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Hopefully a straightforward question but I made a simple figure in R using filled.contour(). It looks fine, and what it should like given the data. However, I want to add a reference line along a contour for 0 (level = 0), and the plotted line doesn't match the colors on the filled.contour figure. The line is close, but not matching with the figure (and eventually crossing over another contour from the filled.contour plot). Any ideas why this is happening?
aa <- c(0.05843150, 0.11300040, 0.15280030, 0.183524400, 0.20772430, 0.228121000)
bb <- c(0.01561055, 0.06520635, 0.10196237, 0.130127650, 0.15314544, 0.172292410)
cc <- c(-0.02166599, 0.02306650, 0.05619421, 0.082193680, 0.10334837, 0.121156780)
dd <- c(-0.05356592, -0.01432910, 0.01546647, 0.039156660, 0.05858709, 0.074953650)
ee <- c(-0.08071987, -0.04654243, -0.02011676, 0.000977798, 0.01855881, 0.033651089)
ff <- c(-0.10343798, -0.07416114, -0.05111547, -0.032481132, -0.01683215, -0.003636035)
gg <- c(-0.12237798, -0.09753544, -0.07785126, -0.061607548, -0.04788856, -0.036169540)
hh <-rbind(aa,bb,cc,dd,ee,ff,gg)
z <- as.matrix(hh)
y <- seq(0.5,1.75,0.25)
x <- seq(1,2.5,0.25)
filled.contour(x,y,z,
key.title = title(main=expression("log"(lambda))),
color.palette = topo.colors) #This works
contour(x,y,z, level=0,add=T,lwd=3) #This line doesn't match plot
This is completely answered in the ?filled.contour help page. In the Notes section it states
The output produced by filled.contour is actually a combination of two plots; one is the filled contour and one is the legend. Two separate coordinate systems are set up for these two plots, but they are only used internally – once the function has returned these coordinate systems are lost. If you want to annotate the main contour plot, for example to add points, you can specify graphics commands in the plot.axes argument. See the examples.
And the examples given in that help page show how to annotate on top of the main plot. In this particular case, the correct way would be
filled.contour(x,y,z,
key.title = title(main=expression("log"(lambda))),
color.palette = topo.colors,
plot.axes = {
axis(1)
axis(2)
contour(x,y,z, level=0,add=T,lwd=3)
}
)
which produces
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 have found that the beanplot is the best way to represent my data. I want to look at multiple beanplots together to visualize my data. Each of my plots contains 3 variables, so each one looks something like what would be generated by this code:
library(beanplot)
a <- rnorm(100)
b <- rnorm(100)
c <- rnorm(100)
beanplot(a, b ,c ,ylim = c(-4, 4), main = "Beanplot",
col = c("#CAB2D6", "#33A02C", "#B2DF8A"), border = "#CAB2D6")
(Would have just included an image but my reputation score is not high enough, sorry)
I have 421 of these that I want to put into one long PDF (EDIT: One plot per page is fine, this was just poor wording on my part). The approach I have taken was to first generate the beanplots in a for loop and store them in a list at each iteration. Then I will use the multiplot function (from the R Cookbook page on multiplot) to display all of my plots on one long column so I can begin my analysis.
The problem is that the beanplot function does not appear to be set up to assign plot objects as a variable. Example:
library(beanplot)
a <- rnorm(100)
b <- rnorm(100)
plot1 <- beanplot(a, b, ylim = c(-5,5), main = "Beanplot",
col = c("#CAB2D6", "#33A02C", "#B2DF8A"), border = "#CAB2D6")
plot1
If you then type plot1 into the R console, you will get back two of the plot parameters but not the plot itself. This means that when I store the plots in the list, I am unable to graph them with multiplot. It will simply return the plot parameters and a blank plot.
This behavior does not seem to be the case with qplot for example which will return a plot when you recall the stored plot. Example:
library(ggplot2)
a <- rnorm(100)
b <- rnorm(100)
plot2 <- qplot(a,b)
plot2
There is no equivalent to the beanplot that I know of in ggplot. Is there some sort of workaround I can use for this issue?
Thank you.
You can simply open a PDF device with pdf() and keep the default parameter onefile=TRUE. Then call all your beanplot()s, one after the other. They will all be in one PDF document, each one on a separate page. See here.
I am trying to figure out how to use grconvertX/grconvertX in ggplot. My ultimate goal is to to add annotation to a ggplot2 figure (and possibly lattice) with grid.text and grid.lines by going from user coordinates to device coordinates. I know it can be done with grobs but I am wondering if there is an easier way.
The following code allows me to pass values from user coordinates to ndc coordinates and use those values to annotate the plot with grid.text.
graphics.off() # close graphics windows
library(grid)
library(gridBase)
test= data.frame(
x = c(1,2,3),
y = c(12,10,3),
n = c(75,76,73)
)
par(mar = c(13,5,2,3))
plot(test$y ~ test$x,type="b", ann=F)
for (i in 1:nrow(test))
{
X=grconvertX(i , from="user", to="ndc")
grid.text(x=X, y =0.2, label=paste("GRID.text at\nuser.x=", i, "\n", "ndc.x=", (signif( X, 5)) ) )
grid.lines(x=c(X, X), y = c(0.28, 0.33) )
}
#add some code to save as PDF ...
The code is based on the solution from one of my previous posts: Mixing X and Y coordinate systems . You can see how x coordinates from the original plot were converted to ndc. The advantage of this approach is that I can use device coordinates for Y.
I assumed I could easily do the same in ggplot2 (and possibly in lattice).
library(ggplot2)
graphics.off() # close graphics windows
qplot(x=x, y=y, data=test)+geom_line()+ opts(plot.margin = unit(c(1,3,8,1), "lines"))
for (i in 1:nrow(test))
{
X=grconvertX(i , from="user", to="ndc")
grid.text(x=X, y =0.2, label=paste("GRID.text at\nuser.x=", i, "\n", "ndc.x=", (signif( X, 5)) ) )
grid.lines(x=c(X, X), y = c(0.28, 0.33) )
}
#add some code to save as PDF...
However, it does not work correctly. The coordinates seem to be a bit off. The vertical lines and text don't correspond to the tick labels on the plot. Can anybody tell me how to fix it? Thanks a lot in advance.
The grconvertX and grconvertY functions work with base graphics while ggplot2 uses grid graphics. In general the 2 different graphics engines don't play nicely together (though you have demonstrated using gridBase to help). Your first example works because you started with a base graphic so the user coordinate system exists with the base graph and grconvertX converts from it. In the second case the user coordinate system was never set in the base graphics, so it looks like it might use the default coordinates of 0,1 which are similar but not identical to the top viewport coordinates so you get something similar but not exactly correct (I am actually surprised that you did not get an error or warning
Generally for grid graphics the equivalent for converting between coordinates is to just create a new viewport with the coordinate system of interest (or push/pop to an existing viewport with the correct coordinate system), then add your annotations in that viewport.
Here is an example that creates your plot, then moves down to the viewport containing the main plot, creates a new viewport with the same dimensions but with clipping turned off, the x scale is based on the data and the y scale is 0,1, then adds some text accordingly:
library(ggplot2)
library(grid)
test= data.frame( x = c(1,2,3), y = c(12,10,3), n = c(75,76,73) )
qplot(x=x, y=y, data=test)+geom_line()+ opts(plot.margin = unit(c(1,3,8,1), "lines"))
current.vpTree()
downViewport('panel-3-4')
pushViewport(dataViewport( test$x, clip='off',yscale=c(0,1)))
for (i in 1:nrow(test)) {
grid.text(x=i, y = -0.2, default.units='native',
label=paste("GRID.text at\nuser.x=", i, "\n" ) )
grid.lines(x=c(i, i), y = c(-0.1, 0), default.units='native' )
}
One of the tricky things here is that ggplot2 does not set the viewport scales to match the data being plotted, but does the conversions itself. In this case setting the scale based on the x data worked, but if ggplot2 does something fancier then this might not work. What we would need is some way to get the back tranformed coordinates from ggplot2 to use in the call to grid.text.
I have a multiple variable time series were some of the variables have rather large ranges. I wish to make a single-page plot with multiple stacked plots of each variable were some of the variables have a log10 y-axis scaling. I am relatively new to lattice and have not been able to figure out how to effectively mix the log10 scaling with non-transformed axes and get a publication quality plot. If print.trellis is used the plots are not aligned and the padding needs some work, if c.trellis is used the layout is good, but only the y-scaling from only one plot is used. Any suggestions for an efficient solution, where I can replicate the output of c.trellis using the different y-scaling for each (original) object?
Example below:
require(lattice)
require(latticeExtra)
# make data.frame
d.date <- as.POSIXct(c("2009-12-15", "2010-01-15", "2010-02-15", "2010-03-15", "2010-04-15"))
CO2dat <- c(100,200,1000,9000,2000)
pHdat <- c(10,9,7,6,7)
tmp <- data.frame(date=d.date ,CO2dat=CO2dat ,pHdat=pHdat)
# make plots
plot1 <- xyplot(pHdat ~ date, data=tmp
, ylim=c(5,11)
, ylab="pHdat"
, xlab="Date"
, origin = 0, border = 0
, scales=list(y=list(alternating=1))
, panel = function(...){
panel.xyarea(...)
panel.xyplot(...)
}
)
# make plot with log y scale
plot2 <- xyplot(CO2dat ~ date, data=tmp
, ylim=c(10,10^4)
, ylab="CO2dat"
, xlab="Date"
, origin = 0, border = 0
, scales=list(y=list(alternating=1,log=10))
, yscale.components = yscale.components.log10ticks
, panel = function(...){
panel.xyarea(...)
panel.xyplot(...)
# plot CO2air uatm
panel.abline(h=log10(390),col="blue",type="l",...)
}
)
# plot individual figures using split
print(plot2, split=c(1,1,1,2), more=TRUE)
print(plot1, split=c(1,2,1,2), more=F)
# combine plots (more convenient)
comb <- c(plot1, plot2, x.same=F, y.same=F, layout = c(1, 2))
# plot combined figure
update(comb, ylab = c("pHdat","log10 CO2dat"))
Using #joran's idea, I can get the axes to be closer but not exact; also, reducing padding gets them closer together but changes the aspect ratio. In the picture below I've reduced the padding perhaps by too much to show the not exactness; if this close were desired, you'd clearly want to remove the x-axis labels on the top as well.
I looked into the code that sets up the layout and the margin on the left side is calculated from the width of the labels, so #joran's idea is probably the only thing that will work based on the printing using split, unless one were to rewrite the plot.trellis command. Perhaps the c method could work but I haven't found a way yet to set the scale components separately depending on the panel. That does seem more promising though.
mtheme <- standard.theme("pdf")
mtheme$layout.heights$bottom.padding <- -10
plot1b <- update(plot1, scales=list(y=list(alternating=1, at=5:10, labels=paste(" ",c(5:10)))))
plot2b <- update(plot2, par.settings=mtheme)
pdf(file="temp.pdf")
print(plot2b, split=c(1,1,1,2), more=TRUE)
print(plot1b, split=c(1,2,1,2), more=F)