I have the below figure shows cdf. I am wondering how can I zoom in to show better the difference between four lines in the left upper part of the figure.
You can use coord_cartesian to zoom in. I don't know what you mean by having the zoom part and the whole in the same figure. If you want to have them side by side, you can use the multiplot function found in the Cookbook for R page. For example:
df <- data.frame(x = c(rnorm(100, 0, 3), rnorm(100, 0, 10)),
g = gl(2, 100))
p <- ggplot(df, aes(x, colour = g)) + stat_ecdf()
p1 <- p
p2 <- p + coord_cartesian(ylim = c(.75, 1))
multiplot(p1, p2)
Edit
Based on #Paul Lemmens' comment, you can use grid's viewport function in the following way:
pdf("~/Desktop/foo.pdf", width = 6, height = 6)
subvp <- viewport(width = .4, height = .4, x = .75, y = .25)
p1
print(p2, vp = subvp)
dev.off()
which gives the following output - adjust the details for your specific example:
Related
I'd like to make a plot using ggplot2 where some of the fill values are clipped, i.e. values above or below the limits of the color scale are displayed as the minimum/maximum color. I can get this to work like this, using a combination of limit and oob (out of bounds):
library(ggplot2)
library(scales)
ggplot() + ... + scale_fill_viridis(na.value="white", limit=c(0, 10), oob=squish)
But there is no information in the colorbar that indicates there are values present outside of the limits.
How can I reproduce this matplotlib example in ggplot: https://stackoverflow.com/a/32072348
Specifically, how to get the triangles at the end of the colorbar?
As far as I'm aware there is not a package that implements triangle ends for colourbars in ggplot2 (but please let me know if there is!). However, we can implement our own. We'd need a constructor for our custom guide and a way to draw it. Most of the stuff is already implemented in guide_colourbar() and methods for their class, so what we need to do is just tag on our own class and expand the guide_gengrob method. The code below should work for vertically oriented colourbars. You'd need to know some stuff about the grid package and gtable package to follow along.
library(ggplot2)
library(gtable)
library(grid)
my_triangle_colourbar <- function(...) {
guide <- guide_colourbar(...)
class(guide) <- c("my_triangle_colourbar", class(guide))
guide
}
guide_gengrob.my_triangle_colourbar <- function(...) {
# First draw normal colourbar
guide <- NextMethod()
# Extract bar / colours
is_bar <- grep("^bar$", guide$layout$name)
bar <- guide$grobs[[is_bar]]
extremes <- c(bar$raster[1], bar$raster[length(bar$raster)])
# Extract size
width <- guide$widths[guide$layout$l[is_bar]]
height <- guide$heights[guide$layout$t[is_bar]]
short <- min(convertUnit(width, "cm", valueOnly = TRUE),
convertUnit(height, "cm", valueOnly = TRUE))
# Make space for triangles
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar] - 1)
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar])
# Draw triangles
top <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(0, 1, 0), "npc"),
gp = gpar(fill = extremes[1], col = NA)
)
bottom <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(1, 0, 1), "npc"),
gp = gpar(fill = extremes[2], col = NA)
)
# Add triangles to guide
guide <- gtable_add_grob(
guide, top,
t = guide$layout$t[is_bar] - 1,
l = guide$layout$l[is_bar]
)
guide <- gtable_add_grob(
guide, bottom,
t = guide$layout$t[is_bar] + 1,
l = guide$layout$l[is_bar]
)
return(guide)
}
You can then use your custom guide as the guide argument in a scale.
g <- ggplot(mtcars, aes(mpg, wt)) +
geom_point(aes(colour = drat))
g + scale_colour_viridis_c(
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
There isn't really a natural way to colour out-of-bounds values differently, but you can make very small slices near the extremes a different colour.
g + scale_colour_gradientn(
colours = c("red", scales::viridis_pal()(255), "hotpink"),
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
Created on 2021-07-19 by the reprex package (v1.0.0)
library(gg.layers)
library(ggplot2)
library(rcolors)
brk <- c(-Inf, -1, 0, 1, 3, 6, 9, Inf)
nbrk <- length(brk) - 1
cols <- get_color(rcolors$amwg256, nbrk)
g <- make_colorbar(
at = brk, col = cols, height = 1,
tck = 0.4,
space = "right",
legend.text.location = c(0.3, 0.5),
legend.text.just = c(0.5, 0.5),
# legend.text = list(fontfamily = "Times", cex = 1.1),
hjust = 0.05
)
p <- ggplot(mtcars, aes(mpg, disp)) + geom_point()
p + g
https://github.com/rpkgs/gg.layers
Triangles? No idea. Colors? You can set a gradient with custom values where your normal range is manually defined and your extremes are something else.
library(ggplot2)
# example taken from ?viridis::scale_colour_viridis, even if I don't use that function
dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
d <- ggplot(dsub, aes(x, y, colour=diff)) + geom_point()
d +
scale_color_gradientn(
colours=c("red", "red", "blue", "green", "yellow", "red", "red"),
values = c(0, 0.1-1e-9, 0.1, 0.5, 0.9, 0.9+1e-9, 1),
breaks = c(-0.51, -.4, 0, .4, .62),
label = function(z) replace(z, c(1, length(z)), c("Min", "Max"))) +
theme_bw()
I doubled "red" on each end so that there would be no gradient transition with the neighboring colors. You can choose a different color for one end (while in this case it's clear if it's extreme-high or extreme-low).
I chose to manually control values= and labels= to include arbitrary points and labels for the extremes. This can be improved based on your preferences.
The disadvantage to this is that you have to define the viridis colors manually; should not be too difficult. I've hastily approximated it here, I'm confident you can choose better colors for the internal gradient portion.
I'd like to make a plot using ggplot2 where some of the fill values are clipped, i.e. values above or below the limits of the color scale are displayed as the minimum/maximum color. I can get this to work like this, using a combination of limit and oob (out of bounds):
library(ggplot2)
library(scales)
ggplot() + ... + scale_fill_viridis(na.value="white", limit=c(0, 10), oob=squish)
But there is no information in the colorbar that indicates there are values present outside of the limits.
How can I reproduce this matplotlib example in ggplot: https://stackoverflow.com/a/32072348
Specifically, how to get the triangles at the end of the colorbar?
As far as I'm aware there is not a package that implements triangle ends for colourbars in ggplot2 (but please let me know if there is!). However, we can implement our own. We'd need a constructor for our custom guide and a way to draw it. Most of the stuff is already implemented in guide_colourbar() and methods for their class, so what we need to do is just tag on our own class and expand the guide_gengrob method. The code below should work for vertically oriented colourbars. You'd need to know some stuff about the grid package and gtable package to follow along.
library(ggplot2)
library(gtable)
library(grid)
my_triangle_colourbar <- function(...) {
guide <- guide_colourbar(...)
class(guide) <- c("my_triangle_colourbar", class(guide))
guide
}
guide_gengrob.my_triangle_colourbar <- function(...) {
# First draw normal colourbar
guide <- NextMethod()
# Extract bar / colours
is_bar <- grep("^bar$", guide$layout$name)
bar <- guide$grobs[[is_bar]]
extremes <- c(bar$raster[1], bar$raster[length(bar$raster)])
# Extract size
width <- guide$widths[guide$layout$l[is_bar]]
height <- guide$heights[guide$layout$t[is_bar]]
short <- min(convertUnit(width, "cm", valueOnly = TRUE),
convertUnit(height, "cm", valueOnly = TRUE))
# Make space for triangles
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar] - 1)
guide <- gtable_add_rows(guide, unit(short, "cm"),
guide$layout$t[is_bar])
# Draw triangles
top <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(0, 1, 0), "npc"),
gp = gpar(fill = extremes[1], col = NA)
)
bottom <- polygonGrob(
x = unit(c(0, 0.5, 1), "npc"),
y = unit(c(1, 0, 1), "npc"),
gp = gpar(fill = extremes[2], col = NA)
)
# Add triangles to guide
guide <- gtable_add_grob(
guide, top,
t = guide$layout$t[is_bar] - 1,
l = guide$layout$l[is_bar]
)
guide <- gtable_add_grob(
guide, bottom,
t = guide$layout$t[is_bar] + 1,
l = guide$layout$l[is_bar]
)
return(guide)
}
You can then use your custom guide as the guide argument in a scale.
g <- ggplot(mtcars, aes(mpg, wt)) +
geom_point(aes(colour = drat))
g + scale_colour_viridis_c(
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
There isn't really a natural way to colour out-of-bounds values differently, but you can make very small slices near the extremes a different colour.
g + scale_colour_gradientn(
colours = c("red", scales::viridis_pal()(255), "hotpink"),
limits = c(3, 4), oob = scales::oob_squish,
guide = my_triangle_colourbar()
)
Created on 2021-07-19 by the reprex package (v1.0.0)
library(gg.layers)
library(ggplot2)
library(rcolors)
brk <- c(-Inf, -1, 0, 1, 3, 6, 9, Inf)
nbrk <- length(brk) - 1
cols <- get_color(rcolors$amwg256, nbrk)
g <- make_colorbar(
at = brk, col = cols, height = 1,
tck = 0.4,
space = "right",
legend.text.location = c(0.3, 0.5),
legend.text.just = c(0.5, 0.5),
# legend.text = list(fontfamily = "Times", cex = 1.1),
hjust = 0.05
)
p <- ggplot(mtcars, aes(mpg, disp)) + geom_point()
p + g
https://github.com/rpkgs/gg.layers
Triangles? No idea. Colors? You can set a gradient with custom values where your normal range is manually defined and your extremes are something else.
library(ggplot2)
# example taken from ?viridis::scale_colour_viridis, even if I don't use that function
dsub <- subset(diamonds, x > 5 & x < 6 & y > 5 & y < 6)
dsub$diff <- with(dsub, sqrt(abs(x-y))* sign(x-y))
d <- ggplot(dsub, aes(x, y, colour=diff)) + geom_point()
d +
scale_color_gradientn(
colours=c("red", "red", "blue", "green", "yellow", "red", "red"),
values = c(0, 0.1-1e-9, 0.1, 0.5, 0.9, 0.9+1e-9, 1),
breaks = c(-0.51, -.4, 0, .4, .62),
label = function(z) replace(z, c(1, length(z)), c("Min", "Max"))) +
theme_bw()
I doubled "red" on each end so that there would be no gradient transition with the neighboring colors. You can choose a different color for one end (while in this case it's clear if it's extreme-high or extreme-low).
I chose to manually control values= and labels= to include arbitrary points and labels for the extremes. This can be improved based on your preferences.
The disadvantage to this is that you have to define the viridis colors manually; should not be too difficult. I've hastily approximated it here, I'm confident you can choose better colors for the internal gradient portion.
I have a log-normal density with a mean of -0.4 and standard deviation of 2.5.
At x = 0.001 the height is over 5 (I double checked this value with the formula for the log-normal PDF):
dlnorm(0.001, -0.4, 2.5)
5.389517
When I plot it using the curve function over the input range 0-6 it looks like with a height just over 1.5:
curve(dlnorm(x, -.4, 2.5), xlim = c(0, 6), ylim = c(0, 6))
When I adjust the input range to 0-1 the height is nearly 4:
curve(dlnorm(x, -.4, 2.5), xlim = c(0, 1), ylim = c(0, 6))
Similarly with ggplot2 (output not shown, but looks like the curve plots above):
library(ggplot2)
ggplot(data = data.frame(x = 0), mapping = aes(x = x)) +
stat_function(fun = function(x) dlnorm(x, -0.4, 2.5)) +
xlim(0, 6) +
ylim(0, 6)
ggplot(data = data.frame(x = 0), mapping = aes(x = x)) +
stat_function(fun = function(x) dlnorm(x, -0.4, 2.5)) +
xlim(0, 1) +
ylim(0, 6)
Does someone know why the density height is changing when the x-axis scale is adjusted? And why neither attempt above seems to reach the correct height? I tried this with just the normal density and this doesn't happen.
curves generates a set of discrete points in the range you give it. By default it generates n = 101 points, so there is a step problem. If you increase the number of points you will have almost the correct value:
curve(dlnorm(x, -.4, 2.5), xlim = c(0, 1), ylim = c(0, 6), n = 1000)
In the first case you propose curve generates 101 points in the interval x <- c(0,6), while in the second case generates 101 points in the interval x <- c(0,1), so the step is more dense
I want to add a standard 1:1 line (with intercept 0 and slope 1) and some text (for example R-square) on my hexbinplot. The trial codes could be something like
require(hexbin)
x1 <- rnorm(10000)
x2 <- rnorm(10000)
df <- data.frame(cbind(x1, x2))
hex <- hexbin(x1, x2, xbins = 300)
hexbinplot(x2 ~ x1, data = df, aspect = '1', xbins = 300, xlim = c(-5, 5), ylim = c(-5, 5))
hexVP.abline(hexViewport(hex), 0, 1)
This gives me the plot below
The line added have two issues which are
It should be from the lower left corner to the top right corner，but the slope (should be 1) and intercept (should be 0) looks change when I zoom in/out the graphic window in RStudio and
the both ends of the line does not extend to the plot border
Another question is about how to add a text on the plot.
The ideal plot could be looks like
Cute that a package still uses lattice for graphics. That's really retro!
Here is the lattice way:
hexbinplot(x2 ~ x1, data = df, aspect = '1', xbins = 300, xlim = c(-5, 5), ylim = c(-5, 5),
panel = function(x, y, ...) {
panel.hexbinplot(x, y, ...)
lattice::panel.abline(a = 0, b = 1)
})
(Edit: After you added additional requirements: Use panel.text to add text to a lattice plot.)
Personally, I'd use ggplot2 and its geom_hex:
library(ggplot2)
ggplot(df, aes(x = x1, y = x2)) +
geom_hex(bins = 300) +
xlim(-5, 5) + ylim(-5, 5) +
geom_abline(intercept = 0, slope = 1)
I am trying to add the file name outside a plot area obtained with ggplot, using facet_wrap. I was pretty sure I found the solution in this post:
Add filename or other annotation to ggplot figures. However, applying the solution to my problem gives a distorted image.
The code to generate this is here:
require("gridExtra")
library(tidyverse)
df <- data.frame(x =runif(100, 1, 10),
y = runif(100, 1, 10),
myfacet = c("one", "two"))
p <- ggplot(data = df,
aes(x = x,
y = y)) +
geom_point() +
facet_wrap(~myfacet)
print(p)
script.name <- "myscript.R"
sub.label = textGrob(script.name,
gp=gpar(fontsize=6),
x = unit(1, "npc"),
hjust = 1,
vjust = 1)
ggsave(filename="../plots/myplot.png",
plot = arrangeGrob(p,
sub = sub.label,
clip = FALSE))
If I just use
ggsave(filename="../plots/myplot2.png",
plot = p)
I get the following image:
Please note that I need a solution that works outside the facets.
Could anyone provide a hint as to what is going on? Thank you!
grid.arrange(p, bottom = sub.label)