I'm trying to implement the log_2(x + 1) transformation in ggplot2 but am running into issues.
Here is an MWE
library(ggplot2)
x <- rexp(100)
y <- rexp(100)
df <- data.frame(x = x, y = y)
p <- ggplot(df, aes(x = x, y = y)) + geom_point(colour = "blue") +
scale_x_continuous(trans = "log2") +
scale_y_continuous(trans = "log2")
print(p)
However, I'm unsure how to best go about transforming the axes, as well as labelling the axes as log_2{x + 1) and log_2(y + 1).
You could use log2_trans from scales with a function to add 1 like this:
library(ggplot2)
library(scales)
x <- rexp(100)
y <- rexp(100)
df <- data.frame(x = x, y = y)
p <- ggplot(df, aes(x = x, y = y)) + geom_point(colour = "blue") +
scale_x_continuous(trans = log2_trans(),
breaks = trans_breaks("log2", function(x) x + 1),
labels = trans_format("log2", math_format(.x + 1))) +
scale_y_continuous(trans = log2_trans(),
breaks = trans_breaks("log2", function(x) x + 1),
labels = trans_format("log2", math_format(.x + 1)))
print(p)
Created on 2022-11-04 with reprex v2.0.2
Related
I am creating several plots in order to create frames for a gif. It is supposed to show growing points over time. (see plot 1 and 2 - the values increase). Using size aesthetic is problematic, because the scaling is done for each plot individually.
I tried to set breaks with scale_size_area() to provide a sequence of absolute values, in order to scale on 'all values' rather than only the values present in each plot. (no success).
Plot 3 shows how the points should be scaled, but this scaling should be achieved in each plot.
library(tidyverse)
df1 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25)
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
ggplot(df1, aes(x, y, size = size2)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
df2 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25) %>% gather(key, value, y:size2)
ggplot(df2, aes(x, value, size = value)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
Created on 2019-05-12 by the reprex package (v0.2.1)
Pass lower and upper bound to limits argument in scale_size_area function:
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
labs(
title = "Y on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
ggplot(df1, aes(x, y, size = size2 )) +
geom_point() +
labs(
title = "size2 on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
How about this?
library("ggplot2")
df1 <- data.frame(x = letters[1:5],
y = 1:5)
ggplot(data = df1,
aes(x = x,
y = y,
size = y)) +
geom_point() +
scale_size_area(breaks = seq(1,25,1),
limits = c(1, 25))
Bug? Possibly related to this..
dat = data.frame(x = 1:4, y = ordered(c(4,'>5',1,1), levels = c(1:5, '>5')), grp = 1)
ggplot(dat, aes(x, y, group=grp)) + geom_step() +
scale_y_discrete(breaks = levels(dat$y), labels = levels(dat$y))
ggplot(dat, aes(x, y, group=grp)) + geom_step() +
scale_y_discrete(breaks = levels(dat$y), labels = levels(dat$y), drop = FALSE)
Using geom_blank I want to add some new factor levels, but I can't seem to do this and keep the NA level
library('ggplot2')
pl <- ggplot(data.frame(x = factor(c(1:2, NA)), y = 1), aes(x, y)) + geom_point()
pl
pl + geom_blank(data = data.frame(x = addNA(factor(c(0:3, NA))), y = 1))
I would like to have the x at 0,1,2,3,NA using geom_blank
As I hoped in the comments, a work-around could be re-ordering the layers if the plot is already made, and this should work for ggplots in general.
library('ggplot2')
pl <- ggplot(data.frame(x = factor(c(1:2, NA)), y = 1), aes(x, y)) +
geom_point() +
geom_blank(data = data.frame(x = addNA(factor(c(0:3, NA))), y = 1))
## not what I want
pl
## this is what I want
pl$layers <- rev(pl$layers)
pl
Unfortunately, when you have multiple layers with separate datasets, factor levels can get messed overridden. You can fix this by setting the limits on the discrete scale:
pl <- ggplot(data.frame(x = factor(c(1:2, NA)), y = 1), aes(x, y)) + geom_point()
pl +
geom_blank(data = data.frame(x = addNA(factor(c(0:3, NA))), y = 1)) +
scale_x_discrete(limits=addNA(factor(c(0:3, NA))))
For some reason, this messes up the expansion on the axis and the NA break is at the right edge.
[
This can be fixed by manually setting the expand parameter to your liking.
pl <- ggplot(data.frame(x = factor(c(1:2, NA)), y = 1), aes(x, y)) + geom_point()
pl +
geom_blank(data = data.frame(x = addNA(factor(c(0:3, NA))), y = 1)) +
scale_x_discrete(limits=addNA(factor(c(0:3, NA))), expand=c(0.25,0.25))
I am using the ggplot function to plot this kind of graph
image
I want to add the specific value of the x-axis as shown in the picture
this is my code :
quantiles <- quantile(mat,prob = quant)
x <- as.vector(mat)
d <- as.data.frame(x=x)
p <- ggplot(data = d,aes(x=x)) + theme_bw() +
geom_histogram(aes(y = ..density..), binwidth=0.001,color="black",fill="white") +
geom_density(aes(x=x, y = ..density..),fill="blue", alpha=0.5, color = 'black')
x.dens <- density(x)
df.dens <- data.frame(x = x.dens$x, y = x.dens$y)
p <- p + geom_area(data = subset(df.dens, x <= quantiles), aes(x=x,y=y),
fill = 'green', alpha=0.6)
print(p)
I created a plot with several geom_area according to the following code :
library(ggplot2)
set.seed(1)
dat <- data.frame(matrix(rnorm(100, 10, 2), 100, 1))
dat_density <- data.frame(density(dat[, 1])[c("x", "y")])
quant <- quantile(dat[, 1], probs = seq(0, 1, 0.10))
library(RColorBrewer)
color_pal <- brewer.pal(length(quant)-1, "RdYlBu")
dens <- ggplot(data = dat_density, aes(x = x, y = y)) +
geom_line(size = 2)
for(i in 1:(length(color_pal))){
dens <- dens +
geom_area(data = subset(dat_density, x > quant[[i]] & x < quant[[i + 1]]), fill = color_pal[i])
}
dens
How can I add a common legend with each color of the color_pal vector (corresponding to all the 10% area of data) ?
The easiest way is to define the groups in your dataset
dat_density$quant <- cut(dat_density$x, breaks = c(-Inf, quant, Inf))
ggplot(data = dat_density, aes(x = x, y = y, fill = quant)) +
geom_line(size = 2) +
geom_area() +
scale_fill_brewer(palette = "RdYlBu")