I know there are ways to update settings of a given ggplot graph.
I want to save two files, one with the standard setting as png and another one as pdf but with a different label size.
df <- data.frame(a=c('a;b;c','d;e;f'), b=c('A;B;C','D;E;F'),
x=c(1,2), y=c(2,3))
g <- ggplot(df, aes(x,y)) + geom_point() + geom_text(aes(label=a))
ggsave('test1.png',g)
ggsave('test2.pdf',g + geom_text(aes(label=a), size=10))
Is there a way to remove or update the old geom_text layer and not just add a layer to the graph?
Look into str(g) and update relevant bits, in your case following should work:
g$layers[[2]]$geom_params$size <- 10
One option would be to have a simpler basic object g and then add the layers you need
g <- ggplot(df, aes(x,y)) + geom_point()
ggsave('test1.png', g + geom_text(aes(label=a)))
ggsave('test2.pdf', g + geom_text(aes(label=a), size=10))
You can also put the size argument inside the aes and then use scale_size_manual to get different scales.
g <- ggplot(df, aes(x,y)) +
geom_point() +
geom_text(aes(label=a, size="size"), show_guide = FALSE)
g + scale_size_manual(values=c(size = 10))
Related
I have an R package as a back-end engine for a GUI (JASP). I want to be able to have people convert their images to greyscale (for publication printing). For most plots, I can use scale_colour_grey(), but that doesn't work when colors are specified within stats_summary. For example:
# simulate data
set.seed(1212)
y = rnorm(100)
g = sample(c("a", "b"), 100, T)
d = data.frame(y=y, g=g)
### create ggplot
plot = ggplot(data=d, aes(x=g, y=y)) +
geom_jitter() +
stat_summary(fun="mean", geom="point", size=3, color="red") +
stat_summary(geom="errorbar", size=3, color="red") +
theme_bw()
### converting to greyscale doesn't work
plot + scale_colour_grey()
After some research, I learned you can dissect the ggplot object then rebuilt it:
### can rebuilt ggplot object, but not ideal
q <- ggplot_build(plot)
q$data[[2]]$colour <- "black"
q$data[[3]]$colour <- "black"
q <- ggplot_gtable(q)
plot = ggplotify::as.ggplot(q)
plot
That works fine, but I try to include as few dependencies in my R packages as possible. Do I really have to resort to another package (ggplotify) in order to modify the color coming from stat_summary?
A few notes: I don't want to have to modify the original stat_summary statement. Let's just pretend that cannot be modified. Also, let's assume I cannot add another package (aside from ggplot2, which is already loaded). Remember, I'm a "guest" in the JASP framework and I don't want to have to add another package to the list of packages they have to store.
You can actually get at the ggplot object before it is built, reach into the layer that has the coloured object and change it. Here's a full reprex:
library(ggplot2)
# simulate data
set.seed(1212)
y = rnorm(100)
g = sample(c("a", "b"), 100, T)
d = data.frame(y=y, g=g)
### create ggplot
p <- ggplot(data=d, aes(x=g, y=y)) +
geom_jitter() +
stat_summary(fun="mean", geom="point", size=3, color="red") +
stat_summary(geom="errorbar", size=3, color="red") +
theme_bw()
p
p$layers[[3]]$aes_params$colour <- "gray50"
p
If you want a more general approach to change a particular geom's colour, you could do something like this function:
recolour_geom <- function(gg_plot, geom, colour = "gray50")
{
ss <- which(sapply(p$layers, function(l) {
paste(gsub("GEOM|GG|PROTO", "", toupper(class(l$geom))),
collapse = "")}) %in% toupper(geom))
if (length(ss) > 0)
{
for (i in ss)
{
p$layers[[i]]$aes_params$colour <- colour
}
}
p
}
Which allows you to do, for example
recolour_geom(p, "errorbar", "blue")
recolour_geom(p, "point", "green")
Be aware though that this changes the ggplot by reference, so p is changed as a side effect of the function.
Created on 2020-08-14 by the reprex package (v0.3.0)
I have a data frame with more than 40 factor levels and I would like to assign different shapes for each level. However, as shown in the scale_shapes_table of ggplot2, I can assign only 26 of them and some !,",# and so on.
But I know that in python or jmp you can assign many shapes (like asteriks, left triangle ,right triangle, rectangle etc.). Is it also possible also in ggplot2?
data=data.frame(gr=seq(1,40), x1=runif(40), y1=runif(40))
library(ggplot2)
ggplot(data=data,aes(x=x1,y=y1,shape=factor(gr),col=factor(gr)))+
geom_point(alpha = 0.3,size=4,stroke=1.4) +
scale_shape_manual(values=c(0:40))
A large set of symbols is available using the emojifont package with Font Awasome (see the complete list here). More details are given here.
library(ggplot2)
library(emojifont)
set.seed(1234)
symbls <- c('fa-github', 'fa-binoculars', 'fa-twitter', 'fa-android', 'fa-coffee',
'fa-cube', 'fa-ambulance','fa-check','fa-cutlery','fa-cogs','fa-dot-circle-o','fa-car',
'fa-building','fa-fire', 'fa-flag','fa-female','fa-gratipay','fa-heart','fa-magnet',
'fa-lock','fa-map','fa-puzzle-piece','fa-shopping-cart','fa-star','fa-sticky-note',
'fa-stop-circle-o','fa-volume-down','fa-anchor', 'fa-beer','fa-book','fa-cloud',
'fa-comment','fa-eject','fa-chrome','fa-child','fa-bomb', 'fa-certificate',
'fa-desktop','fa-fire-extinguisher','fa-diamond')
idx <- order(symbls)
fa <- fontawesome(symbls)
k <- length(fa)
data=data.frame(gr=factor(fa, levels=fa[idx]), x1=runif(k), y1=runif(k))
data$gr <- factor(data$gr, levels=fa[idx])
ggplot(data, aes(x1, y1, colour=gr, label=gr)) +
xlab(NULL) + ylab(NULL) + geom_point(size=-1) +
geom_text(family='fontawesome-webfont', size=6, show.legend=FALSE) +
theme(legend.text=element_text(family='fontawesome-webfont')) +
scale_colour_discrete("Points",guide=guide_legend(override.aes=list(size=4)))
Warning: if you want to use the code in Rstudio, first reassign the graphing device as follows:
devtools::install_github("coatless/balamuta")
library("balamuta")
external_graphs()
Would using a combination of 5 or 10 distinct shapes with distinct colors sufficient to distinguish the 40 points work better? I see these as being visually easier to differentiate the 40 elements than using/resorting to unusual symbols.
ggplot(data=data,aes(x=x1,y=y1, shape=factor(gr), col=factor(gr)))+
geom_point(alpha = 0.5, size=4, stroke=1.4) +
scale_shape_manual(values=rep(c(0:2,5:6,9:10,11:12,14), times=4))
Or take advantage of the 5 unique shapes that take fill colors.
ggplot(data=data,aes(x=x1,y=y1, shape=factor(gr), fill=factor(gr), col=factor(gr)))+
geom_point(alpha = 0.5, size=4, stroke=1.4) +
scale_shape_manual(values=rep(c(21:25), times=8))
Maybe use gr as labels, using ggrepel, easier to find a number than comparing shapes:
library(ggrepel)
ggplot(data = data, aes(x = x1, y = y1, label = gr))+
geom_point() +
geom_label_repel()
I'd like to label a horizontal line on a ggplot with multiple series, without associating the line with a series. R ggplot2: Labelling a horizontal line on the y axis with a numeric value asks about the single-series case, for which geom_text solves. However, geom_text associates the label with one of the series via color and legend.
Consider the same example from that question, with another color column:
library(ggplot2)
df <- data.frame(y=1:10, x=1:10, col=c("a", "b")) # Added col
h <- 7.1
plot1 <- ggplot(df, aes(x=x, y=y, color=col)) + geom_point()
plot2 <- plot1 + geom_hline(aes(yintercept=h))
# Applying top answer https://stackoverflow.com/a/12876602/1840471
plot2 + geom_text(aes(0, h, label=h, vjust=-1))
How can I label the line without associating the label to one of the series?
Is this what you had in mind?
library(ggplot2)
df <- data.frame(y=1:10, x=1:10, col=c("a", "b")) # Added col
h <- 7.1
ggplot(df, aes(x=x,y=y)) +
geom_point(aes(color=col)) +
geom_hline(yintercept=h) +
geom_text(data=data.frame(x=0,y=h), aes(x, y), label=h, vjust=-1)
First, you can make the color mapping local to the points layer. Second, you do not have to put all the aesthetics into calls to aes(...) - only those you want mapped to columns of the dataset. Three, you can have layer-specific datasets using data=... in the calls to a specific geom_*.
You can use annotate instead:
plot2 + annotate(geom="text", label=h, x=1, y=h, vjust=-1)
Edit: Removed drawback that x is required, since that's also true of geom_text.
I've become quite fond of boxplots in which jittered points are overlain over the boxplots to represent the actual data, as below:
set.seed(7)
l1 <- gl(3, 1, length=102, labels=letters[1:3])
l2 <- gl(2, 51, length=102, labels=LETTERS[1:2]) # Will use this later
y <- runif(102)
d <- data.frame(l1, l2, y)
ggplot(d, aes(x=l1, y=y)) +
geom_point(position=position_jitter(width=0.2), alpha=0.5) +
geom_boxplot(fill=NA)
(These are particularly helpful when there are very different numbers of data points in each box.)
I'd like to use this technique when I am also (implicitly) using position_dodge to separate boxplots by a second variable, e.g.
ggplot(d, aes(x=l1, y=y, colour=l2)) +
geom_point(position=position_jitter(width=0.2), alpha=0.5) +
geom_boxplot(fill=NA)
However, I can't figure out how to dodge the points by the colour variable (here, l2) and also jitter them.
Here is an approach that manually performs the jittering and dodging.
# a plot with no dodging or jittering of the points
dp <- ggplot(d, aes(x=l1, y=y, colour=l2)) +
geom_point(alpha=0.5) +
geom_boxplot(fill=NA)
# build the plot for rendering
foo <- ggplot_build(dp)
# now replace the 'x' values in the data for layer 1 (unjittered and un-dodged points)
# with the appropriately dodged and jittered points
foo$data[[1]][['x']] <- jitter(foo$data[[2]][['x']][foo$data[[1]][['group']]],amount = 0.2)
# now draw the plot (need to explicitly load grid package)
library(grid)
grid.draw(ggplot_gtable(foo))
# note the following works without explicitly loading grid
plot(ggplot_gtable(foo))
I don't think you'll like it, but I've never found a way around this except to produce your own x values for the points. In this case:
d$l1.num <- as.numeric(d$l1)
d$l2.num <- (as.numeric(d$l2)/3)-(1/3 + 1/6)
d$x <- d$l1.num + d$l2.num
ggplot(d, aes(l1, y, colour = l2)) + geom_boxplot(fill = NA) +
geom_point(aes(x = x), position = position_jitter(width = 0.15), alpha = 0.5) + theme_bw()
It's certainly a long way from ideal, but becomes routine pretty quickly. If anyone has an alternative solution, I'd be very happy!
The new position_jitterdodge() works for this. However, it requires the fill aesthetic to tell it how to group points, so you have to specify a manual fill to get uncolored boxes:
ggplot(d, aes(x=l1, y=y, colour=l2, fill=l2)) +
geom_point(position=position_jitterdodge(width=0.2), alpha=0.5) +
geom_boxplot() + scale_fill_manual(values=rep('white', length(unique(l2))))
I'm using a newer version of ggplot2 (ggplot2_2.2.1.9000) and I was struggling to find an answer that worked for a similar plot of my own. #John Didon's answer produced an error for me; Error in position_jitterdodge(width = 0.2) : unused argument (width = 0.2). I had previous code that worked with geom_jitter that stopped working after downloading the newer version of ggplot2. This is how I solved it below - minimal-fuss code....
ggplot(d, aes(x=l1, y=y, colour=l2, fill=l2)) +
geom_point(position = position_jitterdodge(dodge.width = 1,
jitter.width = 0.5), alpha=0.5) +
geom_boxplot(position = position_dodge(width = 1), fill = NA)
Another option would be to use facets:
set.seed(7)
l1 <- gl(3, 1, length=102, labels=letters[1:3])
l2 <- gl(2, 51, length=102, labels=LETTERS[1:2]) # Will use this later
y <- runif(102)
d <- data.frame(l1, l2, y)
ggplot(d, aes(x=l1, y=y, colour=l2)) +
geom_point(position=position_jitter(width=0.2), alpha=0.5) +
geom_boxplot(fill=NA) +
facet_grid(.~l2) +
theme_bw()
Sorry, don´t have enough points to post the resulting graph.
Does anyone know how to create a scatterplot in R to create plots like these in PRISM's graphpad:
I tried using boxplots but they don't display the data the way I want it. These column scatterplots that graphpad can generate show the data better for me.
Any suggestions would be appreciated.
As #smillig mentioned, you can achieve this using ggplot2. The code below reproduces the plot that you are after pretty well - warning it is quite tricky. First load the ggplot2 package and generate some data:
library(ggplot2)
dd = data.frame(values=runif(21), type = c("Control", "Treated", "Treated + A"))
Next change the default theme:
theme_set(theme_bw())
Now we build the plot.
Construct a base object - nothing is plotted:
g = ggplot(dd, aes(type, values))
Add on the points: adjust the default jitter and change glyph according to type:
g = g + geom_jitter(aes(pch=type), position=position_jitter(width=0.1))
Add on the "box": calculate where the box ends. In this case, I've chosen the average value. If you don't want the box, just omit this step.
g = g + stat_summary(fun.y = function(i) mean(i),
geom="bar", fill="white", colour="black")
Add on some error bars: calculate the upper/lower bounds and adjust the bar width:
g = g + stat_summary(
fun.ymax=function(i) mean(i) + qt(0.975, length(i))*sd(i)/length(i),
fun.ymin=function(i) mean(i) - qt(0.975, length(i)) *sd(i)/length(i),
geom="errorbar", width=0.2)
Display the plot
g
In my R code above I used stat_summary to calculate the values needed on the fly. You could also create separate data frames and use geom_errorbar and geom_bar.
To use base R, have a look at my answer to this question.
If you don't mind using the ggplot2 package, there's an easy way to make similar graphics with geom_boxplot and geom_jitter. Using the mtcars example data:
library(ggplot2)
p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_boxplot() + geom_jitter() + theme_bw()
which produces the following graphic:
The documentation can be seen here: http://had.co.nz/ggplot2/geom_boxplot.html
I recently faced the same problem and found my own solution, using ggplot2.
As an example, I created a subset of the chickwts dataset.
library(ggplot2)
library(dplyr)
data(chickwts)
Dataset <- chickwts %>%
filter(feed == "sunflower" | feed == "soybean")
Since in geom_dotplot() is not possible to change the dots to symbols, I used the geom_jitter() as follow:
Dataset %>%
ggplot(aes(feed, weight, fill = feed)) +
geom_jitter(aes(shape = feed, col = feed), size = 2.5, width = 0.1)+
stat_summary(fun = mean, geom = "crossbar", width = 0.7,
col = c("#9E0142","#3288BD")) +
scale_fill_manual(values = c("#9E0142","#3288BD")) +
scale_colour_manual(values = c("#9E0142","#3288BD")) +
theme_bw()
This is the final plot:
For more details, you can have a look at this post:
http://withheadintheclouds1.blogspot.com/2021/04/building-dot-plot-in-r-similar-to-those.html?m=1