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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r}
#| include: false
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.width = 5,
fig.height = 3
)
```
# scales <a href="https://scales.r-lib.org/"><img src="man/figures/logo.png" align="right" height="138" alt="scales website" /></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=scales)
[](https://github.com/r-lib/scales/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/r-lib/scales)
<!-- badges: end -->
One of the most difficult parts of any graphics package is scaling, converting from data values to perceptual properties. The inverse of scaling, making guides (legends and axes) that can be used to read the graph, is often even harder! The scales packages provides the internal scaling infrastructure used by [ggplot2](https://ggplot2.tidyverse.org/), and gives you tools to override the default breaks, labels, transformations and palettes.
## Installation
```{r}
#| eval: false
# Scales is installed when you install ggplot2 or the tidyverse.
# But you can install just scales from CRAN:
install.packages("scales")
# Or the development version from Github:
# install.packages("pak")
pak::pak("r-lib/scales")
```
## Usage
### Breaks and labels
The most common use of the scales package is to control the appearance of axis and legend labels. Use a `break_` function to control how breaks are generated from the limits, and a `label_` function to control how breaks are turned in to labels.
```{r}
#| label: labels
#| fig-alt: >
#| A line plot created with ggplot2, showing property sales in Texas. The x scale
#| uses `scales::break_width()` to place breaks every second year, and
#| `scales::label_date()` to create a custom format for the labels. The y-scale
#| uses `scales::label_number()` to reformat the labels with
#| `scales::cut_short_scale()`.
library(ggplot2)
library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)
txhousing %>%
mutate(date = make_date(year, month, 1)) %>%
group_by(city) %>%
filter(min(sales) > 5e2) %>%
ggplot(aes(date, sales, group = city)) +
geom_line(na.rm = TRUE) +
scale_x_date(
NULL,
breaks = scales::breaks_width("2 years"),
labels = scales::label_date("'%y")
) +
scale_y_log10(
"Total sales",
labels = scales::label_number(scale_cut = scales::cut_short_scale())
)
```
```{r}
#| fig-alt: >
#| A line plot created with ggplot2, showing personal expenses between 1967 and
#| 1970. The x axis uses `scales::break_width()` to put a break every 3 months
#| and `scales::label_date_short()` to only show the year on the first occuring
#| break of that year. The y axis uses `scales::breaks_extended()` to request
#| 8 breaks, though only 6 are ultimately provided, and
#| `scales::label_dollar()` to format the label as a dollar value.
economics %>%
filter(date < ymd("1970-01-01")) %>%
ggplot(aes(date, pce)) +
geom_line() +
scale_x_date(NULL,
breaks = scales::breaks_width("3 months"),
labels = scales::label_date_short()
) +
scale_y_continuous("Personal consumption expenditures",
breaks = scales::breaks_extended(8),
labels = scales::label_dollar()
)
```
Generally, I don't recommend running `library(scales)` because when you type (e.g.) `scales::label_` autocomplete will provide you with a list of labelling functions to jog your memory.
### Advanced features
Scales colour palettes are used to power the scales in ggplot2, but you can use them in any plotting system. The following example shows how you might apply them to a base plot.
```{r}
#| label: palettes
#| fig-alt: >
#| A scatterplot created with base plot showing the relationship between
#| sepal length and sepal width in the Iris dataset. The points are coloured
#| according to species and the `scales::pal_brewer()` are used to provide the
#| colours.
library(scales)
# pull a list of colours from any palette
pal_viridis()(4)
# use in combination with baseR `palette()` to set new defaults
palette(pal_brewer(palette = "Set2")(4))
par(mar = c(5, 5, 1, 1))
plot(Sepal.Length ~ Sepal.Width, data = iris, col = Species, pch = 20)
```
scales also gives users the ability to define and apply their own custom
transformation functions for repeated use.
```{r}
#| label: transforms
#| fig-alt: >
#| A scatterplot created with ggplot2 showing the relationship between diamond
#| price and its carat for a subset of the data in the diamonds dataset. The
#| y scale uses a custom log transform created with `scales::new_transform()`.
# use new_transform to build a new transformation
transform_logp3 <- new_transform(
name = "logp",
transform = function(x) log(x + 3),
inverse = function(x) exp(x) - 3,
breaks = log_breaks()
)
dsamp <- sample_n(diamonds, 100)
ggplot(dsamp, aes(carat, price, colour = color)) +
geom_point() +
scale_y_continuous(trans = transform_logp3)
```