Package 'ggformula'

Description

Creates a function that can be passed to scales for creating discrete breaks at multilples of resolution.

Usage

discrete_breaks(resolution = 1)

Arguments

Value

A function that can be passed to scales functions as the breaks argument.

Examples

x <- rbinom(100, 100, 0.4)
p <- gf_bar( ~ x)
p |> gf_refine(scale_x_continuous(breaks = discrete_breaks()))
p |> gf_refine(scale_x_continuous(breaks = discrete_breaks(5)))
p |> gf_refine(scale_x_continuous(breaks = discrete_breaks(2)))

Description

Some packages like expss provide mechanisms for providing longer labels to R objects. These labels can be used when labeling plots and tables, for example, without requiring long or awkward variable names. This is an experimental feature and currently only supports expss or any other system that stores a label in the label attribute of a vector.

Usage

get_variable_labels(...)

Arguments

Details

get_variable_labels() is a synonym of labelled::var_label().

See Also

labelled::var_label(), labelled::set_variable_labels()

Examples

KF <-
  mosaicData::KidsFeet |>
  set_variable_labels(
      length      = 'foot length (cm)',
      width       = 'foot width (cm)',
      birthmonth  = 'birth month',
      birthyear   = 'birth year',
      biggerfoot  = 'bigger foot',
      domhand     = 'dominant hand'
  )
KF |>
  gf_point(length ~ width, color = ~ domhand)
get_variable_labels(KF)

Description

These functions create layers that display lines described i various ways. Unlike most of the plotting functions in ggformula, these functions do not take a formula as input for describing positional attributes of the plot.

Usage

gf_abline(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  slope,
  intercept,
  color,
  linetype,
  linewidth,
  alpha,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  show.legend = NA,
  show.help = NULL,
  inherit = FALSE,
  environment = parent.frame()
)

gf_hline(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  yintercept,
  color,
  linetype,
  linewidth,
  alpha,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  show.legend = NA,
  show.help = NULL,
  inherit = FALSE,
  environment = parent.frame()
)

gf_vline(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  xintercept,
  color,
  linetype,
  linewidth,
  alpha,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  show.legend = NA,
  show.help = NULL,
  inherit = FALSE,
  environment = parent.frame()
)

gf_coefline(object = NULL, coef = NULL, model = NULL, ...)

Arguments

See Also

ggplot2::geom_abline(), ggplot2::geom_vline(), ggplot2::geom_hline()

Examples

mtcars2 <- df_stats(wt ~ cyl, data = mtcars, median_wt = median)
gf_point(wt ~ hp, size = ~wt, color = ~cyl, data = mtcars) |>
  gf_abline(slope = ~0, intercept = ~median_wt, color = ~cyl, data = mtcars2)

gf_point(wt ~ hp, size = ~wt, color = ~cyl, data = mtcars) |>
  gf_abline(slope = 0, intercept = 3, color = "green")

# avoid warnings by using formulas:

gf_point(wt ~ hp, size = ~wt, color = ~cyl, data = mtcars) |>
  gf_abline(slope = ~0, intercept = ~3, color = "green")

gf_point(wt ~ hp, size = ~wt, color = ~cyl, data = mtcars) |>
  gf_hline(yintercept = ~median_wt, color = ~cyl, data = mtcars2)

gf_point(mpg ~ hp, color = ~cyl, size = ~wt, data = mtcars) |>
  gf_abline(color = "red", slope = ~ - 0.10, intercept = ~ 35)

gf_point(mpg ~ hp, color = ~cyl, size = ~wt, data = mtcars) |>
  gf_abline(
    color = "red", slope = ~slope, intercept = ~intercept,
    data = data.frame(slope = -0.10, intercept = 33:35)
  )

# We can set the color of the guidelines while mapping color in other layers
gf_point(mpg ~ hp, color = ~cyl, size = ~ wt, data = mtcars) |>
  gf_hline(color = "navy", yintercept = ~ c(20, 25), data = NA) |>
  gf_vline(color = "brown", xintercept = ~ c(200, 300), data = NA)

# If we want to map the color of the guidelines, it must work with the
# scale of the other colors in the plot.
gf_point(mpg ~ hp, size = ~wt, data = mtcars, alpha = 0.3) |>
  gf_hline(color = ~"horizontal", yintercept = ~ c(20, 25), data = NA) |>
  gf_vline(color = ~"vertical", xintercept = ~ c(100, 200, 300), data = NA)

gf_point(mpg ~ hp, size = ~wt, color = ~ factor(cyl), data = mtcars, alpha = 0.3) |>
  gf_hline(color = "orange", yintercept = ~ 20) |>
  gf_vline(color = ~ c("4", "6", "8"), xintercept = ~ c(80, 120, 250), data = NA)

gf_point(mpg ~ hp, size = ~wt, color = ~ factor(cyl), data = mtcars, alpha = 0.3) |>
  gf_hline(color = "orange", yintercept = ~ 20) |>
  gf_vline(color = c("green", "red", "blue"), xintercept = ~ c(80, 120, 250),
    data = NA)

# reversing the layers requires using inherit = FALSE
gf_hline(color = "orange", yintercept = ~ 20) |>
  gf_vline(color = ~ c("4", "6", "8"), xintercept = ~ c(80, 120, 250), data = NA) |>
  gf_point(mpg ~ hp,
    size = ~wt, color = ~ factor(cyl), data = mtcars, alpha = 0.3,
    inherit = FALSE
  )

Description

For each x value, geom_ribbon() displays a y interval defined by ymin and ymax. geom_area() is a special case of geom_ribbon(), where the ymin is fixed to 0 and y is used instead of ymax.

Usage

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

Arguments

See Also

ggplot2::geom_area()

Examples

if (require(dplyr) && require(mosaicData)) {
  Temps <- Weather |>
    filter(city == "Chicago", year == 2016, month <= 4)
  gf_linerange(low_temp + high_temp ~ date, color = ~high_temp, data = Temps)
  gf_ribbon(low_temp + high_temp ~ date, data = Temps, color = "navy", alpha = 0.3)
  gf_area(high_temp ~ date, data = Temps, color = "navy", alpha = 0.3)

  gf_ribbon(low_temp + high_temp ~ date, data = Weather, alpha = 0.3) |>
    gf_facet_grid(city ~ .)

  gf_linerange(low_temp + high_temp ~ date, color = ~high_temp, data = Weather) |>
    gf_facet_grid(city ~ .) |>
    gf_refine(scale_colour_gradientn(colors = rev(rainbow(5))))
}

Description

An ASH plot is the average over all histograms of a fixed bin width. geom_ash() and gf_ash() provide ways to create ASH plots using ggplot2 or ggformula.

Usage

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

stat_ash(
  mapping = NULL,
  data = NULL,
  geom = "line",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  binwidth = NULL,
  adjust = 1,
  ...
)

geom_ash(
  mapping = NULL,
  data = NULL,
  stat = "ash",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  binwidth = NULL,
  adjust = 1,
  ...
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

geom_histogram(), link{gf_histogram}().

Examples

data(penguins, package = "palmerpenguins")
gf_ash(~bill_length_mm, color = ~species, data = penguins)
gf_ash(~bill_length_mm, color = ~species, data = penguins, adjust = 2)
gf_ash(~bill_length_mm, color = ~species, data = penguins, binwidth = 1)
gf_ash(~bill_length_mm, color = ~species, data = penguins, binwidth = 1, adjust = 2)
ggplot(faithful, aes(x = eruptions)) +
  geom_histogram(aes(y = stat(density)),
    fill = "lightskyblue", colour = "gray50", alpha = 0.2
  ) +
  geom_ash(colour = "red") +
  geom_ash(colour = "forestgreen", adjust = 2) +
  geom_ash(colour = "navy", adjust = 1 / 2) +
  theme_minimal()

Description

There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x position. geom_col() uses stat_identity(): it leaves the data as is.

Usage

gf_bar(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  width = NULL,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "bar",
  stat = "count",
  position = "stack",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_counts(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  width = NULL,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "bar",
  stat = "count",
  position = "stack",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_props(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  xlab,
  ylab = "proportion",
  title,
  subtitle,
  caption,
  geom = "bar",
  stat = "count",
  position = "stack",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame(),
  denom = ~PANEL
)

gf_percents(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  xlab,
  ylab = "percent",
  title,
  subtitle,
  caption,
  geom = "bar",
  stat = "count",
  position = "stack",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame(),
  denom = ~PANEL
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_bar()

Examples

gf_bar(~substance, data = mosaicData::HELPrct)
gf_bar(~substance, data = mosaicData::HELPrct, fill = ~sex)
gf_bar(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge()
)
# gf_counts() is another name for gf_bar()
gf_counts(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge()
)
# gf_props() and gf_percents() use proportions or percentages instead of counts
# use denom to control which denominators are used.
gf_props(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge()
)
gf_props(substance ~ .,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge(),
  orientation = 'y'
)
gf_props(substance ~ .,
  data = mosaicData::HELPrct, fill = ~sex,
  position = "dodge"
)

gf_percents(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge()
)
gf_percents(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge(),
  denom = ~x
)
gf_percents(~substance,
  data = mosaicData::HELPrct, fill = ~sex,
  position = position_dodge(),
  denom = ~fill
)
gf_percents(~substance | sex,
  data = mosaicData::HELPrct, fill = ~homeless,
  position = position_dodge()
)
gf_percents(~substance | sex,
  data = mosaicData::HELPrct,
  fill = ~homeless,
  denom = ~fill,
  position = position_dodge()
)
gf_percents(~substance | sex,
  data = mosaicData::HELPrct,
  fill = ~homeless,
  denom = ~interaction(fill, PANEL),
  position = position_dodge()
)
if (require(scales)) {
  gf_percents(~substance,
    data = mosaicData::HELPrct, fill = ~sex,
    position = position_dodge(),
    denom = ~ x,
  ) |>
    gf_refine(scale_y_continuous(labels = scales::percent))
}

Description

These functions were wrappers around functions from ggstance from an era before ggplot2 supported horizonally oriented geoms. ggstance has not been updated to comply with the current version of ggplot2, and since the functionalilty is now available by other means, these functions have been deprecated.

Usage

gf_barh(...)

gf_countsh(...)

gf_colh(...)

gf_propsh(...)

gf_percentsh(...)

gf_boxploth(...)

gf_linerangeh(...)

gf_pointrangeh(...)

gf_crossbarh(...)

gf_violinh(...)

gf_errorbarh(...)

Arguments

Examples

gf_violin(carat ~ color, data = diamonds)
gf_violin(carat ~ color, data = diamonds) |>
  gf_refine(coord_flip())
gf_violin(color ~ carat, data = diamonds)
gf_density(~ carat, data = diamonds)
gf_density(carat ~ ., data = diamonds)

Description

geom_bin2d() uses ggplot2::stat_bin2d() to bin the data before using gf_tile() to display the results.

Usage

gf_bin2d(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "tile",
  stat = "bin2d",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_bin2d(), gf_tile()

Examples

gf_bin2d(eruptions ~ waiting, data = faithful, bins = 15) |>
  gf_refine(scale_fill_viridis_c(begin = 0.1, end = 0.9))

Description

The blank geom draws nothing, but can be a useful way of ensuring common scales between different plots. See expand_limits() for more details.

Usage

gf_blank(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "blank",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_frame(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "blank",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_blank()

Examples

gf_point((c(0, 1)) ~ (c(0, 5)))
gf_frame((c(0, 1)) ~ (c(0, 5)))
gf_blank((c(0, 1)) ~ (c(0, 5)))
# gf_blank() can be used to expand the view
gf_point((c(0, 1)) ~ (c(0, 5))) |>
  gf_blank((c(0, 3)) ~ (c(-2, 7)))

Description

The boxplot compactly displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all "outlying" points individually.

Usage

gf_boxplot(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  coef,
  outlier.color = NULL,
  outlier.fill = NULL,
  outlier.shape = 19,
  outlier.size = 1.5,
  outlier.stroke = 0.5,
  outlier.alpha = NULL,
  notch = FALSE,
  notchwidth = 0.5,
  varwidth = FALSE,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "boxplot",
  stat = "boxplot",
  position = "dodge",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

References

McGill, R., Tukey, J. W. and Larsen, W. A. (1978) Variations of box plots. The American Statistician 32, 12-16.

See Also

ggplot2::geom_boxplot(), fivenum(), df_stats()

Examples

gf_boxplot(age ~ substance, data = mosaicData::HELPrct)
gf_boxplot(age ~ substance, data = mosaicData::HELPrct, varwidth = TRUE)
gf_boxplot(age ~ substance, data = mosaicData::HELPrct, color = ~sex)
gf_boxplot(age ~ substance,
  data = mosaicData::HELPrct,
  color = ~sex, outlier.color = "gray50"
)
# longer whiskers
gf_boxplot(age ~ substance,
  data = mosaicData::HELPrct,
  color = ~sex, coef = 2
)

# Note: width for boxplots is full width of box.
#       For jittering, it is the half-width.
gf_boxplot(age ~ substance | sex,
  data = mosaicData::HELPrct,
  coef = 5, width = 0.4
) |>
  gf_jitter(width = 0.2, alpha = 0.3)
# move boxplots away a bit by adjusting dodge
gf_boxplot(age ~ substance,
  data = mosaicData::HELPrct,
  color = ~sex, position = position_dodge(width = 0.9)
)

Description

There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x position. geom_col() uses stat_identity(): it leaves the data as is.

Usage

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

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_col()

Examples

SomeData <- data.frame(
  group = LETTERS[1:3],
  count = c(20, 25, 18)
)
gf_col(count ~ group, data = SomeData)

# A Pareto chart

if (require(dplyr) && require(mosaicData)) {
  HELPrct |>
    group_by(substance) |>
    summarise(count = n()) |>
    ungroup() |>
    dplyr::arrange(-count) |>
    mutate(
      cumcount = cumsum(count),
      substance = reorder(substance, -count)
    ) |>
    gf_col(count ~ substance, fill = "skyblue") |>
    gf_point(cumcount ~ substance) |>
    gf_line(cumcount ~ substance, group = 1) |>
    gf_refine(
      scale_y_continuous(sec.axis = sec_axis(~ . / nrow(HELPrct)))
    )
}

Description

ggplot2 can not draw true 3D surfaces, but you can use geom_contour(), geom_contour_filled(), and geom_tile() to visualise 3D surfaces in 2D.

These functions require regular data, where the x and y coordinates form an equally spaced grid, and each combination of x and y appears once. Missing values of z are allowed, but contouring will only work for grid points where all four corners are non-missing. If you have irregular data, you'll need to first interpolate on to a grid before visualising, using interp::interp(), akima::bilinear(), or similar.

Usage

gf_contour(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "contour",
  stat = "contour",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_contour_filled(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "contour_filled",
  stat = "contour_filled",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_contour(), gf_density_2d()

Examples

gf_density_2d(eruptions ~ waiting, data = faithful, alpha = 0.5, color = "navy") |>
  gf_contour(density ~ waiting + eruptions, data = faithfuld, bins = 10, color = "red")
gf_contour_filled(density ~ waiting + eruptions, data = faithfuld, bins = 10,
    show.legend = FALSE) |>
  gf_jitter(eruptions ~ waiting, data = faithful, color = "white", alpha = 0.5,
    inherit = FALSE)

Description

This is a variant geom_point() that counts the number of observations at each location, then maps the count to point area. It useful when you have discrete data and overplotting.

Usage

gf_count(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  shape,
  size,
  stroke,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "point",
  stat = "sum",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_count()

Examples

# Best used in conjunction with scale_size_area which ensures that
# counts of zero would be given size 0. This doesn't make much difference
# here because the smallest count is already close to 0.

gf_count(hwy ~ cty, data = mpg, alpha = 0.3) |>
  gf_refine(scale_size_area())

Description

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

Usage

gf_crossbar(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  fatten = 2.5,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "crossbar",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_crossbar()

Examples

if (require(mosaicData) && require(dplyr)) {
  HELP2 <- HELPrct |>
    summarise(.by = c(substance, sex),
      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 = 0.7, 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 = 0.7, width = 0.2, height = 0, color = "skyblue")  |>
    gf_errorbar(lo + hi ~ substance, data = HELP2, inherit = FALSE) |>
    gf_facet_grid(~sex)

  gf_jitter(age ~ substance, data = HELPrct,
      alpha = 0.7, width = 0.2, height = 0, color = "skyblue") |>
    gf_crossbar(mean.age + lo + hi ~ substance, data = HELP2,
      fill = "transparent") |>
    gf_facet_grid(~sex)

  gf_jitter(substance ~ age, data = HELPrct,
      alpha = 0.7, height = 0.2, width = 0, color = "skyblue") |>
    gf_crossbar(substance ~ mean.age + lo + hi, data = HELP2,
      fill = "transparent", color = "red") |>
    gf_facet_grid(~sex)
}

Description

geom_segment() draws a straight line between points (x, y) and (xend, yend). geom_curve() draws a curved line. See the underlying drawing function grid::curveGrob() for the parameters that control the curve.

Usage

gf_curve(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  curvature = 0.5,
  angle = 90,
  ncp = 5,
  arrow = NULL,
  lineend = "butt",
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "curve",
  stat = "identity",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_curve()

Examples

D <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)
gf_point(mpg ~ wt, data = mtcars) |>
  gf_curve(y1 + y2 ~ x1 + x2, data = D, color = "navy") |>
  gf_segment(y1 + y2 ~ x1 + x2, data = D, color = "red")

Description

Computes and draws a kernel density estimate, which is a smoothed version of the histogram and is a useful alternative when the data come from an underlying smooth distribution. The only difference between gf_dens() and gf_density() is the default geom used to show the density curve: gf_density() uses an area geom (which can be filled). gf_dens() using a line geom (which cannot be filled).

Usage

gf_density(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha = 0.5,
  color,
  fill,
  group,
  linetype,
  linewidth,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "area",
  stat = "density",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_dens(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha = 0.5,
  color,
  fill = NA,
  group,
  linetype,
  linewidth,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "line",
  stat = "density",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_dens2(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha = 0.5,
  color,
  fill = NA,
  group,
  linetype,
  linewidth,
  kernel = "gaussian",
  n = 512,
  trim = FALSE,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "density_line",
  stat = "density",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

gf_ash(), ggplot2::geom_density()

Examples

gf_dens()
data(penguins, package = "palmerpenguins")
gf_density(~bill_length_mm, fill = ~species, data = penguins)
gf_dens(~bill_length_mm, color = ~species, data = penguins)
gf_dens2(~bill_length_mm, color = ~species, fill = ~species, data = penguins)
gf_freqpoly(~bill_length_mm, color = ~species, data = penguins, bins = 15)
# Chaining in the data
data(penguins, package = "palmerpenguins")
penguins |> gf_dens(~bill_length_mm, color = ~species)
# horizontal orientation
penguins |> gf_dens(bill_length_mm ~ ., color = ~species)

Description

Perform a 2D kernel density estimation using MASS::kde2d() and display the results with contours. This can be useful for dealing with overplotting. This is a 2D version of geom_density(). geom_density_2d() draws contour lines, and geom_density_2d_filled() draws filled contour bands.

Usage

gf_density_2d(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  contour = TRUE,
  n = 100,
  h = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "density_2d",
  stat = "density_2d",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_density_2d_filled(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  contour = TRUE,
  n = 100,
  h = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "density_2d_filled",
  stat = "density_2d_filled",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_density2d(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  contour = TRUE,
  n = 100,
  h = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "density2d",
  stat = "density2d",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

gf_density2d_filled(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  linetype,
  linewidth,
  contour = TRUE,
  n = 100,
  h = NULL,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "density2d_filled",
  stat = "density_2d_filled",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_density_2d()

Examples

gf_jitter(avg_drinks ~ age,
  alpha = 0.2, data = mosaicData::HELPrct,
  width = 0.4, height = 0.4
) |>
  gf_density_2d(avg_drinks ~ age, data = mosaicData::HELPrct)
gf_density_2d_filled(avg_drinks ~ age, data = mosaicData::HELPrct, show.legend = FALSE) |>
  gf_jitter(avg_drinks ~ age,
    alpha = 0.3, data = mosaicData::HELPrct,
    width = 0.4, height = 0.4,
    color = "white"
)
gf_jitter(avg_drinks ~ age,
  alpha = 0.2, data = mosaicData::HELPrct,
  width = 0.4, height = 0.4
) |>
  gf_density2d(avg_drinks ~ age, data = mosaicData::HELPrct)
gf_density2d_filled(avg_drinks ~ age, data = mosaicData::HELPrct, show.legend = FALSE) |>
  gf_jitter(avg_drinks ~ age,
    alpha = 0.4, data = mosaicData::HELPrct,
    width = 0.4, height = 0.4,
    color = "white"
)

Description

Create a layer displaying a probability distribution.

Usage

gf_dist(
  object = ggplot(),
  dist,
  ...,
  xlim = NULL,
  kind = c("density", "cdf", "qq", "qqstep", "histogram"),
  resolution = 5000L,
  eps = 1e-06,
  params = NULL
)

Arguments

Examples

gf_dhistogram(~ rnorm(100), bins = 20) |>
  gf_dist("norm", color = "red")

# shading tails -- but see pdist() for this
gf_dist("norm", fill = ~ (abs(x) <= 2), geom = "area")
gf_dist("norm", color = "red", kind = "cdf")
gf_dist("norm", fill = "red", kind = "histogram")
gf_dist("norm", color = "red", kind = "qqstep", resolution = 25) |>
  gf_dist("norm", color = "black", kind = "qq", resolution = 25, linewidth = 2, alpha = 0.5)
# size is used as parameter for binomial distribution
gf_dist("binom", size = 20, prob = 0.25)
# If we want to adjust size argument for plots, we have two choices:
gf_dist("binom", size = 20, prob = 0.25, plot_size = 2)
gf_dist("binom", params = list(size = 20, prob = 0.25), size = 2)

Description

Scatterplots in ggformula.

Usage

gf_dotplot(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  fill,
  group,
  binwidth = NULL,
  binaxis = "x",
  method = "dotdensity",
  binpositions = "bygroup",
  stackdir = "up",
  stackratio = 1,
  dotsize = 1,
  stackgroups = FALSE,
  origin = NULL,
  right = TRUE,
  width = 0.9,
  drop = FALSE,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Details

There are two basic approaches: dot-density and histodot. With dot-density binning, the bin positions are determined by the data and binwidth, which is the maximum width of each bin. See Wilkinson (1999) for details on the dot-density binning algorithm. With histodot binning, the bins have fixed positions and fixed widths, much like a histogram.

When binning along the x axis and stacking along the y axis, the numbers on y axis are not meaningful, due to technical limitations of ggplot2. You can hide the y axis, as in one of the examples, or manually scale it to match the number of dots.

Value

a gg object

Warning

Dotplots in ggplot2 (and hence in ggformula) often require some fiddling because the default y-axis is meaningless and the ideal size of the dots depends on the aspect ratio of the plot.

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

References

Wilkinson, L. (1999) Dot plots. The American Statistician, 53(3), 276-281.

See Also

ggplot2::geom_dotplot()

Examples

data(penguins, package = "palmerpenguins")
gf_dotplot(~bill_length_mm, fill = ~species, data = penguins)

Description

The empirical cumulative distribution function (ECDF) provides an alternative visualization of distribution. Compared to other visualizations that rely on density (like histograms or density plots) the ECDF doesn't require any tuning parameters and handles both continuous and categorical variables. The downside is that it requires more training to accurately interpret, and the underlying visual tasks are somewhat more challenging.

Usage

gf_ecdf(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  group,
  pad,
  n = NULL,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "step",
  stat = "ecdf",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

Examples

Data <- data.frame(
  x = c(rnorm(100, 0, 1), rnorm(100, 0, 3), rt(100, df = 3)),
  g = gl(3, 100, labels = c("N(0, 1)", "N(0, 3)", "T(df = 3)") )
)
gf_ecdf( ~ x, data = Data)
# Don't go to positive/negative infinity
gf_ecdf( ~ x, data = Data, pad = FALSE)

# Multiple ECDFs
gf_ecdf( ~ x, data = Data, color = ~ g)

Description

Formula interface to ggplot2::stat_ellipse().

Usage

gf_ellipse(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  alpha,
  color,
  group,
  type = "t",
  level = 0.95,
  segments = 51,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "path",
  stat = "ellipse",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = TRUE,
  environment = parent.frame()
)

Arguments

See Also

ggplot2::stat_ellipse()

Examples

gf_ellipse()
gf_point(eruptions ~ waiting, data = faithful) |>
  gf_ellipse(alpha = 0.5)

gf_point(eruptions ~ waiting, data = faithful, color = ~ (eruptions > 3)) |>
  gf_ellipse(alpha = 0.5)

gf_point(eruptions ~ waiting, data = faithful, color = ~ (eruptions > 3)) |>
  gf_ellipse(type = "norm", linetype = ~ "norm") |>
  gf_ellipse(type = "t",    linetype = ~ "t")

gf_point(eruptions ~ waiting, data = faithful, color = ~ (eruptions > 3)) |>
  gf_ellipse(type = "norm",   linetype = ~ "norm") |>
  gf_ellipse(type = "euclid", linetype = ~ "euclid", level = 3) |>
  gf_refine(coord_fixed())

# Use geom = "polygon" to enable fill
gf_point(eruptions ~ waiting, data = faithful, fill = ~ (eruptions > 3)) |>
  gf_ellipse(geom = "polygon", alpha = 0.3, color = "black")

gf_point(eruptions ~ waiting, data = faithful, fill = ~ (eruptions > 3)) |>
  gf_ellipse(geom = "polygon", alpha = 0.3) |>
  gf_ellipse(alpha = 0.3, color = "black")

gf_ellipse(eruptions ~ waiting, data = faithful, show.legend = FALSE,
  alpha = 0.3, fill = ~ (eruptions > 3), geom = "polygon") |>
  gf_ellipse(level = 0.68, geom = "polygon", alpha = 0.3) |>
  gf_point(data = faithful, color = ~ (eruptions > 3), show.legend = FALSE)

Description

This is primarily useful as a way to start a sequence of piped plot layers.

Usage

gf_empty(environment = parent.frame())

Arguments

Value

A plot with now layers.

Examples

gf_empty()
data(penguins, package = "palmerpenguins")
gf_empty() |>
  gf_point(bill_length_mm ~ bill_depth_mm, data = penguins, color = ~species)

Description

For each x value, geom_ribbon() displays a y interval defined by ymin and ymax. geom_area() is a special case of geom_ribbon(), where the ymin is fixed to 0 and y is used instead of ymax.

Usage

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

Arguments

See Also

ggplot2::geom_errorbar()

Examples

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

Description

These functions provide more control over faceting than is possible using the formula interface.

Usage

gf_facet_wrap(object, ...)

gf_facet_grid(object, ...)

Arguments

See Also

ggplot2::facet_grid(), ggplot2::facet_wrap().

Examples

gf_histogram(~avg_drinks, data = mosaicData::HELPrct) |>
  gf_facet_grid(~substance)
gf_histogram(~avg_drinks, data = mosaicData::HELPrct) |>
  gf_facet_grid(~substance, scales = "free")
gf_histogram(~avg_drinks, data = mosaicData::HELPrct) |>
  gf_facet_grid(~substance, scales = "free", space = "free")
gf_line(births ~ date, data = mosaicData::Births, color = ~wday) |>
  gf_facet_wrap(~year, scales = "free_x", nrow = 5) |>
  gf_theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(), axis.ticks.x = element_blank()
  ) |>
  gf_labs(color = "Day")

Description

MASS::fitdistr() is used to fit coefficients of a specified family of distributions and the resulting density curve is displayed.

Usage

gf_fitdistr(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  dist = "dnorm",
  start = NULL,
  alpha,
  color,
  fill,
  group,
  linetype,
  linewidth,
  size,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = "path",
  stat = "fitdistr",
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = FALSE,
  environment = parent.frame()
)

Arguments

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

mosaicCore::fit_distr_fun()

Examples

gf_fitdistr(~length, data = mosaicData::KidsFeet, inherit = FALSE) |>
  gf_dhistogram(~length, data = mosaicData::KidsFeet, binwidth = 0.5, alpha = 0.25)

gf_dhistogram(~length, data = mosaicData::KidsFeet, binwidth = 0.5, alpha = 0.25) |>
  gf_fitdistr()

set.seed(12345)
Dat <- data.frame(
  f = rf(500, df1 = 3, df2 = 47),
  g = rgamma(500, 3, 10)
)
gf_dhistogram(~g, data = Dat) |>
  gf_fitdistr(dist = "dgamma", linewidth = 1.4)

gf_dhistogram(~g, data = Dat) |>
  gf_fun(mosaicCore::fit_distr_fun(~g, data = Dat, dist = "dgamma"))

gf_dhistogram(~f, data = Dat) |>
  gf_fitdistr(dist = "df", start = list(df1 = 2, df2 = 50))

# fitted parameters are default argument values
args(
  mosaicCore::fit_distr_fun(~f,
    data = Dat, dist = "df",
    start = list(df1 = 2, df2 = 50)
  )
)
args(mosaicCore::fit_distr_fun(~g, data = Dat, dist = "dgamma"))