https://github.com/r-lib/scales

Tools for ggplot2 scales
https://github.com/r-lib/scales

ggplot2 r

Last synced: about 2 months ago
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Tools for ggplot2 scales

• Host: GitHub
• URL: https://github.com/r-lib/scales
• Owner: r-lib
• License: other
• Created: 2010-11-08T00:44:23.000Z (almost 14 years ago)
• Default Branch: main
• Last Pushed: 2024-05-14T14:55:27.000Z (4 months ago)
• Last Synced: 2024-06-27T10:54:52.518Z (3 months ago)
• Topics: ggplot2, r
• Language: R
• Homepage: https://scales.r-lib.org
• Size: 31 MB
• Stars: 388
• Watchers: 18
• Forks: 106
• Open Issues: 37
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

• awesome-starred - scales - Tools for ggplot2 scales (R)
• jimsghstars - r-lib/scales - Tools for ggplot2 scales (R)

README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  fig.width = 5,

  fig.height = 3

)

```

# scales 

 

[![CRAN status](https://www.r-pkg.org/badges/version/scales)](https://CRAN.R-project.org/package=scales)

[![R-CMD-check](https://github.com/r-lib/scales/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/r-lib/scales/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/r-lib/scales/branch/main/graph/badge.svg)](https://app.codecov.io/gh/r-lib/scales?branch=main)

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 labels}

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())

  )

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, palettes}

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 transforms}

# 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)

```