Why does this graph not show overlaps
Some of the cars in this dataset share the same combination for x and y (displ and hwy).
For example for displ = 2 and hwy = 29, there are: 1 midsize; 6 compact and 3 subcompact.
However, in this spot there is only a green dot showing only 1 midsize. What am I misunderstanding about this graph?
Thank you so much!
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
Carsten,
The call to goem_point() will map coordinates over each other, hence you will see only one point, this is especially true for small datasets. You can address this by using geom_jitter(), which allows you to insert noise into the plot allowing you to see all points.
Solution: geom_jitter()
Here we use geom_jitter(), to insert noise into the plot data allowing us to see all overlapping points.
if (require(ggplot2) ) install.packages("ggplot2")
data(mtcars)
ggplot(data = mpg) +
geom_jitter(mapping = aes(x = displ, y = hwy, color = class))
Plot Output: (Points slightly shifted to distinguish each point)
Note how the inserted "noise" allows you to distinguish the plot points.
nb. The jitter geom is a convenient shortcut for geom_point(position = "jitter"). It adds a small amount of random variation to the location of each point, and is a useful way of handling overplotting caused by discreteness in smaller datasets.
Apart from jitter, you can also change the alpha argument in geom_point() to 0.3 or 0.4, by default it is 1, which means 100% opaque.
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class, alpha = 0.3))
This will highlight areas of over-plotting
The geom_jitter solution and alpha changing solution are both excellent. A third possibility is to map the size of the marker to the number of observations at those coordinates (along with an alpha adjustment) using geom_count():
library(ggplot2)
data(mtcars)
ggplot(data = mpg) +
geom_count(mapping = aes(x = displ, y = hwy, color = class), alpha = .5)
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)
Hi I am trying to code for a scatter plot for three variables in R:
Race= [0,1]
YOI= [90,92,94]
ASB_mean = [1.56, 1.59, 1.74]
Antisocial <- read.csv(file = 'Antisocial.csv')
Table_1 <- ddply(Antisocial, "YOI", summarise, ASB_mean = mean(ASB))
Table_1
Race <- unique(Antisocial$Race)
Race
ggplot(data = Table_1, aes(x = YOI, y = ASB_mean, group_by(Race))) +
geom_point(colour = "Black", size = 2) + geom_line(data = Table_1, aes(YOI,
ASB_mean), colour = "orange", size = 1)
Image of plot: https://drive.google.com/file/d/1E-ePt9DZJaEr49m8fguHVS0thlVIodu9/view?usp=sharing
Data file: https://drive.google.com/file/d/1UeVTJ1M_eKQDNtvyUHRB77VDpSF1ASli/view?usp=sharing
Can someone help me understand where I am making mistake? I want to plot mean ASB vs YOI grouped by Race. Thanks.
I am not sure what is your desidered output. Maybe, if I well understood your question I Think that you want somthing like this.
g_Antisocial <- Antisocial %>%
group_by(Race) %>%
summarise(ASB = mean(ASB),
YOI = mean(YOI))
Antisocial %>%
ggplot(aes(x = YOI, y = ASB, color = as_factor(Race), shape = as_factor(Race))) +
geom_point(alpha = .4) +
geom_point(data = g_Antisocial, size = 4) +
theme_bw() +
guides(color = guide_legend("Race"), shape = guide_legend("Race"))
and this is the output:
#Maninder: there are a few things you need to look at.
First of all: The grammar of graphics of ggplot() works with layers. You can add layers with different data (frames) for the different geoms you want to plot.
The reason why your code is not working is that you mix the layer call and or do not really specify (and even mix) what is the scatter and line visualisation you want.
(I) Use ggplot() + geom_point() for a scatter plot
The ultimate first layer is: ggplot(). Think of this as your drawing canvas.
You then speak about adding a scatter plot layer, but you actually do not do it.
For example:
# plotting antisocal data set
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race)))
will plot your Antiscoial data set using the scatter, i.e. geom_point() layer.
Note that I put Race as a factor to have a categorical colour scheme otherwise you might end up with a continous palette.
(II) line plot
In analogy to above, you would get for the line plot the following:
# plotting Table_1
ggplot() +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean))
I save showing the plot of the line.
(III) combining different layers
# putting both together
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race))) +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean)) +
## this is to set the legend title and have a nice(r) name in your colour legend
labs(colour = "Race")
This yields:
That should explain how ggplot-layering works. Keep an eye on the datasets and geoms that you want to use. Before working with inheritance in aes, I recommend to keep the data= and aes() call in the geom_xxxx. This avoids confustion.
You may want to explore with geom_jitter() instead of geom_point() to get a bit of a better presentation of your dataset. The "few" points plotted are the result of many datapoints in the same position (and overplotted).
Moving away from plotting to your question "I want to plot mean ASB vs YOI grouped by Race."
I know too little about your research to fully comprehend what you mean with that.
I take it that the mean ASB you calculated over the whole population is your reference (aka your Table_1), and you would like to see how the Race groups feature vs this population mean.
One option is to group your race data points and show them as boxplots for each YOI.
This might be what you want. The boxplot gives you the median and quartiles, and you can compare this per group against the calculated ASB mean.
For presentation purposes, I highlighted the line by increasing its size and linetype. You can play around with the colours, etc. to give you the aesthetics you aim for.
Please note, that for the grouped boxplot, you also have to treat your integer variable YOI, I coerced into a categorical factor. Boxplot works with fill for the body (colour sets only the outer line). In this setup, you also need to supply a group value to geom_line() (I just assigned it to 1, but that is arbitrary - in other contexts you can assign another variable here).
ggplot() +
geom_boxplot(data = Antisocial, aes(x = as.factor(YOI), y = ASB, fill = as.factor(Race))) +
geom_line(data = Table_1, aes(x = as.factor(YOI), y = ASB_mean, group = 1)
, size = 2, linetype = "dashed") +
labs(x = "YOI", fill = "Race")
Hope this gets you going!
I want to make some points stand out on a ggplot2 chart by giving them less transparency while the rest fade to the background. But no matter what two alpha values I give the sets of points, their relative transparency is the same.
Here's 0.8 vs 0.7:
x <- mtcars
x$opacity <- ifelse(x$cyl == 6, 0.8, 0.7)
ggplot(x, aes(x = wt, y = mpg, color = cyl, alpha = opacity)) +
geom_point()
And here's 0.8 vs 0.1 -- looks the same:
x$opacity <- ifelse(x$cyl == 6, 0.8, 0.1)
ggplot(x, aes(x = wt, y = mpg, color = cyl, alpha = opacity)) +
geom_point()
How can I fine-tune that relative alpha so that the two sets are closer in transparency? Right now the values of the two numbers don't seem to matter. Specifically, in this case I want the darker points (with the higher alpha) to be more transparent.
Since you are trying to pass actual alpha values to the aesthetic mapping, be sure to use
scale_alpha_identity()
Otherwise ggplot will rescale your values just like it created the colors for you automatically.
Add scale_alpha_continuous to your plot and define the range. e.g.
scale_alpha_continuous(range = c(0.7, 0.8))
You're mapping the values 0.7 and 0.8 to alpha, not necessarily using them for alpha. A quicker way is to map the condition and then set alpha:
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg, color = cyl, alpha = cyl == 6)) +
geom_point() +
scale_alpha_discrete(range = c(0.2, 0.8))
#> Warning: Using alpha for a discrete variable is not advised.
I have a plot looking principally like this when based on the mpg-dataset:
library(datasets)
plot2 <- ggplot(mapping = aes(
x = cty,
y = hwy,
group = as.factor(cyl),
shape = as.factor(cyl),
linetype = as.factor(cyl)),
data = mpg) +
geom_point() +
geom_smooth(method = lm, se = F, color = "black") +
theme(legend.key.width = unit(4,"cm"))
plot2
I would like to be able to control the size of the symbols in the legend without affecting the thickness of the lines. Trying with the idea of using override.aes from similar threads gives bigger symbols but at the same time thicker lines.
plot2 + guides(shape = guide_legend(override.aes = list(size=5)))
This question is partly described in Modifying legends in ggplot2 with interactions and guides; however, the question in the link adresses changes to Geom_Path and not Geom_Smooth and does not explain how to find the exact definition of the ggproto()-object to change. It would be helpful if someone could supply this information - then I could probably modify the code myself in a similar fashion.
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.