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Formula interface to geom_linerange() and geom_pointrange()

Source: R/gf_functions.R
gf_linerange.Rd

Various ways of representing a vertical interval defined by x, ymin and ymax. Each case draws a single graphical object.

Usage

gf_linerange(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "linerange",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_pointrange(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  size,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "pointrange",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_summary(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth = 1,
  size,
  fun.y = NULL,
  fun.ymax = NULL,
  fun.ymin = NULL,
  fun.args = list(),
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "pointrange",
  stat = "summary",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

object

When chaining, this holds an object produced in the earlier portions of the chain. Most users can safely ignore this argument. See details and examples.

gformula

A formula with shape ymin + ymax ~ x. Faceting can be achieved by including | in the formula.

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. A function can be created from a formula (e.g. ~ head(.x, 10)).

...

Additional arguments. Typically these are (a) ggplot2 aesthetics to be set with attribute = value, (b) ggplot2 aesthetics to be mapped with attribute = ~ expression, or (c) attributes of the layer as a whole, which are set with attribute = value.

alpha

Opacity (0 = invisible, 1 = opaque).

color

Set or map color.

group

Use to set or map group.

linetype, linewidth

Set or map style of the line.

xlab

Label for x-axis. See also gf_labs().

ylab

Label for y-axis. See also gf_labs().

title, subtitle, caption

Title, sub-title, and caption for the plot. See also gf_labs().

geom

The geometric object to use to display the data for this layer. When using a stat_*() function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

  • A Geom ggproto subclass, for example GeomPoint.

  • A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".

  • For more information and other ways to specify the geom, see the layer geom documentation.

stat

The statistical transformation to use on the data for this layer. When using a geom_*() function to construct a layer, the stat argument can be used to override the default coupling between geoms and stats. The stat argument accepts the following:

  • A Stat ggproto subclass, for example StatCount.

  • A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".

  • For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

  • The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.

  • A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".

  • For more information and other ways to specify the position, see the layer position documentation.

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. It can also be a named logical vector to finely select the aesthetics to display. To include legend keys for all levels, even when no data exists, use TRUE. If NA, all levels are shown in legend, but unobserved levels are omitted.

show.help

If TRUE, display some minimal help.

inherit

A logical indicating whether default attributes are inherited.

environment

An environment in which to look for variables not found in data.

size

size aesthetic for points (gf_pointrange()).

fun.ymin, fun.y, fun.ymax

[Deprecated] Use the versions specified above instead.

fun.args

Optional additional arguments passed on to the functions.

See also

ggplot2::geom_linerange()

ggplot2::geom_pointrange()

ggplot2::geom_pointrange(), ggplot2::stat_summary()

Examples

gf_linerange()
#> gf_linerange() uses 
#>     * a formula with shape ymin + ymax ~ x or y ~ xmin + xmax. 
#>     * geom:  linerange 
#>     * key attributes:  alpha, color, group, linetype, linewidth
#> 
#> For more information, try ?gf_linerange

gf_ribbon(low_temp + high_temp ~ date,
  data = mosaicData::Weather,
  fill = ~city, alpha = 0.4
) |>
  gf_theme(theme = theme_minimal())

gf_linerange(
  low_temp + high_temp ~ date | city ~ .,
  data = mosaicData::Weather,
  color = ~ ((low_temp + high_temp) / 2)
) |>
  gf_refine(scale_colour_gradientn(colors = rev(rainbow(5)))) |>
  gf_labs(color = "mid-temp")


gf_ribbon(low_temp + high_temp ~ date | city ~ ., data = mosaicData::Weather)


# Chaining in the data
mosaicData::Weather |>
  gf_ribbon(low_temp + high_temp ~ date, alpha = 0.4) |>
  gf_facet_grid(city ~ .)

if (require(mosaicData) && require(dplyr)) {
  HELP2 <- HELPrct |>
    group_by(substance, sex) |>
    summarise(
      mean.age = mean(age),
      median.age = median(age),
      max.age = max(age),
      min.age = min(age),
      sd.age = sd(age),
      lo = mean.age - sd.age,
      hi = mean.age + sd.age
    )

  gf_jitter(age ~ substance, data = HELPrct,
      alpha = 0.5, width = 0.2, height = 0, color = "skyblue") |>
    gf_pointrange(mean.age + lo + hi ~ substance, data = HELP2) |>
    gf_facet_grid(~sex)

  gf_jitter(age ~ substance, data = HELPrct,
    alpha = 0.5, width = 0.2, height = 0, color = "skyblue") |>
    gf_errorbar(lo + hi ~ substance, data = HELP2, inherit = FALSE) |>
    gf_facet_grid(~sex)

  # width is defined differently for gf_boxplot() and gf_jitter()
  #   * for gf_boxplot() it is the full width of the box.
  #   * for gf_jitter() it is half that -- the maximum amount added or subtracted.
  gf_boxplot(age ~ substance, data = HELPrct, width = 0.4) |>
    gf_jitter(width = 0.4, height = 0, color = "skyblue", alpha = 0.5)

  gf_boxplot(age ~ substance, data = HELPrct, width = 0.4) |>
    gf_jitter(width = 0.2, height = 0, color = "skyblue", alpha = 0.5)
}
#> `summarise()` has grouped output by 'substance'. You can override using the
#> `.groups` argument.

p <- gf_jitter(mpg ~ cyl, data = mtcars, height = 0, width = 0.15); p

p |> gf_summary(fun.data = "mean_cl_boot", color = "red", size = 2, linewidth = 1.3)

# You can supply individual functions to summarise the value at
# each x:
p |> gf_summary(fun.y = "median", color = "red", size = 3, geom = "point")
#> No summary function supplied, defaulting to `mean_se()`

p |>
  gf_summary(fun.y = "mean", color = "red", size = 3, geom = "point") |>
  gf_summary(fun.y = mean, geom = "line")
#> No summary function supplied, defaulting to `mean_se()`
#> No summary function supplied, defaulting to `mean_se()`

p |>
  gf_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, color = "red")
#> No summary function supplied, defaulting to `mean_se()`

if (FALSE) { # \dontrun{
  p |>
  gf_summary(fun.ymin = min, fun.ymax = max, color = "red", geom = "linerange")
} # }

gf_bar(~ cut, data = diamonds)

gf_col(price ~ cut, data = diamonds, stat = "summary_bin", fun.y = "mean")
#> No summary function supplied, defaulting to `mean_se()`


# Don't use gf_lims() to zoom into a summary plot - this throws the
# data away
p <- gf_summary(mpg ~ cyl, data = mtcars, fun.y = "mean", geom = "point")
p
#> No summary function supplied, defaulting to `mean_se()`

p |> gf_lims(y = c(15, 30))
#> No summary function supplied, defaulting to `mean_se()`
#> Warning: Removed 9 rows containing non-finite outside the scale range
#> (`stat_summary()`).

# Instead use coord_cartesian()
p |> gf_refine(coord_cartesian(ylim = c(15, 30)))
#> No summary function supplied, defaulting to `mean_se()`

# A set of useful summary functions is provided from the Hmisc package.
if (FALSE) { # \dontrun{
p <- gf_jitter(mpg ~ cyl, data = mtcars, width = 0.15, height = 0); p
p |> gf_summary(fun.data = mean_cl_boot, color = "red")
p |> gf_summary(fun.data = mean_cl_boot, color = "red", geom = "crossbar")
p |> gf_summary(fun.data = mean_sdl, group = ~ cyl, color = "red",
                   geom = "crossbar", width = 0.3)
p |> gf_summary(group = ~ cyl, color = "red", geom = "crossbar", width = 0.3,
        fun.data = mean_sdl, fun.args = list(mult = 1))
p |> gf_summary(fun.data = median_hilow, group = ~ cyl, color = "red",
        geom = "crossbar", width = 0.3)
} # }

# An example with highly skewed distributions:
if (require("ggplot2movies")) {
  set.seed(596)
  Mov <- movies[sample(nrow(movies), 1000), ]
  m2 <- gf_jitter(votes ~ factor(round(rating)), data = Mov, width = 0.15, height = 0, alpha = 0.3)
  m2 <- m2 |>
    gf_summary(fun.data = "mean_cl_boot", geom = "crossbar",
               colour = "red", width = 0.3) |>
    gf_labs(x = "rating")
  m2
  # Notice how the overplotting skews off visual perception of the mean
  # supplementing the raw data with summary statistics is _very_ important

  # Next, we'll look at votes on a log scale.

  # Transforming the scale means the data are transformed
  # first, after which statistics are computed:
  m2 |> gf_refine(scale_y_log10())
  # Transforming the coordinate system occurs after the
  # statistic has been computed. This means we're calculating the summary on the raw data
  # and stretching the geoms onto the log scale.  Compare the widths of the
  # standard errors.
  m2 |> gf_refine(coord_trans(y="log10"))
}
#> Loading required package: ggplot2movies
#> Warning: `coord_trans()` was deprecated in ggplot2 4.0.0.
#> ℹ Please use `coord_transform()` instead.