I am trying to use geom_point to illustrate the count of my data. I would also like to annotate a few of the points in my graph with geom_text. When I add the call to geom_text, it appears that it is plotting something underneath the points in the legend. I've tried reversing the order of the layers to no avail. I can't wrap my head around why it is doing this. Has anyone seen this before?
set.seed(42)
df <- data.frame(x = 1:10
, y = 1:10
, label = sample(LETTERS,10, replace = TRUE)
, count = sample(1:300, 10, replace = FALSE)
)
p <- ggplot(data = df, aes(x = x, y = y, size = count)) + geom_point()
p + geom_text(aes(label = label, size = 150, vjust = 2))
This happened to me all the time. The trick is knowing that aes() maps data to aesthetics. If there's no data to map (e.g., if you have a single value that you determine), there's no reason to use aes(). I believe that only things inside of an aes() will show up in your legend.
Furthermore, when you specify mappings inside of ggplot(aes()), those mappings apply to every subsequent layer. That's good for your x and y, since both geom_point and geom_text use them. That's bad for size = count, as that only applies to the points.
So these are my two rules to prevent this kind of thing:
Only put data-based mappings inside of aes(). If the argument is taking a single pre-determined value, pass it to the layer outside of aes().
Map data only for those layers that will use it. Corollary: only map data inside of ggplot(aes()) if you're confident that every subsequent layer will use it. Otherwise, map it at the layer level.
So I would plot this thusly:
p <- ggplot(data = df, aes(x = x, y = y)) + geom_point(aes(size = count))
p + geom_text(aes(label = label), size = 4, vjust = 2)
or, if you need to specify the size of text inside the aes, then legend = FALSE suppress drawing the legends of the geom:
p <- ggplot(data = df, aes(x = x, y = y, size = count)) + geom_point()
p + geom_text(aes(label = label, size = 150, vjust = 2), show_guide = FALSE)
Related
I would like to plot a background that captures the density of points in one dimension in a scatter plot. This would serve a similar purpose to a marginal density plot or a rug plot. I have a way of doing it that is not particularly elegant, I am wondering if there's some built-in functionality I can use to produce this kind of plot.
Mainly there are a few issues with the current approach:
Alpha overlap at boundaries causes banding at lower resolution as seen here. - Primary objective, looking for a geom or other solution that draws a nice continuous band filled with specific colour. Something like geom_density_2d() but with the stat drawn from only the X axis.
"Background" does not cover expanded area, can use coord_cartesian(expand = FALSE) but would like to cover regular margins. - Not a big deal, is nice-to-have but not required.
Setting scale_fill "consumes" the option for the plot, not allowing it to be set independently for the points themselves. - This may not be easily achievable, independent palettes for layers appears to be a fundamental issue with ggplot2.
data(iris)
dns <- density(iris$Sepal.Length)
dns_df <- tibble(
x = dns$x,
density = dns$y
)%>%
mutate(
start = x - mean(diff(x))/2,
end = x + mean(diff(x))/2
)
ggplot() +
geom_rect(
data = dns_df,
aes(xmin = start, xmax = end, fill = density),
ymin = min(iris$Sepal.Width),
ymax = max(iris$Sepal.Width),
alpha = 0.5) +
scale_fill_viridis_c(option = "A") +
geom_point(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_rug(data = iris, aes(x = Sepal.Length))
This is a bit of a hacky solution because it (ab)uses knowledge of how objects are internally parametrised to get what you want, which will yield some warnings, but gets you want you'd want.
First, we'll use a geom_raster() + stat_density() decorated with some choice after_stat()/stage() delayed evaluation. Normally, this would result in a height = 1 strip, but by setting the internal parameters ymin/ymax to infinitives, we'll have the strip extend the whole height of the plot. Using geom_raster() resolves the alpha issue you were having.
library(ggplot2)
p <- ggplot(iris) +
geom_raster(
aes(Sepal.Length,
y = mean(Sepal.Width),
fill = after_stat(density),
ymin = stage(NULL, after_scale = -Inf),
ymax = stage(NULL, after_scale = Inf)),
stat = "density", alpha = 0.5
)
#> Warning: Ignoring unknown aesthetics: ymin, ymax
p
#> Warning: Duplicated aesthetics after name standardisation: NA
Next, we add a fill scale, and immediately follow that by ggnewscale::new_scale_fill(). This allows another layer to use a second fill scale, as demonstrated with fill = Species.
p <- p +
scale_fill_viridis_c(option = "A") +
ggnewscale::new_scale_fill() +
geom_point(aes(Sepal.Length, Sepal.Width, fill = Species),
shape = 21) +
geom_rug(aes(Sepal.Length))
p
#> Warning: Duplicated aesthetics after name standardisation: NA
Lastly, to get rid of the padding at the x-axis, we can manually extend the limits and then shrink in the expansion. It allows for an extended range over which the density can be estimated, making the raster fill the whole area. There is some mismatch between how ggplot2 and scales::expand_range() are parameterised, so the exact values are a bit of trial and error.
p +
scale_x_continuous(
limits = ~ scales::expand_range(.x, mul = 0.05),
expand = c(0, -0.2)
)
#> Warning: Duplicated aesthetics after name standardisation: NA
Created on 2022-07-04 by the reprex package (v2.0.1)
This doesn't solve your problem (I'm not sure I understand all the issues correctly), but perhaps it will help:
Background does not cover expanded area, can use coord_cartesian(expand = FALSE) but would like to cover regular margins.
If you make the 'background' larger and use coord_cartesian() you can get the same 'filled-to-the-edges' effect; would this work for your use-case?
Alpha overlap at boundaries causes banding at lower resolution as seen here.
I wasn't able to fix the banding completely, but my approach below appears to reduce it.
Setting scale_fill "consumes" the option for the plot, not allowing it to be set independently for the points themselves.
If you use geom_segment() you can map density to colour, leaving fill available for e.g. the points. Again, not sure if this is a useable solution, just an idea that might help.
library(tidyverse)
data(iris)
dns <- density(iris$Sepal.Length)
dns_df <- tibble(
x = dns$x,
density = dns$y
) %>%
mutate(
start = x - mean(diff(x))/2,
end = x + mean(diff(x))/2
)
ggplot() +
geom_segment(
data = dns_df,
aes(x = start, xend = end,
y = min(iris$Sepal.Width) * 0.9,
yend = max(iris$Sepal.Width) * 1.1,
color = density), alpha = 0.5) +
coord_cartesian(ylim = c(min(iris$Sepal.Width),
max(iris$Sepal.Width)),
xlim = c(min(iris$Sepal.Length),
max(iris$Sepal.Length))) +
scale_color_viridis_c(option = "A", alpha = 0.5) +
scale_fill_viridis_d() +
geom_point(data = iris, aes(x = Sepal.Length,
y = Sepal.Width,
fill = Species),
shape = 21) +
geom_rug(data = iris, aes(x = Sepal.Length))
Created on 2022-07-04 by the reprex package (v2.0.1)
I want to output two plots in a grid using the same function but with different input for x. I am using ggplot2 with stat_function as per this post and I have combined the two plots as per this post and this post.
f01 <- function(x) {1 - abs(x)}
ggplot() +
stat_function(data = data.frame(x=c(-1, 1)), aes(x = x, color = "red"), fun = f01) +
stat_function(data = data.frame(x=c(-2, 2)), aes(x = x, color = "black"), fun = f01)
With the following outputs:
Plot:
Message:
`mapping` is not used by stat_function()`data` is not used by stat_function()`mapping` is not used by stat_function()`data` is not used by stat_function()
I don't understand why stat_function() won't use neither of the arguments. I would expect to plot two graphs one with x between -1:1 and the second with x between -2:2. Furthermore it takes the colors as labels, which I also don't understand why. I must be missing something obvious.
The issue is that according to the docs the data argument is
Ignored by stat_function(), do not use.
Hence, at least in the second call to stat_function the data is ignored.
Second, the
The function is called with a grid of evenly spaced values along the x axis, and the results are drawn (by default) with a line.
Therefore both functions are plotted over the same range of x values.
If you simply want to draw functions this can be achievd without data and mappings like so:
library(ggplot2)
f01 <- function(x) {1 - abs(x)}
ggplot() +
stat_function(color = "black", fun = f01, xlim = c(-2, 2)) +
stat_function(color = "red", fun = f01, xlim = c(-1, 1))
To be honest, I'm not really sure what happens here with ggplot and its inner workings. It seems that the functions are always applied to the complete range, here -2 to 2. Also, there is an issue on github regarding a wrong error message for stat_function.
However, you can use the xlim argument for your stat_function to limit the range on which a function is drawn. Also, if you don't specify the colour argument by a variable, but by a manual label, you need to tell which colours should be used for which label with scale_colour_manual (easiest with a named vector). I also adjusted the line width to show the function better:
library(ggplot2)
f01 <- function(x) {1 - abs(x)}
cols <- c("red" = "red", "black" = "black")
ggplot() +
stat_function(data = data.frame(x=c(-1, 1)), aes(x = x, colour = "red"), fun = f01, size = 1.5, xlim = c(-1, 1)) +
stat_function(data = data.frame(x=c(-2, 2)), aes(x = x, colour = "black"), fun = f01) +
scale_colour_manual(values = cols)
I wish to add the number of observations to this boxplot, not by group but separated by factor. Also, I wish to display the number of observations in addition to the x-axis label that it looks something like this: ("PF (N=12)").
Furthermore, I would like to display the mean value of each box inside of the box, displayed in millions in order not to have a giant number for each box.
Here is what I have got:
give.n <- function(x){
return(c(y = median(x)*1.05, label = length(x)))
}
mean.n <- function(x){x <- x/1000000
return(c(y = median(x)*0.97, label = round(mean(x),2)))
}
ggplot(Soils_noctrl) +
geom_boxplot(aes(x=Slope,y=Events.g_Bacteria, fill = Detergent),
varwidth = TRUE) +
stat_summary(aes(x = Slope, y = Events.g_Bacteria), fun.data = give.n, geom = "text",
fun = median,
position = position_dodge(width = 0.75))+
ggtitle("Cell Abundance")+
stat_summary(aes(x = Slope, y = Events.g_Bacteria),
fun.data = mean.n, geom = "text", fun = mean, colour = "red")+
facet_wrap(~ Location, scale = "free_x")+
scale_y_continuous(name = "Cell Counts per Gram (Millions)",
breaks = round (seq(min(0),
max(100000000), by = 5000000),1),
labels = function(y) y / 1000000)+
xlab("Sample")
And so far it looks like this:
As you can see, the mean value is at the bottom of the plot and the number of observations are in the boxes but not separated
Thank you for your help! Cheers
TL;DR - you need to supply a group= aesthetic, since ggplot2 does not know on which column data it is supposed to dodge the text geom.
Unfortunately, we don't have your data, but here's an example set that can showcase the rationale here and the function/need for group=.
set.seed(1234)
df1 <- data.frame(detergent=c(rep('EDTA',15),rep('Tween',15)), cells=c(rnorm(15,10,1),rnorm(15,10,3)))
df2 <- data.frame(detergent=c(rep('EDTA',20),rep('Tween',20)), cells=c(rnorm(20,1.3,1),rnorm(20,4,2)))
df3 <- data.frame(detergent=c(rep('EDTA',30),rep('Tween',30)), cells=c(rnorm(30,5,0.8),rnorm(30,3.3,1)))
df1$smp='Sample1'
df2$smp='Sample2'
df3$smp='Sample3'
df <- rbind(df1,df2,df3)
Instead of using stat_summary(), I'm just going to create a separate data frame to hold the mean values I want to include as text on my plot:
summary_df <- df %>% group_by(smp, detergent) %>% summarize(m=mean(cells))
Now, here's the plot and use of geom_text() with dodging:
p <- ggplot(df, aes(x=smp, y=cells)) +
geom_boxplot(aes(fill=detergent))
p + geom_text(data=summary_df,
aes(y=m, label=round(m,2)),
color='blue', position=position_dodge(0.8)
)
You'll notice the numbers are all separated along y= just fine, but the "dodging" is not working. This is because we have not supplied any information on how to do the dodging. In this case, the group= aesthetic can be supplied to let ggplot2 know that this is the column by which to use for the dodging:
p + geom_text(data=summary_df,
aes(y=m, label=round(m,2), group=detergent),
color='blue', position=position_dodge(0.8)
)
You don't have to supply the group= aesthetic if you supply another aesthetic such as color= or fill=. In cases where you give both a color= and group= aesthetic, the group= aesthetic will override any of the others for dodging purposes. Here's an example of the same, but where you don't need a group= aesthetic because I've moved color= up into the aes() (changing fill to greyscale so that you can see the text):
p + geom_text(data=summary_df,
aes(y=m, label=round(m,2), color=detergent),
position=position_dodge(0.8)
) + scale_fill_grey()
FUN FACT: Dodging still works even if you supply geom_text() with a nonsensical aesthetic that would normally work for dodging, such as fill=. You get a warning message Ignoring unknown aesthetics: fill, but the dodging still works:
p + geom_text(data=summary_df,
aes(y=m, label=round(m,2), fill=detergent),
position=position_dodge(0.8)
)
# gives you the same plot as if you just supplied group=detergent, but with black text
In your case, changing your stat_summary() line to this should work:
stat_summary(aes(x = Slope, y = Events.g_Bacteria, group = Detergent),...
I am trying to plot a histogram of two overlapping distributions in ggplot2. Unfortunately, the graphic needs to be in black and white. I tried representing the two categories with different shades of grey, with transparency, but the result is not as clear as I would like. I tried adding outlines to the bars with different linetypes, but this produced some strange results.
require(ggplot2)
set.seed(65)
a = rnorm(100, mean = 1, sd = 1)
b = rnorm(100, mean = 3, sd = 1)
dat <- data.frame(category = rep(c('A', 'B'), each = 100),
values = c(a, b))
ggplot(data = dat, aes(x = values, linetype = category, fill = category)) +
geom_histogram(colour = 'black', position = 'identity', alpha = 0.4, binwidth = 1) +
scale_fill_grey()
Notice that one of the lines that should appear dotted is in fact solid (at a value of x = 4). I think this must be a result of it actually being two lines - one from the 3-4 bar and one from the 4-5 bar. The dots are out of phase so they produce a solid line. The effect is rather ugly and inconsistent.
Is there any way of fixing this overlap?
Can anyone suggest a more effective way of clarifying the difference between the two categories, without resorting to colour?
Many thanks.
One possibility would be to use a 'hollow histogram', as described here:
# assign your original plot object to a variable
p1 <- ggplot(data = dat, aes(x = values, linetype = category, fill = category)) +
geom_histogram(colour = 'black', position = 'identity', alpha = 0.4, binwidth = 0.4) +
scale_fill_grey()
# p1
# extract relevant variables from the plot object to a new data frame
# your grouping variable 'category' is named 'group' in the plot object
df <- ggplot_build(p1)$data[[1]][ , c("xmin", "y", "group")]
# plot using geom_step
ggplot(data = df, aes(x = xmin, y = y, linetype = factor(group))) +
geom_step()
If you want to vary both linetype and fill, you need to plot a histogram first (which can be filled). Set the outline colour of the histogram to transparent. Then add the geom_step. Use theme_bw to avoid 'grey elements on grey background'
p1 <- ggplot() +
geom_histogram(data = dat, aes(x = values, fill = category),
colour = "transparent", position = 'identity', alpha = 0.4, binwidth = 0.4) +
scale_fill_grey()
df <- ggplot_build(p1)$data[[1]][ , c("xmin", "y", "group")]
df$category <- factor(df$group, labels = c("A", "B"))
p1 +
geom_step(data = df, aes(x = xmin, y = y, linetype = category)) +
theme_bw()
First, I would recommend theme_set(theme_bw()) or theme_set(theme_classic()) (this sets the background to white, which makes it (much) easier to see shades of gray).
Second, you could try something like scale_linetype_manual(values=c(1,3)) -- this won't completely eliminate the artifacts you're unhappy about, but it might make them a little less prominent since linetype 3 is sparser than linetype 2.
Short of drawing density plots instead (which won't work very well for small samples and may not be familiar to your audience), dodging the positions of the histograms (which is ugly), or otherwise departing from histogram conventions, I can't think of a better solution.
I'm trying to produce a histogram that illustrates observed points(a sub-set) on a histogram of all observations. To make it meaningful, I need to color each point differently and place a legend on the plot. My problem is, I can't seem to get a scale to show up on the plot. Below is an example of what I've tried.
subset <-1:8
results = data.frame(x_data = rnorm(5000),TestID=1:5000)
m <- ggplot(results,aes(x=x_data))
m+stat_bin(aes(y=..density..))+
stat_density(colour="blue", fill=NA)+
geom_point(data = results[results$TestID %in% subset,],
aes(x = x_data, y = 0),
colour = as.factor(results$TestID[results$TestID %in% subset]),
size = 5)+
scale_colour_brewer(type="seq", palette=3)
Ideally, I'd like the points to be positioned on the density line(but I'm really unsure of how to make that work, so I'll settle to position them at y = 0). What I need most urgently is a legend which indicates the TestID that corresponds to each of the points in subset.
Thanks a lot to anyone who can help.
This addresses your second point - if you want a legend, you need to include that variable as an aesthetic and map it to a variable (colour in this case). So all you really need to do is move colour = as.factor(results$TestID[results$TestID %in% subset]) inside the call to aes() like so:
ggplot(results,aes(x=x_data)) +
stat_bin(aes(y=..density..))+
stat_density(colour="blue", fill=NA)+
geom_point(data = results[results$TestID %in% subset,],
aes(x = x_data,
y = 0,
colour = as.factor(results$TestID[results$TestID %in% subset])
),
size = 5) +
scale_colour_brewer("Fancy title", type="seq", palette=3)