---
title: "Changing Axes"
output:
rmarkdown::html_vignette:
fig_width: 7
vignette: >
%\VignetteIndexEntry{Changing Axes}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r}
library(ggplot2)
library(ggprism)
library(patchwork)
```
The ["Frame and Origin"](https://www.graphpad.com/guides/prism/latest/user-guide/frame_and_axes.htm)
tab in Prism allows you to change the appearance of the graph's axes. Some of
these options have been implemented in `ggprism` as axis guides. This vignette
will go through how to use the 4 axis guides included in this package.
## Minor ticks
Adding minor ticks to graphs is very simple. There are two mains ways, using
the continuous scale functions such as `scale_x_continuous()` or using the
`guides()` function, both from `ggplot2`. Note that `guide_prism_minor()`
_does not_ work with discrete axes as they do not have minor breaks.
```{r, fig.height=3.5}
# Compare methods for adding minor ticks
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = guide_prism_minor())
p2 <- p + guides(y = guide_prism_minor())
p1 + p2
```
Note that if you are happy with the defaults you can refer to the axis guide
as a string instead of calling the function.
```{r, fig.height=3.5}
# refer to guide as string
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = "prism_minor")
p2 <- p + guides(y = "prism_minor")
p1 + p2
```
To adjust the number of minor ticks, you just change the number of minor breaks
using the `minor_breaks` argument of the continuous scale functions. The vector
you give the `minor_breaks` argument will define the position of each minor
tick.
```{r, fig.height=3.5}
# compare 1 minor ticks (default) vs 4 minor ticks per major tick
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
stat_summary(aes(fill = factor(dose)), na.rm = TRUE,
geom = "col", fun = mean, colour = "black", linewidth = 0.9) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = "prism_minor",
limits = c(0, 30),
expand = c(0, 0))
p2 <- p + scale_y_continuous(guide = "prism_minor",
limits = c(0, 30),
expand = c(0, 0),
minor_breaks = seq(0, 30, 2))
p1 + p2
```
To get log10 minor ticks, just use a log10 scale and then modify the n
`minor_breaks` argument as above. Remember the vector you give the
`minor_breaks` argument will define the position of each minor tick.
```{r, fig.height=3.5}
p <- ggplot(msleep, aes(bodywt, brainwt)) +
geom_point(na.rm = TRUE) +
theme_prism()
p1 <- p + scale_x_log10(limits = c(1e0, 1e4),
guide = "prism_minor")
p2 <- p + scale_x_log10(limits = c(1e0, 1e4),
minor_breaks = rep(1:9, 4)*(10^rep(0:3, each = 9)),
guide = "prism_minor")
p1 + p2
```
You can use the `theme()` function with the `prism.ticks.length` argument to
change the length of the minor ticks. This works in the same way as the
`axis.ticks.length` argument to change the length of major ticks.
```{r, fig.height=3.5}
# change minor tick length
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
stat_summary(aes(fill = factor(dose)), na.rm = TRUE,
geom = "col", fun = mean, colour = "black", linewidth = 0.9) +
theme_prism() +
scale_y_continuous(guide = "prism_minor",
limits = c(0, 30),
expand = c(0, 0),
minor_breaks = seq(0, 30, 2))
p1 <- p + theme(legend.position = "none")
p2 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(20, "pt"))
p1 + p2
```
You can change the direction of minor ticks just by making their length negative.
```{r, fig.height=3.5}
# change minor tick length
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
stat_summary(aes(fill = factor(dose)), na.rm = TRUE,
geom = "col", fun = mean, colour = "black", linewidth = 0.9) +
theme_prism() +
scale_y_continuous(guide = "prism_minor",
limits = c(0, 30),
expand = c(0, 0),
minor_breaks = seq(0, 30, 2))
p1 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(20, "pt"))
p2 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(-20, "pt"))
p1 + p2
```
The colour (and other aesthetic attributes) of minor ticks will change when
you change the colour of major ticks using the `axis.ticks` argument of the
`theme()` function.
```{r, fig.height=3.5}
# change how ticks look
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
stat_summary(aes(fill = factor(dose)), na.rm = TRUE,
geom = "col", fun = mean, colour = "black", linewidth = 0.9) +
theme_prism() +
scale_y_continuous(guide = "prism_minor",
limits = c(0, 30),
expand = c(0, 0),
minor_breaks = seq(0, 30, 2))
p1 <- p + theme(legend.position = "none")
p2 <- p + theme(legend.position = "none",
axis.ticks.y = element_line(colour = "blue",
linewidth = 2,
lineend = "round"))
p1 + p2
```
## Offset axis
One popular axis option in Prism is the offset axis. This has been implemented
in `ggprism` as the `guide_prism_offset()` function. This function works by
only drawing the axis line to the outer most tick mark (either
major or minor tick). Technically, it works with both continuous and discrete
scales, but it should probably only be used with continuous scales.
If you want an offset axis with minor ticks, see the `guide_prism_offset_minor()`
function below.
```{r, fig.height=3.5}
# show that offset axis looks better when you specify the axis limits
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = "prism_offset")
p2 <- p + scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p1 + p2
```
As with a normal ggplot, you can adjust the appearance of the offset axis line
with the `theme()` function and the `axis.line` argument.
```{r, fig.height=3.5}
# change appearance of offset axis
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p1 <- p + theme(legend.position = "none")
p2 <- p + theme(legend.position = "none",
axis.line.y = element_line(colour = "blue",
linewidth = 2,
lineend = "round"))
p1 + p2
```
## Offset axis with minor ticks
The `guide_prism_offset_minor()` function is similar to `guide_prism_minor()`
except the axis line is offset.
```{r, fig.height=3.5}
# compare prism_minor with prism_offset_minor
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = "prism_offset")
p2 <- p + scale_y_continuous(guide = "prism_offset_minor")
p1 + p2
```
As with `guide_prism_offset()`, the axis tends to look better if you explicitly
set the axis limits.
```{r, fig.height=3.5}
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(guide = "prism_offset_minor")
p2 <- p + scale_y_continuous(limits = c(0, 40),
guide = "prism_offset_minor")
p1 + p2
```
As with `guide_prism_minor()` you can change the number of minor ticks by
adjusting the `minor_breaks`.
```{r, fig.height=3.5}
# compare 1 minor tick to 4 minor ticks per major
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(limits = c(0, 40),
guide = "prism_offset_minor")
p2 <- p + scale_y_continuous(limits = c(0, 40),
minor_breaks = seq(0, 40, 2),
guide = "prism_offset_minor")
p1 + p2
```
And as with `guide_prism_minor()` you can change the length of minor ticks by
adjusting the `prism_ticks_length` argument of the `theme()` function.
```{r, fig.height=3.5}
# change minor tick length and direction
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
scale_y_continuous(limits = c(0, 40),
minor_breaks = seq(0, 40, 2),
guide = "prism_offset_minor")
p1 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(20, "pt"))
p2 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(-20, "pt"))
p1 + p2
```
Lastly, the colour (and other aesthetic attributes) of minor ticks will change
when you change the colour of major ticks using the `axis.ticks` argument of the
`theme()` function.
```{r, fig.height=3.5}
# change minor tick colour, thickness, and lineend
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
theme_prism() +
scale_y_continuous(limits = c(0, 40),
minor_breaks = seq(0, 40, 2),
guide = "prism_offset_minor")
p1 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(20, "pt"))
p2 <- p + theme(legend.position = "none",
prism.ticks.length.y = unit(20, "pt"),
axis.ticks.y = element_line(colour = "blue",
linewidth = 2,
lineend = "round"))
p1 + p2
```
## Brackets axis
Brackets are not an axis option in GraphPad Prism. Rather the idea comes
from the [`lemon`](https://github.com/stefanedwards/lemon) package functions
`brackets_horisontal()` and `brackets_vertical()`. I wanted brackets for my
graphs, therefore they have been re-implemented in `ggprism` as the
`guide_prism_brackets()` axis guide.
This axis guide works best with discrete axes.
```{r, fig.height=3.5}
# show bracket axis guide
p1 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p2 <- p1 + scale_x_discrete(guide = "prism_bracket")
p1 + p2
```
The guide works fine with flipped plots.
```{r, fig.height=3.5}
# show bracket axis guide with flipped plot
p1 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset") +
scale_x_discrete(guide = "prism_bracket")
p2 <- p1 + coord_flip()
p1 + p2
```
By default, the function tries to guess how wide the brackets should be.
However you can control the bracket width with the `width` argument. Try numbers
between 0 and 1. In this example we make the width of `geom_jitter()` and the
width of `guide_prism_bracket()` both 0.2 which seems to work well.
```{r, fig.height=3.5}
# control bracket width
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p1 <- p + scale_x_discrete(guide = "prism_bracket")
p2 <- p + scale_x_discrete(guide = guide_prism_bracket(width = 0.2))
p1 + p2
```
You can change the bracket direction with the `outside` argument. By default,
`outside = TRUE` which means the brackets point outward.
```{r, fig.height=3.5}
# compare brackets outside or inside
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none") +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p1 <- p + scale_x_discrete(guide = "prism_bracket")
p2 <- p + scale_x_discrete(guide = guide_prism_bracket(outside = FALSE))
p1 + p2
```
Making the brackets point inside makes the space between the axis text and the
brackets smaller. You can increase this distance again by changing the margin
of the relevent `axis.text` element.
```{r, fig.height=3.5}
# adjust text spacing with inside pointing brackets
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
scale_y_continuous(limits = c(0, 40), guide = "prism_offset") +
scale_x_discrete(guide = guide_prism_bracket(outside = FALSE))
p1 <- p + theme(legend.position = "none")
p2 <- p + theme(legend.position = "none",
axis.text.x = element_text(margin = margin(t = 10)))
p1 + p2
```
## Border with minor ticks
Say you want a graph, with a border, and with minor ticks. There are a couple
of ways one might do this, one of which is outlined here. The other way
involved messing around with secondary axes and is buggy so we'll ignore it
for now.
First we define a base plot.
```{r, fig.asp=0.9, fig.width=5}
# define a base plot
base <- ggplot(mpg, aes(x = displ, y = cty)) +
geom_point(aes(colour = class))
base
```
Then we'll apply `theme_prism()` with a border, move the legend into the
plotting area, and turn clipping off so the border thickness is accurate.
```{r, fig.asp=0.9, fig.width=5}
# apply theme_prism and turn clipping off for the border
p <- base + theme_prism(border = TRUE) +
guides(colour = guide_legend(position = "inside")) +
theme(legend.position.inside = c(0.8, 0.75)) +
coord_cartesian(clip = "off")
p
```
Now we'll add minor ticks to both primary axes.
```{r, fig.asp=0.9, fig.width=5}
# add axis guides
p <- p + guides(x = "prism_minor", y = "prism_minor")
p
```
One way to add minor ticks all around the border is to use the
`annotation_ticks()` function in `ggprism`. This is the way I would recommend
to avoid the issues that arise using secondary axes.
Here we add ticks as a plot annotation (i.e. not a proper axis) with the
following arguments:
1. Put the ticks on the top and right sides
1. Draw both major and minor ticks (as opposed to just `"minor"` or `"major"`)
1. Make the thickness = 1
1. Make them point outside (must turn clipping off!)
1. Try to match the lengths with the other tick marks (bit of trial and error)
```{r, fig.asp=0.9, fig.width=5}
# add tick annotations
p_annot <- p + annotation_ticks(sides = "tr", type = "both", linewidth = 1,
outside = TRUE,
tick.length = unit(14/2, "pt"),
minor.length = unit(14/4, "pt"))
p_annot
```
You can adjust the number of minor ticks using the continuous scale
functions as before.
```{r, fig.asp=0.9, fig.width=5}
# tick annotations will mirror adjustments to the actual axis ticks
p_annot <- p_annot + scale_x_continuous(minor_breaks = seq(1, 7, 0.2))
p_annot
```
## Discontinuous axis
Generally using discontinuous axes is discouraged. However as it is an option
in GraphPad Prism, an example of how to do this with `ggprism` is given below.
First we'll take the `ToothGrowth` data set and change a value in the `len`
column to be an obvious outlier.
```{r}
# multiply one of the len values by 100
tg <- ToothGrowth
tg[2, "len"] <- tg[2, "len"] * 100
```
Next we'll see what a plot without a discontinuous axis would look like.
The majority of the data has been compressed near the bottom of the plot in
an attempt to fit the outlier point. In this situation one might want to use a
discontinuous axis (although honestly it is probably better to add an inset
plot using the `patchwork` package).
```{r, fig.width=4, fig.height=5}
ggplot(tg, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none")
```
We'll use the `patchwork` package to create our discontinuous
axis plot. Indeed, we will be making two plots (one zoomed in on the main data
and the other zoomed in on the outlier), using a slightly different theme for
each plot, and then combining them.
First we'll make a plot zoomed in on the main data. It is important to use
`coord_cartesian()` to change the axis limits instead of `scale_y_continuous()`
as `coord_cartesian()` does not exclude any data, unlike `scale_y_continuous()`.
```{r, fig.width=4, fig.height=3}
p_bottom <- ggplot(tg, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
coord_cartesian(ylim = c(0, 60)) +
guides(x = "prism_bracket", y = "prism_offset_minor") +
theme_prism() +
theme(legend.position = "none")
p_bottom
```
Next we'll make a plot zoomed in on the outlier, and we'll make a new theme for
the top plot specifically, called `theme_outlier()`.
```{r, fig.width=4, fig.height=1.5}
p_top <- ggplot(tg, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
coord_cartesian(ylim = c(1140, 1160)) +
scale_y_continuous(breaks = c(1140, 1160)) +
guides(y = "prism_offset_minor")
theme_outlier <- function(palette = "black_and_white",
base_size = 14,
base_family = "sans",
base_fontface = "bold",
base_line_size = base_size/14,
base_rect_size = base_size/14,
axis_text_angle = 0,
border = FALSE) {
theme_prism(palette = palette,
base_size = base_size,
base_family = base_family,
base_fontface = base_fontface,
base_line_size = base_line_size,
base_rect_size = base_rect_size,
axis_text_angle = axis_text_angle,
border = border) %+replace%
theme(axis.title = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank(),
legend.position = "none")
}
p_top <- p_top + theme_outlier()
p_top
```
Now we'll combine the two plots with `patchwork`. We can easily change theme
elements of both plots using `& theme()`.
```{r, fig.width=4, fig.height=5}
p_top / p_bottom +
plot_layout(heights = c(1, 4)) &
theme(axis.text.y = element_text(colour = "red"))
```