I would like to draw a histogram with a density curve and then put a boxplot above the top margin. I know how to do this using the hist(), boxplot() and layout() functions, or using functions from the ggplot2 and grid packages. However, I am looking for a specific solution using ggplot2 and the ggMarginal() function within the ggExtra package. Let's simulate some data before I present my problem:
library(ggplot2)
library(ggExtra)
set.seed(1234)
vdat = data.frame(V1 = c(sample(1:10, 100, T), 99))
vname = colnames(vdat)[1]
boxplot(vdat[[vname]], horizontal = T)
To note, I explicitly insert an outlier 99 into a sample of numbers from 1 to 10. Hence, when I draw the boxplot, 99 should be displayed as an outlier.
I can easily draw a histogram using ggplot2.
p = ggplot(data=vdat, aes_string(x=vname)) +
geom_histogram(aes(y=stat(density)),
bins=nclass.Sturges(vdat[[vname]])+1,
color="black", fill="steelblue", na.rm=T) +
geom_density(na.rm=T) +
theme_bw()
p
When I try to use ggMarginal to add a marginal boxplot, the added boxplots are not right.
p1 = ggMarginal(p, type="boxplot")
p1
The boxplot on the right might be right. But the one on top, which is the very one I need, is definitely wrong. The outlier 99 is not there and the median is clearly not right.
When I try not to provide p1, but the original data, x, and y as suggested by the help documentation, I get the right boxplot but the histogram is now gone.
p2 = ggMarginal(data=vdat, x=vname, y=NA, type="boxplot", margins="x")
p2
How can I combine the correct parts of p1 and p2 such that I have the histogram from p1 and the boxplot from p2?
I am trying something like
p1 + p2
or
ggMarginal(p1, data=vdat, x=vname, y=NA, type="boxplot", margins="x")
But they are not working.
According to ggMarginal's documentation, p is expected to be a ggplot scatterplot. We can insert the following line as the first geom layer in p:
geom_point(aes(y = 0.01), alpha = 0)
y = 0.01 was chosen as a value within the existing plot's y-axis range, and alpha = 0 ensures this layer isn't visible.
Running your code with this p should give you the boxplot with outlier.
p <- ggplot(data=vdat, aes_string(x=vname)) +
geom_point(aes(y = 0.01), alpha = 0) +
geom_histogram(aes(y=stat(density)),
bins=nclass.Sturges(vdat[[vname]])+1,
color="black", fill="steelblue", na.rm=T) +
geom_density(na.rm=T) +
theme_bw()
p1 = ggMarginal(p, type="boxplot", margins = "x")
p1
By the way, I don't think it really makes sense to plot a boxplot to the right in this instance, since you have not assigned any variable to y.
Related
I have some biological data for two individuals, and I graph it using R as a scatterplot using ggplot like this:
p1<-ggplot(data, aes(meth_matrix$sample1, meth_matrix$sample3)) +
geom_point() +
theme_minimal()
which works perfect, but I want to add lines to it: the abline that divides the scatterplot in half:
p1 + geom_abline(color="blue")
and my question is: how can I draw two red lines parallel to that diagonal (y intercept would be 0.2, slope would be the same as the blue line) ??
Also: how can I draw the difference of both samples in a similar scatterplot (it will look like a horizontal scatterplot) with ggplot? right now I can only do it with plot like:
dif_samples<-meth_matrix$sample1- meth_matrix$sample3
plot(dif_samples, main="difference",
xlab="CpGs ", ylab="Methylation ", pch=19)
(also I'd like adding the horizontal blue line and the red lines paralllel to the blue line)
Please help!!!
Thank you very much.
You can specify slopes and intercepts in the geom_abline() function. I'll use the iris dataset that comes with ggplot2 to illustrate:
# I'll use the iris dataset. I normalise by dividing the variables by their max so that
# a line through the origin will be visible
library(ggplot2)
p1 <- ggplot(iris, aes(Sepal.Length/max(Sepal.Length), Sepal.Width/max(Sepal.Width))) +
geom_point() + theme_minimal()
# Draw lines by specifying their slopes and intercepts. since all lines
# share a slope I just give one insted of a vector of slopes
p1 + geom_abline(intercept = c(0, .2, -.2), slope = 1,
color = c("blue", "red", "red"))
I'm not as clear on exactly what you want for the second plot, but you can plot differences directly in the call to ggplot() and you can add horizontal lines with geom_hline():
# Now lets plot the difference between sepal length and width
# for each observation
p2 <- ggplot(iris, aes(x = 1:nrow(iris),
y = (Sepal.Length - Sepal.Width) )) +
geom_point() + theme_minimal()
# we'll add horizontal lines -- you can pick values that make sense for your problem
p2 + geom_hline(yintercept = c(3, 3.2, 2.8),
color = c("blue", "red", "red"))
Created on 2018-03-21 by the reprex package (v0.2.0).
I would like to show in the same plot interpolated data and a histogram of the raw data of each predictor. I have seen in other threads like this one, people explain how to do marginal histograms of the same data shown in a scatter plot, in this case, the histogram is however based on other data (the raw data).
Suppose we see how price is related to carat and table in the diamonds dataset:
library(ggplot2)
p = ggplot(diamonds, aes(x = carat, y = table, color = price)) + geom_point()
We can add a marginal frequency plot e.g. with ggMarginal
library(ggExtra)
ggMarginal(p)
How do we add something similar to a tile plot of predicted diamond prices?
library(mgcv)
model = gam(price ~ s(table, carat), data = diamonds)
newdat = expand.grid(seq(55,75, 5), c(1:4))
names(newdat) = c("table", "carat")
newdat$predicted_price = predict(model, newdat)
ggplot(newdat,aes(x = carat, y = table, fill = predicted_price)) +
geom_tile()
Ideally, the histograms go even beyond the margins of the tileplot, as these data points also influence the predictions. I would, however, be already very happy to know how to plot a histogram for the range that is shown in the tileplot. (Maybe the values that are outside the range could just be added to the extreme values in different color.)
PS. I managed to more or less align histograms to the margins of the sides of a tile plot, using the method of the accepted answer in the linked thread, but only if I removed all kind of labels. It would be particularly good to keep the color legend, if possible.
EDIT:
eipi10 provided an excellent solution. I tried to modify it slightly to add the sample size in numbers and to graphically show values outside the plotted range since they also affect the interpolated values.
I intended to include them in a different color in the histograms at the side. I hereby attempted to count them towards the lower and upper end of the plotted range. I also attempted to plot the sample size in numbers somewhere on the plot. However, I failed with both.
This was my attempt to graphically illustrate the sample size beyond the plotted area:
plot_data = diamonds
plot_data <- transform(plot_data, carat_range = ifelse(carat < 1 | carat > 4, "outside", "within"))
plot_data <- within(plot_data, carat[carat < 1] <- 1)
plot_data <- within(plot_data, carat[carat > 4] <- 4)
plot_data$carat_range = as.factor(plot_data$carat_range)
p2 = ggplot(plot_data, aes(carat, fill = carat_range)) +
geom_histogram() +
thm +
coord_cartesian(xlim=xrng)
I tried to add the sample size in numbers with geom_text. I tried fitting it in the far right panel but it was difficult (/impossible for me) to adjust. I tried to put it on the main graph (which would anyway probably not be the best solution), but it didn’t work either (it removed the histogram and legend, on the right side and it did not plot all geom_texts). I also tried to add a third row of plots and writing it there. My attempt:
n_table_above = nrow(subset(diamonds, table > 75))
n_table_below = nrow(subset(diamonds, table < 55))
n_table_within = nrow(subset(diamonds, table >= 55 & table <= 75))
text_p = ggplot()+
geom_text(aes(x = 0.9, y = 2, label = paste0("N(>75) = ", n_table_above)))+
geom_text(aes(x = 1, y = 2, label = paste0("N = ", n_table_within)))+
geom_text(aes(x = 1.1, y = 2, label = paste0("N(<55) = ", n_table_below)))+
thm
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, text_p, ggplot(), widths=c(6,1), heights =c(6,1))
I would be very happy to receive help on either or both tasks (adding sample size as text & adding values outside plotted range in a different color).
Based on your comment, maybe the best approach is to roll your own layout. Below is an example. We create the marginal plots as separate ggplot objects and lay them out with the main plot. We also extract the legend and put it outside the marginal plots.
Set-up
library(ggplot2)
library(cowplot)
# Function to extract legend
#https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend) }
thm = list(theme_void(),
guides(fill=FALSE),
theme(plot.margin=unit(rep(0,4), "lines")))
xrng = c(0.6,4.4)
yrng = c(53,77)
Plots
p1 = ggplot(newdat, aes(x = carat, y = table, fill = predicted_price)) +
geom_tile() +
theme_classic() +
coord_cartesian(xlim=xrng, ylim=yrng)
leg = g_legend(p1)
p1 = p1 + thm[-1]
p2 = ggplot(diamonds, aes(carat)) +
geom_line(stat="density") +
thm +
coord_cartesian(xlim=xrng)
p3 = ggplot(diamonds, aes(table)) +
geom_line(stat="density") +
thm +
coord_flip(xlim=yrng)
plot_grid(
plot_grid(plotlist=list(p2, ggplot(), p1, p3), ncol=2,
rel_widths=c(4,1), rel_heights=c(1,4), align="hv", scale=1.1),
leg, rel_widths=c(5,1))
UPDATE: Regarding your comment about the space between the plots: This is an Achilles heel of plot_grid and I don't know if there's a way to fix it. Another option is ggarrange from the experimental egg package, which doesn't add so much space between plots. Also, you need to save the output of ggarrange first and then lay out the saved object with the legend. If you run ggarrange inside grid.arrange you get two overlapping copies of the plot:
# devtools::install_github('baptiste/egg')
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, widths=c(6,1))
I have data from 2 populations.
I'd like to get the histogram and density plot of both on the same graphic.
With one color for one population and another color for the other one.
I've tried this (example):
library(ggplot2)
AA <- rnorm(100000, 70,20)
BB <- rnorm(100000,120,20)
valores <- c(AA,BB)
grupo <- c(rep("AA", 100000),c(rep("BB", 100000)))
todo <- data.frame(valores, grupo)
ggplot(todo, aes(x=valores, fill=grupo, color=grupo)) +
geom_histogram(aes(y=..density..), binwidth=3)+ geom_density(aes(color=grupo))
But I'm just getting a graphic with a single line and a single color.
I would like to have different colors for the the two density lines. And if possible the histograms as well.
I've done it with ggplot2 but base R would also be OK.
or I don't know what I've changed and now I get this:
ggplot(todo, aes(x=valores, fill=grupo, color=grupo)) +
geom_histogram( position="identity", binwidth=3, alpha=0.5)+
geom_density(aes(color=grupo))
but the density lines were not plotted.
or even strange things like
I suggest this ggplot2 solution:
ggplot(todo, aes(valores, color=grupo)) +
geom_histogram(position="identity", binwidth=3, aes(y=..density.., fill=grupo), alpha=0.5) +
geom_density()
#skan: Your attempt was close but you plotted the frequencies instead of density values in the histogram.
A base R solution could be:
hist(AA, probability = T, col = rgb(1,0,0,0.5), border = rgb(1,0,0,1),
xlim=range(AA,BB), breaks= 50, ylim=c(0,0.025), main="AA and BB", xlab = "")
hist(BB, probability = T, col = rgb(0,0,1,0.5), border = rgb(0,0,1,1), add=T)
lines(density(AA))
lines(density(BB), lty=2)
For alpha I used rgb. But there are more ways to get it in. See alpha() in the scales package for instance. I added also the breaks parameter for the plot of the AAs to increase the binwidth compared to the BB group.
I'm trying to create a density curve in R using a set of random numbers between 1000, and shade the part that is less than or equal to a certain value. There are a lot of solutions out there involving geom_area or geom_ribbon, but they all require a yval, which I don't have (it's just a vector of 1000 numbers). Any ideas on how I could do this?
Two other related questions:
Is it possible to do the same thing for a cumulative density function (I'm currently using stat_ecdf to generate one), or shade it at all?
Is there any way to edit geom_vline so it will only go up to the height of the density curve, rather than the whole y axis?
Code: (the geom_area is a failed attempt to edit some code I found. If I set ymax manually, I just get a column taking up the whole plot, instead of just the area under the curve)
set.seed(100)
amount_spent <- rnorm(1000,500,150)
amount_spent1<- data.frame(amount_spent)
rand1 <- runif(1,0,1000)
amount_spent1$pdf <- dnorm(amount_spent1$amount_spent)
mean1 <- mean(amount_spent1$amount_spent)
#density/bell curve
ggplot(amount_spent1,aes(amount_spent)) +
geom_density( size=1.05, color="gray64", alpha=.5, fill="gray77") +
geom_vline(xintercept=mean1, alpha=.7, linetype="dashed", size=1.1, color="cadetblue4")+
geom_vline(xintercept=rand1, alpha=.7, linetype="dashed",size=1.1, color="red3")+
geom_area(mapping=aes(ifelse(amount_spent1$amount_spent > rand1,amount_spent1$amount_spent,0)), ymin=0, ymax=.03,fill="red",alpha=.3)+
ylab("")+
xlab("Amount spent on lobbying (in Millions USD)")+
scale_x_continuous(breaks=seq(0,1000,100))
There are a couple of questions that show this ... here and here, but they calculate the density prior to plotting.
This is another way, more complicated than required im sure, that allows ggplot to do some of the calculations for you.
# Your data
set.seed(100)
amount_spent1 <- data.frame(amount_spent=rnorm(1000, 500, 150))
mean1 <- mean(amount_spent1$amount_spent)
rand1 <- runif(1,0,1000)
Basic density plot
p <- ggplot(amount_spent1, aes(amount_spent)) +
geom_density(fill="grey") +
geom_vline(xintercept=mean1)
You can extract the x and y positions for the area to shade from the plot object using ggplot_build. Linear interpolation was used to get the y value at x=rand1
# subset region and plot
d <- ggplot_build(p)$data[[1]]
p <- p + geom_area(data = subset(d, x > rand1), aes(x=x, y=y), fill="red") +
geom_segment(x=rand1, xend=rand1,
y=0, yend=approx(x = d$x, y = d$y, xout = rand1)$y,
colour="blue", size=3)
I am plotting a forest plot in ggplot2 and am having issues with the ordering of the labels in the legend matching the order of the labels in the data set. Here is my code below.
data code
d<-data.frame(x=c("Co-K(W) N=720", "IH-K(W) N=67", "IF-K(W) N=198", "CO-K(B)N=78", "IH-K(B) N=13", "CO=A(W) N=874","D-Sco Ad(W) N=346","DR-Ad (W) N=892","CE_A(W) N=274","CO-Ad(B) N=66","D-So Ad(B) N=215","DR-Ad(B) N=123","CE-Ad(B) N=79"),
y = rnorm(13, 0, 0.1))
d <- transform(d, ylo = y-1/13, yhi=y+1/13)
d$x <- factor(d$x, levels=rev(d$x)) # reverse ordering
forest plot code
credplot.gg <- function(d){
# d is a data frame with 4 columns
# d$x gives variable names
# d$y gives center point
# d$ylo gives lower limits
# d$yhi gives upper limits
require(ggplot2)
p <- ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi,group=x,colour=x,)) +
geom_pointrange(size=1) +
theme_bw() +
scale_color_discrete(name="Sample") +
coord_flip() +
theme(legend.key=element_rect(fill='cornsilk2')) +
guides(colour = guide_legend(override.aes = list(size=0.5))) +
geom_hline(aes(x=0), colour = 'red', lty=2) +
xlab('Cohort') + ylab('CI') + ggtitle('Forest Plot')
return(p)
}
credplot.gg(d)
This is what I get. As you can see the labels on the y axis matches the labels in the order that it is in the data. However, it is not the same order in the legend. I'm not sure how to correct this. This is my first time creating a plot in ggplot2. Any feedback is well appreciated.Thanks in advanced
Nice plot, especially for a first ggplot! I've not tested, but I think all you need is to add reverse=TRUE inside your colour's guide_legend(found this in the Cookbook for R).
If I were to make one more comment, I'd say that ordering your vertical factor by numeric value often makes comparisons easier when alphabetical order isn't particularly meaningful. (Though maybe your alpha order is meaningful.)