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* Edit label_number_si() to use SI prefixes

* Update test to send another argument to number()

* Fix unicode error emitted by R CMD check

* Remove non-ASCII character from docs

* Fix unicode mismatch on Windows

* Another attempt to resolve Windows unicode

* Share SI prefixes with label_bytes

* Restore whitespace

* Add billion_scale argument to label_dollar()

* Remove wikipedia hyperlink

* Rename argument as rescale_large

* Work with accuracy and scale arguments

* Clarify short scale used internationally for finance

* Refactor common code in rescale_by_suffix()

* Set default accuracy to NULL

* Fix conflicting factor levels on R 3.4

* Rename short/long scale functions

* Move SI prefixes into SI file

* label_bytes() uses rescale_by_suffix()

* label_number_si() supports scale argument

* Remove sep argument from label_number_si()
This wasn't actually doing anything, because user inputs were overwritten.

* First argument of label_number_si() is unit

* Require unit argument

* Update NEWS

* NEWS update

* Document when `scale` argument is useful

* Remove headings from NEWS

* Fix docs typo

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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, and gives you tools to override the default breaks, labels, transformations and palettes.


# Scales is installed when you install ggplot2 or the tidyverse.
# But you can install just scales from CRAN:

# Or the development version from Github:
# install.packages("devtools")


Breaks and labels

The most common use of the scales package is to customise 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.

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) + 
    breaks = scales::breaks_width("2 years"), 
    labels = scales::label_date("'%y")
  ) + 
    "Total sales",
    labels = scales::label_number_si()

economics %>% 
  filter(date < ymd("1970-01-01")) %>% 
  ggplot(aes(date, pce)) + 
  geom_line() + 
    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 job 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.

# pull a list of colours from any palette
#> [1] "#440154FF" "#31688EFF" "#35B779FF" "#FDE725FF"

# use in combination with baseR `palette()` to set new defaults
palette(brewer_pal(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.

# use trans_new to build a new transformation
logp3_trans <- trans_new(
  name = "logp",
  trans = 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 = logp3_trans)