Center x and y axis with ggplot2 - r

Question

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.

Answer 1

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)

Answer 2

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()

Answer 3

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.

Answer 4

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

Answer 5

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)