I currently have a plot and have used facet_zoom to focus on records between 0 and 10 in the x axis. The following code reproduces an example:
require(ggplot2)
require(ggforce)
require(dplyr)
x <- rnorm(10000, 50, 25)
y <- rexp(10000)
data <- data.frame(x, y)
ggplot(data, aes(x = x, y = y)) +
geom_point() +
facet_zoom(x = dplyr::between(x, 0, 10))
I want to change the breaks on the zoomed portion of the graph to be the equivalent of:
ggplot(data, aes(x = x, y = y)) +
geom_point() +
facet_zoom(x = dplyr::between(x, 0, 10)) +
scale_x_continuous(breaks = seq(0,10,2))
But this changes the breaks of the original plot as well. Is it possible to just change the breaks of the zoomed portion whilst leaving the original plot as default?
This works for your use case:
ggplot(data, aes(x = x, y = y)) +
geom_point() +
facet_zoom(x = between(x, 0, 10)) +
scale_x_continuous(breaks = pretty)
From ?scale_x_continuous, breaks would accept the following (emphasis added):
One of:
NULL for no breaks
waiver() for the default breaks computed by the transformation object
A numeric vector of positions
A function that takes the limits as input and returns breaks as output
pretty() is one such function. It doesn't offer very fine control, but does allow you to have some leeway to specify breaks across different facets with very different scales.
For illustration, here are two examples with different desired number of breaks. See ?pretty for more details on the other arguments this function accepts.
p <- ggplot(data, aes(x = x, y = y)) +
geom_point() +
facet_zoom(x = between(x, 0, 10))
cowplot::plot_grid(
p + scale_x_continuous(breaks = function(x) pretty(x, n = 3)),
p + scale_x_continuous(breaks = function(x) pretty(x, n = 10)),
labels = c("n = 3", "n = 10"),
nrow = 1
)
Of course, you can also define your own function to convert plot limits into desired breaks, (e.g. something like p + scale_x_continuous(breaks = function(x) seq(min(x), max(x), length.out = 5))), but I generally find these functions require more tweaking to get right, & pretty() is often good enough.
Related
I am working on percentage changes between periods and struggling with logaritmic transformation of labels. Here is an example based on the storms dataset:
library(dplyr)
library(ggplot2)
library(scales)
df <- storms |>
group_by(year) |>
summarise(wind = mean(wind)) |>
mutate(lag = lag(wind, n = 1)) |>
mutate(perc = (wind / lag) - 1) |>
tidyr::drop_na()
I want to visualize the distribution of percentages, making the percentage change symmetrical (log difference) with log1p.
ggplot(df, aes(x = log1p(perc))) +
geom_histogram(bins = 5)
x-axis with log1p values
At this point I wanted to transform the x-axis label back to the original percentage value.
I tried to create my own transformation with trans_new, and applied it to the labels in scale_x_continuous, but I can't make it work.
trans_perc <- trans_new(
name = "trans_perc",
transform = log1p_trans(),
inverse = function(x)
expm1(x),
breaks = breaks_log(),
format = percent_format(),
domain = c(-Inf, Inf)
)
ggplot(df, aes(x = log1p(perc))) +
geom_histogram(bins = 5) +
scale_x_continuous(labels = trans_perc)
Currently, the result is:
Error in get_labels():
! breaks and labels are different lengths
Run rlang::last_error() to see where the error occurred.
Thanks!
EDIT
I am adding details on the different output I am getting from Alan's first answer:
trans_perc <- trans_new(
name = "trans_perc",
transform = log1p,
inverse = expm1,
breaks = pretty_breaks(5),
format = percent_format(),
domain = c(-Inf, Inf)
)
library(ggpubr)
a <- ggplot(df, aes(x = log1p(perc))) +
geom_histogram(bins = 5)
b <- ggplot(df, aes(x = log1p(perc))) +
geom_histogram(bins = 5) +
scale_x_continuous(trans = trans_perc)
c <- ggplot(df, aes(x = perc)) +
geom_histogram(bins = 5) +
scale_x_continuous(trans = trans_perc)
ggarrange(a, b, c,
ncol = 3,
labels = c("Log on Value only",
"Log on Value and X",
"Log on X only"))
[different outcomes]:(https://i.stack.imgur.com/dCW2m.png
If I understand you correctly, you want to keep the shape of the histogram, but change the labels so that they reflect the value of the perc column rather the transformed log1p(perc) value. If that is the case, there is no need for a transformer object. You can simply put the reverse transformation (plus formatting) as a function into the labels argument of scale_x_continuous.
ggplot(df, aes(x = log1p(perc))) +
geom_histogram(bins = 5) +
scale_x_continuous("Percentage Change",
breaks = log1p(pretty(df$perc, 5)),
labels = ~ percent(expm1(.x)))
Note that although the histogram remains symmetrical in shape, the axis labels represent the back-transformed values of the original axis labels.
The point of a transformer object is to do all this for you without having to pass a transformed data set (i.e. without having to pass log1p(perc)). So in your case, you could do:
trans_perc <- trans_new(
name = "trans_perc",
transform = log1p,
inverse = expm1,
format = percent_format(),
domain = c(-Inf, Inf)
)
ggplot(df, aes(x = perc)) +
geom_histogram(bins = 5) +
scale_x_continuous(trans = trans_perc)
Which gives essentially the same result
Having a tibble and a simple scatterplot:
p <- tibble(
x = rnorm(50, 1),
y = rnorm(50, 10)
)
ggplot(p, aes(x, y)) + geom_point()
I get something like this:
I would like to align (center, left, right, as the case may be) the title of the x-axis - here rather blandly x - with a specific value on the axis, say the off-center 0 in this case. Is there a way to do that declaratively, without having to resort to the dumb (as in "free of context") trial-and-error element_text(hjust=??). The ?? are rather appropriate here because every value is a result of experimentation (my screen and PDF export in RStudio never agree on quite some plot elements). Any change in the data or the dimensions of the rendering may (or may not) invalidate the hjust value and I am looking for a solution that graciously repositions itself, much like the axes do.
Following the suggestions in the comments by #tjebo I dug a little deeper into the coordinate spaces. hjust = 0.0 and hjust = 1.0 clearly align the label with the Cartesian coordinate system extent (but magically left-aligned and right-aligned, respectively) so when I set specific limits, calculation of the exact value of hjust is straightforward (aiming for 0 and hjust = (0 - -1.5) / (3.5 - -1.5) = 0.3):
ggplot(p, aes(x, y)) +
geom_point() +
coord_cartesian(ylim = c(8, 12.5), xlim = c(-1.5, 3.5), expand=FALSE) +
theme(axis.title.x = element_text(hjust = 0.3))
This gives an acceptable result for a label like x, but for longer labels the alignment is off again:
ggplot(p %>% mutate(`Longer X label` = x), aes(x = `Longer X label`, y = y)) +
geom_point() +
coord_cartesian(ylim = c(8, 12.5), xlim = c(-1.5, 3.5), expand=FALSE) +
theme(axis.title.x = element_text(hjust = 0.3))
Any further suggestions much appreciated.
Another option (different enough hopefully to justify the second answer) is as already mentioned to create the annotation as a separate plot. This removes the range problem. I like {patchwork} for this.
library(tidyverse)
library(patchwork)
p <- tibble( x = rnorm(50, 1), y = rnorm(50, 10))
p1 <- tibble( x = rnorm(50, 1), y = 100*rnorm(50, 10))
## I like to define constants outside my ggplot call
mylab <- "longer_label"
x_demo <- c(-1, 2)
demo_fct <- function(p){
p1 <- ggplot(p, aes(x, y)) +
geom_point() +
labs(x = NULL) +
theme(plot.margin = margin())
p2 <- ggplot(p, aes(x, y)) +
## you need that for your correct alignment with the first plot
geom_blank() +
annotate(geom = "text", x = x_demo, y = 1,
label = mylab, hjust = 0) +
theme_void() +
# you need that for those annoying margin reasons
coord_cartesian(clip = "off")
p1 / p2 + plot_layout(heights = c(1, .05))
}
demo_fct(p) + plot_annotation(title = "demo1 with x at -1 and 2")
demo_fct(p1) + plot_annotation(title = "demo2 with larger data range")
Created on 2021-12-04 by the reprex package (v2.0.1)
I still think you will fair better and easier with custom annotation. There are typically two ways to do that. Either direct labelling with a text layer (for single labels I prefer annotate(geom = "text"), or you create a separate plot and stitch both together, e.g. with patchwork.
The biggest challenge is the positioning in y dimension. For this I typically take a semi-automatic approach where I only need to define one constant, and set the coordinates relative to the data range, so changes in range should in theory not matter much. (they still do a bit, because the panel dimensions also change). Below showing examples of exact label positioning for two different data ranges (using the same constant for both)
library(tidyverse)
# I only need patchwork for demo purpose, it is not required for the answer
library(patchwork)
p <- tibble( x = rnorm(50, 1), y = rnorm(50, 10))
p1 <- tibble( x = rnorm(50, 1), y = 100*rnorm(50, 10))
## I like to define constants outside my ggplot call
y_fac <- .1
mylab <- "longer_label"
x_demo <- c(-1, 2)
demo_fct <- function(df, x) {map(x_demo,~{
## I like to define constants outside my ggplot call
ylims <- range(df$y)
ggplot(df, aes(x, y)) +
geom_point() +
## set hjust = 0 for full positioning control
annotate(geom = "text", x = ., y = min(ylims) - y_fac*mean(ylims),
label = mylab, hjust = 0) +
coord_cartesian(ylim = ylims, clip = "off") +
theme(plot.margin = margin(b = .5, unit = "in")) +
labs(x = NULL)
})
}
demo_fct(p, x_demo) %>% wrap_plots() + plot_annotation(title = "demo 1, label at x = -1 and x = 2")
demo_fct(p1, x_demo) %>% wrap_plots() + plot_annotation(title = "demo 2 - different data range")
Created on 2021-12-04 by the reprex package (v2.0.1)
I am using ggplot2 in R to create a histogram and I would like to customise the values on the y axis. At present the values on the Y axis range from one and have an interval of 3. I would like to make all the values on the on the y axis visible i.e. 1,2,3 and so on.
How do I do this?
plot_2 <-
ggplot(Tennis, aes(x=winner)) +
geom_bar(data = subset(top_wins, tournament == "French Open")) +
ggtitle("French Open")
You can use the scale_y_continuous() function. Below is an example where the y axis will go from 0 to 20.
ggplot() + geom_point(data = iris, aes(x = Petal.Width, y = Petal.Length,color = Species)) +
+ scale_y_continuous(limits = c(0, 20), breaks = seq(0, 20, by = 1)
I wish to use ggrepel to add labels to the ends of the lines of a ggplot. To do that, I need to make space for the labels. To do that, I use scale_x_continuous ot extend the x-axis. Not sure that's correct and am open to other strategies.
I can do it when the x_axis type is friendly numeric.
library("tidyverse")
library("ggrepel")
p <- tibble (
x = c(1991, 1999),
y = c(3, 5)
)
ggplot(p, aes(x, y)) + geom_line() + scale_x_continuous(limits = c(1991, 2020)) +
geom_text_repel(data = p[2,], aes(label = "Minimum Wage"), size = 4, nudge_x = 1, nudge_y = 0, colour = "gray50")
However, when I try something similar except the x-axis is of the evil date type, I get the error:
Error in as.Date.numeric(value) : 'origin' must be supplied
p <- tibble (
x = c(as.Date("1991-01-01"), as.Date("1999-01-01")),
y = c(2, 5)
)
range <- c(as.Date("1991-01-01"), as.Date("2020-01-01"))
ggplot(p, aes(x, y)) + geom_line() + scale_x_continuous(limits = range)
How can I get this to work with my arch nemesis, date?
Use scale_x_date instead of scale_x_continuous:
p <- tibble (
x = c(as.Date("1991-01-01"), as.Date("1999-01-01")),
y = c(2, 5)
)
range <- c(as.Date("1991-01-01"), as.Date("2020-01-01"))
ggplot(p, aes(x, y)) + geom_line() + scale_x_date(limits = range)
Note that scale_x_date() has an expand argument which allows exact control over where the x-axis starts and ends. You could try expand = c(0,0) to include only the dates specified in your limits = argument or expand = c(f, f) where f is the fraction of days relative to the entire time series record you should include in your plot beyond the range of dates specified via your limit = argument. For example, f could be 0.01.
I usually save the plots from ggplot2 using the the png device. The width and the height of the output are set by the arguments of the function. Blank zones are drawn when the "natural proportions" of the graph dont't suit the proportions of the device. In order to avoid this and use the whole defined canvas, the proportions of the plot must be known. ¿Is there a way to find out this value without trial and error?
This code can be used as an example:
x <- seq(from = 0, to = 1, by = 0.1)
y <- seq(from = 1, to = 2, by = 0.1)
df <- expand.grid(x = x, y = y)
df <- cbind(df, z = rnorm(ncol(df), 0, 1))
p <- ggplot(df, aes(x,y, fill = z)) + geom_raster() + coord_fixed()
ppi <- 300
#Value 0.4 is used to change inches into milimeters
png("plot.png", width = 16*0.4*ppi, height = 20*0.4*ppi, res = ppi)
print(p)
dev.off()
It can be seen that some blank space is added at the top and at the bottom to fill the png file. This could be easily corrected by using a proportion different from 20/16, which is not optimal.
You can modify the ratio arg inside coord_fixed():
p <- ggplot(df, aes(x,y, fill = z)) +
geom_raster() +
coord_fixed(ratio = 20/16)
Alteratively you can specify the aspect.ratio inside the theme():
p <- ggplot(df, aes(x,y, fill = z)) +
geom_raster() +
theme(aspect.ratio = 20/16)
The result is the same: