I've been learning ggplot2, and hope to use it for all my R graphing. However, I've yet to find a way to make a contour plot that looks analogous to a conventional contour plot, like what can be obtained using lattice:filled.contour(). For example:
#define data
x<-seq(1,11,1)
y<-seq(1,11,1)
xyz.func<-function(x,y) {-10.4+6.53*x+6.53*y-0.167*x^2-0.167*y^2+0.0500*x*y}
#contour plot using lattice graphics and R Color Brewer
library(lattice) #for filled.contour()
library(RColorBrewer) #for brewer.pal()
z.lattice<-outer(x,y,xyz.func)
filled.contour(x,y,z.lattice,nlevels=6,col=brewer.pal(6,"YlOrRd"))
This gives me a nice contour plot.
Now, let's try the same thing in ggplot2. The best I can come up with, based on everything I've read (particularly Drawing labels on flat section of contour lines in ggplot2) is:
#contour plot using ggplot2
library(ggplot2)
library(reshape2) #for melt()
z.molten<-melt(z.lattice)
names(z.molten) <- c("x", "y", "z")
v<-ggplot(z.molten, aes(x,y,z=z))+
geom_tile(aes(fill=z))+
stat_contour(bins=6,aes(x,y,z=z), color="black", size=0.6)+
scale_fill_gradientn(colours=brewer.pal(6,"YlOrRd"))
v
This graph has the same basic idea as filled.contour(), but the colored tiles don't conform to the contours very well.
I haven't been successful with changing the sizes of the tiles, either.
Any suggestions on how to make ggplot2's output closer to filled.contour()'s output?
The essence of your question, it seems, is how to produce a contour plot in ggplot with discrete filled contours, rather than continuous contours as you would get using the conventional geom_tile(...) approach. Here is one way.
x<-seq(1,11,.03) # note finer grid
y<-seq(1,11,.03)
xyz.func<-function(x,y) {-10.4+6.53*x+6.53*y-0.167*x^2-0.167*y^2+0.0500*x*y}
gg <- expand.grid(x=x,y=y)
gg$z <- with(gg,xyz.func(x,y)) # need long format for ggplot
library(ggplot2)
library(RColorBrewer) #for brewer.pal()
brks <- cut(gg$z,breaks=seq(0,100,len=6))
brks <- gsub(","," - ",brks,fixed=TRUE)
gg$brks <- gsub("\\(|\\]","",brks) # reformat guide labels
ggplot(gg,aes(x,y)) +
geom_tile(aes(fill=brks))+
scale_fill_manual("Z",values=brewer.pal(6,"YlOrRd"))+
scale_x_continuous(expand=c(0,0))+
scale_y_continuous(expand=c(0,0))+
coord_fixed()
The use of, e.g., scale_x_continuos(...) is just to get rid of the extra space ggplot puts around the axis limits; fine for most things but distracting in contour plots. The use of coord_fixed(...) is just to set the aspect ratio to 1:1. These are optional.
Related
I found some similar questions but the answers didn't solve my problem.
I try to plot a time series of to variables as a scatterplot and using the date to color the points. In this example, I created a simple dataset (see below) and I want to plot all data with timesteps in the 1960ties, 70ties, 80ties and 90ties with one colour respectively.
Using the standard plot command (plot(x,y,...)) it works the way it should, as I try using the ggplot library some strange happens, I guess I miss something. Has anyone an idea how to solve this and generate a correct plot?
Here is my code using the standard plot command with a colorbar
# generate data frame with test data
x <- seq(1,40)
y <- seq(1,40)
year <- c(rep(seq(1960,1969),2),seq(1970,1989,2),seq(1990,1999))
df <- data.frame(x,y,year)
# define interval and assing color to interval
myinterval <- seq(1959,1999,10)
mycolors <- rainbow(4)
colbreaks <- findInterval(df$year, vec = myinterval, left.open = T)
# basic plot
layout(array(1:2,c(1,2)),widths =c(5,1)) # divide the device area in two panels
par(oma=c(0,0,0,0), mar=c(3,3,3,3))
plot(x,y,pch=20,col = mycolors[colbreaks])
# add colorbar
ncols <- length(myinterval)-1
colbarlabs <- seq(1960,2000,10)
par(mar=c(5,0,5,5))
image(t(array(1:ncols, c(ncols,1))), col=mycolors, axes=F)
box()
axis(4, at=seq(0.5/(ncols-1)-1/(ncols-1),1+1/(ncols-1),1/(ncols-1)), labels=colbarlabs, cex.axis=1, las=1)
abline(h=seq(0.5/(ncols-1),1,1/(ncols-1)))
mtext("year",side=3,line=0.5,cex=1)
As I would like to use ggplot package, as I do for other plots, I tried this version with ggplot
# plot with ggplot
require(ggplot2)
ggplot(df, aes(x=x,y=y,color=year)) + geom_point() +
scale_colour_gradientn(colours= mycolors[colbreaks])
but it didn't work the way I thought it would. Obviously, there is something wrong with the color coding. Also, the colorbar looks strange. I also tried it with scale_color_manual and scale_color_gradient2 but I got more errors (Error in continuous_scale).
Any idea how to solve this and generate a plot according to the standard plot 3 including a colorbar.
I have a scatterplot in a log/log space
plot(a,b,log="xy")
or in ggplot2
qplot(a,b,data="time",log="xy")
Now I would like to impose upon this scatter plot the curve f(x)=x*x+2. Butthe function woudl need to be plotted in the logarithmic space as well. How would I do this? Is there an way to do this in ggplot2?
As you guessed, curve is the command that you're looking for in base graphics.
#Make up some data
set.seed(0)
a <- 1:10
b <-(a^2+2)*exp(0.1*rnorm(10))
plot(a,b,log='xy')
curve(x^2+2,add=TRUE)
in ggplot2 world:
qplot(a,b,data=time)+stat_function(fun=function(x){x^2+2}) + coord_trans(xtrans = "log10",ytrans="log10")
from Plotting in R using stat_function on a logarithmic scale seems to do what you're after.
I'm trying to inset a plot using ggplot2 and annotation_custom (the plot is actually a map that I'm using to replace the legend). However, I'm also using facet_wrap to generate multiple panels, but when used with annotation_custom, this reproduces the plot in each facet. Is there an easy way to insert the plot only once, preferably outside the plotting area?
Here is a brief example:
#Generate fake data
set.seed(9)
df=data.frame(x=rnorm(100),y=rnorm(100),facets=rep(letters[1:2]),
colors=rep(c("red","blue"),each=50))
#Create base plot
p=ggplot(df,aes(x,y,col=colors))+geom_point()+facet_wrap(~facets)
#Create plot to replace legend
legend.plot=ggplotGrob(
ggplot(data=data.frame(colors=c("red","blue"),x=c(1,1),y=c(1,2)),
aes(x,y,shape=colors,col=colors))+geom_point(size=16)+
theme(legend.position="none") )
#Insert plot using annotation_custom
p+annotation_custom(legend.plot)+theme(legend.position="none")
#this puts plot on each facet!
This produces the following plot:
When I would like something more along the lines of:
Any help is appreciated. Thanks!
In the help of annotation_custom() it is said that annotations "are the same in every panel", so it is expected result to have your legend.plot in each facet (panel).
One solution is to add theme(legend.position="none") to your base plot and then use grid.arrange() (library gridExtra) to plot both plots.
library(gridExtra)
p=ggplot(df,aes(x,y,col=colors))+geom_point()+facet_wrap(~facets)+
theme(legend.position="none")
grid.arrange(p,legend.plot,ncol=2,widths=c(3/4,1/4))
I generate a plot using the package hexbin:
# install.packages("hexbin", dependencies=T)
library(hexbin)
set.seed(1234)
x <- rnorm(1e6)
y <- rnorm(1e6)
hbin <- hexbin(
x = x
, y = y
, xbin = 50
, xlab = expression(alpha)
, ylab = expression(beta)
)
## Using plot method for hexbin objects:
plot(hbin, style = "nested.lattice")
abline(h=0)
This seems to generate an S4 object (hbin), which I then plot using plot.
Now I'd like to add a horizontal line to that plot using abline, but unfortunately this gives the error:
plot.new has not yet been called
I have also no idea, how I can manipulate e.g. the position of the axis labels (alpha and beta are within the numbers), change the position of the legend, etc.
I'm familiar with OOP, but so far I could not find out how plot() handles the object (does it call certain methods of the object?) and how I can manipulate the resulting plot.
Why can't I simply draw a line onto the plot?
How can I manipulate axis labels?
Use lattice version of hex bin - hexbinplot(). With panel you can add your line, and with style you can choose different ways of visualizing hexagons. Check help for hexbinplot for more.
library(hexbin)
library(lattice)
x <- rnorm(1e6)
y <- rnorm(1e6)
hexbinplot(x ~ y, aspect = 1, bins=50,
xlab = expression(alpha), ylab = expression(beta),
style = "nested.centroids",
panel = function(...) {
panel.hexbinplot(...)
panel.abline(h=0)
})
hexbin uses grid graphics, not base. There is a similar function, grid.abline, which can draw lines on plots by specifying a slope and intercept, but the co-ordinate system used is confusing:
grid.abline(325,0)
gets approximately what you want, but the intercept here was found by eye.
You will have more luck using ggplot2:
library(ggplot2)
ggplot(data,aes(x=alpha,y=beta)) + geom_hex(bins=10) + geom_hline(yintercept=0.5)
I had a lot of trouble finding a lot of basic plot adjustments (axis ranges, labels, etc.) with the hexbin library but I figured out how to export the points into any other plotting function:
hxb<-hexbin(x=c(-15,-15,75,75),
y=c(-15,-15,75,75),
xbins=12)
hxb#xcm #gives the x co-ordinates of each hex tile
hxb#ycm #gives the y co-ordinates of each hex tile
hxb#count #gives the cell size for each hex tile
points(x=hxb#xcm, y=hxb#ycm, pch=hxb#count)
You can just feed these three vectors into any plotting tool you normally use.. there is the usual tweaking of size scaling, etc. but it's far better than the stubborn hexplot function. The problem I found with the ggplot2 stat_binhex is that I couldn't get the hexes to be different sizes... just different colors.
if you really want hexagons, plotrix has a hexagon drawing function that i think is fine.
I'm relatively new to ggplot2, and I'm having trouble adding appropriate labels to my contours. I would love to be able to add the labels without the directlabels package, but I haven't found a way to, so if you know of a way to customize labels without directlabels, I would love to here it.
Using the classic volcano example, I can add labels to the default contour plot using the directlabels packet in the following way:
library(plyr)
library(ggplot2)
library(directlabels)
library(reshape)
volcano<-melt(volcano)
v<-ggplot(volcano, aes(x,y,z=z))
e<-v + stat_contour(aes(colour=..level..))
direct.label(e)
In the above example, the labels are added appropriately, but things become more complicated if I try to specify my own break points for the contours:
e<-v + stat_contour(aes(breaks=c(160, 170, 180), colour=..level..))
direct.label(e)
Now, the contours are specified by the breaks I have provided, but labels still appear for all of the default contours. How do I only plot only labels for the graphed contours?
A related issue, how would I plot labels for contour levels not included in the default? Say a break of 165:
e<-v + stat_contour(aes(breaks=c(165), colour=..level..))
direct.label(e)
Thanks for any help!
The current development version (directlabels_2013.6.15 with ggplot2_0.9.3.1) should fix your problem (as the author of the directlabels package explained to me). You can install it with:
install.packages("directlabels", repos="http://r-forge.r-project.org")
And then:
library(plyr)
library(ggplot2)
library(directlabels)
library(reshape)
volcano<-melt(volcano)
v<-ggplot(volcano, aes(X1,X2,z=value))
e<-v + stat_contour(aes(colour=..level..), breaks=c(165))
direct.label(e)
I noted several other limitations with simple workarounds:
the first ggplot call must contain the z aesthetic
this works only with the stat_contour (and not with the geom_contour)
the colour aesthetic must be defined in the stat_contour call and set to ..level..
Finally, if you want to control the label and contour line colours (black labels and blue contour lines for instance), you can achieved this as follow:
e<-v + stat_contour(aes(colour=..level..), colour = "blue", breaks=c(165))
e<-e + scale_colour_continuous(low = "#FF0000", high = "#FF0000")
direct.label(e)