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$dose <- as.factor(c(0.5, 1, 2))
# Plot with 3 added blue points
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1) +
geom_point(data = TrueVals, color = "blue")
Related
I've been trying to generate a Manhattan plot using ggplot, which I finally got to work. However, I cannot get the points to be colored by chromosome, despite having tried several different examples I've seen online. I'm attaching my code and the resulting plot below. Can anyone see why the code is failing to color points by chromosome?
library(tidyverse)
library(vroom)
# threshold to drop really small -log10 p values so I don't have to plot millions of uninformative points. Just setting to 0 since I'm running for a small subset
min_p <- 0.0
# reading in data to brassica_df2, converting to data frame, removing characters from AvsDD p value column, converting to numeric, filtering by AvsDD (p value)
brassica_df2 <- vroom("manhattan_practice_data.txt", col_names = c("chromosome", "position", "num_SNPs", "prop_SNPs_coverage", "min_coverage", "AvsDD", "AvsWD", "DDvsWD"))
brassica_df2 <- as.data.frame(brassica_df2)
brassica_df2$AvsDD <- gsub("1:2=","",as.character(brassica_df2$AvsDD))
brassica_df2$AvsDD <- as.numeric(brassica_df2$AvsDD)
brassica_df2 <- filter(brassica_df2, AvsDD > min_p)
# setting significance threshhold
sig_cut <- -log10(1)
# settin ylim for graph
ylim <- (max(brassica_df2$AvsDD) + 2)
# setting up labels for x axis
axisdf <- as.data.frame(brassica_df2 %>% group_by(chromosome) %>% summarize(center=( max(position) + min(position) ) / 2 ))
# making manhattan plot of statistically significant SNP shifts
manhplot <- ggplot(data = filter(brassica_df2, AvsDD > sig_cut), aes(x=position, y=AvsDD), color=as.factor(chromosome)) +
geom_point(alpha = 0.8) +
scale_x_continuous(label = axisdf$chromosome, breaks= axisdf$center) +
scale_color_manual(values = rep(c("#276FBF", "#183059"), unique(length(axisdf$chromosome)))) +
geom_hline(yintercept = sig_cut, lty = 2) +
ylab("-log10 p value") +
ylim(c(0,ylim)) +
theme_classic() +
theme(legend.position = "n")
print(manhplot)
I think you just need to move your color=... argument inside the call to aes():
ggplot(
data = filter(brassica_df2, AvsDD > sig_cut),
aes(x=position, y=AvsDD),
color=as.factor(chromosome))
becomes...
ggplot(
data = filter(brassica_df2, AvsDD > sig_cut),
aes(x=position, y=AvsDD, color=as.factor(chromosome)))
The following sample or R code displays contour levels and the data points used in generating the contours.
n <- 10
x <- c(rnorm(n,-1,0.5), rnorm(n,1,0.5))
y <- c(rnorm(n,-1,1), rnorm(n,1,0.5))
df <- data.frame(x,y)
# categorise the points
df$cat <- sample(c(1,2), n, replace=T)
library(ggplot2)
p <- ggplot(df)
# for manual colouring of points, but not showing contours due to error
#p <- p + geom_point(aes(x=x,y=y,col=factor(cat)))
#cols <- c("1"="red", "2"="blue")
#p <- p + scale_color_manual(values=cols)
# this works fine except I am not controlling the colours
p <- p + geom_point(aes(x=x,y=y,col=cat))
p <- p + geom_density2d(aes(x=x,y=y,color=..level..))
print(p)
I am able to colour the points according to their binary category (see commented out code above) manually if I do not display the contours, but adding the contours results in a "Continuous value supplied to discrete scale" error.
Various attempts have failed.
The question: Is it possible to colour the points (according to category) and independently colour the contour levels (according to height)?
You can try
library(tidyverse)
df %>%
ggplot(aes(x=x,y=y)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon") +
geom_point(aes(color=factor(cat)), size=5) +
theme_bw()
Or switch to points where fill is working like shape=21
df %>%
ggplot(aes(x=x,y=y)) +
geom_density2d(aes(color=..level..))+
geom_point(aes(fill=factor(cat)),color="black",shape=21, size=5) +
theme_bw() +
scale_fill_manual(values = c(2,4)) +
scale_color_continuous(low = "green", high = "orange")
or try to add scale_color_gradientn(colours = rainbow(10)) instead.
I have discreet data that looks like this:
height <- c(1,2,3,4,5,6,7,8)
weight <- c(100,200,300,400,500,600,700,800)
person <- c("Jack","Jim","Jill","Tess","Jack","Jim","Jill","Tess")
set <- c(1,1,1,1,2,2,2,2)
dat <- data.frame(set,person,height,weight)
I'm trying to plot a graph with same x-axis(person), and 2 different y-axis (weight and height). All the examples, I find is trying to plot the secondary axis (sec_axis), or discreet data using base plots.
Is there an easy way to use sec_axis for discreet data on ggplot2?
Edit: Someone in the comments suggested I try the suggested reply. However, I run into this error now
Here is my current code:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("height",sec_axis(~.*1.2, name="height"))
p2
I get the error: Error in x < range[1] :
comparison (3) is possible only for atomic and list types
Alternately, now I have modified the example to match this example posted.
p <- ggplot(dat, aes(x = person))
p <- p + geom_line(aes(y = height, colour = "Height"))
# adding the relative weight data, transformed to match roughly the range of the height
p <- p + geom_line(aes(y = weight/100, colour = "Weight"))
# now adding the secondary axis, following the example in the help file ?scale_y_continuous
# and, very important, reverting the above transformation
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*100, name = "Relative weight [%]"))
# modifying colours and theme options
p <- p + scale_colour_manual(values = c("blue", "red"))
p <- p + labs(y = "Height [inches]",
x = "Person",
colour = "Parameter")
p <- p + theme(legend.position = c(0.8, 0.9))+ facet_wrap(~set, scales="free")
p
I get an error that says
"geom_path: Each group consists of only one observation. Do you need to
adjust the group aesthetic?"
I get the template, but no points get plotted
R function arguments are fed in by position if argument names are not specified explicitly. As mentioned by #Z.Lin in the comments, you need sec.axis= before your sec_axis function to indicate that you are feeding this function into the sec.axis argument of scale_y_continuous. If you don't do that, it will be fed into the second argument of scale_y_continuous, which by default, is breaks=. The error message is thus related to you not feeding in an acceptable data type for the breaks argument:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("weight", sec.axis = sec_axis(~.*1.2, name="height"))
p2
The first argument (name=) of scale_y_continuous is for the first y scale, where as the sec.axis= argument is for the second y scale. I changed your first y scale name to correct that.
I'm trying to generate a ggplot only C.D.F. plot for some of my data. I am also looking to be able to plot an arbitrary number of percentiles as points on top. I have a solution that works for adding a single point to my curve but fails for multiple values.
This works for plotting one percentile value
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.5)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$Values, percentiles),
y = percentiles))
However this fails
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.25,0.5,0.75)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$Values, percentiles),
y = percentiles))
With error
Error: Aesthetics must be either length 1 or the same as the data (1000): x, y
How can I add an arbitrary number of points to a stat_ecdf() plot?
You need to define a new dataset, outside of the aesthetics. aes refers to the original dataframe that you used for making the CDF (in the original ggplot argument).
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(data = data.frame(x=quantile(TestDf$Values, percentiles),
y=percentiles), aes(x=x, y=y))
I have two graphs with the same x axis - the range of x is 0-5 in both of them.
I would like to combine both of them to one graph and I didn't find a previous example.
Here is what I got:
c <- ggplot(survey, aes(often_post,often_privacy)) + stat_smooth(method="loess")
c <- ggplot(survey, aes(frequent_read,often_privacy)) + stat_smooth(method="loess")
How can I combine them?
The y axis is "often privacy" and in each graph the x axis is "often post" or "frequent read".
I thought I can combine them easily (somehow) because the range is 0-5 in both of them.
Many thanks!
Example code for Ben's solution.
#Sample data
survey <- data.frame(
often_post = runif(10, 0, 5),
frequent_read = 5 * rbeta(10, 1, 1),
often_privacy = sample(10, replace = TRUE)
)
#Reshape the data frame
survey2 <- melt(survey, measure.vars = c("often_post", "frequent_read"))
#Plot using colour as an aesthetic to distinguish lines
(p <- ggplot(survey2, aes(value, often_privacy, colour = variable)) +
geom_point() +
geom_smooth()
)
You can use + to combine other plots on the same ggplot object. For example, to plot points and smoothed lines for both pairs of columns:
ggplot(survey, aes(often_post,often_privacy)) +
geom_point() +
geom_smooth() +
geom_point(aes(frequent_read,often_privacy)) +
geom_smooth(aes(frequent_read,often_privacy))
Try this:
df <- data.frame(x=x_var, y=y1_var, type='y1')
df <- rbind(df, data.frame(x=x_var, y=y2_var, type='y2'))
ggplot(df, aes(x, y, group=type, col=type)) + geom_line()