library(tidyverse)
library(ggQC)
set.seed(5555)
Golden_Egg_df <- data.frame(month = 1:12,
egg_diameter = rnorm(n = 12, mean = 1.5, sd = 0.2))
Golden_Egg_df$egg_diameter[3] <- 2.5
Example data is generated above... to create the plot shown below. I want all points above or below the red lines highlighted, preferably with a circle around said point, in our example the third point.
I know I can accomplish this adding a geom_point() with an ifelse statement. I don't know how to strip out the upper 'red line value' and 'lower red line' values from stat_QC() to allow me to utilize my proposed method.
Here's hoping you know how and can provide the answer.
XmR_Plot <- ggplot(Golden_Egg_df, aes(x = month, y = egg_diameter)) +
geom_point() + geom_line() +
stat_QC(method = "XmR")
Initial plot:
library(ggplot2)
p <- ggplot(Golden_Egg_df, aes(x = month, y = egg_diameter)) +
geom_point() + geom_line() +
stat_QC(method = "XmR")
Here is one option where we use ggplot_build to extract the data from the red lines. You can read more about a ggplot_build object here: https://rud.is/books/creating-ggplot2-extensions/demystifying-ggplot2.html#the-ggplot_built-object
pb <- ggplot_build(p)
thres <- range(pb$data[[3]]$yintercept) # you need to inspect pb$data to find the right element
thres contains y-values of the red lines.
thres
#[1] 0.7319105 2.3820961
If you now want to highlight only the point above (or below) these values, add another point layer with a subset of the initial data
p + geom_point(
data = subset(Golden_Egg_df,
egg_diameter > max(thres) | egg_diameter < min(thres)),
shape = 21,
size = 4,
col = "red"
)
Related
I've made this multiple histogram plot in ggplot and now I want to add a legend for both the light purple part and the dark purple part. I know the conventional way is to to it with aes, but I can't seem to figure out how I integrate this feature as one into my multiple histogram plot.
I don't shy manual labour, but more sophisticated solutions are preferred. Anyone help me out?
#dataframe
set.seed(20)
df <- data.frame(expl = rbinom(n=100, size = 1, prob=0.08),
resp = sample(50:100, size = 100, replace = T))
#graph
graph <- ggplot(data = df, aes(x = resp))
graph +
geom_histogram(fill = "#BEBADA", alpha = 0.5, bins = 10) +
geom_histogram(data = subset(df, expl == '1'), fill = "#BEBADA", bins = 10)
Your data is already in the long format that is well suited for ggplot; you just need to map expl to alpha. In general, if you find yourself making multiples of the same geom, you probably want to rethink either the shape of your data or your approach for feeding it into geoms.
library(tidyverse)
set.seed(20)
df <- data.frame(expl = rbinom(n=100, size = 1, prob=0.08),
resp = sample(50:100, size = 100, replace = T))
To map expl onto alpha, make it a factor, and then assign that to alpha inside your aes. Then you can set the alpha scale to values of 0.5 and 1.
ggplot(df, aes(x = resp, alpha = as.factor(expl))) +
geom_histogram(fill = "#bebada", bins = 10) +
scale_alpha_manual(values = c(0.5, 1))
However, differentiating by alpha is a little awkward. You could instead map to fill and use light and dark purples:
ggplot(df, aes(x = resp, fill = as.factor(expl))) +
geom_histogram(bins = 10) +
scale_fill_manual(values = c("0" = "mediumpurple1", "1" = "mediumpurple4"))
Note also that you can adjust the position of the histogram bars if you need to, by assigning geom_histogram(position = ...), where you could fill in with something such as "dodge" if that's what you'd like.
If you want a legend on the alpha value, the idea is to include it as an aesthetic rather than as a direct argument as you tried. In order to do this, a simple solution is to enrich the data frame used by ggplot:
df2 <- rbind(
cbind(df, filter="all lines"),
cbind(subset(df, expl == '1'), filter="expl==1")
)
df2 corresponds to df after appending the lines from your subset of interest (with a field filter telling from which copy each record comes)
Then, this solves your problem
ggplot(df2, aes(resp, alpha=filter)) +
geom_histogram(fill="#BEBADA", bins=10, position="identity") +
scale_alpha_discrete(range=c(.5,1))
I would like to create a colour blind test, similar to that below, using ggplot.
The basic idea is to use geom_hex (or perhaps a voronoi diagram, or possibly even circles as in the figure above) as the starting point, and define a dataframe that, when plotted in ggplot, produces the image.
We would start by creating a dataset, such as:
df <- data.frame(x = rnorm(10000), y = rnorm(10000))
then plot this:
ggplot(df, aes(x, y)) +
geom_hex() +
coord_equal() +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
which gives the image below:
The main missing step is to create a dataset that actually plots a meaningful symbol (letter or number), and I'm not sure how best to go about this without painstakingly mapping the coordinates. Ideally one would be able to read in the coordinates perhaps from an image file.
Finally, a bit of tidying up could round the plot edges by removing the outlying points.
All suggestions are very welcome!
EDIT
Getting a little closer to what I'm after, we can use the image below of the letter 'e':
Using the imager package, we can read this in and convert it to a dataframe:
img <- imager::load.image("e.png")
df <- as.data.frame(img)
then plot that dataframe using geom_raster:
ggplot(df, aes(x, y)) +
geom_raster(aes(fill = value)) +
coord_equal() +
scale_y_continuous(trans = scales::reverse_trans()) +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
If we use geom_hex instead of geom_raster, we can get the following plot:
ggplot(df %>% filter(value %in% 1), aes(x, y)) +
geom_hex() +
coord_equal() +
scale_y_continuous(trans = scales::reverse_trans()) +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
so, getting there but clearly still a long way off...
Here's an approach for creating this plot:
Packages you need:
library(tidyverse)
library(packcircles)
Get image into a 2D matrix (x and y coordinates) of values. To do this, I downloaded the .png file of the e as "e.png" and saved in my working directory. Then some processing:
img <- png::readPNG("e.png")
# From http://stackoverflow.com/questions/16496210/rotate-a-matrix-in-r
rotate <- function(x) t(apply(x, 2, rev))
# Convert to one colour layer and rotate it to be in right direction
img <- rotate(img[,,1])
# Check that matrix makes sense:
image(img)
Next, create a whole lot of circles! I did this based on this post.
# Create random "circles"
# *** THESE VALUES WAY NEED ADJUSTING
ncircles <- 1200
offset <- 100
rmax <- 80
x_limits <- c(-offset, ncol(img) + offset)
y_limits <- c(-offset, nrow(img) + offset)
xyr <- data.frame(
x = runif(ncircles, min(x_limits), max(x_limits)),
y = runif(ncircles, min(y_limits), max(y_limits)),
r = rbeta(ncircles, 1, 10) * rmax)
# Find non-overlapping arrangement
res <- circleLayout(xyr, x_limits, y_limits, maxiter = 1000)
cat(res$niter, "iterations performed")
#> 1000 iterations performed
# Convert to data for plotting (just circles for now)
plot_d <- circlePlotData(res$layout)
# Check circle arrangement
ggplot(plot_d) +
geom_polygon(aes(x, y, group=id), colour = "white", fill = "skyblue") +
coord_fixed() +
theme_minimal()
Finally, interpolate the image pixel values for the centre of each circle. This will indicate whether a circle is centered over the shape or not. Add some noise to get variance in colour and plot.
# Get x,y positions of centre of each circle
circle_positions <- plot_d %>%
group_by(id) %>%
summarise(x = min(x) + (diff(range(x)) / 2),
y = min(y) + (diff(range(y)) / 2))
# Interpolate on original image to get z value for each circle
circle_positions <- circle_positions %>%
mutate(
z = fields::interp.surface(
list(x = seq(nrow(img)), y = seq(ncol(img)), z = img),
as.matrix(.[, c("x", "y")])),
z = ifelse(is.na(z), 1, round(z)) # 1 is the "empty" area shown earlier
)
# Add a little noise to the z values
set.seed(070516)
circle_positions <- circle_positions %>%
mutate(z = z + rnorm(n(), sd = .1))
# Bind z value to data for plotting and use as fill
plot_d %>%
left_join(select(circle_positions, id, z)) %>%
ggplot(aes(x, y, group = id, fill = z)) +
geom_polygon(colour = "white", show.legend = FALSE) +
scale_fill_gradient(low = "#008000", high = "#ff4040") +
coord_fixed() +
theme_void()
#> Joining, by = "id"
To get colours right, tweak them in scale_fill_gradient
I am trying to generate a (grouped) density plot labelled with sample sizes.
Sample data:
set.seed(100)
df <- data.frame(ab.class = c(rep("A", 200), rep("B", 200)),
val = c(rnorm(200, 0, 1), rnorm(200, 1, 1)))
The unlabelled density plot is generated and looks as follows:
ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4)
What I want to do is add text labels somewhere near the peak of each density, showing the number of samples in each group. However, I cannot find the right combination of options to summarise the data in this way.
I tried to adapt the code suggested in this answer to a similar question on boxplots: https://stackoverflow.com/a/15720769/1836013
n_fun <- function(x){
return(data.frame(y = max(x), label = paste0("n = ",length(x))))
}
ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4) +
stat_summary(geom = "text", fun.data = n_fun)
However, this fails with Error: stat_summary requires the following missing aesthetics: y.
I also tried adding y = ..density.. within aes() for each of the geom_density() and stat_summary() layers, and in the ggplot() object itself... none of which solved the problem.
I know this could be achieved by manually adding labels for each group, but I was hoping for a solution that generalises, and e.g. allows the label colour to be set via aes() to match the densities.
Where am I going wrong?
The y in the return of fun.data is not the aes. stat_summary complains that he cannot find y, which should be specificed in global settings at ggplot(df, aes(x = val, group = ab.class, y = or stat_summary(aes(y = if global setting of y is not available. The fun.data compute where to display point/text/... at each x based on y given in the data through aes. (I am not sure whether I have made this clear. Not a native English speaker).
Even if you have specified y through aes, you won't get desired results because stat_summary compute a y at each x.
However, you can add text to desired positions by geom_text or annotate:
# save the plot as p
p <- ggplot(df, aes(x = val, group = ab.class)) +
geom_density(aes(fill = ab.class), alpha = 0.4)
# build the data displayed on the plot.
p.data <- ggplot_build(p)$data[[1]]
# Note that column 'scaled' is used for plotting
# so we extract the max density row for each group
p.text <- lapply(split(p.data, f = p.data$group), function(df){
df[which.max(df$scaled), ]
})
p.text <- do.call(rbind, p.text) # we can also get p.text with dplyr.
# now add the text layer to the plot
p + annotate('text', x = p.text$x, y = p.text$y,
label = sprintf('n = %d', p.text$n), vjust = 0)
Suppose I make a violin plot, with say 10 violins, using the following code:
library(ggplot2)
library(reshape2)
df <- melt(data.frame(matrix(rnorm(500),ncol=10)))
p <- ggplot(df, aes(x = variable, y = value)) +
geom_violin()
p
I can add a dot representing the mean of each variable as follows:
p + stat_summary(fun.y=mean, geom="point", size=2, color="red")
How can I do something similar but for arbitrary points?
For example, if I generate 10 new points, one drawn from each distribution, how could I plot those as dots on the violins?
You can give any function to stat_summary provided it just returns a single value. So one can use the function sample. Put extra arguments such as size, in the fun.args
p + stat_summary(fun.y = "sample", geom = "point", fun.args = list(size = 1))
Assuming your points are qualified using the same group names (i.e., variable), you should be able to define them manually with:
newdf <- group_by(df, variable) %>% sample_n(10)
p + geom_point(data=newdf)
The points can be anything, including static numbers:
newdf <- data.frame(variable = unique(df$variable), value = seq(-2, 2, len=10))
p + geom_point(data=newdf)
I had a similar problem. Code below exemplifies the toy problem - How does one add arbitrary points to a violin plot? - and solution.
## Visualize data set that comes in base R
head(ToothGrowth)
## Make a violin plot with dose variable on x-axis, len variable on y-axis
# Convert dose variable to factor - Important!
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1)
# Suppose you want to add 3 blue points
# [0.5, 10], [1,20], [2, 30] to the plot.
# Make a new data frame with these points
# and add them to the plot with geom_point().
TrueVals <- ToothGrowth[1:3,]
TrueVals$len <- c(10,20,30)
# Make dose variable a factor - Important for positioning points correctly!
TrueVals$dose <- as.factor(c(0.5, 1, 2))
# Plot with 3 added blue points
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1) +
geom_point(data = TrueVals, color = "blue")
I would like to plot a histogram of my data to show its distribution, but I have a few outliers that are really high compared to most of the values, which are < 1.00. Rather than having one or two bars scrunched up at the far left and then nothing until the very far right side of the graph, I'd like to have a histogram with everything except the outliers and then add a bar at the end where the label underneath it is ">100%". I can do that with ggplot2 using geom_bar() like this:
X <- c(rnorm(1000, mean = 0.5, sd = 0.2),
rnorm(10, mean = 10, sd = 0.5))
Data <- data.frame(table(cut(X, breaks=c(seq(0,1, by=0.05), max(X)))))
library(ggplot2)
ggplot(Data, aes(x = Var1, y = Freq)) + geom_bar(stat = "identity") +
scale_x_discrete(labels = paste0(c(seq(5,100, by = 5), ">100"), "%"))
The problem is that, for the size I need this to be, the labels end up overlapping or needing to be plotted at an angle for readability. I don't really need all of the bars labeled. Is there some way to either
A) plot this in a different manner other than geom_bar() so that I don't need to manually add that last bar or
B) only label some of the bars?
I will try to answer B.
I don't know if there is a parameter that would let you do B) but you can manually define a function to do that for you. I.e.:
library(ggplot2)
X <- c(rnorm(1000, mean = 0.5, sd = 0.2),
rnorm(10, mean = 10, sd = 0.5))
Data <- data.frame(table(cut(X, breaks=c(seq(0,1, by=0.05), max(X)))))
#the function will remove one label every n labels
remove_elem <- function(x,n) {
for (i in (1:length(x))) {
if (i %% n == 0) {x[i]<-''}
}
return(x)
}
#make inital labels outside ggplot (same way as before).
labels <-paste0(c(seq(5,100, by = 5),'>100'),'%')
Now using that function inside the ggplot function:
ggplot(Data, aes(x = Var1, y = Freq)) + geom_bar(stat = "identity") +
scale_x_discrete(labels = remove_elem(labels,2))
outputs:
I don't know if this is what you are looking for but it does the trick!