gf_linerange {ggformula}

Formula interface to geom_linerange() and geom_pointrange()

Description

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,
  fatten = 2,
  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,
  size,
  fun.y = NULL,
  fun.ymax = NULL,
  fun.ymin = NULL,
  fun.args = list(),
  fatten = 2,
  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

A color or a formula used for mapping color.

group

Used for grouping.

linetype

A linetype (numeric or "dashed", "dotted", etc.) or a formula used for mapping linetype.

linewidth

A numerical line width or a formula used for mapping linewidth.

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, either as a ggproto Geom subclass or as a string naming the geom stripped of the geom_ prefix (e.g. "point" rather than "geom_point")

stat

The statistical transformation to use on the data for this layer, either as a ggproto Geom subclass or as a string naming the stat stripped of the stat_ prefix (e.g. "count" rather than "stat_count")

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

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.

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()).

fatten

A multiplicative factor used to increase the size of the middle bar in geom_crossbar() and the middle point in geom_pointrange().

fun.ymin, fun.y, fun.ymax

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_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(
      age = NA,
      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)
}
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")
p |>
  gf_summary(fun.y = "mean", color = "red", size = 3, geom = "point") |>
  gf_summary(fun.y = mean, geom = "line")
p |>
  gf_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, color = "red")
## Not run: 
  p |>
  gf_summary(fun.ymin = min, fun.ymax = max, color = "red", geom = "linerange")

## End(Not run)

gf_bar(~ cut, data = diamonds)
gf_col(price ~ cut, data = diamonds, stat = "summary_bin", fun.y = "mean")

# 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
p |> gf_lims(y = c(15, 30))
# Instead use coord_cartesian()
p |> gf_refine(coord_cartesian(ylim = c(15, 30)))
# A set of useful summary functions is provided from the Hmisc package.
## Not run: 
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)

## End(Not run)

# 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"))
}