Is there a way to get the axes, with labels in the center of a ggplot2 plot, like a traditional graphing calculator? I've looked through the docs and there doesn't seem to be that functionality, but other plotting packages are not as graphically customizable as ggplot2. To clarify, I was looking to go from something like this:
To this:
The first plot is made with the following code:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + geom_point(size = 5)
p + xlim(-2,2) + ylim(-2,2)
The second plot is made with Mathematica. The main problem I am having is figuring out how to make the axis, with labels, go to the center (I can make the theme blank, etc., no problem). There seems to be no theme parameter you can edit to make this a quick fix.
I think this is what you are looking for:
I have constructed a function that does just that:
theme_geometry <- function(xvals, yvals, xgeo = 0, ygeo = 0,
color = "black", size = 1,
xlab = "x", ylab = "y",
ticks = 10,
textsize = 3,
xlimit = max(abs(xvals),abs(yvals)),
ylimit = max(abs(yvals),abs(xvals)),
epsilon = max(xlimit,ylimit)/50){
#INPUT:
#xvals .- Values of x that will be plotted
#yvals .- Values of y that will be plotted
#xgeo .- x intercept value for y axis
#ygeo .- y intercept value for x axis
#color .- Default color for axis
#size .- Line size for axis
#xlab .- Label for x axis
#ylab .- Label for y axis
#ticks .- Number of ticks to add to plot in each axis
#textsize .- Size of text for ticks
#xlimit .- Limit value for x axis
#ylimit .- Limit value for y axis
#epsilon .- Parameter for small space
#Create axis
xaxis <- data.frame(x_ax = c(-xlimit, xlimit), y_ax = rep(ygeo,2))
yaxis <- data.frame(x_ax = rep(xgeo, 2), y_ax = c(-ylimit, ylimit))
#Add axis
theme.list <-
list(
theme_void(), #Empty the current theme
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = xaxis),
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = yaxis),
annotate("text", x = xlimit + 2*epsilon, y = ygeo, label = xlab, size = 2*textsize),
annotate("text", x = xgeo, y = ylimit + 4*epsilon, label = ylab, size = 2*textsize),
xlim(-xlimit - 7*epsilon, xlimit + 7*epsilon), #Add limits to make it square
ylim(-ylimit - 7*epsilon, ylimit + 7*epsilon) #Add limits to make it square
)
#Add ticks programatically
ticks_x <- round(seq(-xlimit, xlimit, length.out = ticks),2)
ticks_y <- round(seq(-ylimit, ylimit, length.out = ticks),2)
#Add ticks of x axis
nlist <- length(theme.list)
for (k in 1:ticks){
#Create data frame for ticks in x axis
xtick <- data.frame(xt = rep(ticks_x[k], 2),
yt = c(xgeo + epsilon, xgeo - epsilon))
#Create data frame for ticks in y axis
ytick <- data.frame(xt = c(ygeo + epsilon, ygeo - epsilon),
yt = rep(ticks_y[k], 2))
#Add ticks to geom line for x axis
theme.list[[nlist + 4*k-3]] <- geom_line(aes(x = xt, y = yt),
data = xtick, size = size,
color = color)
#Add labels to the x-ticks
theme.list[[nlist + 4*k-2]] <- annotate("text",
x = ticks_x[k],
y = ygeo - 2.5*epsilon,
size = textsize,
label = paste(ticks_x[k]))
#Add ticks to geom line for y axis
theme.list[[nlist + 4*k-1]] <- geom_line(aes(x = xt, y = yt),
data = ytick, size = size,
color = color)
#Add labels to the y-ticks
theme.list[[nlist + 4*k]] <- annotate("text",
x = xgeo - 2.5*epsilon,
y = ticks_y[k],
size = textsize,
label = paste(ticks_y[k]))
}
#Add theme
#theme.list[[3]] <-
return(theme.list)
}
As an example you can run the following code to create an image similar to the one above:
simdata <- data.frame(x = rnorm(50), y = rnorm(50))
ggplot(simdata) +
theme_geometry(simdata$x, simdata$y) +
geom_point(aes(x = x, y = y), size = 3, color = "red") +
ggtitle("More geometric example")
ggsave("Example1.png", width = 10, height = 10)
There are some other useful answers, but the following comes closer to the target visual and avoids looping:
library(ggplot2)
library(magrittr)
# constants
axis_begin <- -2
axis_end <- 2
total_ticks <- 21
# DATA ----
# point to plot
my_point <- data.frame(x=1,y=1)
# chart junk data
tick_frame <-
data.frame(ticks = seq(axis_begin, axis_end, length.out = total_ticks),
zero=0) %>%
subset(ticks != 0)
lab_frame <- data.frame(lab = seq(axis_begin, axis_end),
zero = 0) %>%
subset(lab != 0)
tick_sz <- (tail(lab_frame$lab, 1) - lab_frame$lab[1]) / 128
# PLOT ----
ggplot(my_point, aes(x,y)) +
# CHART JUNK
# y axis line
geom_segment(x = 0, xend = 0,
y = lab_frame$lab[1], yend = tail(lab_frame$lab, 1),
size = 0.5) +
# x axis line
geom_segment(y = 0, yend = 0,
x = lab_frame$lab[1], xend = tail(lab_frame$lab, 1),
size = 0.5) +
# x ticks
geom_segment(data = tick_frame,
aes(x = ticks, xend = ticks,
y = zero, yend = zero + tick_sz)) +
# y ticks
geom_segment(data = tick_frame,
aes(x = zero, xend = zero + tick_sz,
y = ticks, yend = ticks)) +
# labels
geom_text(data=lab_frame, aes(x=lab, y=zero, label=lab),
family = 'Times', vjust=1.5) +
geom_text(data=lab_frame, aes(x=zero, y=lab, label=lab),
family = 'Times', hjust=1.5) +
# THE DATA POINT
geom_point(color='navy', size=5) +
theme_void()
A first approximation:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + theme_bw() +
geom_point(size = 5) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0)
Adjust limits as per SlowLearner's answer.
I would just use xlim and ylim.
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) +
geom_point(size = 5) +
xlim(-2, 2) +
ylim(-2, 2)
p
Recently, I've added coord_axes_inside() to ggh4x that might be suitable for this problem.
library(ggplot2)
library(ggh4x)
df <- data.frame(x = c(-1, 1))
ggplot(df, aes(x, x)) +
geom_point() +
theme(axis.line = element_line()) +
coord_axes_inside(labels_inside = TRUE)
Aside from being syntactically pretty convenient, it really plots axes, so they respond to theme elements and guide declarations as you'd expect.
last_plot() +
guides(x = guide_axis(angle = 45)) +
theme(axis.ticks.length = unit(-5, "pt"))
Created on 2022-09-01 by the reprex package (v2.0.0)
For centering, I recommend setting the scale limits to centering functions:
last_plot() +
scale_x_continuous(limits = ~ c(-1, 1) * max(abs(.x))) +
scale_y_continuous(limits = ~ c(-1, 1) * max(abs(.x)))
(Disclaimer: I'm the author of ggh4x)
Related
I want to plot a diagram with a log scales y axis:
data <- data.frame(x = seq_len(5), y = c(2,1,100,500,30))
ggplot(data) + aes(x,y) + geom_col() + scale_y_log10()
But because the horizontal axis always crosses the vertical axis at y = 1, the bar for y = 1 is never drawn.
I know that log(1) = 0, but can I set the plot base to 0.1 to let the bar start below 1?
Can't you just multiply the values of y by 10 but divide the labels by the same amount?
ggplot(data) + aes(x, y * 10) + geom_col() +
scale_y_log10(labels = function(x) x/10, name = "y")
You could do the same with a histogram:
set.seed(2)
ggplot(data.frame(x = rnorm(1000)), aes(x)) +
geom_histogram(aes(y = ..count.. * 10), color = "black", fill = "gold") +
scale_y_log10(labels = function(x) x/10, name = "count")
An alternative solution is to place the bottom of the bars at -Inf, so that the log10(1) still shows up. This is a bit tricky, because you have to reparameterise the bars as rectangles and get the geom to understand that the -Inf is after scale transformation.
library(ggplot2)
data <- data.frame(x = seq_len(5), y = c(2,1,100,500,30))
ggplot(data) +
geom_rect(
aes(xmin = x - 0.45, xmax = x + 0.45,
ymin = stage(1, after_scale = -Inf), ymax = y)
) +
scale_y_log10()
Created on 2020-11-03 by the reprex package (v0.3.0)
As you can see on the image, R automatically assigns the values 0, 0.25... 1 for the size of the point. I was wondering if I could replace the 0, 0.25... 1 and make these text values instead while keeping the actual numerical values from the data.
library(ggplot2)
library(scales)
data(SLC4A1, package="ggplot2")
SLC4A1 <- read.csv(file.choose(), header = TRUE)
# bubble chart showing position of polymorphisms on gene, the frequency of each of these
# polymorphisms, where they are prominent on earth, and p-value
SLC4A1ggplot <- ggplot(SLC4A1, aes(Position, log10(Frequency)))+
geom_jitter(aes(col=Geographical.Location, size =(p.value)))+
labs(subtitle="Frequency of Various Polymorphisms", title="SLC4A1 Gene") +
labs(color = "Geographical Location") +
labs(size = "p-value") + labs(x = "Position of Polymorphism on SLC4A1 Gene") +
scale_size_continuous(range=c(1,4.5), trans = "reverse") +
guides(size = guide_legend(reverse = TRUE))
library(tidyver)
df <- data.frame(x = 1:5, y = 1:5,z = 1:5)
ggplot(df,aes(x = x, y = y, size = z)) +
geom_point()
ggplot(df,aes(x = x, y = y, size = z)) +
geom_point() +
scale_size_continuous(range = 1:2) # control range of circle size
See more here:
https://ggplot2.tidyverse.org/reference/scale_size.html
Is there a way to get the axes, with labels in the center of a ggplot2 plot, like a traditional graphing calculator? I've looked through the docs and there doesn't seem to be that functionality, but other plotting packages are not as graphically customizable as ggplot2. To clarify, I was looking to go from something like this:
To this:
The first plot is made with the following code:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + geom_point(size = 5)
p + xlim(-2,2) + ylim(-2,2)
The second plot is made with Mathematica. The main problem I am having is figuring out how to make the axis, with labels, go to the center (I can make the theme blank, etc., no problem). There seems to be no theme parameter you can edit to make this a quick fix.
I think this is what you are looking for:
I have constructed a function that does just that:
theme_geometry <- function(xvals, yvals, xgeo = 0, ygeo = 0,
color = "black", size = 1,
xlab = "x", ylab = "y",
ticks = 10,
textsize = 3,
xlimit = max(abs(xvals),abs(yvals)),
ylimit = max(abs(yvals),abs(xvals)),
epsilon = max(xlimit,ylimit)/50){
#INPUT:
#xvals .- Values of x that will be plotted
#yvals .- Values of y that will be plotted
#xgeo .- x intercept value for y axis
#ygeo .- y intercept value for x axis
#color .- Default color for axis
#size .- Line size for axis
#xlab .- Label for x axis
#ylab .- Label for y axis
#ticks .- Number of ticks to add to plot in each axis
#textsize .- Size of text for ticks
#xlimit .- Limit value for x axis
#ylimit .- Limit value for y axis
#epsilon .- Parameter for small space
#Create axis
xaxis <- data.frame(x_ax = c(-xlimit, xlimit), y_ax = rep(ygeo,2))
yaxis <- data.frame(x_ax = rep(xgeo, 2), y_ax = c(-ylimit, ylimit))
#Add axis
theme.list <-
list(
theme_void(), #Empty the current theme
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = xaxis),
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = yaxis),
annotate("text", x = xlimit + 2*epsilon, y = ygeo, label = xlab, size = 2*textsize),
annotate("text", x = xgeo, y = ylimit + 4*epsilon, label = ylab, size = 2*textsize),
xlim(-xlimit - 7*epsilon, xlimit + 7*epsilon), #Add limits to make it square
ylim(-ylimit - 7*epsilon, ylimit + 7*epsilon) #Add limits to make it square
)
#Add ticks programatically
ticks_x <- round(seq(-xlimit, xlimit, length.out = ticks),2)
ticks_y <- round(seq(-ylimit, ylimit, length.out = ticks),2)
#Add ticks of x axis
nlist <- length(theme.list)
for (k in 1:ticks){
#Create data frame for ticks in x axis
xtick <- data.frame(xt = rep(ticks_x[k], 2),
yt = c(xgeo + epsilon, xgeo - epsilon))
#Create data frame for ticks in y axis
ytick <- data.frame(xt = c(ygeo + epsilon, ygeo - epsilon),
yt = rep(ticks_y[k], 2))
#Add ticks to geom line for x axis
theme.list[[nlist + 4*k-3]] <- geom_line(aes(x = xt, y = yt),
data = xtick, size = size,
color = color)
#Add labels to the x-ticks
theme.list[[nlist + 4*k-2]] <- annotate("text",
x = ticks_x[k],
y = ygeo - 2.5*epsilon,
size = textsize,
label = paste(ticks_x[k]))
#Add ticks to geom line for y axis
theme.list[[nlist + 4*k-1]] <- geom_line(aes(x = xt, y = yt),
data = ytick, size = size,
color = color)
#Add labels to the y-ticks
theme.list[[nlist + 4*k]] <- annotate("text",
x = xgeo - 2.5*epsilon,
y = ticks_y[k],
size = textsize,
label = paste(ticks_y[k]))
}
#Add theme
#theme.list[[3]] <-
return(theme.list)
}
As an example you can run the following code to create an image similar to the one above:
simdata <- data.frame(x = rnorm(50), y = rnorm(50))
ggplot(simdata) +
theme_geometry(simdata$x, simdata$y) +
geom_point(aes(x = x, y = y), size = 3, color = "red") +
ggtitle("More geometric example")
ggsave("Example1.png", width = 10, height = 10)
There are some other useful answers, but the following comes closer to the target visual and avoids looping:
library(ggplot2)
library(magrittr)
# constants
axis_begin <- -2
axis_end <- 2
total_ticks <- 21
# DATA ----
# point to plot
my_point <- data.frame(x=1,y=1)
# chart junk data
tick_frame <-
data.frame(ticks = seq(axis_begin, axis_end, length.out = total_ticks),
zero=0) %>%
subset(ticks != 0)
lab_frame <- data.frame(lab = seq(axis_begin, axis_end),
zero = 0) %>%
subset(lab != 0)
tick_sz <- (tail(lab_frame$lab, 1) - lab_frame$lab[1]) / 128
# PLOT ----
ggplot(my_point, aes(x,y)) +
# CHART JUNK
# y axis line
geom_segment(x = 0, xend = 0,
y = lab_frame$lab[1], yend = tail(lab_frame$lab, 1),
size = 0.5) +
# x axis line
geom_segment(y = 0, yend = 0,
x = lab_frame$lab[1], xend = tail(lab_frame$lab, 1),
size = 0.5) +
# x ticks
geom_segment(data = tick_frame,
aes(x = ticks, xend = ticks,
y = zero, yend = zero + tick_sz)) +
# y ticks
geom_segment(data = tick_frame,
aes(x = zero, xend = zero + tick_sz,
y = ticks, yend = ticks)) +
# labels
geom_text(data=lab_frame, aes(x=lab, y=zero, label=lab),
family = 'Times', vjust=1.5) +
geom_text(data=lab_frame, aes(x=zero, y=lab, label=lab),
family = 'Times', hjust=1.5) +
# THE DATA POINT
geom_point(color='navy', size=5) +
theme_void()
A first approximation:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + theme_bw() +
geom_point(size = 5) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0)
Adjust limits as per SlowLearner's answer.
I would just use xlim and ylim.
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) +
geom_point(size = 5) +
xlim(-2, 2) +
ylim(-2, 2)
p
Recently, I've added coord_axes_inside() to ggh4x that might be suitable for this problem.
library(ggplot2)
library(ggh4x)
df <- data.frame(x = c(-1, 1))
ggplot(df, aes(x, x)) +
geom_point() +
theme(axis.line = element_line()) +
coord_axes_inside(labels_inside = TRUE)
Aside from being syntactically pretty convenient, it really plots axes, so they respond to theme elements and guide declarations as you'd expect.
last_plot() +
guides(x = guide_axis(angle = 45)) +
theme(axis.ticks.length = unit(-5, "pt"))
Created on 2022-09-01 by the reprex package (v2.0.0)
For centering, I recommend setting the scale limits to centering functions:
last_plot() +
scale_x_continuous(limits = ~ c(-1, 1) * max(abs(.x))) +
scale_y_continuous(limits = ~ c(-1, 1) * max(abs(.x)))
(Disclaimer: I'm the author of ggh4x)
How do you adjust the expansion of limits asymmetrically in ggplot? For example,
library(ggplot2)
ggplot(mtcars) +
geom_bar(aes(x = cyl), width = 1)
I would like the bottom of the bars flush with the bottom of the panel background, but would still like space at the top. I can achieve this with a blank annotation:
ggplot(mtcars) +
geom_bar(aes(x = cyl), width = 1) +
annotate("blank", x = 4, y = 16) +
scale_y_continuous(expand = c(0.0,0))
In previous versions of ggplot, however, I could use the solution provided by Rosen Matev:
library("scales")
scale_dimension.custom_expand <- function(scale, expand = ggplot2:::scale_expand(scale)) {
expand_range(ggplot2:::scale_limits(scale), expand[[1]], expand[[2]])
}
scale_y_continuous <- function(...) {
s <- ggplot2::scale_y_continuous(...)
class(s) <- c('custom_expand', class(s))
s
}
and then use scale_y_continuous(expand = list(c(0,0.1), c(0,0))) which would add a consistently addition to the top of the chart. In the current version, however, I get an error
ggplot(mtcars) +
geom_bar(aes(x = cyl), width = 1) +
scale_y_continuous(expand = list(c(0,0.1), c(0,0)))
# Error in diff(range) * mul : non-numeric argument to binary operator
Is there an effective solution for ggplot2 2.0?
A solution should include the ability to work flexibly with facets, and free_xy scale options. For example,
ggplot(mtcars) +
geom_bar(aes(x = cyl, fill = factor(vs)), width = 1) +
facet_grid(vs ~ ., scales = "free_y")
A solution should provide something like:
ggplot(mtcars) +
geom_bar(aes(x = cyl, fill = factor(vs)), width = 1) +
facet_grid(vs ~ ., scales = "free_y") +
scale_y_continuous(expand = c(0,0)) +
geom_blank(data = data.frame(cyl = c(5,5), y = c(12, 16), vs = c(1,0)), aes(x = cyl, y = y))
ggplot2 v3.0.0 released in July 2018 has expand_scale() option (w/ mult argument) to achieve OP's goal.
Edit: expand_scale() will be deprecated in the future release in favor of expansion(). See News for more information.
library(ggplot2)
### ggplot <= 3.2.1
ggplot(mtcars) +
geom_bar(aes(x = cyl, fill = factor(vs)), width = 1) +
facet_grid(vs ~ ., scales = "free_y") +
scale_y_continuous(expand = expand_scale(mult = c(0, .2)))
### ggplot >= 3.2.1.9000
ggplot(mtcars) +
geom_bar(aes(x = cyl, fill = factor(vs)), width = 1) +
facet_grid(vs ~ ., scales = "free_y") +
scale_y_continuous(expand = expansion(mult = c(0, .2)))
I have now tried to add code for this to ggplot2; see issue #1669 and the corresponding pull request. If it is accepted, the syntax for the expand argument will been changed from c(m, a) to c(m_lower, a_lower, m_uppper, a_upper), for specifying separate expansion values for the lower and upper range limits. (The old syntax will still continue to work, though, as the first two elements will be reused if elements three and/or four are missing.)
With this new syntax, you can use
ggplot(mtcars) +
geom_bar(aes(x = cyl), width = 1) +
scale_y_continuous(expand = c(0, 0, 0.05, 0))
The result looks like this:
It also works with facetting:
ggplot(mtcars) +
geom_bar(aes(x = cyl, fill = factor(vs)), width = 1) +
facet_grid(vs ~ ., scales = "free_y") +
scale_y_continuous(expand = c(0, 0, 0.05, 0))
I used Rosen Matev's solution often, and was disappointed when it broke with ggplot version 2.0. I offer a solution, though not nearly as elegant as Rosen's, but will work on plots with no facetting, facet_wrap, and facet_grid, and with one-way and two-way facet_grid. However, it will not work with more complicated facet grids, nor will it work with coord_flip. There are two functions: one for asymmetric expansion along the y-axis, and one for expansion along the x-axis. The functions perform multiplicative and additive expansions.
The functions gather information from the plot, calculate new limits for the y (or x) axis, then use geom_blank to construct new plots with the desired expansion factors.
First, a function to perform asymmetric expansion along the y-axis.
# Function takes two parameters
# 'p' is the plot
# 'expand' is a list of two vectors:
# First vector contains the multiplicative factors;
# Second vector contains the additive parts.
# First element in each vector refers to the lower boundary;
# Second element refers to the upper boundary.
asymmY = function(p, expand = list(mult = c(0, .2), add = c(0, 0))) {
np = p + coord_cartesian(expand = FALSE) # No expand
gb <- ggplot_build(np)
limits <- sapply(gb$panel$ranges, "[[", "y.range")
range = apply(limits, 2, function(x) max(x) - min(x))
rangeU = range*expand[[1]][2]
rangeL = range*expand[[1]][1]
limits <- limits + rbind(-rangeL, rangeU) # Multiplicative expand
limits[1,] = limits[1,] - expand[[2]][1] # Additive expand
limits[2,] = limits[2,] + expand[[2]][2]
limits = as.vector(limits)
df = facet_type(np, gb, "y", limits) # df with new limits - depends on facet type
np = np + geom_blank(data = df, inherit.aes = FALSE, aes(x = Inf, y = y)) # new plot
# But the x axis expansions were set to false. Put back the default expand
gb <- ggplot_build(np)
if(any(grepl("Discrete", class(gb$panel$x_scale[[1]])))) {
limits <- sapply(gb$panel$ranges, "[[", "x.range")
limits[1,] = ceiling(limits[1,]) - .6
limits[2,] = trunc(limits[2,]) + .6
limits = as.vector(limits)
} else {
limits <- sapply(gb$panel$ranges, "[[", "x.range")
range = apply(limits, 2, function(x) max(x) - min(x))
rangeU = range*.05
rangeL = range*.05
limits <- limits + rbind(-rangeL, rangeU)
limits = as.vector(limits)
}
df = facet_type(np, gb, "x", limits)
np + geom_blank(data = df, inherit.aes = FALSE, aes(x = x, y = Inf))
}
# Function to determine type of facetting
# and to get data frame of new limits.
facet_type = function(np, gb, axis, limits) {
if(class(np$facet)[1] == "null") {
setNames(data.frame(y = limits), axis)
} else
if(class(np$facet)[1] == "wrap") {
facetvar <- as.character(np$facet$facets)
facetlev <- gb$panel$layout[[facetvar]]
setNames(data.frame(rep(facetlev, each = 2), limits), c(facetvar, axis))
} else {
facetvar <- as.character(np$facet$cols)
if(length(facetvar) == 0) facetvar <- as.character(np$facet$rows)
facetlev <- gb$panel$layout[[facetvar]]
setNames(data.frame(rep(facetlev, each = 2), limits), c(facetvar, axis))
}
}
Try it out with some facet wrap and facet grid plots.
# Try asymmetric expand along y-axis
library(ggplot2)
p1 <- ggplot(mtcars) +
geom_bar(aes(x = factor(cyl))) +
facet_grid(am ~ vs , scales = "free_y")
p2 <- ggplot(mtcars) +
geom_bar(aes(x = factor(cyl), fill = factor(vs)), width = .5) +
facet_grid(vs ~ ., scales = "free_y")
p3 <- ggplot(mtcars) +
geom_bar(aes(x = factor(cyl), fill = factor(vs)), width = .5) +
facet_grid(. ~ vs)
p4 <- ggplot(mtcars) +
geom_bar(aes(x = factor(cyl), fill = factor(vs)), width = .5) +
facet_wrap(~vs, scales = "free_y")
asymmY(p1, list(c(0, 0.1), c(0, 0)))
asymmY(p2, list(c(0, 0.1), c(0, 0)))
asymmY(p3, list(c(0, 0.1), c(0, 0)))
asymmY(p4, list(c(0, 0.1), c(0, 0)))
Second, a function to perform asymmetric expansion along the x-axis.
asymmX = function(p, expand = list(mult = c(0, .2), add = c(0, 0))) {
np = p + coord_cartesian(expand = FALSE) # No expand
gb <- ggplot_build(np)
limits <- sapply(gb$panel$ranges, "[[", "x.range")
range = apply(limits, 2, function(x) max(x) - min(x))
rangeU = range*expand[[1]][2]
rangeL = range*expand[[1]][1]
limits <- limits + rbind(-rangeL, rangeU) # Mult expand
limits[1,] = limits[1,] - expand[[2]][1]
limits[2,] = limits[2,] + expand[[2]][2] # Add expand
limits = as.vector(limits)
df = facet_type(np, gb, "x", limits) # df with new limits - depends on facet type
np = np + geom_blank(data = df, inherit.aes = FALSE, aes(x = x, y = Inf)) # new plot
# But the y axis expansions were set to false. Put back the default expand
gb <- ggplot_build(np)
if(any(grepl("Discrete", class(gb$panel$y_scale[[1]])))) {
limits <- sapply(gb$panel$ranges, "[[", "y.range")
limits[1,] = ceiling(limits[1,]) - .6
limits[2,] = trunc(limits[2,]) + .6
limits = as.vector(limits)
} else {
limits <- sapply(gb$panel$ranges, "[[", "y.range")
range = apply(limits, 2, function(x) max(x) - min(x))
rangeU = range*.05
rangeL = range*.05
limits <- limits + rbind(-rangeL, rangeU)
limits = as.vector(limits)
}
df = facet_type(np, gb, "y", limits)
np + geom_blank(data = df, inherit.aes = FALSE, aes(x = Inf, y = y))
}
Try it out.
# Try asymmetric expand along x-axis
df = data.frame(x = c(20, 15, 25, 23, 12, 14),
y = rep(c("a", "b", "c"), 2),
z = rep(c("aaa", "bbb"), each = 3),
w = rep(c("ccc", "ddd", "eee"), each = 2))
p1 = ggplot(df[,-4]) + geom_point(aes(x, y)) +
geom_segment(aes(x = 0, xend = x, y = y, yend = y)) +
geom_text(aes(x = x, y = y, label = x), hjust = -1) +
facet_grid(. ~ z, scales = "free_x")
p2 = ggplot(df[, -4]) + geom_point(aes(x, y)) +
geom_segment(aes(x = 0, xend = x, y = y, yend = y)) +
geom_text(aes(x = x, y = y, label = x), hjust = -1) +
facet_grid(z ~ .)
p3 = ggplot(df) + geom_point(aes(x, y)) +
geom_segment(aes(x = 0, xend = x, y = y, yend = y)) +
geom_text(aes(x = x, y = y, label = x), hjust = -1) +
facet_grid(w ~ z)
p4 = ggplot(df[,-4]) + geom_point(aes(x, y)) +
geom_segment(aes(x = 0, xend = x, y = y, yend = y)) +
geom_text(aes(x = x, y = y, label = x), hjust = -1) +
facet_wrap(~ z)
asymmX(p1, list(c(0, .15), c(0, 0)))
asymmX(p2, list(c(0, 0), c(0, 5)))
asymmX(p3, list(c(0, .2), c(0, 0)))
asymmX(p4, list(c(0, 0), c(9, 5)))
Is there a way to get the axes, with labels in the center of a ggplot2 plot, like a traditional graphing calculator? I've looked through the docs and there doesn't seem to be that functionality, but other plotting packages are not as graphically customizable as ggplot2. To clarify, I was looking to go from something like this:
To this:
The first plot is made with the following code:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + geom_point(size = 5)
p + xlim(-2,2) + ylim(-2,2)
The second plot is made with Mathematica. The main problem I am having is figuring out how to make the axis, with labels, go to the center (I can make the theme blank, etc., no problem). There seems to be no theme parameter you can edit to make this a quick fix.
I think this is what you are looking for:
I have constructed a function that does just that:
theme_geometry <- function(xvals, yvals, xgeo = 0, ygeo = 0,
color = "black", size = 1,
xlab = "x", ylab = "y",
ticks = 10,
textsize = 3,
xlimit = max(abs(xvals),abs(yvals)),
ylimit = max(abs(yvals),abs(xvals)),
epsilon = max(xlimit,ylimit)/50){
#INPUT:
#xvals .- Values of x that will be plotted
#yvals .- Values of y that will be plotted
#xgeo .- x intercept value for y axis
#ygeo .- y intercept value for x axis
#color .- Default color for axis
#size .- Line size for axis
#xlab .- Label for x axis
#ylab .- Label for y axis
#ticks .- Number of ticks to add to plot in each axis
#textsize .- Size of text for ticks
#xlimit .- Limit value for x axis
#ylimit .- Limit value for y axis
#epsilon .- Parameter for small space
#Create axis
xaxis <- data.frame(x_ax = c(-xlimit, xlimit), y_ax = rep(ygeo,2))
yaxis <- data.frame(x_ax = rep(xgeo, 2), y_ax = c(-ylimit, ylimit))
#Add axis
theme.list <-
list(
theme_void(), #Empty the current theme
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = xaxis),
geom_line(aes(x = x_ax, y = y_ax), color = color, size = size, data = yaxis),
annotate("text", x = xlimit + 2*epsilon, y = ygeo, label = xlab, size = 2*textsize),
annotate("text", x = xgeo, y = ylimit + 4*epsilon, label = ylab, size = 2*textsize),
xlim(-xlimit - 7*epsilon, xlimit + 7*epsilon), #Add limits to make it square
ylim(-ylimit - 7*epsilon, ylimit + 7*epsilon) #Add limits to make it square
)
#Add ticks programatically
ticks_x <- round(seq(-xlimit, xlimit, length.out = ticks),2)
ticks_y <- round(seq(-ylimit, ylimit, length.out = ticks),2)
#Add ticks of x axis
nlist <- length(theme.list)
for (k in 1:ticks){
#Create data frame for ticks in x axis
xtick <- data.frame(xt = rep(ticks_x[k], 2),
yt = c(xgeo + epsilon, xgeo - epsilon))
#Create data frame for ticks in y axis
ytick <- data.frame(xt = c(ygeo + epsilon, ygeo - epsilon),
yt = rep(ticks_y[k], 2))
#Add ticks to geom line for x axis
theme.list[[nlist + 4*k-3]] <- geom_line(aes(x = xt, y = yt),
data = xtick, size = size,
color = color)
#Add labels to the x-ticks
theme.list[[nlist + 4*k-2]] <- annotate("text",
x = ticks_x[k],
y = ygeo - 2.5*epsilon,
size = textsize,
label = paste(ticks_x[k]))
#Add ticks to geom line for y axis
theme.list[[nlist + 4*k-1]] <- geom_line(aes(x = xt, y = yt),
data = ytick, size = size,
color = color)
#Add labels to the y-ticks
theme.list[[nlist + 4*k]] <- annotate("text",
x = xgeo - 2.5*epsilon,
y = ticks_y[k],
size = textsize,
label = paste(ticks_y[k]))
}
#Add theme
#theme.list[[3]] <-
return(theme.list)
}
As an example you can run the following code to create an image similar to the one above:
simdata <- data.frame(x = rnorm(50), y = rnorm(50))
ggplot(simdata) +
theme_geometry(simdata$x, simdata$y) +
geom_point(aes(x = x, y = y), size = 3, color = "red") +
ggtitle("More geometric example")
ggsave("Example1.png", width = 10, height = 10)
There are some other useful answers, but the following comes closer to the target visual and avoids looping:
library(ggplot2)
library(magrittr)
# constants
axis_begin <- -2
axis_end <- 2
total_ticks <- 21
# DATA ----
# point to plot
my_point <- data.frame(x=1,y=1)
# chart junk data
tick_frame <-
data.frame(ticks = seq(axis_begin, axis_end, length.out = total_ticks),
zero=0) %>%
subset(ticks != 0)
lab_frame <- data.frame(lab = seq(axis_begin, axis_end),
zero = 0) %>%
subset(lab != 0)
tick_sz <- (tail(lab_frame$lab, 1) - lab_frame$lab[1]) / 128
# PLOT ----
ggplot(my_point, aes(x,y)) +
# CHART JUNK
# y axis line
geom_segment(x = 0, xend = 0,
y = lab_frame$lab[1], yend = tail(lab_frame$lab, 1),
size = 0.5) +
# x axis line
geom_segment(y = 0, yend = 0,
x = lab_frame$lab[1], xend = tail(lab_frame$lab, 1),
size = 0.5) +
# x ticks
geom_segment(data = tick_frame,
aes(x = ticks, xend = ticks,
y = zero, yend = zero + tick_sz)) +
# y ticks
geom_segment(data = tick_frame,
aes(x = zero, xend = zero + tick_sz,
y = ticks, yend = ticks)) +
# labels
geom_text(data=lab_frame, aes(x=lab, y=zero, label=lab),
family = 'Times', vjust=1.5) +
geom_text(data=lab_frame, aes(x=zero, y=lab, label=lab),
family = 'Times', hjust=1.5) +
# THE DATA POINT
geom_point(color='navy', size=5) +
theme_void()
A first approximation:
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) + theme_bw() +
geom_point(size = 5) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0)
Adjust limits as per SlowLearner's answer.
I would just use xlim and ylim.
dat = data.frame(x = 1, y =1)
p = ggplot(data = dat, aes(x=x, y=y)) +
geom_point(size = 5) +
xlim(-2, 2) +
ylim(-2, 2)
p
Recently, I've added coord_axes_inside() to ggh4x that might be suitable for this problem.
library(ggplot2)
library(ggh4x)
df <- data.frame(x = c(-1, 1))
ggplot(df, aes(x, x)) +
geom_point() +
theme(axis.line = element_line()) +
coord_axes_inside(labels_inside = TRUE)
Aside from being syntactically pretty convenient, it really plots axes, so they respond to theme elements and guide declarations as you'd expect.
last_plot() +
guides(x = guide_axis(angle = 45)) +
theme(axis.ticks.length = unit(-5, "pt"))
Created on 2022-09-01 by the reprex package (v2.0.0)
For centering, I recommend setting the scale limits to centering functions:
last_plot() +
scale_x_continuous(limits = ~ c(-1, 1) * max(abs(.x))) +
scale_y_continuous(limits = ~ c(-1, 1) * max(abs(.x)))
(Disclaimer: I'm the author of ggh4x)