animint2: geom_abline – R documentation

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geom_abline

Lines: horizontal, vertical, and specified by slope and intercept.

Description

These paired geoms and stats add straight lines to a plot, either horizontal, vertical or specified by slope and intercept. These are useful for annotating plots.

Usage

geom_abline(
  mapping = NULL,
  data = NULL,
  ...,
  slope,
  intercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_hline(
  mapping = NULL,
  data = NULL,
  ...,
  yintercept,
  na.rm = FALSE,
  show.legend = NA
)

geom_vline(
  mapping = NULL,
  data = NULL,
  ...,
  xintercept,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes` or `aes_`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame.`, and will be used as the layer data.
`...`	other arguments passed on to `layer`. These are often aesthetics, used to set an aesthetic to a fixed value, like `color = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`na.rm`	If `FALSE` (the default), removes missing values with a warning. If `TRUE` silently removes missing values.
`show.legend`	logical. Should this layer be included in the legends? `NA`, the default, includes if any aesthetics are mapped. `FALSE` never includes, and `TRUE` always includes.
`xintercept, yintercept, slope, intercept`	Parameters that control the position of the line. If these are set, `data`, `mapping` and `show.legend` are overridden

Details

These geoms act slightly different to other geoms. You can supply the parameters in two ways: either as arguments to the layer function, or via aesthetics. If you use arguments, e.g. geom_abline(intercept = 0, slope = 1), then behind the scenes the geom makes a new data frame containing just the data you've supplied. That means that the lines will be the same in all facets; if you want them to vary across facets, construct the data frame yourself and use aesthetics.

Unlike most other geoms, these geoms do not inherit aesthetics from the plot default, because they do not understand x and y aesthetics which are commonly set in the plot. They also do not affect the x and y scales.

Aesthetics

These geoms are drawn using with geom_line so support the same aesthetics: alpha, colour, linetype and size. They also each have aesthetics that control the position of the line:

geom_vline: xintercept
geom_hline: yintercept
geom_abline: slope and intercept

Examples

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()

# Fixed values
p + geom_vline(xintercept = 5)
p + geom_vline(xintercept = 1:5)
p + geom_hline(yintercept = 20)

p + geom_abline() # Can't see it - outside the range of the data
p + geom_abline(intercept = 20)

# Calculate slope and intercept of line of best fit
coef(lm(mpg ~ wt, data = mtcars))
p + geom_abline(intercept = 37, slope = -5)
# But this is easier to do with geom_smooth:
p + geom_smooth(method = "lm", se = FALSE)

# To show different lines in different facets, use aesthetics
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  facet_wrap(~ cyl)

mean_wt <- data.frame(cyl = c(4, 6, 8), wt = c(2.28, 3.11, 4.00))
p + geom_hline(aes(yintercept = wt), mean_wt)

# You can also control other aesthetics
ggplot(mtcars, aes(mpg, wt, colour = wt)) +
  geom_point() +
  geom_hline(aes(yintercept = wt, colour = wt), mean_wt) +
  facet_wrap(~ cyl)

animint2

Animated Interactive Grammar of Graphics

v2020.9.18

GPL-3

Authors

Toby Hocking [aut, cre] (Original animint code), Hadley Wickham [aut] (Forked ggplot2 code), Winston Chang [aut] (Forked ggplot2 code), RStudio [cph] (Forked ggplot2 code), Nicholas Lewin-Koh [aut] (hexGrob), Martin Maechler [aut] (hexGrob), Randall Prium [aut] (cut_width), Susan VanderPlas [aut] (Animint GSOC 2013), Carson Sievert [aut] (Animint GSOC 2014), Kevin Ferris [aut] (Animint GSOC 2015), Jun Cai [aut] (Animint GSOC 2015), Faizan Khan [aut] (Animint GSOC 2016-2017), Vivek Kumar [aut] (Animint GSOC 2018), Himanshu Singh [aut] (Animint2 GSoC 2020)

Initial release