .limits, ...)
}
One way is to manipulate interactively and then choose the right one. A slider will appear once you run the following code.
library(manipulate)
manipulate(
plot(cars, xlim=c(x.min,x.max)),
x.min=slider(0,15),
x.max=slider(15,30))
I'm not aware of anyway to do this using plot(doesn't mean there isn't one). ggplot might be the way to go; it lends itself more to be being retroactively changed since it is designed around a layer system.
library(ggplot2)
#Creating our ggplot object
loop_plot <- ggplot(cars, aes(x = speed, y = dist)) +
geom_point()
#pulling out the 'auto' x & y axis limits
rangepull <- t(cbind(
ggplot_build(loop_plot)$panel$ranges[[1]]$x.range,
ggplot_build(loop_plot)$panel$ranges[[1]]$y.range))
#taking the max and min(so we don't cut out data points)
newrange <- list(cor.min = min(rangepull[,1]), cor.max = max(rangepull[,2]))
#changing our plot size to be nice and symmetric
loop_plot <- loop_plot +
xlim(newrange$cor.min, newrange$cor.max) +
ylim(newrange$cor.min, newrange$cor.max)
Note that the loop_plot object is of ggplot class, and wont actually print until its called.
I used the cars dataset in the code above to show whats going on, but just sub in your data set[s] and then do whatever postmortem your end goal is.
You'll also be able to add in titles and the like based off of the dataset name et cetera which will likely end up producing a clearer visualization out of your loop.
Hopefully this works for your needs.
I'm working with some custom functions and I need to draw contours for them based on multiple values for the parameters.
Here is an example function:
I need to draw such a contour plot:
Any idea?
Thanks.
First you construct a function, fourvar that takes those four parameters as arguments. In this case you could have done it with 3 variables one of which was lambda_2 over lambda_1. Alpha1 is fixed at 2 so alpha_1/alpha_2 will vary over 0-10.
fourvar <- function(a1,a2,l1,l2){
a1* integrate( function(x) {(1-x)^(a1-1)*(1-x^(l2/l1) )^a2} , 0 , 1)$value }
The trick is to realize that the integrate function returns a list and you only want the 'value' part of that list so it can be Vectorize()-ed.
Second you construct a matrix using that function:
mat <- outer( seq(.01, 10, length=100),
seq(.01, 10, length=100),
Vectorize( function(x,y) fourvar(a1=2, x/2, l1=2, l2=y/2) ) )
Then the task of creating the plot with labels in those positions can only be done easily with lattice::contourplot. After doing a reasonable amount of searching it does appear that the solution to geom_contour labeling is still a work in progress in ggplot2. The only labeling strategy I found is in an external package. However, the 'directlabels' package's function directlabel does not seem to have sufficient control to spread the labels out correctly in this case. In other examples that I have seen, it does spread the labels around the plot area. I suppose I could look at the code, but since it depends on the 'proto'-package, it will probably be weirdly encapsulated so I haven't looked.
require(reshape2)
mmat <- melt(mat)
str(mmat) # to see the names in the melted matrix
g <- ggplot(mmat, aes(x=Var1, y=Var2, z=value) )
g <- g+stat_contour(aes(col = ..level..), breaks=seq(.1, .9, .1) )
g <- g + scale_colour_continuous(low = "#000000", high = "#000000") # make black
install.packages("directlabels", repos="http://r-forge.r-project.org", type="source")
require(directlabels)
direct.label(g)
Note that these are the index positions from the matrix rather than the ratios of parameters, but that should be pretty easy to fix.
This, on the other hand, is how easilyy one can construct it in lattice (and I think it looks "cleaner":
require(lattice)
contourplot(mat, at=seq(.1,.9,.1))
As I think the question is still relevant, there have been some developments in the contour plot labeling in the metR package. Adding to the previous example will give you nice contour labeling also with ggplot2
require(metR)
g + geom_text_contour(rotate = TRUE, nudge_x = 3, nudge_y = 5)
I just came by the following plot:
And wondered how can it be done in R? (or other softwares)
Update 10.03.11: Thank you everyone who participated in answering this question - you gave wonderful solutions! I've compiled all the solution presented here (as well as some others I've came by online) in a post on my blog.
Make.Funny.Plot does more or less what I think it should do. To be adapted according to your own needs, and might be optimized a bit, but this should be a nice start.
Make.Funny.Plot <- function(x){
unique.vals <- length(unique(x))
N <- length(x)
N.val <- min(N/20,unique.vals)
if(unique.vals>N.val){
x <- ave(x,cut(x,N.val),FUN=min)
x <- signif(x,4)
}
# construct the outline of the plot
outline <- as.vector(table(x))
outline <- outline/max(outline)
# determine some correction to make the V shape,
# based on the range
y.corr <- diff(range(x))*0.05
# Get the unique values
yval <- sort(unique(x))
plot(c(-1,1),c(min(yval),max(yval)),
type="n",xaxt="n",xlab="")
for(i in 1:length(yval)){
n <- sum(x==yval[i])
x.plot <- seq(-outline[i],outline[i],length=n)
y.plot <- yval[i]+abs(x.plot)*y.corr
points(x.plot,y.plot,pch=19,cex=0.5)
}
}
N <- 500
x <- rpois(N,4)+abs(rnorm(N))
Make.Funny.Plot(x)
EDIT : corrected so it always works.
I recently came upon the beeswarm package, that bears some similarity.
The bee swarm plot is a
one-dimensional scatter plot like
"stripchart", but with closely-packed,
non-overlapping points.
Here's an example:
library(beeswarm)
beeswarm(time_survival ~ event_survival, data = breast,
method = 'smile',
pch = 16, pwcol = as.numeric(ER),
xlab = '', ylab = 'Follow-up time (months)',
labels = c('Censored', 'Metastasis'))
legend('topright', legend = levels(breast$ER),
title = 'ER', pch = 16, col = 1:2)
(source: eklund at www.cbs.dtu.dk)
I have come up with the code similar to Joris, still I think this is more than a stem plot; here I mean that they y value in each series is a absolute value of a distance to the in-bin mean, and x value is more about whether the value is lower or higher than mean.
Example code (sometimes throws warnings but works):
px<-function(x,N=40,...){
x<-sort(x);
#Cutting in bins
cut(x,N)->p;
#Calculate the means over bins
sapply(levels(p),function(i) mean(x[p==i]))->meansl;
means<-meansl[p];
#Calculate the mins over bins
sapply(levels(p),function(i) min(x[p==i]))->minl;
mins<-minl[p];
#Each dot is one value.
#X is an order of a value inside bin, moved so that the values lower than bin mean go below 0
X<-rep(0,length(x));
for(e in levels(p)) X[p==e]<-(1:sum(p==e))-1-sum((x-means)[p==e]<0);
#Y is a bin minum + absolute value of a difference between value and its bin mean
plot(X,mins+abs(x-means),pch=19,cex=0.5,...);
}
Try the vioplot package:
library(vioplot)
vioplot(rnorm(100))
(with awful default color ;-)
There is also wvioplot() in the wvioplot package, for weighted violin plot, and beanplot, which combines violin and rug plots. They are also available through the lattice package, see ?panel.violin.
Since this hasn't been mentioned yet, there is also ggbeeswarm as a relatively new R package based on ggplot2.
Which adds another geom to ggplot to be used instead of geom_jitter or the like.
In particular geom_quasirandom (see second example below) produces really good results and I have in fact adapted it as default plot.
Noteworthy is also the package vipor (VIolin POints in R) which produces plots using the standard R graphics and is in fact also used by ggbeeswarm behind the scenes.
set.seed(12345)
install.packages('ggbeeswarm')
library(ggplot2)
library(ggbeeswarm)
ggplot(iris,aes(Species, Sepal.Length)) + geom_beeswarm()
ggplot(iris,aes(Species, Sepal.Length)) + geom_quasirandom()
#compare to jitter
ggplot(iris,aes(Species, Sepal.Length)) + geom_jitter()