I'm trying to use facet_grid or facet_wrap in conjunction with geom_raster. However, in each panel, the z aesthetic is on a different scale. For example,
##Data at end of question
ggplot(dd, aes(x,y)) +
geom_raster(aes(fill=z)) +
facet_grid(type ~ var)
gives
.
However, since the average values of C and D are around 0 and 100 respectively, we lose a lot of resolution. You could also try:
##Change C to D to get other panel
ggplot(subset(dd, var=="C"), aes(x,y))+
geom_raster(aes(fill=z)) +
facet_grid(type ~ var) +
theme(legend.position="bottom")
which gives
and
but I now have two y strips.
Question
Can I alter the first plot to give two legends for the fill aesthetic?
Or, if I do two separate graphs, can I remove the y strip on one of the plots to allow me to press them together - messing about with the theme, suggests this isn't possible.
Data
Data to reproduce graphs
dd = expand.grid(x=1:10, y=1:10)
dd = data.frame(dd, type=rep(LETTERS[1:2], each=100),
var =rep(c("C", "D"), each=200) )
dd$z = rnorm(400, rep(c(0, 100), each=200))
What about this:
library(gridExtra)
p1 <- ggplot(subset(dd, var=="C"), aes(x,y))+
geom_raster(aes(fill=z)) + facet_grid(type ~ var) +
theme(legend.position="bottom", plot.margin = unit(c(1,-1,1,0.2), "line"))
p2 <- ggplot(subset(dd, var=="D"), aes(x,y))+
geom_raster(aes(fill=z)) + facet_grid(type ~ var) +
theme(legend.position="bottom", plot.margin = unit(c(1,1,1,-0.8), "line"),
axis.text.y = element_blank(), axis.ticks.y = element_blank()) + ylab("")
grid.arrange(arrangeGrob(p1, p2, nrow = 1))
also you might want to play around with plot.margin. And it seems that a negative answer to your first question can be found here.
Related
I've created a plot (below) and basically want the left and right ends of each facet to state "Ov" and "Cx." I've tried using scale_x_continuous but the issue is that the x-axis for each facet is different.
What I have right now (image):
What I'd like to get ideally:
all_prm %>% ggplot(aes(y_coord, prominence)) +
geom_point() +
facet_wrap(~interaction(ms, sample), scales="free_x") +
scale_x_continuous(breaks=c(10000), labels=c("Ov")) +
theme(
axis.title.y=element_text(margin=margin(r=7)),
axis.title.x=element_text(margin=margin(t=7)),
panel.background = element_rect(fill='white', color='grey10')) +
xlab("Oviduct-Cervical Axis") +
ylab("Prominence")
You can use a custom function to set the break points, in this case using the range of the x limit values with an adjustment argument to move the labels away from the axis limits relative to their scale.
Using the iris dataset:
break_range <- function(x, adjust = .025) {
rng <- range(x)
rng + diff(rng) * c(adjust, -adjust)
}
ggplot(iris, aes(Petal.Length, Sepal.Length )) +
geom_point() +
facet_wrap(. ~ Species, nrow = 3, scales="free_x") +
scale_x_continuous(breaks = break_range, labels = c("Ov", "Cx")) +
theme(
axis.title.y=element_text(margin=margin(r=7)),
axis.title.x=element_text(margin=margin(t=7)),
panel.background = element_rect(fill='white', color='grey10'))
I'm preparing an appendix plot for a revised manuscript where I need to give information of the within-year ranges (variability) of several variables between years and sites.
I figured the tidiest way to do this (I have 7 sites, 21 years, and 5 variables...) would be to use a rose plot using coord_polar. However, I stumbled upon something that has always frustrated me about ggplot - the default ordering assumptions. While factors are easily reordered based on some value, this seems to only work in a fixed fashion: as far as I've understood, the order needs to apply throughout the data frame.
In this plot, the ordering needs to depend on a value which changes between years, and therefore the colour and fill values need to change in plotting order within the panel.
To demonstrate, I've created a reproducible example coded below (pictured in the way it should not work)
Basically, I always need the Site with the minimum value within a given Year to be plotted first (in the centre), followed outwards by the increase in value of the other sites, in order of the original value (see order and diff columns of the data frame). In other words, some years Site a will be at the centre, some years Site c will be in the centre, etc.
Any help would be massively appreciated.
library('ggplot2')
library('reshape2')
library("plyr")
## reproducible example of problem: create dummy data
madeup <- data.frame(Year = rep(2000:2015, each=20), Site=rep(c("a","b","c","d"), each=5, times=16),
var1 = rnorm(n=16*20, mean=20, sd=5), var2= rnorm(n=16*20, mean=50, sd=1))
## create ranges of the data by Year and Site
myRange <- function(dat) {range=max(dat, na.rm=TRUE)-min(dat,na.rm = TRUE)}
vardf <- ddply(madeup, .(Site, Year), summarise, var1=myRange(var1),
var2=myRange(var2))
varmelt <- melt(vardf, id.vars = c("Site","Year"))
varmelt$Site <- as.character(varmelt$Site) # this to preserve the new order when rbind called
varmelt <- by(varmelt, list(varmelt$Year, varmelt$variable), function(x) {x <- x[order(x$value),]
x$order <- 1:nrow(x)
return(x)})
varmelt <- do.call(rbind, varmelt)
## create difference between these values so that each site gets plotted cumulatively on the rose plot
## (otherwise areas close to the centre become uninterpretable)
vartest <- by(varmelt, list(varmelt$Year, varmelt$variable), function(x) {
x$diff <- c(x$value[1], diff(x$value))
return(x)
})
vartest <- do.call(rbind,vartest)
## plot rose plot to display how ranges in variables vary by year and between sites
## for this test example we'll just take one variable, but the idea is to facet by variable
max1 <- max(vartest$value[vartest$variable=='var1'])
yearlength <- length(2000:2015)
ggplot(vartest[vartest$variable=="var1",], aes(x=factor(Year), y=diff)) +
theme_bw() +
geom_hline(yintercept = seq(0,max1, by=1), size=0.3, col="grey60",lty=3) +
geom_vline(xintercept=seq(1,yearlength,1), size=0.3, col='grey30', lty=2) +
geom_bar(stat='identity', width=1, size=0.5, aes(col=Site, fill=Site)) +
scale_x_discrete() +
coord_polar() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
As long as you don't use stacked bars (position = "stack", which is the default for geom_bar), ggplot2 will actually use the order of the rows in your data for the plotting order. So all you need to do, is use the original values for the y-axis (rather than the cumulatively differenced ones) along with position = "identity", and order your data from largest to smallest value before plotting:
ordered_data <- vartest[order(-vartest$value), ]
ggplot(ordered_data, aes(factor(Year), value)) +
geom_col(aes(fill = Site), position = "identity", width = 1) +
coord_polar() +
facet_wrap(~ variable)
Created on 2018-02-17 by the reprex package (v0.2.0).
PS. When generating random data for an example, consider using set.seed so that your results can be reproduced exactly.
You can start with a single plot of the largest site, and then layer smaller sites on top like so:
a <- ggplot(vartest[vartest$variable=="var1"& vartest$order==4,], aes(x=factor(Year), y=value,group=order)) +
theme_bw() +
geom_hline(yintercept = seq(0,max1, by=1), size=0.3, col="grey60",lty=3) +
geom_vline(xintercept=seq(1,yearlength,1), size=0.3, col='grey30', lty=2) +
geom_bar(stat='identity', width=1, size=0.5, aes(col=Site, fill=Site)) +
scale_x_discrete() +
coord_polar() +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
b <- a + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==3,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
c <- b + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==2,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
c + geom_bar(data = vartest[vartest$variable=="var1"& vartest$order==1,],
stat='identity', width=1, size=0.5, aes(x=factor(Year), y=value,col=Site, fill=Site))
This produces the following:
Is that what you wanted?
I've got three subplots I want to put together into one plot, and faceting would be a natural way to do it. However, one of these subplots would be easier/more natural to read with a reversed x-axis (whereas I'd like to leave the others alone). Is there a way to accomplish this using facet_grid() or facet_wrap()?
The other alternative I've considered is grid.arrange(), and the chief problem I've run into there is getting it to align the subplots based on plot area (inside the axes), rather than based on the edges of the images. (My axis titles and labels are not the same size, so the default behavior is fairly ugly.)
Edited to add a MWE with some data for context. Here, since larger is "better" for the beta and R-squared subplots, it would be more natural to reverse the axis for the p subplot. (In this case it would probably also be better to add the log transform to that scale, but my real problem doesn't need to get that fancy.)
df <- data.frame(z=c(rep("R-squared",15),rep("p",15),rep("beta",15)),
x=c(runif(15),exp(-runif(15,1,10)),rnorm(15,1,0.5)),
y=rep(letters[1:15],3))
plot <- ggplot(df) + geom_point(aes(x=x,y=y)) + facet_grid(.~z, scales="free_x", switch="x")
Here's a solution using patchwork
library(ggplot2)
library(dplyr)
df <- data.frame(z=c(rep("R-squared",15),rep("p",15),rep("beta",15)),
x=c(runif(15),exp(-runif(15,1,10)),rnorm(15,1,0.5)),
y=rep(letters[1:15],3))
p1 <- ggplot(filter(df, z == "beta"), aes(x, y)) +
geom_point()
p2 <- ggplot(filter(df, z == "p"), aes(x, y)) +
geom_point() +
scale_x_reverse() +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
p3 <- ggplot(filter(df, z == "R-squared"), aes(x, y)) +
geom_point() +
theme(axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
#devtools::install_github("thomasp85/patchwork")
library(patchwork)
p1 + p2 + p3
I want to combine these two graphs :
p1 <- ggplot(iris, aes(Sepal.Length)) +
geom_density() +
facet_wrap(~ Species)
p2 <- ggplot(iris, aes(Sepal.Length)) +
geom_density()
To combine, I do :
multiplot(p1, p2, cols = 2)
But it is not the desired shape.
I would like the graph p2 has the same dimensions than others and is situated just next to the last faceted graph.
Thanks for help
Not sure if this is applicable in you generic case, but with facet_grid instead of facet_wrap, you can use the margins argument:
library(ggplot2)
ggplot(iris, aes(Sepal.Length)) +
geom_density() +
facet_grid(. ~ Species, margins = T)
If you question is more generic the answer probably lies in grid.arrange.
Something like this could be a start:
library(gridExtra)
grid.arrange(arrangeGrob(p1, p2,
widths = c(3,1),
heights = c(1,20),
layout_matrix = matrix(c(1,1,NA,2),2)))
As you can see there are several problems (different axes, top strip), but working with grid could gets complicated quickly.
This code should work:
p1 <- ggplot(iris, aes(Sepal.Length)) +
geom_density() +
ylim(limits = c(0, 1.25))+
facet_wrap(~ Species)
p2 <- ggplot(iris, aes(Sepal.Length)) +
geom_density() +
ggtitle("") + # ad empty title as place holder
labs(y = "", x = "") + # hide axis labels
ylim(limits = c(0, 1.25)) + # y axis values should be fixed in both plots
coord_fixed(ratio=20/1) + # ratio of x- and y-axis to reduce width of plot
theme(axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.line.y = element_blank(),
plot.margin=unit(c(0,0,0.65,-10), "lines")) # margin between plots = "0.65"
I fiddled a bit and used just different styling options to produce this result. If you have more plots than this one I would recommend to use one theme for all.
You can use either the multiplot function that you are already using
multiplot(p1, p2, cols = 2)
or you install the packages gridExtra and grid and use that one:
grid.arrange(p1, p2, ncol=2)
Hope this helps!
I have a empirical PDF + CDF combo I'd like to plot on the same panel. distro.df has columns pdf, cdf, and day. I'd like the pdf values to be plotted as bars, and the cdf as lines. This does the trick for making the plot:
p <- ggplot(distro.df, aes(x=day))
p <- p + geom_bar(aes(y=pdf/max(pdf)), stat="identity", width=0.95, fill=fillCol)
p <- p + geom_line(aes(y=cdf))
p <- p + xlab("Day") + ylab("")
p <- p + theme_bw() + theme_update(panel.background = element_blank(), panel.border=element_blank())
However, I'm having trouble getting a legend to appear. I'd like a line for the cdf and a filled block for the pdf. I've tried various contortions with guides, but can't seem to get anything to appear.
Suggestions?
EDIT:
Per #Henrik's request: to make a suitable distro.df object:
df <- data.frame(day=0:10)
df$pdf <- runif(length(df$day))
df$pdf <- df$pdf / sum(df$pdf)
df$cdf <- cumsum(df$pdf)
Then the above to make the plot, then invoke p to see the plot.
This generally involves moving fill into aes and using it in both the geom_bar and geom_line layers. In this case, you also need to add show_guide = TRUE to geom_line.
Once you have that, you just need to set the fill colors in scale_fill_manual so CDF doesn't have a fill color and use override.aes to do the same thing for the lines. I didn't know what your fill color was, so I just used red.
ggplot(df, aes(x=day)) +
geom_bar(aes(y=pdf/max(pdf), fill = "PDF"), stat="identity", width=0.95) +
geom_line(aes(y=cdf, fill = "CDF"), show_guide = TRUE) +
xlab("Day") + ylab("") +
theme_bw() +
theme_update(panel.background = element_blank(),
panel.border=element_blank()) +
scale_fill_manual(values = c(NA, "red"),
breaks = c("PDF", "CDF"),
name = element_blank(),
guide = guide_legend(override.aes = list(linetype = c(0,1))))
I'd still like a solution to the above (and will checkout #aosmith's answer), but I am currently going with a slightly different approach to eliminate the need to solve the problem:
p <- ggplot(distro.df, aes(x=days, color=pdf, fill=pdf))
p <- p + geom_bar(aes(y=pdf/max(pdf)), stat="identity", width=0.95)
p <- p + geom_line(aes(y=cdf), color="black")
p <- p + xlab("Day") + ylab("CDF")
p <- p + theme_bw() + theme_update(panel.background = element_blank(), panel.border=element_blank())
p
This also has the advantage of displaying some of the previously missing information, namely the PDF values.