I want to generate a PDF with many plots, one per page. These plots contain marginal distributions on the sides, generated with ggExtra:
library(ggplot2)
library(ggExtra)
set.seed(1)
pdf("example.pdf", width = 5, height=5, bg = "white")
for(i in 1:10){
plot.df <- data.frame(x = rnorm(1000), y = rnorm(1000))
p1 <- ggplot(plot.df, aes(x = x, y = y)) +
geom_point() +
ggtitle(i)
p1 <- ggMarginal(p1)
plot(p1)
}
dev.off()
However, the plots appear with a grey grid background instead of white background (see below). How can I make all the background white? Also, the output PDF contains a blank page at the beginning. How can avoid it being created?
You need to change the theme of the plot. You can choose for example theme_void(), or make your own using theme(), and add it to your plot.
p1 <- ggplot(plot.df, aes(x = x, y = y)) +
geom_point() +
ggtitle(i) +
theme_void()
Example of theme_void() :
Related
I used the following code to plot a packing circle graph and I want to add the numbers (values) for each bubble in addition to the text. How do I do that?
Another question is whether someone knows how to deal with a large number of categories (about 200) which makes some of the plot unreadable. Is there another visualization that might be more useful in this case?
Thanks in advance!
library(packcircles)
library(ggplot2)
library(viridis)
library(ggiraph)
packing <- circleProgressiveLayout(data$Number, sizetype='area')
data <- cbind(data, packing)
dat.gg <- circleLayoutVertices(packing, npoints=50)
ggplot() +
geom_polygon(data = dat.gg, aes(x, y, fill=as.factor(id), colour = "black", alpha = 0.6)) +
geom_text(data = data, aes(x, y, size=Number, label = Journal)) +
scale_size_continuous(range = c(2,4)) +
theme_void() +
theme(legend.position="none")+
coord_equal()```
Here I have 2-dim numeric array dataset and numeric 1-dim array of labels clustring. Then I plot it with the following code:
s = data.frame(x = dataset[,1], y = dataset[,2])
p = ggplot(s, aes(x, y))
p + geom_point(aes(colour = factor(clustering)))
which displays beautiful picture:
Now I want to remove legend completely, so here I've found possible solution:
# Remove legend for a particular aesthetic (fill)
p + guides(fill=FALSE)
# It can also be done when specifying the scale
p + scale_fill_discrete(guide=FALSE)
# This removes all legends
p + theme(legend.position="none")
but none of such commands wont help. It shows empty plot instead:
So how do I remove the legend from my plot?
Try this:
library(ggplot2)
s = data.frame(x = rnorm(20), y = rnorm(20), clustering = rep(c(1, 2), 10))
p <- ggplot(s, aes(x, y))+
guides(fill=FALSE)+
geom_point(aes(colour = factor(clustering)))+
scale_fill_discrete(guide=FALSE)+
theme(legend.position="none")
p
In your code, you are not saving the plot again after each time you add something to it. You can fix this by changing the lines that add to the plot:
# Remove legend for a particular aesthetic (fill)
p = p + guides(fill=FALSE)
But the way I wrote is is more common R formatting.
Use show.legend = FALSE within geom_point. Here is an example using ggplot2's diamonds dataset.
s <- diamonds
p <- ggplot(data = s, aes(x = depth, y = price))
p + geom_point(aes(colour = factor(cut)), show.legend = FALSE)
Just try this:
p + geom_point(aes(colour = factor(clustering)),show.legend=FALSE)
I have a plot with three different lines. I want one of those lines to have points on as well. I also want the two lines without points to be thicker than the one without points. I have managed to get the plot I want, but I the legend isn't keeping up.
library(ggplot2)
y <- c(1:10, 2:11, 3:12)
x <- c(1:10, 1:10, 1:10)
testnames <- c(rep('mod1', 10), rep('mod2', 10), rep('meas', 10))
df <- data.frame(testnames, y, x)
ggplot(data=df, aes(x=x, y=y, colour=testnames)) +
geom_line(aes(size=testnames)) +
scale_size_manual("", values=c(0.5,1,1)) +
geom_point(aes(alpha=testnames), size=5, shape=4) +
scale_alpha_manual("", values=c(1, 0, 0))
I can remove the second (black) legend:
ggplot(data = df, aes(x=x, y=y, colour=testnames)) +
geom_line(aes(size=testnames)) +
scale_size_manual("", values=c(0.5,1,1), guide='none') +
geom_point(aes(alpha=testnames), size=5, shape=4) +
scale_alpha_manual("", values=c(1, 0.05, 0.05), guide='none')
But what I really want is a merge of the two legends - a legend with colours, cross only on the first variable (meas) and the lines of mod1 and mod2 thicker than the first line. I have tried guide and override, but with little luck.
You don't need transparency to hide the shapes for mod1 and mod2. You can omit these points from the plot and legend by setting their shape to NA in scale_shape_manual:
ggplot(data = df, aes(x = x, y = y, colour = testnames, size = testnames)) +
geom_line() +
geom_point(aes(shape = testnames), size = 5) +
scale_size_manual(values=c(0.5, 2, 2)) +
scale_shape_manual(values=c(8, NA, NA))
This gives the following plot:
NOTE: I used some more distinct values in the size-scale and another shape in order to better illustrate the effect.
I would like two separate plots. I am using them in different frames of a beamer presentation and I will add one line to the other (eventually, not in example below). Thus I do not want the presentation to "skip" ("jump" ?) from one slide to the next slide. I would like it to look like the line is being added naturally. The below code I believe shows the problem. It is subtle, but not how the plot area of the second plot is slightly larger than of the first plot. This happens because of the y axis label.
library(ggplot2)
dfr1 <- data.frame(
time = 1:10,
value = runif(10)
)
dfr2 <- data.frame(
time = 1:10,
value = runif(10, 1000, 1001)
)
p1 <- ggplot(dfr1, aes(time, value)) + geom_line() + scale_y_continuous(breaks = NULL) + scale_x_continuous(breaks = NULL) + ylab(expression(hat(z)==hat(gamma)[1]*time+hat(gamma)[4]*time^2))
print(p1)
dev.new()
p2 <- ggplot(dfr2, aes(time, value)) + geom_line() + scale_y_continuous(breaks = NULL) + scale_x_continuous(breaks = NULL) + ylab(".")
print(p2)
I would prefer to not have a hackish solution such as setting the size of the axis label manually or adding spaces on the x-axis (see one reference below), because I will use this technique in several settings and the labels can change at any time (I like reproducibility so want a flexible solution).
I'm searched a lot and have found the following:
Specifying ggplot2 panel width
How can I make consistent-width plots in ggplot (with legends)?
https://groups.google.com/forum/#!topic/ggplot2/2MNoYtX8EEY
How can I add variable size y-axis labels in R with ggplot2 without changing the plot width?
They do not work for me, mainly because I need separate plots, so it is not a matter of aligning them virtically on one combined plot as in some of the above solutions.
haven't tried, but this might work,
gl <- lapply(list(p1,p2), ggplotGrob)
library(grid)
widths <- do.call(unit.pmax, lapply(gl, "[[", "widths"))
heights <- do.call(unit.pmax, lapply(gl, "[[", "heights"))
lg <- lapply(gl, function(g) {g$widths <- widths; g$heights <- heights; g})
grid.newpage()
grid.draw(lg[[1]])
grid.newpage()
grid.draw(lg[[2]])
How about using this for p2:
p2 <- ggplot(dfr2, aes(time, value)) + geom_line() +
scale_y_continuous(breaks = NULL) +
scale_x_continuous(breaks = NULL) +
ylab(expression(hat(z)==hat(gamma)[1]*time+hat(gamma)[4]*time^2)) +
theme(axis.title.y=element_text(color=NA))
This has the same label as p1, but the color is NA so it doesn't display. You could also use color="white".
I am using ggplot2 to produce a plot that has 3 facets. Because I am comparing two different data sets, I would like to then be able to plot a second data set using the same y scale for the facets as in the first plot. However, I cannot find a simple way to save the settings of the first plot to then re-use them with the second plot. Since each facet has its own y scale, it will be a pain to specify them by hand for the second plot. Does anyone know of a quick way of re-using scales? To make this concrete, here is how I am generating first my plot:
p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + facet_wrap(~ cyl, scales = "free_y")
EDIT
When applying one of the suggestions below, I found out that my problem was more specific than described in the original post, and it had to do specifically with scaling of the error bars. Concretely, the error bars look weird when I rescale the second plot as suggested. Does anyone have any suggestions on how to keep the same scale for both plots and dtill display the error bars correctly? I am attaching example below for concreteness:
#Create sample data
d1 <- data.frame(fixtype=c('ff','ff','fp','fp'), detype=c('det','pro','det','pro'),
diffscore=c(-1,-15,3,-17),se=c(2,3,1,2))
d2 <- data.frame(fixtype=c('ff','ff','fp','fp'), detype=c('det','pro','det','pro'),
diffscore=c(-1,-3,-2,-1),se=c(4,3,5,3))
#Plot for data frame 1, this is the scale I want to keep
lim_d1 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_point(aes(size=1), shape=15) +
geom_errorbar(lim_d1, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#Plot for data frame 2 original scale
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d2, aes(colour=detype, y=diffscore, x=detype)) +
geom_point(aes(size=1), shape=15) +
geom_errorbar(lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#Plot for data frame 2 adjusted scale. This is where things go wrong!
#As suggested below, first I plot the first plot, then I draw a blank screen and try
#to plot the second data frame on top.
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_blank() +
geom_point(data=d2, aes(size=1), shape=15) +
geom_errorbar(lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#If the error bars are fixed, by adding data=d2 to geom_errorbar(), then
#the error bars are displayed correctly but the scale gets distorted again
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_blank() +
geom_point(data=d2, aes(size=1), shape=15) +
geom_errorbar(data=d2,lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
You may first call ggplot on your original data where you add a geom_blank as a first layer. This sets up a plot area, with axes and legends based on the data provided in ggplot.
Then add geoms which use data other than the original data. In the example, I use a simple subset of the original data.
From ?geom_blank: "The blank geom draws nothing, but can be a useful way of ensuring common scales between different plots.".
ggplot(data = mtcars, aes(mpg, wt)) +
geom_blank() +
geom_point(data = subset(mtcars, wt < 3)) +
facet_wrap(~ cyl, scales = "free_y")
Here is an ugly hack that assumes you have an identical facetting layout in both plots.
It replaces the panel element of the ggplot build.
p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p1 <- p + facet_wrap(~ cyl, scales = "free_y") + labs(title = 'original')
# create "other" data.frame
n <- nrow(mtcars)
set.seed(201405)
mtcars2 <- mtcars[sample(seq_len(n ),n-15),]
# create this second plot
p2 <- p1 %+% mtcars2 + labs(title = 'new data')
# and a copy so we can attempt to fix
p3 <- p2 + labs(title = 'new data original scale')
# use ggplot_build to construct the plots for rendering
p1b <- ggplot_build(p1)
p3b <- ggplot_build(p3)
# replace the 'panel' information in plot 2 with that
# from plot 1
p3b[['panel']] <- p1b[['panel']]
# render the revised plot
# for comparison
library(gridExtra)
grid.arrange(p1 , p2, ggplot_gtable(p3b))