I am plotting a density in base R and then in ggplot2.
When I use base R the plot comes out alright, but in ggplot2 the margins are cut out.
This is the plot in base R:
library(tidyverse)
library(mvtnorm)
library(reshape2)
#>
#> Attaching package: 'reshape2'
#> The following object is masked from 'package:tidyr':
#>
#> smiths
sd <- 1 / 2
# sigma
s1 <- sd^2
mu1 <- c(0, 0)
sigma1 <- matrix(c(s1^2, 0, 0, s1^2), nrow = 2)
# first two vectors
x.points <- seq(-3, 3, length.out = 100)
y.points <- seq(-3, 3, length.out = 100)
# the third vector is a density
z <- matrix(0, nrow = 100, ncol = 100)
z[] <- dmvnorm(expand.grid(x.points, y.points), mean = mu1, sigma = sigma1)
contour(x.points, y.points, z, xlim = range(-3, 3), ylim = c(-3, 3), nlevels = 5, drawlabels = TRUE)
And this is the plot in ggplot2:
df <- reshape2::melt(z)
df <- transform(
df,
x = x.points[Var1],
y = y.points[Var2]
)
ggplot(df, aes(x, y)) +
geom_contour(aes(z = value)) +
xlim(-3,3) +
ylim(-3,3) +
theme_classic()
Created on 2021-04-08 by the reprex package (v0.3.0)
When I started working on the plots, both plots were coming out well. I was running par(pty = "s") (unfortunately if I include the par command in the reprex(), something goes wrong and there is no plot.) The par command was working and giving me a square plot for both base R and ggplot2. Then I added a line and some points to the ggplot2 plot:
points <- data.frame(
x = c(0, 1, 1.5, 1, 0),
y = c(-3, -2.5, 0, 2.5, 3)
)
ggplot(df, aes(x, y)) +
geom_contour(aes(z = value)) +
xlim(-3,3) +
ylim(-3,3) +
geom_path(mapping = aes(x=points[,1], y=points[,2]), points) +
geom_point(mapping = aes(x=points[,1], y=points[,2]), points) +
theme_classic()
After I added the points, the ggplot2 plot started cutting out the margins.
I have tried adding dev.new(width=10, height=10) following this advice, but of course, it just opens a new graphing design and, in addition, the margins are also cut. I have also tried to reset the graphing device with dev.off(), and restarting the R session.
The issue was the classic theme theme_classic(): it looks like that no matter the type of plot, theme_classic() is going to draw only two sides of the box around the plot.
library(tidyverse)
library(mvtnorm)
library(reshape2)
#>
#> Attaching package: 'reshape2'
#> The following object is masked from 'package:tidyr':
#>
#> smiths
sd <- 1 / 2
# sigma
s1 <- sd^2
mu1 <- c(0, 0)
sigma1 <- matrix(c(s1^2, 0, 0, s1^2), nrow = 2)
# first two vectors
x.points <- seq(-3, 3, length.out = 100)
y.points <- seq(-3, 3, length.out = 100)
# the third vector is a density
z <- matrix(0, nrow = 100, ncol = 100)
z[] <- dmvnorm(expand.grid(x.points, y.points), mean = mu1, sigma = sigma1)
df <- reshape2::melt(z)
df <- transform(
df,
x = x.points[Var1],
y = y.points[Var2]
)
ggplot(df, aes(x, y)) +
geom_contour(aes(z = value)) +
xlim(-3,3) +
ylim(-3,3) +
theme_bw()
Created on 2021-04-08 by the reprex package (v0.3.0)
Related
I have plotted a density function in base R and I would like to replicate the plot in ggplot2.
This is the plot in base R:
library(tidyverse)
library(mvtnorm)
sd <- 1 / 2
# sigma
s1 <- sd^2
# first two vectors
x.points <- seq(-3, 3, length.out = 100)
y.points <- seq(-3, 3, length.out = 100)
# the third vector is a density
z <- matrix(0, nrow = 100, ncol = 100)
mu1 <- c(0, 0)
sigma1 <- matrix(c(s1^2, 0, 0, s1^2), nrow = 2)
for (i in 1:100) {
for (j in 1:100) {
z[i, j] <- dmvnorm(c(x.points[i], y.points[j]),
mean = mu1, sigma = sigma1
)
}
}
contour(x.points, y.points, z, xlim = range(-3, 3), ylim = c(-3, 3), nlevels = 5, drawlabels = TRUE)
To obtain the same result in ggplot2, I am following this example:
library(ggplot2)
library(reshape2) # for melt
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Basic plot
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour()
But in my case vector z has a different length than x.points and y.points. From the errors I get below, it looks like the three vectors should have the same length. How can I transform the dataset presented above so that it can be run through ggplot2?
data1 <- as.data.frame(cbind(x.points, y.points))
p <- ggplot(data = data1, mapping = aes(x.points, y.points, z=z))
p + geom_contour()
#> Error: Aesthetics must be either length 1 or the same as the data (100): z
p + stat_contour()
#> Error: Aesthetics must be either length 1 or the same as the data (100): z
p + stat_function(fun = contour) + xlim(-3,3)
#> Error: Aesthetics must be either length 1 or the same as the data (100): z
Created on 2021-04-08 by the reprex package (v0.3.0)
The problem is likely that your data isn't in long format: for every value of the z matrix, you need the x and y position, which is different from the base R approach, wherein you just need these positions for every row/column.
We can transform the matrix z to a long format using reshape2::melt and then grab the correct positions from your vectors.
library(tidyverse)
library(mvtnorm)
sd <- 1 / 2
# sigma
s1 <- sd^2
# first two vectors
x.points <- seq(-3, 3, length.out = 100)
y.points <- seq(-3, 3, length.out = 100)
# the third vector is a density
z <- matrix(0, nrow = 100, ncol = 100)
mu1 <- c(0, 0)
sigma1 <- matrix(c(s1^2, 0, 0, s1^2), nrow = 2)
for (i in 1:100) {
for (j in 1:100) {
z[i, j] <- dmvnorm(c(x.points[i], y.points[j]),
mean = mu1, sigma = sigma1
)
}
}
# Here be the reshaping bit
df <- reshape2::melt(z)
df <- transform(
df,
x = x.points[Var1],
y = y.points[Var2]
)
ggplot(df, aes(x, y)) +
geom_contour(aes(z = value))
Created on 2021-04-08 by the reprex package (v1.0.0)
I am combining a persp graph and a ggplot graph in the same window using plot_grid. However, the persp graph is too small, how can I make it bigger?
library(pacman)
p_load(tidyverse)
p_load(mvtnorm)
p_load(cowplot)
p_load(gridGraphics)
p_load(GA)
my_mean<-c(25,65)
mycors<-seq(-1,1,by=.25)
sd_vec<-c(5,7)
i<-3
temp_cor<-matrix(c(1,mycors[i],
mycors[i],1),
byrow = T,ncol=2)
V<-sd_vec %*% t(sd_vec) *temp_cor
my_x<-seq(my_mean[1]-3*sd_vec[1], my_mean[1]+3*sd_vec[1], length.out=20)
my_y<-seq(my_mean[2]-3*sd_vec[2], my_mean[2]+3*sd_vec[2], length.out=20)
temp_f<-function(a,b){dmvnorm(cbind(a,b), my_mean,V)}
my_z<-outer(my_x, my_y,temp_f)
nlevels<-20
my_zlim <- range(my_z, finite = TRUE)
my_levels <- pretty(my_zlim, nlevels)
zz <- (my_z[-1, -1] + my_z[-1, -ncol(my_z)] + my_z[-nrow(my_z), -1] + my_z[-nrow(my_z),
-ncol(my_z)])/4
cols <- jet.colors(length(my_levels) - 1)
zzz <- cut(zz, breaks = my_levels, labels = cols)
persp(my_x, my_y, my_z, theta = -25, phi = 45, expand = 0.5,xlab="x",ylab="y",zlab="f(x,y)",col = as.character(zzz))
p1 <- recordPlot()
data.grid <- expand.grid(x = seq(my_mean[1]-3*sd_vec[1], my_mean[1]+3*sd_vec[1], length.out=200),
y = seq(my_mean[2]-3*sd_vec[2], my_mean[2]+3*sd_vec[2], length.out=200))
q.samp <- cbind(data.grid, prob = dmvnorm(data.grid, mean = my_mean, sigma = V))
p2<-ggplot(q.samp, aes(x, y, z = prob)) +
geom_contour(aes(color = ..level..), bins = 11, size = 1) +
scale_color_gradientn(colours = jet.colors(11)) +
theme_bw()
plot_grid(p1, p2)
Created on 2020-10-31 by the reprex package (v0.3.0)
I think there are two things you need to do:
Set par(mar = c(0, 0, 0, 0)) before calling persp. Ensure you save your default parameters before and reset them afterwards.
Resize your plotting window to give it a wider aspect ratio
So basically you can change your persp call to:
par_store <- par()
par(mar = c(0, 0, 0, 0))
persp(my_x, my_y, my_z, theta = -25, phi = 45, expand = 0.5,
xlab = "x", ylab = "y", zlab = "f(x,y)", col = as.character(zzz))
p1 <- recordPlot()
par(par_store)
And after resizing the plotting window you get:
I frequently use kernel density plots to illustrate distributions. These are easy and fast to create in R like so:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
#or in one line like this: plot(density(rnorm(100)^2))
Which gives me this nice little PDF:
I'd like to shade the area under the PDF from the 75th to 95th percentiles. It's easy to calculate the points using the quantile function:
q75 <- quantile(draws, .75)
q95 <- quantile(draws, .95)
But how do I shade the the area between q75 and q95?
With the polygon() function, see its help page and I believe we had similar questions here too.
You need to find the index of the quantile values to get the actual (x,y) pairs.
Edit: Here you go:
x1 <- min(which(dens$x >= q75))
x2 <- max(which(dens$x < q95))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
Output (added by JDL)
Another solution:
dd <- with(dens,data.frame(x,y))
library(ggplot2)
qplot(x,y,data=dd,geom="line")+
geom_ribbon(data=subset(dd,x>q75 & x<q95),aes(ymax=y),ymin=0,
fill="red",colour=NA,alpha=0.5)
Result:
An expanded solution:
If you wanted to shade both tails (copy & paste of Dirk's code) and use known x values:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
q2 <- 2
q65 <- 6.5
qn08 <- -0.8
qn02 <- -0.2
x1 <- min(which(dens$x >= q2))
x2 <- max(which(dens$x < q65))
x3 <- min(which(dens$x >= qn08))
x4 <- max(which(dens$x < qn02))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
with(dens, polygon(x=c(x[c(x3,x3:x4,x4)]), y= c(0, y[x3:x4], 0), col="gray"))
Result:
This question needs a lattice answer. Here's a very basic one, simply adapting the method employed by Dirk and others:
#Set up the data
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
#Put in a simple data frame
d <- data.frame(x = dens$x, y = dens$y)
#Define a custom panel function;
# Options like color don't need to be hard coded
shadePanel <- function(x,y,shadeLims){
panel.lines(x,y)
m1 <- min(which(x >= shadeLims[1]))
m2 <- max(which(x <= shadeLims[2]))
tmp <- data.frame(x1 = x[c(m1,m1:m2,m2)], y1 = c(0,y[m1:m2],0))
panel.polygon(tmp$x1,tmp$y1,col = "blue")
}
#Plot
xyplot(y~x,data = d, panel = shadePanel, shadeLims = c(1,3))
Here's another ggplot2 variant based on a function that approximates the kernel density at the original data values:
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
Using the original data (rather than producing a new data frame with the density estimate's x and y values) has the benefit of also working in faceted plots where the quantile values depend on the variable by which the data is being grouped:
Code used
library(tidyverse)
library(RColorBrewer)
# dummy data
set.seed(1)
n <- 1e2
dt <- tibble(value = rnorm(n)^2)
# function that approximates the density at the provided values
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
probs <- c(0.75, 0.95)
dt <- dt %>%
mutate(dy = approxdens(value), # calculate density
p = percent_rank(value), # percentile rank
pcat = as.factor(cut(p, breaks = probs, # percentile category based on probs
include.lowest = TRUE)))
ggplot(dt, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
scale_fill_brewer(guide = "none") +
theme_bw()
# dummy data with 2 groups
dt2 <- tibble(category = c(rep("A", n), rep("B", n)),
value = c(rnorm(n)^2, rnorm(n, mean = 2)))
dt2 <- dt2 %>%
group_by(category) %>%
mutate(dy = approxdens(value),
p = percent_rank(value),
pcat = as.factor(cut(p, breaks = probs,
include.lowest = TRUE)))
# faceted plot
ggplot(dt2, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
facet_wrap(~ category, nrow = 2, scales = "fixed") +
scale_fill_brewer(guide = "none") +
theme_bw()
Created on 2018-07-13 by the reprex package (v0.2.0).
I frequently use kernel density plots to illustrate distributions. These are easy and fast to create in R like so:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
#or in one line like this: plot(density(rnorm(100)^2))
Which gives me this nice little PDF:
I'd like to shade the area under the PDF from the 75th to 95th percentiles. It's easy to calculate the points using the quantile function:
q75 <- quantile(draws, .75)
q95 <- quantile(draws, .95)
But how do I shade the the area between q75 and q95?
With the polygon() function, see its help page and I believe we had similar questions here too.
You need to find the index of the quantile values to get the actual (x,y) pairs.
Edit: Here you go:
x1 <- min(which(dens$x >= q75))
x2 <- max(which(dens$x < q95))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
Output (added by JDL)
Another solution:
dd <- with(dens,data.frame(x,y))
library(ggplot2)
qplot(x,y,data=dd,geom="line")+
geom_ribbon(data=subset(dd,x>q75 & x<q95),aes(ymax=y),ymin=0,
fill="red",colour=NA,alpha=0.5)
Result:
An expanded solution:
If you wanted to shade both tails (copy & paste of Dirk's code) and use known x values:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
q2 <- 2
q65 <- 6.5
qn08 <- -0.8
qn02 <- -0.2
x1 <- min(which(dens$x >= q2))
x2 <- max(which(dens$x < q65))
x3 <- min(which(dens$x >= qn08))
x4 <- max(which(dens$x < qn02))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
with(dens, polygon(x=c(x[c(x3,x3:x4,x4)]), y= c(0, y[x3:x4], 0), col="gray"))
Result:
This question needs a lattice answer. Here's a very basic one, simply adapting the method employed by Dirk and others:
#Set up the data
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
#Put in a simple data frame
d <- data.frame(x = dens$x, y = dens$y)
#Define a custom panel function;
# Options like color don't need to be hard coded
shadePanel <- function(x,y,shadeLims){
panel.lines(x,y)
m1 <- min(which(x >= shadeLims[1]))
m2 <- max(which(x <= shadeLims[2]))
tmp <- data.frame(x1 = x[c(m1,m1:m2,m2)], y1 = c(0,y[m1:m2],0))
panel.polygon(tmp$x1,tmp$y1,col = "blue")
}
#Plot
xyplot(y~x,data = d, panel = shadePanel, shadeLims = c(1,3))
Here's another ggplot2 variant based on a function that approximates the kernel density at the original data values:
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
Using the original data (rather than producing a new data frame with the density estimate's x and y values) has the benefit of also working in faceted plots where the quantile values depend on the variable by which the data is being grouped:
Code used
library(tidyverse)
library(RColorBrewer)
# dummy data
set.seed(1)
n <- 1e2
dt <- tibble(value = rnorm(n)^2)
# function that approximates the density at the provided values
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
probs <- c(0.75, 0.95)
dt <- dt %>%
mutate(dy = approxdens(value), # calculate density
p = percent_rank(value), # percentile rank
pcat = as.factor(cut(p, breaks = probs, # percentile category based on probs
include.lowest = TRUE)))
ggplot(dt, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
scale_fill_brewer(guide = "none") +
theme_bw()
# dummy data with 2 groups
dt2 <- tibble(category = c(rep("A", n), rep("B", n)),
value = c(rnorm(n)^2, rnorm(n, mean = 2)))
dt2 <- dt2 %>%
group_by(category) %>%
mutate(dy = approxdens(value),
p = percent_rank(value),
pcat = as.factor(cut(p, breaks = probs,
include.lowest = TRUE)))
# faceted plot
ggplot(dt2, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
facet_wrap(~ category, nrow = 2, scales = "fixed") +
scale_fill_brewer(guide = "none") +
theme_bw()
Created on 2018-07-13 by the reprex package (v0.2.0).
I frequently use kernel density plots to illustrate distributions. These are easy and fast to create in R like so:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
#or in one line like this: plot(density(rnorm(100)^2))
Which gives me this nice little PDF:
I'd like to shade the area under the PDF from the 75th to 95th percentiles. It's easy to calculate the points using the quantile function:
q75 <- quantile(draws, .75)
q95 <- quantile(draws, .95)
But how do I shade the the area between q75 and q95?
With the polygon() function, see its help page and I believe we had similar questions here too.
You need to find the index of the quantile values to get the actual (x,y) pairs.
Edit: Here you go:
x1 <- min(which(dens$x >= q75))
x2 <- max(which(dens$x < q95))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
Output (added by JDL)
Another solution:
dd <- with(dens,data.frame(x,y))
library(ggplot2)
qplot(x,y,data=dd,geom="line")+
geom_ribbon(data=subset(dd,x>q75 & x<q95),aes(ymax=y),ymin=0,
fill="red",colour=NA,alpha=0.5)
Result:
An expanded solution:
If you wanted to shade both tails (copy & paste of Dirk's code) and use known x values:
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
plot(dens)
q2 <- 2
q65 <- 6.5
qn08 <- -0.8
qn02 <- -0.2
x1 <- min(which(dens$x >= q2))
x2 <- max(which(dens$x < q65))
x3 <- min(which(dens$x >= qn08))
x4 <- max(which(dens$x < qn02))
with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray"))
with(dens, polygon(x=c(x[c(x3,x3:x4,x4)]), y= c(0, y[x3:x4], 0), col="gray"))
Result:
This question needs a lattice answer. Here's a very basic one, simply adapting the method employed by Dirk and others:
#Set up the data
set.seed(1)
draws <- rnorm(100)^2
dens <- density(draws)
#Put in a simple data frame
d <- data.frame(x = dens$x, y = dens$y)
#Define a custom panel function;
# Options like color don't need to be hard coded
shadePanel <- function(x,y,shadeLims){
panel.lines(x,y)
m1 <- min(which(x >= shadeLims[1]))
m2 <- max(which(x <= shadeLims[2]))
tmp <- data.frame(x1 = x[c(m1,m1:m2,m2)], y1 = c(0,y[m1:m2],0))
panel.polygon(tmp$x1,tmp$y1,col = "blue")
}
#Plot
xyplot(y~x,data = d, panel = shadePanel, shadeLims = c(1,3))
Here's another ggplot2 variant based on a function that approximates the kernel density at the original data values:
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
Using the original data (rather than producing a new data frame with the density estimate's x and y values) has the benefit of also working in faceted plots where the quantile values depend on the variable by which the data is being grouped:
Code used
library(tidyverse)
library(RColorBrewer)
# dummy data
set.seed(1)
n <- 1e2
dt <- tibble(value = rnorm(n)^2)
# function that approximates the density at the provided values
approxdens <- function(x) {
dens <- density(x)
f <- with(dens, approxfun(x, y))
f(x)
}
probs <- c(0.75, 0.95)
dt <- dt %>%
mutate(dy = approxdens(value), # calculate density
p = percent_rank(value), # percentile rank
pcat = as.factor(cut(p, breaks = probs, # percentile category based on probs
include.lowest = TRUE)))
ggplot(dt, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
scale_fill_brewer(guide = "none") +
theme_bw()
# dummy data with 2 groups
dt2 <- tibble(category = c(rep("A", n), rep("B", n)),
value = c(rnorm(n)^2, rnorm(n, mean = 2)))
dt2 <- dt2 %>%
group_by(category) %>%
mutate(dy = approxdens(value),
p = percent_rank(value),
pcat = as.factor(cut(p, breaks = probs,
include.lowest = TRUE)))
# faceted plot
ggplot(dt2, aes(value, dy)) +
geom_ribbon(aes(ymin = 0, ymax = dy, fill = pcat)) +
geom_line() +
facet_wrap(~ category, nrow = 2, scales = "fixed") +
scale_fill_brewer(guide = "none") +
theme_bw()
Created on 2018-07-13 by the reprex package (v0.2.0).