ggproto classes for ggiraph

Description

ggproto classes for ggiraph

Geoms

All ⁠geom_*_interactive⁠ functions (like geom_point_interactive) return a layer that contains a ⁠GeomInteractive*⁠ object (like GeomInteractivePoint). The ⁠Geom*⁠ object is responsible for rendering the data in the plot.

See Geom for more information.

Create interactive annotations

Description

The layer is based on ggplot2::annotate(). See the documentation for that function for more details.

Usage

annotate_interactive(...)

Arguments

Details for annotate_*_interactive functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe(), interactive_parameters, annotation_raster_interactive()

Examples

# add interactive annotation to a ggplot -------
library(ggplot2)
library(ggiraph)

gg <- ggplot(mtcars, aes(x = disp, y = qsec )) +
  geom_point(size=2) +
  annotate_interactive(
    "rect", xmin = 100, xmax = 400, fill = "red",
    data_id = "an_id", tooltip = "a tooltip",
    ymin = 18, ymax = 20, alpha = .5)

x <- girafe(ggobj = gg, width_svg = 5, height_svg = 4)
if( interactive() ) print(x)

Create interactive raster annotations

Description

The layer is based on ggplot2::annotation_raster(). See the documentation for that function for more details.

Usage

annotation_raster_interactive(...)

Arguments

Details for annotate_*_interactive functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Examples

# add interactive raster annotation to a ggplot -------
library(ggplot2)
library(ggiraph)

# Generate data
rainbow <- matrix(hcl(seq(0, 360, length.out = 50 * 50), 80, 70), nrow = 50)
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  annotation_raster_interactive(rainbow, 15, 20, 3, 4, tooltip = "I am an image!")
x <- girafe(ggobj = p)
if( interactive() ) print(x)

# To fill up whole plot
p <- ggplot(mtcars, aes(mpg, wt)) +
  annotation_raster_interactive(rainbow, -Inf, Inf, -Inf, Inf, tooltip = "I am an image too!") +
  geom_point()
x <- girafe(ggobj = p)
if( interactive() ) print(x)

SVG Graphics Driver

Description

This function produces SVG files (compliant to the current w3 svg XML standard) where elements can be made interactive.

In order to generate the output, used fonts must be available on the computer used to create the svg, used fonts must also be available on the computer used to render the svg.

Usage

dsvg(
  file = "Rplots.svg",
  width = 6,
  height = 6,
  bg = "white",
  pointsize = 12,
  standalone = TRUE,
  setdims = TRUE,
  canvas_id = "svg_1",
  title = NULL,
  desc = NULL,
  fonts = list()
)

Arguments

See Also

Devices

Examples

fileout <- tempfile(fileext = ".svg")
dsvg(file = fileout)
plot(rnorm(10), main="Simple Example", xlab = "", ylab = "")
dev.off()

Run plotting code and view svg in RStudio Viewer or web broswer.

Description

This is useful primarily for testing. Requires the htmltools package.

Usage

dsvg_view(code, ...)

Arguments

Examples

dsvg_view(plot(1:10))
dsvg_view(hist(rnorm(100)))

Create interactive theme elements

Description

With these functions the user can add interactivity to various ggplot2::theme() elements.

They are based on ggplot2::element_rect(), ggplot2::element_line() and ggplot2::element_text() See the documentation for those functions for more details.

Usage

element_line_interactive(...)

element_rect_interactive(...)

element_text_interactive(...)

Arguments

Details for element_*_interactive functions

The interactive parameters can be supplied as arguments in the relevant function and they should be scalar values.

For theme text elements (element_text_interactive()), the interactive parameters can also be supplied while setting a label value, via the ggplot2::labs() family of functions or when setting a scale/guide title or key label. Instead of setting a character value for the element, function label_interactive() can be used to define interactive parameters to go along with the label. When the parameters are supplied that way, they override the default values that are set at the theme via element_text_interactive() or via the guide's theme parameters.

See Also

girafe()

Examples

# add interactive theme elements -------
library(ggplot2)
library(ggiraph)

dataset <- structure(list(qsec = c(16.46, 17.02, 18.61, 19.44, 17.02, 20.22
), disp = c(160, 160, 108, 258, 360, 225), carname = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant"), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46)), row.names = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant"), class = "data.frame")

# plots
gg_point = ggplot(data = dataset) +
  geom_point_interactive(aes(
    x = wt,
    y = qsec,
    color = disp,
    tooltip = carname,
    data_id = carname
  )) +
  theme_minimal() +
  theme(
    plot.title = element_text_interactive(
      data_id = "plot.title",
      tooltip = "plot title",
      hover_css = "fill:red;stroke:none;font-size:12pt"
    ),
    plot.subtitle = element_text_interactive(
      data_id = "plot.subtitle",
      tooltip = "plot subtitle",
      hover_css = "fill:none;"
    ),
    axis.title.x = element_text_interactive(
      data_id = "axis.title.x",
      tooltip = "Description for x axis",
      hover_css = "fill:red;stroke:none;"
    ),
    axis.title.y = element_text_interactive(
      data_id = "axis.title.y",
      tooltip = "Description for y axis",
      hover_css = "fill:red;stroke:none;"
    ),
    panel.grid.major = element_line_interactive(
      data_id = "panel.grid",
      tooltip = "Major grid lines",
      hover_css = "fill:none;stroke:red;"
    )
  ) +
  labs(
    title = "Interactive points example!",
    subtitle = label_interactive(
      "by ggiraph",
      tooltip = "Click me!",
      onclick = "window.open(\"https://davidgohel.github.io/ggiraph/\")",
      hover_css = "fill:magenta;cursor:pointer;"
    )
  )

x <- girafe(ggobj = gg_point)
if( interactive() ) print(x)

Create interactive grid facets

Description

These facets are based on ggplot2::facet_grid().

To make a facet interactive, it is mandatory to use labeller_interactive() for argument labeller.

Usage

facet_grid_interactive(..., interactive_on = "text")

Arguments

Value

An interactive facetting object.

See Also

girafe()

Create interactive wraped facets

Description

These facets are based on ggplot2::facet_wrap().

To make a facet interactive, it is mandatory to use labeller_interactive() for argument labeller.

Usage

facet_wrap_interactive(..., interactive_on = "text")

Arguments

Value

An interactive facetting object.

See Also

girafe()

Check if font family exists.

Description

Check if a font family exists in system fonts.

Usage

font_family_exists(font_family = "sans")

Arguments

Value

A logical value

See Also

Other functions for font management: match_family(), validated_fonts()

Examples

font_family_exists("sans")
font_family_exists("Arial")
font_family_exists("Courier")

Create interactive reference lines

Description

These geometries are based on ggplot2::geom_abline(), ggplot2::geom_hline() and ggplot2::geom_vline().

Usage

geom_abline_interactive(...)

geom_hline_interactive(...)

geom_vline_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

girafe()

girafe()

Examples

# add diagonal interactive reference lines to a ggplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mtcars, aes(wt, mpg)) + geom_point()
g <- p + geom_abline_interactive(intercept = 20, tooltip = 20)
x <- girafe(ggobj = g)
if (interactive())
  print(x)

l <- coef(lm(mpg ~ wt, data = mtcars))
g <- p + geom_abline_interactive(
  intercept = l[[1]],
  slope = l[[2]],
  tooltip = paste("intercept:", l[[1]], "\nslope:", l[[2]]),
  data_id="abline"
)
x <- girafe(ggobj = g)
x <- girafe_options(x = x,
                    opts_hover(css = "cursor:pointer;fill:orange;stroke:orange;"))
if (interactive())
  print(x)

# add horizontal interactive reference lines to a ggplot -------
library(ggplot2)
library(ggiraph)

if( requireNamespace("dplyr", quietly = TRUE)){
  g1 <- ggplot(economics, aes(x = date, y = unemploy)) +
    geom_point() + geom_line()

  gg_hline1 <- g1 + geom_hline_interactive(
    aes(yintercept = mean(unemploy),
        tooltip = round(mean(unemploy), 2)), size = 3)
  x <- girafe(ggobj = gg_hline1)
  if( interactive() ) print(x)
}

dataset <- data.frame(
  x = c(1, 2, 5, 6, 8),
  y = c(3, 6, 2, 8, 7),
  vx = c(1, 1.5, 0.8, 0.5, 1.3),
  vy = c(0.2, 1.3, 1.7, 0.8, 1.4),
  year = c(2014, 2015, 2016, 2017, 2018)
)

dataset$clickjs <- rep(paste0("alert(\"", mean(dataset$y), "\")"), 5)


g2 <- ggplot(dataset, aes(x = year, y = y)) +
  geom_point() + geom_line()

gg_hline2 <- g2 + geom_hline_interactive(
  aes(yintercept = mean(y),
      tooltip = round(mean(y), 2),
      data_id = y, onclick = clickjs))

x <- girafe(ggobj = gg_hline2)
if( interactive() ) print(x)

# add vertical interactive reference lines to a ggplot -------
library(ggplot2)
library(ggiraph)

if (requireNamespace("dplyr", quietly = TRUE)) {
  g1 <- ggplot(diamonds, aes(carat)) +
    geom_histogram()

  gg_vline1 <- g1 + geom_vline_interactive(
    aes(xintercept = mean(carat),
        tooltip = round(mean(carat), 2),
        data_id = carat), size = 3)
  x <- girafe(ggobj = gg_vline1)
  if( interactive() ) print(x)
}

dataset <- data.frame(x = rnorm(100))

dataset$clickjs <- rep(paste0("alert(\"",
                              round(mean(dataset$x), 2), "\")"), 100)

g2 <- ggplot(dataset, aes(x)) +
  geom_density(fill = "#000000", alpha = 0.7)
gg_vline2 <- g2 + geom_vline_interactive(
  aes(xintercept = mean(x), tooltip = round(mean(x), 2),
      data_id = x, onclick = clickjs), color = "white")

x <- girafe(ggobj = gg_vline2)
x <- girafe_options(x = x,
                    opts_hover(css = "cursor:pointer;fill:orange;stroke:orange;") )
if( interactive() ) print(x)

Create interactive bars

Description

The geometries are based on ggplot2::geom_bar() and ggplot2::geom_col(). See the documentation for those functions for more details.

Usage

geom_bar_interactive(...)

geom_col_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive bar -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mpg, aes( x = class, tooltip = class,
        data_id = class ) ) +
  geom_bar_interactive()

x <- girafe(ggobj = p)
if( interactive() ) print(x)

dat <- data.frame( name = c( "David", "Constance", "Leonie" ),
    gender = c( "Male", "Female", "Female" ),
    height = c(172, 159, 71 ) )
p <- ggplot(dat, aes( x = name, y = height, tooltip = gender,
                      data_id = name ) ) +
  geom_col_interactive()

x <- girafe(ggobj = p)
if( interactive() ) print(x)

# an example with interactive guide ----
dat <- data.frame(
  name = c( "Guy", "Ginette", "David", "Cedric", "Frederic" ),
  gender = c( "Male", "Female", "Male", "Male", "Male" ),
  height = c(169, 160, 171, 172, 171 ) )
p <- ggplot(dat, aes( x = name, y = height, fill = gender,
                      data_id = name ) ) +
  geom_bar_interactive(stat = "identity") +
    scale_fill_manual_interactive(
      values = c(Male = "#0072B2", Female = "#009E73"),
      data_id = c(Female = "Female", Male = "Male"),
      tooltip = c(Male = "Male", Female = "Female")
    )
x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive heatmaps of 2d bin counts

Description

The geometry is based on ggplot2::geom_bin_2d(). See the documentation for those functions for more details.

Usage

geom_bin_2d_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive bin2d heatmap to a ggplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(diamonds, aes(x, y, fill=cut)) + xlim(4, 10) + ylim(4, 10)+
  geom_bin2d_interactive(aes(tooltip = cut), bins = 30)

x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive boxplot

Description

The geometry is based on ggplot2::geom_boxplot(). See the documentation for that function for more details.

Usage

geom_boxplot_interactive(...)

Arguments

Details

You can supply ⁠interactive parameters⁠ for the outlier points by prefixing them with outlier. prefix. For example: aes(outlier.tooltip = 'bla', outlier.data_id = 'blabla').

IMPORTANT: when supplying outlier interactive parameters, the correct group aesthetic must be also supplied. Otherwise the default group calculation will be incorrect, which will result in an incorrect plot.

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive boxplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mpg, aes(x = class, y = hwy, tooltip = class)) +
  geom_boxplot_interactive()

x <- girafe(ggobj = p)
if (interactive()) print(x)

p <- ggplot(mpg) +
  geom_boxplot_interactive(
    aes(
      x = drv, y = hwy,
      fill = class,
      data_id = class,
      tooltip = after_stat({
        paste0(
          "class: ", .data$fill,
          "\nQ1: ", prettyNum(.data$lower),
          "\nQ3: ", prettyNum(.data$upper),
          "\nmedian: ", prettyNum(.data$middle)
        )
      })
    ),
    outlier.colour = "red"
  ) +
  guides(fill = "none") +
  theme_minimal()

x <- girafe(ggobj = p)
if (interactive()) print(x)


p <- ggplot(mpg) +
  geom_boxplot_interactive(
    aes(
      x = drv, y = hwy,
      fill = class, group = paste(drv, class),
      data_id = class,
      tooltip = after_stat({
        paste0(
          "class: ", .data$fill,
          "\nQ1: ", prettyNum(.data$lower),
          "\nQ3: ", prettyNum(.data$upper),
          "\nmedian: ", prettyNum(.data$middle)
        )
      }),
      outlier.tooltip = paste(
        "I am an outlier!\nhwy:", hwy, "\ndrv:", drv, "\nclass:", class
      )
    ),
    outlier.colour = "red"
  ) +
  guides(fill = "none") +
  theme_minimal()

x <- girafe(ggobj = p)
if (interactive()) print(x)

Create interactive 2d contours of a 3d surface

Description

These geometries are based on ggplot2::geom_contour() and ggplot2::geom_contour_filled(). See the documentation for those functions for more details.

Usage

geom_contour_interactive(...)

geom_contour_filled_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive contours to a ggplot -------
library(ggplot2)
library(ggiraph)

v <- ggplot(faithfuld, aes(waiting, eruptions, z = density))
p <- v + geom_contour_interactive(aes(
  colour = after_stat(level),
  tooltip = paste("Level:", after_stat(level))
))
x <- girafe(ggobj = p)
if (interactive()) print(x)

if (packageVersion("grid") >= numeric_version("3.6")) {
  p <- v + geom_contour_filled_interactive(aes(
    colour = after_stat(level),
    fill = after_stat(level),
    tooltip = paste("Level:", after_stat(level))
  ))
  x <- girafe(ggobj = p)
  if (interactive()) print(x)
}

Create interactive point counts

Description

The geometry is based on ggplot2::geom_bin2d(). See the documentation for those functions for more details.

Usage

geom_count_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive point counts to a ggplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mpg, aes(cty, hwy)) +
  geom_count_interactive(aes(tooltip=after_stat(n)))
x <- girafe(ggobj = p)
if( interactive() ) print(x)

p2 <- ggplot(diamonds, aes(x = cut, y = clarity)) +
  geom_count_interactive(aes(size = after_stat(prop),
                             tooltip = after_stat(round(prop, 3)), group = 1)) +
  scale_size_area(max_size = 10)
x <- girafe(ggobj = p2)
if (interactive()) print(x)

Create interactive vertical intervals: lines, crossbars & errorbars

Description

These geometries are based on ggplot2::geom_crossbar(), ggplot2::geom_errorbar(), ggplot2::geom_linerange() and ggplot2::geom_pointrange(). See the documentation for those functions for more details.

Usage

geom_crossbar_interactive(...)

geom_errorbar_interactive(...)

geom_linerange_interactive(...)

geom_pointrange_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive intervals -------
library(ggplot2)
library(ggiraph)

# Create a simple example dataset
df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),
  resp = c(1, 5, 3, 4),
  group = factor(c(1, 2, 1, 2)),
  upper = c(1.1, 5.3, 3.3, 4.2),
  lower = c(0.8, 4.6, 2.4, 3.6)
)

p <- ggplot(df, aes(trt, resp, colour = group))
g <- p + geom_linerange_interactive(aes(ymin = lower, ymax = upper, tooltip = group))
x <- girafe(ggobj = g)
if( interactive() ) print(x)

g <- p + geom_pointrange_interactive(aes(ymin = lower, ymax = upper, tooltip = group))
x <- girafe(ggobj = g)
if( interactive() ) print(x)

g <- p + geom_crossbar_interactive(aes(ymin = lower, ymax = upper, tooltip = group), width = 0.2)
x <- girafe(ggobj = g)
if( interactive() ) print(x)

g <- p + geom_errorbar_interactive(aes(ymin = lower, ymax = upper, tooltip = group), width = 0.2)
x <- girafe(ggobj = g)
if( interactive() ) print(x)

Create interactive line segments and curves

Description

The geometries are based on ggplot2::geom_segment() and ggplot2::geom_curve(). See the documentation for those functions for more details.

Usage

geom_curve_interactive(...)

geom_segment_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive segments and curves to a ggplot -------
library(ggplot2)
library(ggiraph)

counts <- as.data.frame(table(x = rpois(100,5)))
counts$x <- as.numeric( as.character(counts$x) )
counts$xlab <- paste0("bar",as.character(counts$x) )

gg_segment_1 <- ggplot(data = counts, aes(x = x, y = Freq,
			yend = 0, xend = x, tooltip = xlab ) ) +
	geom_segment_interactive( size = I(10))
x <- girafe(ggobj = gg_segment_1)
if( interactive() ) print(x)

dataset = data.frame(x=c(1,2,5,6,8),
		y=c(3,6,2,8,7),
		vx=c(1,1.5,0.8,0.5,1.3),
		vy=c(0.2,1.3,1.7,0.8,1.4),
		labs = paste0("Lab", 1:5))
dataset$clickjs = paste0("alert(\"",dataset$labs, "\")" )

gg_segment_2 = ggplot() +
	geom_segment_interactive(data=dataset, mapping=aes(x=x, y=y,
			xend=x+vx, yend=y+vy, tooltip = labs, onclick=clickjs ),
		arrow=grid::arrow(length = grid::unit(0.03, "npc")),
		size=2, color="blue") +
	geom_point(data=dataset, mapping=aes(x=x, y=y),
		size=4, shape=21, fill="white")

x <- girafe(ggobj = gg_segment_2)
if( interactive() ) print(x)

df <- data.frame(x1 = 2.62, x2 = 3.57, y1 = 21.0, y2 = 15.0)
p <- ggplot(df, aes(x = x1, y = y1, xend = x2, yend = y2)) +
  geom_curve_interactive(aes(colour = "curve", tooltip=I("curve"))) +
  geom_segment_interactive(aes(colour = "segment", tooltip=I("segment")))

x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive contours of a 2d density estimate

Description

The geometries are based on ggplot2::geom_density_2d() and ggplot2::geom_density_2d_filled(). See the documentation for those functions for more details.

Usage

geom_density_2d_interactive(...)

geom_density_2d_filled_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive contours to a ggplot -------
library(ggplot2)
library(ggiraph)

m <- ggplot(faithful, aes(x = eruptions, y = waiting)) +
  geom_point_interactive(aes(tooltip = paste("Waiting:", waiting, "\neruptions:", eruptions))) +
  xlim(0.5, 6) +
  ylim(40, 110)
p <- m + geom_density_2d_interactive(aes(tooltip = paste("Level:", after_stat(level))))
x <- girafe(ggobj = p)
if (interactive()) print(x)

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
d <- ggplot(dsmall, aes(x, y))


p <- d + geom_density_2d_interactive(aes(colour = cut, tooltip = cut, data_id = cut))
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:red;stroke-width:3px;") )
if (interactive()) print(x)

p <- d + geom_density_2d_filled_interactive(aes(colour = cut, tooltip = cut, data_id = cut),
                                            contour_var = "count") + facet_wrap(vars(cut))
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:red;stroke-width:3px;") )
if (interactive()) print(x)


p <- d + stat_density_2d(aes(fill = after_stat(nlevel),
                             tooltip = paste("nlevel:", after_stat(nlevel))),
                         geom = "interactive_polygon") +
  facet_grid(. ~ cut) + scale_fill_viridis_c_interactive(tooltip = "nlevel")
x <- girafe(ggobj = p)
if (interactive()) print(x)

Create interactive smoothed density estimates

Description

The geometry is based on ggplot2::geom_density(). See the documentation for those functions for more details.

Usage

geom_density_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive bar -------
library(ggplot2)
library(ggiraph)

p <- ggplot(diamonds, aes(carat)) +
  geom_density_interactive(tooltip="density", data_id="density")
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:orange;stroke-width:3px;") )
if( interactive() ) print(x)

p <- ggplot(diamonds, aes(depth, fill = cut, colour = cut)) +
  geom_density_interactive(aes(tooltip=cut, data_id=cut), alpha = 0.1) +
  xlim(55, 70)
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:yellow;stroke-width:3px;fill-opacity:0.8;") )
if( interactive() ) print(x)


p <- ggplot(diamonds, aes(carat, fill = cut)) +
  geom_density_interactive(aes(tooltip=cut, data_id=cut), position = "stack")
x <- girafe(ggobj = p)
if( interactive() ) print(x)

p <- ggplot(diamonds, aes(carat, after_stat(count), fill = cut)) +
  geom_density_interactive(aes(tooltip=cut, data_id=cut), position = "fill")
x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive dot plots

Description

This geometry is based on ggplot2::geom_dotplot(). See the documentation for those functions for more details.

Usage

geom_dotplot_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive dot plots to a ggplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mtcars, aes(x = mpg, fill = factor(cyl))) +
  geom_dotplot_interactive(
    aes(tooltip = row.names(mtcars)),
    stackgroups = TRUE, binwidth = 1, method = "histodot"
  )

x <- girafe(ggobj = p)
if( interactive() ) print(x)

gg_point = ggplot(
  data = mtcars,
  mapping = aes(
    x = factor(vs), fill = factor(cyl), y = mpg,
    tooltip = row.names(mtcars))) +
  geom_dotplot_interactive(binaxis = "y",
    stackdir = "center", position = "dodge")

x <- girafe(ggobj = gg_point)
if( interactive() ) print(x)

Create interactive horizontal error bars

Description

This geometry is based on ggplot2::geom_errorbarh(). See the documentation for those functions for more details.

Usage

geom_errorbarh_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add horizontal error bars -------
library(ggplot2)
library(ggiraph)

df <- data.frame(
  trt = factor(c(1, 1, 2, 2)),
  resp = c(1, 5, 3, 4),
  group = factor(c(1, 2, 1, 2)),
  se = c(0.1, 0.3, 0.3, 0.2)
)

# Define the top and bottom of the errorbars

p <- ggplot(df, aes(resp, trt, colour = group))
g <- p + geom_point() +
  geom_errorbarh_interactive(aes(xmax = resp + se, xmin = resp - se, tooltip = group))
x <- girafe(ggobj = g)
if( interactive() ) print(x)

g <- p + geom_point() +
  geom_errorbarh_interactive(aes(xmax = resp + se, xmin = resp - se, height = .2, tooltip = group))
x <- girafe(ggobj = g)
if( interactive() ) print(x)

Create interactive histograms and frequency polygons

Description

The geometries are based on ggplot2::geom_histogram() and ggplot2::geom_freqpoly(). See the documentation for those functions for more details.

This interactive version is only providing a single tooltip per group of data (same for data_id). It means it is only possible to associate a single tooltip to a set of bins.

Usage

geom_freqpoly_interactive(...)

geom_histogram_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive histogram -------
library(ggplot2)
library(ggiraph)

p <- ggplot(diamonds, aes(carat)) +
  geom_histogram_interactive(bins=30, aes(tooltip = after_stat(count),
                                          group = 1L) )
x <- girafe(ggobj = p)
if( interactive() ) print(x)

p <- ggplot(diamonds, aes(price, colour = cut, tooltip = cut, data_id = cut)) +
  geom_freqpoly_interactive(binwidth = 500)
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke-width:3px;") )
if( interactive() ) print(x)

Create interactive hexagonal heatmaps

Description

The geometry is based on ggplot2::geom_hex(). See the documentation for those functions for more details.

Usage

geom_hex_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive hexagonal heatmaps to a ggplot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(diamonds, aes(carat, price)) +
  geom_hex_interactive(aes(tooltip = after_stat(count)), bins = 10)
x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive jittered points

Description

The geometry is based on ggplot2::geom_jitter(). See the documentation for those functions for more details.

Usage

geom_jitter_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive paths to a ggplot -------
library(ggplot2)
library(ggiraph)

gg_jitter <- ggplot(mpg, aes(cyl, hwy,
                     tooltip = paste(manufacturer, model, year, trans, sep = "\n")))+
  geom_jitter_interactive()

x <- girafe(ggobj = gg_jitter)
if( interactive() ) print(x)

Create interactive textual annotations

Description

The geometries are based on ggplot2::geom_text() and ggplot2::geom_label(). See the documentation for those functions for more details.

Usage

geom_label_interactive(...)

geom_text_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive labels to a ggplot -------
library(ggplot2)
library(ggiraph)


p <- ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars))) +
  geom_label_interactive(aes(tooltip = paste(rownames(mtcars), mpg, sep = "\n")))
x <- girafe(ggobj = p)
if( interactive() ) print(x)


p <- ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars))) +
  geom_label_interactive(aes(fill = factor(cyl),
                             tooltip = paste(rownames(mtcars), mpg, sep = "\n")),
                         colour = "white",
                         fontface = "bold")
x <- girafe(ggobj = p)
if( interactive() ) print(x)

# add interactive texts to a ggplot -------
library(ggplot2)
library(ggiraph)

## the data
dataset = mtcars
dataset$label = row.names(mtcars)

dataset$tooltip = paste0( "cyl: ", dataset$cyl, "<br/>",
       "gear: ", dataset$gear, "<br/>",
       "carb: ", dataset$carb)

## the plot
gg_text = ggplot(dataset,
                 aes(x = mpg, y = wt, label = label,
                     color = qsec,
                     tooltip = tooltip, data_id = label)) +
  geom_text_interactive(check_overlap = TRUE) +
  coord_cartesian(xlim = c(0,50))

## display the plot
x <- girafe(ggobj = gg_text)
x <- girafe_options(x = x,
                    opts_hover(css = "fill:#FF4C3B;font-style:italic;") )
if( interactive() ) print(x)

Create interactive polygons from a reference map

Description

The geometry is based on ggplot2::geom_map(). See the documentation for those functions for more details.

Usage

geom_map_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive maps to a ggplot -------
library(ggplot2)
library(ggiraph)

crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)

# create tooltips and onclick events
states_ <- sprintf("<p>%s</p>",
                   as.character(crimes$state) )
table_ <- paste0(
  "<table><tr><td>UrbanPop</td>",
  sprintf("<td>%.0f</td>", crimes$UrbanPop),
  "</tr><tr>",
  "<td>Assault</td>",
  sprintf("<td>%.0f</td>", crimes$Assault),
  "</tr></table>"
)

onclick <- sprintf(
  "window.open(\"%s%s\")",
  "http://en.wikipedia.org/wiki/",
  as.character(crimes$state)
)


crimes$labs <- paste0(states_, table_)
crimes$onclick = onclick

if (require("maps") ) {
  states_map <- map_data("state")
  gg_map <- ggplot(crimes, aes(map_id = state))
  gg_map <- gg_map + geom_map_interactive(aes(
                  fill = Murder,
                  tooltip = labs,
                  data_id = state,
                  onclick = onclick
                ),
                map = states_map) +
    expand_limits(x = states_map$long, y = states_map$lat)
  x <- girafe(ggobj = gg_map)
  if( interactive() ) print(x)
}

Create interactive observations connections

Description

These geometries are based on ggplot2::geom_path(), ggplot2::geom_line() and ggplot2::geom_step(). See the documentation for those functions for more details.

Usage

geom_path_interactive(...)

geom_line_interactive(...)

geom_step_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive paths to a ggplot -------
library(ggplot2)
library(ggiraph)

# geom_line_interactive example -----
if( requireNamespace("dplyr", quietly = TRUE)){
  gg <- ggplot(economics_long,
    aes(date, value01, colour = variable, tooltip = variable, data_id = variable,
        hover_css = "fill:none;")) +
    geom_line_interactive(size = .75)
  x <- girafe(ggobj = gg)
  x <- girafe_options(x = x,
                      opts_hover(css = "stroke:red;fill:orange") )
  if( interactive() ) print(x)

}

# geom_step_interactive example -----
if( requireNamespace("dplyr", quietly = TRUE)){
  recent <- economics[economics$date > as.Date("2013-01-01"), ]
  gg = ggplot(recent, aes(date, unemploy)) +
    geom_step_interactive(aes(tooltip = "Unemployement stairstep line", data_id = 1))
  x <- girafe(ggobj = gg)
  x <- girafe_options(x = x,
                      opts_hover(css = "stroke:red;") )
  if( interactive() ) print(x)
}

# create datasets -----
id = paste0("id", 1:10)
data = expand.grid(list(
	variable = c("2000", "2005", "2010", "2015"),
	id = id
	)
)
groups = sample(LETTERS[1:3], size = length(id), replace = TRUE)
data$group = groups[match(data$id, id)]
data$value = runif(n = nrow(data))
data$tooltip = paste0('line ', data$id )
data$onclick = paste0("alert(\"", data$id, "\")" )

cols = c("orange", "orange1", "orange2", "navajowhite4", "navy")
dataset2 <- data.frame(x = rep(1:20, 5),
		y = rnorm(100, 5, .2) + rep(1:5, each=20),
		z = rep(1:20, 5),
		grp = factor(rep(1:5, each=20)),
		color = factor(rep(1:5, each=20)),
		label = rep(paste0( "id ", 1:5 ), each=20),
		onclick = paste0(
		  "alert(\"",
		  sample(letters, 100, replace = TRUE),
		  "\")" )
)


# plots ---
gg_path_1 = ggplot(data, aes(variable, value, group = id,
		colour = group, tooltip = tooltip, onclick = onclick, data_id = id)) +
	geom_path_interactive(alpha = 0.5)

gg_path_2 = ggplot(data, aes(variable, value, group = id, data_id = id,
		tooltip = tooltip)) +
	geom_path_interactive(alpha = 0.5) +
	facet_wrap( ~ group )

gg_path_3 = ggplot(dataset2) +
	geom_path_interactive(aes(x, y, group=grp, data_id = label,
		color = color, tooltip = label, onclick = onclick), size = 1 )

# ggiraph widgets ---
x <- girafe(ggobj = gg_path_1)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke-width:3px;") )
if( interactive() ) print(x)

x <- girafe(ggobj = gg_path_2)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:orange;stroke-width:3px;") )
if( interactive() ) print(x)

x <- girafe(ggobj = gg_path_3)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke-width:10px;") )
if( interactive() ) print(x)

m <- ggplot(economics, aes(unemploy/pop, psavert))
p <- m + geom_path_interactive(aes(colour = as.numeric(date), tooltip=date))
x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive points

Description

The geometry is based on ggplot2::geom_point(). See the documentation for those functions for more details.

Usage

geom_point_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

Note

The following shapes id 3, 4 and 7 to 14 are composite symbols and should not be used.

See Also

girafe()

Examples

# add interactive points to a ggplot -------
library(ggplot2)
library(ggiraph)

dataset <- structure(list(qsec = c(16.46, 17.02, 18.61, 19.44, 17.02, 20.22
), disp = c(160, 160, 108, 258, 360, 225), carname = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant"), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46)), row.names = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant"), class = "data.frame")
dataset

# plots
gg_point = ggplot(data = dataset) +
	geom_point_interactive(aes(x = wt, y = qsec, color = disp,
    tooltip = carname, data_id = carname)) + theme_minimal()

x <- girafe(ggobj = gg_point)
if( interactive() ) print(x)

Create interactive polygons

Description

The geometry is based on ggplot2::geom_polygon(). See the documentation for those functions for more details.

Usage

geom_polygon_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive polygons to a ggplot -------
library(ggplot2)
library(ggiraph)

# create data
ids <- factor(c("1.1", "2.1", "1.2", "2.2", "1.3", "2.3"))

values <- data.frame(
	id = ids,
	value = c(3, 3.1, 3.1, 3.2, 3.15, 3.5) )
positions <- data.frame(
	id = rep(ids, each = 4),
	x = c(2, 1, 1.1, 2.2, 1, 0, 0.3, 1.1, 2.2, 1.1, 1.2, 2.5, 1.1, 0.3,
		0.5, 1.2, 2.5, 1.2, 1.3, 2.7, 1.2, 0.5, 0.6, 1.3),
	y = c(-0.5, 0, 1, 0.5, 0, 0.5, 1.5, 1, 0.5, 1, 2.1, 1.7, 1, 1.5,
		2.2, 2.1, 1.7, 2.1, 3.2, 2.8, 2.1, 2.2, 3.3, 3.2) )

datapoly <- merge(values, positions, by=c("id"))

datapoly$oc = "alert(this.getAttribute(\"data-id\"))"

# create a ggplot -----
gg_poly_1 <- ggplot(datapoly, aes( x = x, y = y ) ) +
	geom_polygon_interactive(aes(fill = value, group = id,
		tooltip = value, data_id = value, onclick = oc))

# display ------
x <- girafe(ggobj = gg_poly_1)
if( interactive() ) print(x)

if (packageVersion("grid") >= "3.6") {
  # As of R version 3.6 geom_polygon() supports polygons with holes
  # Use the subgroup aesthetic to differentiate holes from the main polygon

  holes <- do.call(rbind, lapply(split(datapoly, datapoly$id), function(df) {
    df$x <- df$x + 0.5 * (mean(df$x) - df$x)
    df$y <- df$y + 0.5 * (mean(df$y) - df$y)
    df
  }))
  datapoly$subid <- 1L
  holes$subid <- 2L
  datapoly <- rbind(datapoly, holes)
  p <- ggplot(datapoly, aes(x = x, y = y)) +
    geom_polygon_interactive(aes(fill = value, group = id, subgroup = subid,
                                 tooltip = value, data_id = value, onclick = oc))
  x <- girafe(ggobj = p)
  if( interactive() ) print(x)
}

Create interactive quantile regression

Description

The geometry is based on ggplot2::geom_quantile(). See the documentation for those functions for more details.

Usage

geom_quantile_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive quantiles to a ggplot -------
library(ggplot2)
library(ggiraph)

if (requireNamespace("quantreg", quietly = TRUE)) {
  m <- ggplot(mpg, aes(displ, 1 / hwy)) + geom_point()
  p <- m + geom_quantile_interactive(
    aes(
      tooltip = after_stat(quantile),
      data_id = after_stat(quantile),
      colour = after_stat(quantile)
    ),
    formula = y ~ x,
    size = 2,
    alpha = 0.5
  )
  x <- girafe(ggobj = p)
  x <- girafe_options(x = x,
                      opts_hover(css = "stroke:red;stroke-width:10px;") )
  if (interactive()) print(x)
}

Create interactive raster rectangles

Description

The geometry is based on ggplot2::geom_raster(). See the documentation for those functions for more details.

Usage

geom_raster_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

girafe()

Examples

# add interactive raster to a ggplot -------
library(ggplot2)
library(ggiraph)

df <- expand.grid(x = 0:5, y = 0:5)
df$z <- runif(nrow(df))

gg <- ggplot(df, aes(x, y, fill = z, tooltip = "tooltip")) +
  geom_raster_interactive() +
  scale_fill_gradient_interactive(
    data_id = "coco", onclick = "cici", tooltip = "cucu"
  )

x <- girafe(ggobj = gg)
if( interactive() ) print(x)

Create interactive rectangles

Description

These geometries are based on ggplot2::geom_rect() and ggplot2::geom_tile(). See the documentation for those functions for more details.

Usage

geom_rect_interactive(...)

geom_tile_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

Note

Converting a raster to svg elements could inflate dramatically the size of the svg and make it unreadable in a browser. Function geom_tile_interactive should be used with caution, total number of rectangles should be small.

See Also

girafe()

Examples

# add interactive polygons to a ggplot -------
library(ggplot2)
library(ggiraph)

dataset = data.frame( x1 = c(1, 3, 1, 5, 4),
	x2 = c(2, 4, 3, 6, 6),
	y1 = c( 1, 1, 4, 1, 3),
	y2 = c( 2, 2, 5, 3, 5),
	t = c( 'a', 'a', 'a', 'b', 'b'),
	r = c( 1, 2, 3, 4, 5),
	tooltip = c("ID 1", "ID 2", "ID 3", "ID 4", "ID 5"),
	uid = c("ID 1", "ID 2", "ID 3", "ID 4", "ID 5"),
	oc = rep("alert(this.getAttribute(\"data-id\"))", 5)
)

gg_rect = ggplot() +
	scale_x_continuous(name="x") +
	scale_y_continuous(name="y") +
	geom_rect_interactive(data=dataset,
		mapping = aes(xmin = x1, xmax = x2,
			ymin = y1, ymax = y2, fill = t,
			tooltip = tooltip, onclick = oc, data_id = uid ),
		color="black", alpha=0.5, linejoin = "bevel", lineend = "round") +
	geom_text(data=dataset,
			aes(x = x1 + ( x2 - x1 ) / 2, y = y1 + ( y2 - y1 ) / 2,
					label = r ),
		size = 4 )

x <- girafe(ggobj = gg_rect)
if( interactive() ) print(x)
# add interactive tiles to a ggplot -------
library(ggplot2)
library(ggiraph)

df <- data.frame(
  id = rep(c("a", "b", "c", "d", "e"), 2),
  x = rep(c(2, 5, 7, 9, 12), 2),
  y = rep(c(1, 2), each = 5),
  z = factor(rep(1:5, each = 2)),
  w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2)
)

p <- ggplot(df, aes(x, y, tooltip = id)) + geom_tile_interactive(aes(fill = z))
x <- girafe(ggobj = p)
if( interactive() ) print(x)


# correlation dataset ----
cor_mat <- cor(mtcars)
diag( cor_mat ) <- NA
var1 <- rep( row.names(cor_mat), ncol(cor_mat) )
var2 <- rep( colnames(cor_mat), each = nrow(cor_mat) )
cor <- as.numeric(cor_mat)
cor_mat <- data.frame( var1 = var1, var2 = var2,
  cor = cor, stringsAsFactors = FALSE )
cor_mat[["tooltip"]] <-
  sprintf("<i>`%s`</i> vs <i>`%s`</i>:</br><code>%.03f</code>",
  var1, var2, cor)

p <- ggplot(data = cor_mat, aes(x = var1, y = var2) ) +
  geom_tile_interactive(aes(fill = cor, tooltip = tooltip), colour = "white") +
  scale_fill_gradient2_interactive(low = "#BC120A", mid = "white", high = "#BC120A",
                                   limits = c(-1, 1), data_id = "cormat", tooltip = "cormat") +
  coord_equal()
x <- girafe(ggobj = p)
if( interactive() ) print(x)

Create interactive ribbons and area plots

Description

The geometries are based on ggplot2::geom_ribbon() and ggplot2::geom_area(). See the documentation for those functions for more details.

Usage

geom_ribbon_interactive(...)

geom_area_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive bar -------
library(ggplot2)
library(ggiraph)

# Generate data
huron <- data.frame(year = 1875:1972, level = as.vector(LakeHuron))
h <- ggplot(huron, aes(year))

g <- h +
  geom_ribbon_interactive(aes(ymin = level - 1, ymax = level + 1),
                          fill = "grey70", tooltip = "ribbon1", data_id="ribbon1",
                          outline.type = "both",
                          hover_css = "stroke:red;stroke-width:inherit;") +
  geom_line_interactive(aes(y = level), tooltip = "level", data_id="line1",
                        hover_css = "stroke:orange;fill:none;")
x <- girafe(ggobj = g)
x <- girafe_options(x = x,
                    opts_hover(css = girafe_css(
                      css = "stroke:orange;stroke-width:3px;",
                      area = "fill:blue;"
                    )))
if( interactive() ) print(x)


g <- h + geom_area_interactive(aes(y = level), tooltip = "area1")
x <- girafe(ggobj = g)
if( interactive() ) print(x)

Create interactive sf objects

Description

These geometries are based on ggplot2::geom_sf(), ggplot2::geom_sf_label() and ggplot2::geom_sf_text(). See the documentation for those functions for more details.

Usage

geom_sf_interactive(...)

geom_sf_label_interactive(...)

geom_sf_text_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive sf objects to a ggplot -------
library(ggplot2)
library(ggiraph)

## original code: see section examples of ggplot2::geom_sf help file
if (requireNamespace("sf",
                     quietly = TRUE,
                     versionCheck = c(op = ">=", version = "0.7-3"))) {
  nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
  gg <- ggplot(nc) +
    geom_sf_interactive(aes(fill = AREA, tooltip = NAME, data_id = NAME))
  x <- girafe(ggobj = gg)
  if( interactive() ) print(x)

  nc_3857 <- sf::st_transform(nc, "+init=epsg:3857")

  # Unfortunately if you plot other types of feature you'll need to use
  # show.legend to tell ggplot2 what type of legend to use
  nc_3857$mid <- sf::st_centroid(nc_3857$geometry)
  gg <- ggplot(nc_3857) +
    geom_sf(colour = "white") +
    geom_sf_interactive(aes(geometry = mid,
        size = AREA, tooltip = NAME, data_id = NAME),
      show.legend = "point")
  x <- girafe( ggobj = gg)
  if( interactive() ) print(x)

  # Example with texts.
  gg <- ggplot(nc_3857[1:3, ]) +
    geom_sf(aes(fill = AREA)) +
    geom_sf_text_interactive(aes(label = NAME, tooltip = NAME), color="white")
  x <- girafe( ggobj = gg)
  if( interactive() ) print(x)

  # Example with labels.
  gg <- ggplot(nc_3857[1:3, ]) +
    geom_sf(aes(fill = AREA)) +
    geom_sf_label_interactive(aes(label = NAME, tooltip = NAME))
  x <- girafe( ggobj = gg)
  if( interactive() ) print(x)
}

Create interactive smoothed conditional means

Description

The geometry is based on ggplot2::geom_smooth(). See the documentation for those functions for more details.

Usage

geom_smooth_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive bar -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth_interactive(aes(tooltip="smoothed line", data_id="smooth"))
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:orange;stroke-width:3px;") )
if( interactive() ) print(x)

p <- ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth_interactive(method = lm, se = FALSE, tooltip="smooth", data_id="smooth")
x <- girafe(ggobj = p)
if( interactive() ) print(x)

p <- ggplot(mpg, aes(displ, hwy, colour = class, tooltip = class, data_id = class)) +
  geom_point_interactive() +
  geom_smooth_interactive(se = FALSE, method = lm)
x <- girafe(ggobj = p)
x <- girafe_options(x = x,
                    opts_hover(css = "stroke:red;stroke-width:3px;") )
if( interactive() ) print(x)

Create interactive line segments parameterised by location, direction and distance

Description

The geometry is based on ggplot2::geom_spoke(). See the documentation for those functions for more details.

Usage

geom_spoke_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive line segments parameterised by location,
# direction and distance to a ggplot -------
library(ggplot2)
library(ggiraph)

df <- expand.grid(x = 1:10, y=1:10)
df$angle <- runif(100, 0, 2*pi)
df$speed <- runif(100, 0, sqrt(0.1 * df$x))

p <- ggplot(df, aes(x, y)) +
  geom_point() +
  geom_spoke_interactive(aes(angle = angle, tooltip=round(angle, 2)), radius = 0.5)
x <- girafe(ggobj = p)
if( interactive() ) print(x)

p2 <- ggplot(df, aes(x, y)) +
  geom_point() +
  geom_spoke_interactive(aes(angle = angle, radius = speed,
                             tooltip=paste(round(angle, 2), round(speed, 2), sep="\n")))
x2 <- girafe(ggobj = p2)
if( interactive() ) print(x2)

Create interactive repulsive textual annotations

Description

The geometries are based on ggrepel::geom_text_repel() and ggrepel::geom_label_repel(). See the documentation for those functions for more details.

Usage

geom_text_repel_interactive(...)

geom_label_repel_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

Note

The ggrepel package is required for these geometries

See Also

girafe()

Examples

# add interactive repulsive texts to a ggplot -------
library(ggplot2)
library(ggiraph)

# geom_text_repel_interactive
if (requireNamespace("ggrepel", quietly = TRUE)) {
  dataset = mtcars
  dataset$label = row.names(mtcars)
  dataset$tooltip = paste0(dataset$label, "<br/>", "cyl: ", dataset$cyl, "<br/>",
                           "gear: ", dataset$gear, "<br/>",
                           "carb: ", dataset$carb)
  p <- ggplot(dataset, aes(wt, mpg, color = qsec ) ) +
    geom_point_interactive(aes(tooltip = tooltip, data_id = label))

  gg_text = p +
    geom_text_repel_interactive(
      aes(label = label, tooltip = tooltip, data_id = label),
      size = 3
    )

  x <- girafe(ggobj = gg_text)
  x <- girafe_options(x = x,
                      opts_hover(css = "fill:#FF4C3B;") )
  if (interactive()) print(x)
}

# geom_label_repel_interactive
if (requireNamespace("ggrepel", quietly = TRUE)) {
  gg_label = p +
    geom_label_repel_interactive(
      aes(label = label, tooltip = tooltip, data_id = label),
      size = 3,
      max.overlaps = 12
    )

  x2 <- girafe(ggobj = gg_label)
  x2 <- girafe_options(x = x2,
                      opts_hover(css = ggiraph::girafe_css(
                       css = ";",
                       area = "fill:#FF4C3B;"
                      )) )
  if (interactive()) print(x2)
}

Create interactive violin plot

Description

The geometry is based on ggplot2::geom_violin(). See the documentation for those functions for more details.

Usage

geom_violin_interactive(...)

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See Also

girafe()

Examples

# add interactive violin plot -------
library(ggplot2)
library(ggiraph)

p <- ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_violin_interactive(aes(fill = cyl, tooltip = cyl))
x <- girafe(ggobj = p)
if( interactive() ) print(x)

# Show quartiles
p2 <- ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_violin_interactive(aes(tooltip=after_stat(density)),
                          draw_quantiles = c(0.25, 0.5, 0.75))
x2 <- girafe(ggobj = p2)
if( interactive() ) print(x2)

Create a ggiraph object (defunct)

Description

Create an interactive graphic to be used in a web browser.

This function is now defunct, users should now use function girafe().

Usage

ggiraph(...)

Arguments

Create a ggiraph output element

Description

Render a ggiraph within an application page.

This function is now defunct, users should now use function girafeOutput().

Usage

ggiraphOutput(...)

Arguments

Create a girafe object

Description

Create an interactive graphic with a ggplot object to be used in a web browser. The function should replace function ggiraph.

Usage

girafe(
  code,
  ggobj = NULL,
  pointsize = 12,
  width_svg = NULL,
  height_svg = NULL,
  options = list(),
  dependencies = NULL,
  ...
)

Arguments

Details

Use geom_zzz_interactive to create interactive graphical elements.

Difference from original functions is that some extra aesthetics are understood: the interactive_parameters.

Tooltips can be displayed when mouse is over graphical elements.

If id are associated with points, they get animated when mouse is over and can be selected when used in shiny apps.

On click actions can be set with javascript instructions. This option should not be used simultaneously with selections in Shiny applications as both features are "on click" features.

When a zoom effect is set, "zoom activate", "zoom desactivate" and "zoom init" buttons are available in a toolbar.

When selection type is set to 'multiple' (in Shiny applications), lasso selection and lasso anti-selections buttons are available in a toolbar.

Widget options

girafe animations can be customized with function girafe_options(). Options are available to customize tooltips, hover effects, zoom effects selection effects and toolbar.

Widget sizing

girafe graphics are responsive, which mean, they will be resized according to their container. There are two responsive behavior implementations: one for Shiny applications and flexdashboard documents and one for other documents (i.e. R markdown and saveWidget).

Graphics are created by an R graphic device (i.e pdf, png, svg here) and need arguments width and height to define a graphic region. Arguments width_svg and height_svg are used as corresponding values. They are defining the aspect ratio of the graphic. This proportion is always respected when the graph is displayed.

When a girafe graphic is in a Shiny application, graphic will be resized according to the arguments width and height of the function girafeOutput. Default values are '100\ outer bounding box of the graphic (the HTML element that will contain the graphic with an aspect ratio).

When a girafe graphic is in an R markdown document (producing an HTML document), the graphic will be resized according to the argument width of the function girafe. Its value is beeing used to define a relative width of the graphic within its HTML container. Its height is automatically adjusted regarding to the argument width and the aspect ratio.

See Also

girafe_options(), validated_fonts(), dsvg()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname <- row.names(mtcars)

gg_point <- ggplot(
  data = dataset,
  mapping = aes(
    x = wt, y = qsec, color = disp,
    tooltip = carname, data_id = carname
  )
) +
  geom_point_interactive() +
  theme_minimal()

x <- girafe(ggobj = gg_point)

if (interactive()) {
  print(x)
}

Create a girafe output element

Description

Render a girafe within an application page.

Usage

girafeOutput(outputId, width = "100%", height = NULL)

Arguments

Size control

If you want to control a fixed size, use opts_sizing(rescale = FALSE) and set the chart size with girafe(width_svg=..., height_svg=...).

If you want the graphic to fit the available width, use opts_sizing(rescale = TRUE) and set the size of the graphic with girafe(width_svg=..., height_svg=...), this size will define the aspect ratio.

CSS creation helper

Description

It allows specifying individual styles for various SVG elements.

Usage

girafe_css(
  css,
  text = NULL,
  point = NULL,
  line = NULL,
  area = NULL,
  image = NULL
)

Arguments

Value

css as scalar character

See Also

girafe_css_bicolor(), girafe()

Examples

library(ggiraph)

girafe_css(
  css = "fill:orange;stroke:gray;",
  text = "stroke:none; font-size: larger",
  line = "fill:none",
  area = "stroke-width:3px",
  point = "stroke-width:3px",
  image = "outline:2px red"
)

Helper for a 'girafe' css string

Description

It allows the creation of a css set of individual styles for animation of 'girafe' elements. The used model is based on a simple pattern that works most of the time for girafe hover effects and selection effects.

It sets properties based on a primary and a secondary color.

Usage

girafe_css_bicolor(primary = "orange", secondary = "gray")

Arguments

See Also

girafe_css(), girafe()

Examples

library(ggplot2)
library(ggiraph)

dat <- mtcars
dat$id <- "id"
dat$label <- "a line"
dat <- dat[order(dat$wt), ]

p <- ggplot(
  data = dat,
  mapping = aes(
    x = wt, y = mpg, data_id = id, tooltip = label)) +
  geom_line_interactive(color = "white", size  = .75,
                        hover_nearest = TRUE) +
  theme_dark() +
  theme(plot.background = element_rect(fill="black"),
        panel.background = element_rect(fill="black"),
        text = element_text(colour = "white"),
        axis.text = element_text(colour = "white")
        )

x <- girafe(
  ggobj = p,
  options = list(
    opts_hover(
      css = girafe_css_bicolor(
        primary = "yellow", secondary = "black"))
))
if (interactive()) print(x)

Get girafe defaults formatting properties

Description

The current formatting properties are automatically applied to every girafe you produce. These default values are returned by this function.

Usage

girafe_defaults(name = NULL)

Arguments

Value

a list containing default values or an element selected with argument name.

See Also

Other girafe animation options: girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

girafe_defaults()

Set girafe options

Description

Defines the animation options related to a girafe() object.

Usage

girafe_options(x, ...)

Arguments

See Also

girafe(), girafe_css(), girafe_css_bicolor()

Other girafe animation options: girafe_defaults(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)
library(htmlwidgets)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg_point = ggplot( data = dataset,
    mapping = aes(x = wt, y = qsec, color = disp,
    tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg_point)
x <- girafe_options(x = x,
    opts_tooltip(opacity = .7),
    opts_zoom(min = .5, max = 4),
    sizingPolicy(defaultWidth = "100%", defaultHeight = "300px"),
    opts_hover(css = "fill:red;stroke:orange;r:5pt;") )

if(interactive()){
  print(x)
}

Create interactive bins guide

Description

The guide is based on ggplot2::guide_bins(). See the documentation for that function for more details.

Usage

guide_bins_interactive(...)

Arguments

Value

An interactive guide object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

interactive_parameters, girafe()

Examples

# add interactive bins guide to a ggplot -------
library(ggplot2)
library(ggiraph)

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
p <- ggplot(dsmall, aes(x, y)) +
  stat_density_2d(
    aes(
      fill = after_stat(nlevel),
      tooltip = paste("nlevel:", after_stat(nlevel))
    ),
    geom = "interactive_polygon"
  ) +
  facet_grid(. ~ cut)

# add interactive binned scale and guide
p1 <- p + scale_fill_viridis_b_interactive(
  data_id = "nlevel",
  tooltip = "nlevel",
  guide = "bins"
)
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# set the keys separately
p2 <- p + scale_fill_viridis_b_interactive(
  data_id = function(breaks) {
    sapply(seq_along(breaks), function(i) {
      if (i < length(breaks)) {
        paste(
          min(breaks[i], breaks[i + 1], na.rm = TRUE),
          max(breaks[i], breaks[i + 1], na.rm = TRUE),
          sep = "-"
        )
      } else {
        NA_character_
      }
    })
  },
  tooltip = function(breaks) {
    sapply(seq_along(breaks), function(i) {
      if (i < length(breaks)) {
        paste(
          min(breaks[i], breaks[i + 1], na.rm = TRUE),
          max(breaks[i], breaks[i + 1], na.rm = TRUE),
          sep = "-"
        )
      } else {
        NA_character_
      }
    })
  },
  guide = "bins"
)
x <- girafe(ggobj = p2)
if (interactive()) print(x)


# make the title and labels interactive
p3 <- p + scale_fill_viridis_c_interactive(
  data_id = function(breaks) {
    sapply(seq_along(breaks), function(i) {
      if (i < length(breaks)) {
        paste(
          min(breaks[i], breaks[i + 1], na.rm = TRUE),
          max(breaks[i], breaks[i + 1], na.rm = TRUE),
          sep = "-"
        )
      } else {
        NA_character_
      }
    })
  },
  tooltip = function(breaks) {
    sapply(seq_along(breaks), function(i) {
      if (i < length(breaks)) {
        paste(
          min(breaks[i], breaks[i + 1], na.rm = TRUE),
          max(breaks[i], breaks[i + 1], na.rm = TRUE),
          sep = "-"
        )
      } else {
        NA_character_
      }
    })
  },
  guide = "bins",
  name = label_interactive("nlevel",
    data_id = "nlevel",
    tooltip = "nlevel"
  ),
  labels = function(breaks) {
    label_interactive(
      as.character(breaks),
      data_id = as.character(breaks),
      onclick = paste0("alert(\"", as.character(breaks), "\")"),
      tooltip = as.character(breaks)
    )
  }
)
x <- girafe(ggobj = p3)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

Create interactive continuous colour bar guide

Description

The guide is based on ggplot2::guide_colourbar(). See the documentation for that function for more details.

Usage

guide_colourbar_interactive(...)

guide_colorbar_interactive(...)

Arguments

Value

An interactive guide object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

interactive_parameters, girafe()

Examples

# add interactive colourbar guide to a ggplot -------
library(ggplot2)
library(ggiraph)

df <- expand.grid(x = 0:5, y = 0:5)
df$z <- runif(nrow(df))

p <- ggplot(df, aes(x, y, fill = z, tooltip = "tooltip")) +
  geom_raster_interactive()

# add an interactive scale (guide is colourbar)
p1 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar"
)
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make the legend title interactive
p2 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  name = label_interactive(
    "z",
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  )
)
x <- girafe(ggobj = p2)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# make the legend labels interactive
p3 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  name = label_interactive(
    "z",
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  ),
  labels = function(breaks) {
    lapply(breaks, function(abreak) label_interactive(
      as.character(abreak),
      data_id = paste0("colourbar", abreak),
      onclick = "alert(\"colourbar\")",
      tooltip = paste0("colourbar", abreak)
    ))
  }
)
x <- girafe(ggobj = p3)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# also via the guide
p4 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  guide = guide_colourbar_interactive(
    title.theme = element_text_interactive(
      size = 8,
      data_id = "colourbar",
      onclick = "alert(\"colourbar\")",
      tooltip = "colourbar"
    ),
    label.theme = element_text_interactive(
      size = 8,
      data_id = "colourbar",
      onclick = "alert(\"colourbar\")",
      tooltip = "colourbar"
    )
  )
)
x <- girafe(ggobj = p4)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# make the legend background interactive
p5 <- p4 + theme(
  legend.background = element_rect_interactive(
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  )
)
x <- girafe(ggobj = p5)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

Create interactive colorsteps guide

Description

The guide is based on ggplot2::guide_coloursteps(). See the documentation for that function for more details.

Usage

guide_coloursteps_interactive(...)

guide_colorsteps_interactive(...)

Arguments

Value

An interactive guide object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

interactive_parameters, girafe()

Examples

# add interactive coloursteps guide to a ggplot -------
library(ggplot2)
library(ggiraph)

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000),]
p <- ggplot(dsmall, aes(x, y)) +
  stat_density_2d(aes(
    fill = after_stat(nlevel),
    tooltip = paste("nlevel:", after_stat(nlevel))
  ),
  geom = "interactive_polygon") +
  facet_grid(. ~ cut)

# add interactive binned scale, by default the guide is colorsteps
p1 <- p + scale_fill_viridis_b_interactive(data_id = "nlevel",
                                           tooltip = "nlevel")
x <- girafe(ggobj = p1)
if (interactive()) print(x)


# make the title and labels interactive
p2 <- p + scale_fill_viridis_b_interactive(
  data_id = "nlevel",
  tooltip = "nlevel",
  name = label_interactive("nlevel", data_id = "nlevel",
                           tooltip = "nlevel"),
  labels = function(breaks) {
    l <- lapply(breaks, function(br) {
      label_interactive(
        as.character(br),
        data_id = as.character(br),
        onclick = paste0("alert(\"", as.character(br), "\")"),
        tooltip = as.character(br)
      )
    })
    l
  }
)
x <- girafe(ggobj = p2)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

Create interactive legend guide

Description

The guide is based on ggplot2::guide_legend(). See the documentation for that function for more details.

Usage

guide_legend_interactive(...)

Arguments

Value

An interactive guide object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

interactive_parameters, girafe()

Examples

# add interactive discrete legend guide to a ggplot -------
library(ggplot2)
library(ggiraph)

dat <- data.frame(
  name = c( "Guy", "Ginette", "David", "Cedric", "Frederic" ),
  gender = c( "Male", "Female", "Male", "Male", "Male" ),
  height = c(169, 160, 171, 172, 171 ) )
p <- ggplot(dat, aes( x = name, y = height, fill = gender,
                      data_id = name ) ) +
  geom_bar_interactive(stat = "identity")

# add interactive scale (guide is legend)
p1 <- p +
  scale_fill_manual_interactive(
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = c(Female = "Female", Male = "Male"),
    tooltip = c(Male = "Male", Female = "Female")
  )
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make the title interactive too
p2 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = c(Female = "Female", Male = "Male"),
    tooltip = c(Male = "Male", Female = "Female")
  )
x <- girafe(ggobj = p2)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# the interactive params can be functions too
p3 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = function(breaks) { as.character(breaks)},
    tooltip = function(breaks) { as.character(breaks)},
    onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") }
  )
x <- girafe(ggobj = p3)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# also via the guide
p4 <- p + scale_fill_manual_interactive(
  values = c(Male = "#0072B2", Female = "#009E73"),
  data_id = function(breaks) { as.character(breaks)},
  tooltip = function(breaks) { as.character(breaks)},
  onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") },
  guide = guide_legend_interactive(
    title.theme = element_text_interactive(
      size = 8,
      data_id = "legend.title",
      onclick = "alert(\"Gender levels\")",
      tooltip = "Gender levels"
    ),
    label.theme = element_text_interactive(
      size = 8
    )
  )
)
x <- girafe(ggobj = p4)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# make the legend labels interactive
p5 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = function(breaks) { as.character(breaks)},
    tooltip = function(breaks) { as.character(breaks)},
    onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") },
    labels = function(breaks) {
      lapply(breaks, function(br) {
        label_interactive(
          as.character(br),
          data_id = as.character(br),
          onclick = paste0("alert(\"", as.character(br), "\")"),
          tooltip = as.character(br)
        )
      })
    }
  )
x <- girafe(ggobj = p5)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)
# add interactive continuous legend guide to a ggplot -------
library(ggplot2)
library(ggiraph)

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000),]
p <- ggplot(dsmall, aes(x, y)) +
  stat_density_2d(aes(
    fill = after_stat(nlevel),
    tooltip = paste("nlevel:", after_stat(nlevel))
  ),
  geom = "interactive_polygon") +
  facet_grid(. ~ cut)

# add interactive scale, by default the guide is a colourbar
p1 <- p + scale_fill_viridis_c_interactive(data_id = "nlevel",
                                           tooltip = "nlevel")
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make it legend
p2 <- p + scale_fill_viridis_c_interactive(data_id = "nlevel",
                                           tooltip = "nlevel",
                                           guide = "legend")
x <- girafe(ggobj = p2)
if (interactive()) print(x)

# set the keys separately
p3 <- p + scale_fill_viridis_c_interactive(
  data_id = function(breaks) {
    as.character(breaks)
  },
  tooltip = function(breaks) {
    as.character(breaks)
  },
  guide = "legend"
)
x <- girafe(ggobj = p3)
if (interactive()) print(x)


# make the title and labels interactive
p4 <- p + scale_fill_viridis_c_interactive(
  data_id = function(breaks) {
    as.character(breaks)
  },
  tooltip = function(breaks) {
    as.character(breaks)
  },
  guide = "legend",
  name = label_interactive("nlevel", data_id = "nlevel",
                           tooltip = "nlevel"),
  labels = function(breaks) {
    label_interactive(
      as.character(breaks),
      data_id = as.character(breaks),
      onclick = paste0("alert(\"", as.character(breaks), "\")"),
      tooltip = as.character(breaks)
    )
  }
)
x <- girafe(ggobj = p4)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

Re-init animation defaults options

Description

Re-init all defaults options with the package defaults.

Usage

init_girafe_defaults()

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Create interactive circles grob

Description

The grob is based on circleGrob(). See the documentation for that function for more details.

Usage

interactive_circle_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive curve grob

Description

The grob is based on curveGrob(). See the documentation for that function for more details.

Usage

interactive_curve_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Interactive parameters

Description

Throughout ggiraph there are functions that add interactivity to ggplot plot elements. The user can control the various aspects of interactivity by supplying a special set of parameters to these functions.

Arguments

Details for interactive geom functions

The interactive parameters can be supplied with two ways:

• As aesthetics with the mapping argument (via ggplot2::aes()). In this way they can be mapped to data columns and apply to a set of geometries.

• As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

Details for annotate_*_interactive functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

Details for element_*_interactive functions

The interactive parameters can be supplied as arguments in the relevant function and they should be scalar values.

For theme text elements (element_text_interactive()), the interactive parameters can also be supplied while setting a label value, via the ggplot2::labs() family of functions or when setting a scale/guide title or key label. Instead of setting a character value for the element, function label_interactive() can be used to define interactive parameters to go along with the label. When the parameters are supplied that way, they override the default values that are set at the theme via element_text_interactive() or via the guide's theme parameters.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

Custom interactive parameters

The argument extra_interactive_params can be passed to any of the *_interactive functions (geoms, grobs, scales, labeller, labels and theme elements), It should be a character vector of additional names to be treated as interactive parameters when evaluating the aesthetics. The values will eventually end up as attributes in the SVG elements of the output.

Intended only for expert use.

See Also

girafe_options(), girafe()

Create interactive path grob

Description

The grob is based on pathGrob(). See the documentation for that function for more details.

Usage

interactive_path_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive points grob

Description

The grob is based on pointsGrob(). See the documentation for that function for more details.

Usage

interactive_points_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive polygon grob

Description

The grob is based on polygonGrob(). See the documentation for that function for more details.

Usage

interactive_polygon_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive polyline grob

Description

These grobs are based on polylineGrob() and linesGrob(). See the documentation for those functions for more details.

Usage

interactive_polyline_grob(...)

interactive_lines_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive raster grob

Description

The grob is based on rasterGrob(). See the documentation for that function for more details.

Usage

interactive_raster_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

interactive_parameters, girafe()

Create interactive rectangle grob

Description

The grob is based on rectGrob(). See the documentation for that function for more details.

Usage

interactive_rect_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive rectangle grob

Description

The grob is based on roundrectGrob(). See the documentation for that function for more details.

Usage

interactive_roundrect_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive segments grob

Description

The grob is based on segmentsGrob. See the documentation for that function for more details.

Usage

interactive_segments_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create interactive text grob

Description

The grob is based on textGrob. See the documentation for that function for more details.

Usage

interactive_text_grob(...)

Arguments

Value

An interactive grob object.

Details for interactive_*_grob functions

The interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors depending on params on base function.

See Also

girafe()

Create an interactive label

Description

This function returns an object that can be used as a label via the ggplot2::labs() family of functions or when setting a scale/guide name/title or key label. It passes the interactive parameters to a theme element created via element_text_interactive() or via an interactive guide.

Usage

label_interactive(label, ...)

Arguments

Value

an interactive label object

See Also

interactive_parameters, labeller_interactive()

Examples

library(ggplot2)
library(ggiraph)

gg_jitter <- ggplot(
  mpg, aes(cyl, hwy, group = cyl)) +
  geom_boxplot() +
  labs(title =
         label_interactive(
           "title",
           data_id = "id_title",
           onclick = "alert(\"title\")",
           tooltip = "title" )
  ) +
  theme(plot.title = element_text_interactive())

x <- girafe(ggobj = gg_jitter)
if( interactive() ) print(x)

Construct interactive labelling specification for facet strips

Description

This function is a wrapper around ggplot2::labeller() that allows the user to turn facet strip labels into interactive labels via label_interactive().

It requires that the ggplot2::theme()'s strip.text elements are defined as interactive theme elements via element_text_interactive(), see details.

Usage

labeller_interactive(.mapping = NULL, ...)

Arguments

Details

The aesthetics set provided via .mapping is evaluated against the data provided by the ggplot2 facet. This means that the variables for each facet are available for using inside the aesthetic mappings. In addition the .label variable provides access to the produced label. See the examples.

The plot's theme is required to have the strip texts as interactive text elements. This involves strip.text or individually strip.text.x and strip.text.y: theme(strip.text.x = element_text_interactive()) theme(strip.text.y = element_text_interactive())

See Also

ggplot2::labeller(), label_interactive(), ggplot2::labellers

Examples

# use interactive labeller
library(ggplot2)
library(ggiraph)

p1 <- ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point_interactive(aes(tooltip = row.names(mtcars)))

# Always remember to set the theme's strip texts as interactive
# no need to set any interactive parameters, they'll be assigned from the labels
p1 <- p1 +
  theme(
    strip.text.x = element_text_interactive(),
    strip.text.y = element_text_interactive()
  )

# simple facet
p <- p1 + facet_wrap_interactive(
  vars(gear),
  labeller = labeller_interactive(aes(tooltip = paste("Gear:", gear)))
)
x <- girafe(ggobj = p)
if (interactive()) print(x)

# With two vars. When the .multi_line labeller argument is TRUE (default),
# supply a different labeller for each var
p <- p1 + facet_wrap_interactive(
  vars(gear, vs),
  labeller = labeller_interactive(
    gear = labeller_interactive(aes(tooltip = paste("Gear:", gear))),
    vs = labeller_interactive(aes(tooltip = paste("VS:", vs)))
  )
)
x <- girafe(ggobj = p)
if (interactive()) print(x)

# When the .multi_line argument is FALSE, the labels are joined and
# the same happens with the data, so we can refer to both variables in the aesthetics!
p <- p1 + facet_wrap_interactive(
  vars(gear, vs),
  labeller = labeller_interactive(
    aes(tooltip = paste0("Gear: ", gear, "\nVS: ", vs)),
    .multi_line = FALSE
  )
)
x <- girafe(ggobj = p)
if (interactive()) print(x)

# Example with facet_grid:
p <- p1 + facet_grid_interactive(
  vs + am ~ gear,
  labeller = labeller(
    gear = labeller_interactive(aes(
      tooltip = paste("gear:", gear), data_id = paste0("gear_", gear)
    )),
    vs = labeller_interactive(aes(
      tooltip = paste("VS:", vs), data_id = paste0("vs_", vs)
    )),
    am = labeller_interactive(aes(
      tooltip = paste("AM:", am), data_id = paste0("am_", am)
    ))
  )
)
x <- girafe(ggobj = p)
if (interactive()) print(x)

# Same with .rows and .cols and .multi_line = FALSE
p <- p1 + facet_grid_interactive(
  vs + am ~ gear,
  labeller = labeller(
    .cols = labeller_interactive(
      .mapping = aes(tooltip = paste("gear:", gear))
    ),
    .rows = labeller_interactive(
      aes(tooltip = paste0("VS: ", vs, "\nAM: ", am)),
      .multi_line = FALSE
    )
  )
)
x <- girafe(ggobj = p)
if (interactive()) print(x)

# a more complex example
p2 <- ggplot(msleep, aes(x = sleep_total, y = awake)) +
  geom_point_interactive(aes(tooltip = name)) +
  theme(
    strip.text.x = element_text_interactive(),
    strip.text.y = element_text_interactive()
  )

# character vector as lookup table
conservation_status <- c(
  cd = "Conservation Dependent",
  en = "Endangered",
  lc = "Least concern",
  nt = "Near Threatened",
  vu = "Vulnerable",
  domesticated = "Domesticated"
)

# function to capitalize a string
capitalize <- function(x) {
  substr(x, 1, 1) <- toupper(substr(x, 1, 1))
  x
}

# function to cut a string and append an ellipsis
cut_str <- function(x, width = 10) {
  ind <- !is.na(x) & nchar(x) > width
  x[ind] <- paste0(substr(x[ind], 1, width), "...")
  x
}

replace_nas <- function(x) {
  ifelse(is.na(x), "Not available", x)
}

# in this example we use the '.label' variable to access the produced label
# and we set the 'label' aesthetic to modify the label
p <- p2 + facet_grid_interactive(
  vore ~ conservation,
  labeller = labeller(
    vore = labeller_interactive(
      aes(tooltip = paste("Vore:", replace_nas(.label))),
      .default = capitalize
    ),
    conservation = labeller_interactive(
      aes(
        tooltip = paste("Conservation:\n", replace_nas(.label)),
        label = cut_str(.label, 3)
      ),
      .default = conservation_status
    )
  )
)

x <- girafe(ggobj = p)
if (interactive()) print(x)

Find best family match with systemfonts

Description

match_family() returns the best font family match.

Usage

match_family(font = "sans", bold = TRUE, italic = TRUE, debug = NULL)

Arguments

See Also

Other functions for font management: font_family_exists(), validated_fonts()

Examples

match_family("sans")
match_family("serif")

Hover effect settings

Description

Allows customization of the rendering of graphic elements when the user hovers over them with the cursor (mouse pointer). Use opts_hover for interactive geometries in panels, opts_hover_key for interactive scales/guides and opts_hover_theme for interactive theme elements. Use opts_hover_inv for the effect on the rest of the geometries, while one is hovered (inverted operation).

Usage

opts_hover(css = NULL, reactive = FALSE, nearest_distance = NULL)

opts_hover_inv(css = NULL)

opts_hover_key(css = NULL, reactive = FALSE)

opts_hover_theme(css = NULL, reactive = FALSE)

Arguments

Note

IMPORTANT: When applying a fill style with the css argument, be aware that the browser's CSS engine will apply it also to line elements, if there are any that use the hovering feature. This will cause an undesired effect.

To overcome this, supply the argument css using girafe_css(), in order to set the fill style only for the desired elements.

See Also

girafe_css(), girafe_css_bicolor()

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_hover(css = "fill:wheat;stroke:orange;r:5pt;") )
if( interactive() ) print(x)

Selection effect settings

Description

Allows customization of the rendering of selected graphic elements. Use opts_selection for interactive geometries in panels, opts_selection_key for interactive scales/guides and opts_selection_theme for interactive theme elements. Use opts_selection_inv for the effect on the rest of the geometries, while some are selected (inverted operation).

Usage

opts_selection(
  css = NULL,
  type = c("multiple", "single", "none"),
  only_shiny = TRUE,
  selected = character(0)
)

opts_selection_inv(css = NULL)

opts_selection_key(
  css = NULL,
  type = c("single", "multiple", "none"),
  only_shiny = TRUE,
  selected = character(0)
)

opts_selection_theme(
  css = NULL,
  type = c("single", "multiple", "none"),
  only_shiny = TRUE,
  selected = character(0)
)

Arguments

Note

IMPORTANT: When applying a fill style with the css argument, be aware that the browser's CSS engine will apply it also to line elements, if there are any that use the selection feature. This will cause an undesired effect.

To overcome this, supply the argument css using girafe_css(), in order to set the fill style only for the desired elements.

See Also

girafe_css(), girafe_css_bicolor()

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_selection(type = "multiple", only_shiny = FALSE,
    css = "fill:red;stroke:gray;r:5pt;") )
if( interactive() ) print(x)

Girafe sizing settings

Description

Allows customization of the svg style sizing

Usage

opts_sizing(rescale = TRUE, width = 1)

Arguments

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_toolbar(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_sizing(rescale = FALSE) )
if( interactive() ) print(x)

Toolbar settings

Description

Allows customization of the toolbar

Usage

opts_toolbar(
  position = c("topright", "top", "bottom", "topleft", "bottomleft", "bottomright"),
  saveaspng = TRUE,
  pngname = "diagram",
  tooltips = NULL,
  hidden = NULL,
  fixed = FALSE,
  delay_mouseover = 200,
  delay_mouseout = 500
)

Arguments

Note

saveaspng relies on JavaScript promises, so any browsers that don't natively support the standard Promise object will need to have a polyfill (e.g. Internet Explorer with version less than 11 will need it).

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_tooltip(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_toolbar(position = "top") )
if( interactive() ) print(x)

Tooltip settings

Description

Settings to be used with girafe() for tooltip customisation.

Usage

opts_tooltip(
  css = NULL,
  offx = 10,
  offy = 0,
  use_cursor_pos = TRUE,
  opacity = 0.9,
  use_fill = FALSE,
  use_stroke = FALSE,
  delay_mouseover = 200,
  delay_mouseout = 500,
  placement = c("auto", "doc", "container"),
  zindex = 999
)

Arguments

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_zoom(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_tooltip(opacity = .7,
    offx = 20, offy = -10,
    use_fill = TRUE, use_stroke = TRUE,
    delay_mouseout = 1000) )
if( interactive() ) print(x)

Zoom settings

Description

Allows customization of the zoom.

Usage

opts_zoom(min = 1, max = 1, duration = 300)

Arguments

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), set_girafe_defaults()

Examples

library(ggplot2)

dataset <- mtcars
dataset$carname = row.names(mtcars)

gg <- ggplot(
  data = dataset,
  mapping = aes(x = wt, y = qsec, color = disp,
                tooltip = carname, data_id = carname) ) +
  geom_point_interactive() + theme_minimal()

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = .7, max = 2) )
if( interactive() ) print(x)

Reactive version of girafe

Description

Makes a reactive version of girafe object for use in Shiny.

Usage

renderGirafe(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())

Arguments

Reactive version of ggiraph object

Description

Makes a reactive version of a ggiraph object for use in Shiny.

This function is now defunct, users should now use function renderGirafe().

Usage

renderggiraph(...)

Arguments

Run shiny examples and see corresponding code

Description

Run shiny examples and see corresponding code

Usage

run_girafe_example(name = "crimes")

Arguments

Create interactive scales for alpha transparency

Description

These scales are based on ggplot2::scale_alpha(), ggplot2::scale_alpha_continuous(), ggplot2::scale_alpha_discrete(), ggplot2::scale_alpha_binned(), ggplot2::scale_alpha_ordinal(), ggplot2::scale_alpha_date(), ggplot2::scale_alpha_datetime(). See the documentation for those functions for more details.

Usage

scale_alpha_interactive(...)

scale_alpha_continuous_interactive(...)

scale_alpha_discrete_interactive(...)

scale_alpha_binned_interactive(...)

scale_alpha_ordinal_interactive(...)

scale_alpha_date_interactive(...)

scale_alpha_datetime_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Create interactive colorbrewer scales

Description

These scales are based on ggplot2::scale_colour_brewer(), ggplot2::scale_fill_brewer(), ggplot2::scale_colour_distiller(), ggplot2::scale_fill_distiller(), ggplot2::scale_colour_fermenter(), ggplot2::scale_fill_fermenter(). See the documentation for those functions for more details.

Usage

scale_colour_brewer_interactive(...)

scale_color_brewer_interactive(...)

scale_fill_brewer_interactive(...)

scale_colour_distiller_interactive(...)

scale_color_distiller_interactive(...)

scale_fill_distiller_interactive(...)

scale_colour_fermenter_interactive(...)

scale_color_fermenter_interactive(...)

scale_fill_fermenter_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Create interactive colour scales

Description

These scales are based on ggplot2::scale_colour_continuous(), ggplot2::scale_fill_continuous(), ggplot2::scale_colour_grey(), ggplot2::scale_fill_grey(), ggplot2::scale_colour_hue(), ggplot2::scale_fill_hue(), ggplot2::scale_colour_binned(), ggplot2::scale_fill_binned(), ggplot2::scale_colour_discrete(), ggplot2::scale_fill_discrete(), ggplot2::scale_colour_date(), ggplot2::scale_fill_date(), ggplot2::scale_colour_datetime() and ggplot2::scale_fill_datetime(). See the documentation for those functions for more details.

Usage

scale_colour_continuous_interactive(...)

scale_color_continuous_interactive(...)

scale_fill_continuous_interactive(...)

scale_colour_grey_interactive(...)

scale_color_grey_interactive(...)

scale_fill_grey_interactive(...)

scale_colour_hue_interactive(...)

scale_color_hue_interactive(...)

scale_fill_hue_interactive(...)

scale_colour_binned_interactive(...)

scale_color_binned_interactive(...)

scale_fill_binned_interactive(...)

scale_colour_discrete_interactive(...)

scale_color_discrete_interactive(...)

scale_fill_discrete_interactive(...)

scale_colour_date_interactive(...)

scale_color_date_interactive(...)

scale_fill_date_interactive(...)

scale_colour_datetime_interactive(...)

scale_color_datetime_interactive(...)

scale_fill_datetime_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Create interactive binned gradient colour scales

Description

These scales are based on ggplot2::scale_colour_steps(), ggplot2::scale_fill_steps(), ggplot2::scale_colour_steps2(), ggplot2::scale_fill_steps2(), ggplot2::scale_colour_stepsn() and ggplot2::scale_fill_stepsn(). See the documentation for those functions for more details.

Usage

scale_colour_steps_interactive(...)

scale_color_steps_interactive(...)

scale_fill_steps_interactive(...)

scale_colour_steps2_interactive(...)

scale_color_steps2_interactive(...)

scale_fill_steps2_interactive(...)

scale_colour_stepsn_interactive(...)

scale_color_stepsn_interactive(...)

scale_fill_stepsn_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Create interactive gradient colour scales

Description

These scales are based on ggplot2::scale_colour_gradient(), ggplot2::scale_fill_gradient(), ggplot2::scale_colour_gradient2(), ggplot2::scale_fill_gradient2(), ggplot2::scale_colour_gradientn() and ggplot2::scale_fill_gradientn(). See the documentation for those functions for more details.

Usage

scale_colour_gradient_interactive(...)

scale_color_gradient_interactive(...)

scale_fill_gradient_interactive(...)

scale_colour_gradient2_interactive(...)

scale_color_gradient2_interactive(...)

scale_fill_gradient2_interactive(...)

scale_colour_gradientn_interactive(...)

scale_color_gradientn_interactive(...)

scale_fill_gradientn_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Examples

# add interactive gradient colour scale to a ggplot -------
library(ggplot2)
library(ggiraph)

df <- expand.grid(x = 0:5, y = 0:5)
df$z <- runif(nrow(df))

p <- ggplot(df, aes(x, y, fill = z, tooltip = "tooltip")) +
  geom_raster_interactive()

# add an interactive scale (guide is colourbar)
p1 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar"
)
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make the legend title interactive
p2 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  name = label_interactive(
    "z",
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  )
)
x <- girafe(ggobj = p2)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# make the legend labels interactive
p3 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  name = label_interactive(
    "z",
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  ),
  labels = function(breaks) {
    lapply(breaks, function(abreak) label_interactive(
      as.character(abreak),
      data_id = paste0("colourbar", abreak),
      onclick = "alert(\"colourbar\")",
      tooltip = paste0("colourbar", abreak)
    ))
  }
)
x <- girafe(ggobj = p3)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# also via the guide
p4 <- p + scale_fill_gradient_interactive(
  data_id = "colourbar",
  onclick = "alert(\"colourbar\")",
  tooltip = "colourbar",
  guide = guide_colourbar_interactive(
    title.theme = element_text_interactive(
      size = 8,
      data_id = "colourbar",
      onclick = "alert(\"colourbar\")",
      tooltip = "colourbar"
    ),
    label.theme = element_text_interactive(
      size = 8,
      data_id = "colourbar",
      onclick = "alert(\"colourbar\")",
      tooltip = "colourbar"
    )
  )
)
x <- girafe(ggobj = p4)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

# make the legend background interactive
p5 <- p4 + theme(
  legend.background = element_rect_interactive(
    data_id = "colourbar",
    onclick = "alert(\"colourbar\")",
    tooltip = "colourbar"
  )
)
x <- girafe(ggobj = p5)
x <- girafe_options(
  x,
  opts_hover_key(girafe_css("stroke:red", text = "stroke:none;fill:red"))
)
if (interactive()) print(x)

Create interactive scales for line patterns

Description

These scales are based on ggplot2::scale_linetype(), ggplot2::scale_linetype_continuous(), ggplot2::scale_linetype_discrete() and ggplot2::scale_linetype_binned(). See the documentation for those functions for more details.

Usage

scale_linetype_interactive(...)

scale_linetype_continuous_interactive(...)

scale_linetype_discrete_interactive(...)

scale_linetype_binned_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_manual_interactive, scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Create your own interactive discrete scale

Description

These scales are based on ggplot2::scale_colour_manual(), ggplot2::scale_fill_manual(), ggplot2::scale_size_manual(), ggplot2::scale_shape_manual(), ggplot2::scale_linetype_manual(), ggplot2::scale_alpha_manual() and ggplot2::scale_discrete_manual(). See the documentation for those functions for more details.

Usage

scale_colour_manual_interactive(...)

scale_color_manual_interactive(...)

scale_fill_manual_interactive(...)

scale_size_manual_interactive(...)

scale_shape_manual_interactive(...)

scale_linetype_manual_interactive(...)

scale_alpha_manual_interactive(...)

scale_discrete_manual_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_shape_interactive(), scale_size_interactive(), scale_viridis_interactive

Examples

# add interactive manual fill scale to a ggplot -------
library(ggplot2)
library(ggiraph)

dat <- data.frame(
  name = c( "Guy", "Ginette", "David", "Cedric", "Frederic" ),
  gender = c( "Male", "Female", "Male", "Male", "Male" ),
  height = c(169, 160, 171, 172, 171 ) )
p <- ggplot(dat, aes( x = name, y = height, fill = gender,
                      data_id = name ) ) +
  geom_bar_interactive(stat = "identity")

# add interactive scale (guide is legend)
p1 <- p +
  scale_fill_manual_interactive(
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = c(Female = "Female", Male = "Male"),
    tooltip = c(Male = "Male", Female = "Female")
  )
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make the title interactive too
p2 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = c(Female = "Female", Male = "Male"),
    tooltip = c(Male = "Male", Female = "Female")
  )
x <- girafe(ggobj = p2)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# the interactive params can be functions too
p3 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = function(breaks) { as.character(breaks)},
    tooltip = function(breaks) { as.character(breaks)},
    onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") }
  )
x <- girafe(ggobj = p3)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# also via the guide
p4 <- p + scale_fill_manual_interactive(
  values = c(Male = "#0072B2", Female = "#009E73"),
  data_id = function(breaks) { as.character(breaks)},
  tooltip = function(breaks) { as.character(breaks)},
  onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") },
  guide = guide_legend_interactive(
    title.theme = element_text_interactive(
      size = 8,
      data_id = "legend.title",
      onclick = "alert(\"Gender levels\")",
      tooltip = "Gender levels"
    ),
    label.theme = element_text_interactive(
      size = 8
    )
  )
)
x <- girafe(ggobj = p4)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

# make the legend labels interactive
p5 <- p +
  scale_fill_manual_interactive(
    name = label_interactive("gender", tooltip="Gender levels", data_id="legend.title"),
    values = c(Male = "#0072B2", Female = "#009E73"),
    data_id = function(breaks) { as.character(breaks)},
    tooltip = function(breaks) { as.character(breaks)},
    onclick = function(breaks) { paste0("alert(\"", as.character(breaks), "\")") },
    labels = function(breaks) {
      lapply(breaks, function(br) {
        label_interactive(
          as.character(br),
          data_id = as.character(br),
          onclick = paste0("alert(\"", as.character(br), "\")"),
          tooltip = as.character(br)
        )
      })
    }
  )
x <- girafe(ggobj = p5)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

Create interactive scales for shapes

Description

These scales are based on ggplot2::scale_shape(), ggplot2::scale_shape_continuous(), ggplot2::scale_shape_discrete(), ggplot2::scale_shape_binned() and ggplot2::scale_shape_ordinal(). See the documentation for those functions for more details.

Usage

scale_shape_interactive(...)

scale_shape_continuous_interactive(...)

scale_shape_discrete_interactive(...)

scale_shape_binned_interactive(...)

scale_shape_ordinal_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_size_interactive(), scale_viridis_interactive

Create interactive scales for area or radius

Description

These scales are based on ggplot2::scale_size(), ggplot2::scale_size_area(), ggplot2::scale_size_continuous(), ggplot2::scale_size_discrete(), ggplot2::scale_size_binned(), ggplot2::scale_size_binned_area(), ggplot2::scale_size_date(), ggplot2::scale_size_datetime(), ggplot2::scale_size_ordinal() and ggplot2::scale_radius(). See the documentation for those functions for more details.

Usage

scale_size_interactive(...)

scale_size_area_interactive(...)

scale_size_continuous_interactive(...)

scale_size_discrete_interactive(...)

scale_size_binned_interactive(...)

scale_size_binned_area_interactive(...)

scale_size_date_interactive(...)

scale_size_datetime_interactive(...)

scale_size_ordinal_interactive(...)

scale_radius_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_viridis_interactive

Create interactive viridis colour scales

Description

These scales are based on ggplot2::scale_colour_viridis_d(), ggplot2::scale_fill_viridis_d(), ggplot2::scale_colour_viridis_c(), ggplot2::scale_fill_viridis_c(), ggplot2::scale_colour_viridis_b(), ggplot2::scale_fill_viridis_b(), ggplot2::scale_colour_ordinal(), ggplot2::scale_fill_ordinal(). See the documentation for those functions for more details.

Usage

scale_colour_viridis_d_interactive(...)

scale_color_viridis_d_interactive(...)

scale_fill_viridis_d_interactive(...)

scale_colour_viridis_c_interactive(...)

scale_color_viridis_c_interactive(...)

scale_fill_viridis_c_interactive(...)

scale_colour_viridis_b_interactive(...)

scale_color_viridis_b_interactive(...)

scale_fill_viridis_b_interactive(...)

scale_colour_ordinal_interactive(...)

scale_color_ordinal_interactive(...)

scale_fill_ordinal_interactive(...)

Arguments

Value

An interactive scale object.

Details for interactive scale and interactive guide functions

For scales, the interactive parameters can be supplied as arguments in the relevant function and they can be scalar values or vectors, depending on the number of breaks (levels) and the type of the guide used. The guides do not accept any interactive parameter directly, they receive them from the scales.

When guide of type legend, bins, colourbar or coloursteps is used, it will be converted to a guide_legend_interactive(), guide_bins_interactive(), guide_colourbar_interactive() or guide_coloursteps_interactive() respectively, if it's not already.

The length of each scale interactive parameter vector should match the length of the breaks. It can also be a named vector, where each name should correspond to the same break name. It can also be defined as function that takes the breaks as input and returns a named or unnamed vector of values as output.

For binned guides like bins and coloursteps the breaks include the label breaks and the limits. The number of bins will be one less than the number of breaks and the interactive parameters can be constructed for each bin separately (look at the examples). For colourbar guide in raster mode, the breaks vector, is scalar 1 always, meaning the interactive parameters should be scalar too. For colourbar guide in non-raster mode, the bar is drawn using rectangles, and the breaks are the midpoints of each rectangle.

The interactive parameters here, give interactivity only to the key elements of the guide.

To provide interactivity to the rest of the elements of a guide, (title, labels, background, etc), the relevant theme elements or relevant guide arguments can be used. The guide arguments title.theme and label.theme can be defined as element_text_interactive (in fact, they will be converted to that if they are not already), either directly or via the theme. See the element_*_interactive section for more details.

See Also

girafe()

Other interactive scale: scale_alpha_interactive(), scale_colour_brewer_interactive(), scale_colour_interactive, scale_colour_steps_interactive(), scale_gradient_interactive, scale_linetype_interactive(), scale_manual_interactive, scale_shape_interactive(), scale_size_interactive()

Examples

# add interactive viridis scale to a ggplot -------
library(ggplot2)
library(ggiraph)

set.seed(4393)
dsmall <- diamonds[sample(nrow(diamonds), 1000),]
p <- ggplot(dsmall, aes(x, y)) +
  stat_density_2d(aes(
    fill = after_stat(nlevel),
    tooltip = paste("nlevel:", after_stat(nlevel))
  ),
  geom = "interactive_polygon") +
  facet_grid(. ~ cut)

# add interactive scale, by default the guide is a colourbar
p1 <- p + scale_fill_viridis_c_interactive(data_id = "nlevel",
                                           tooltip = "nlevel")
x <- girafe(ggobj = p1)
if (interactive()) print(x)

# make it legend
p2 <- p + scale_fill_viridis_c_interactive(data_id = "nlevel",
                                           tooltip = "nlevel",
                                           guide = "legend")
x <- girafe(ggobj = p2)
if (interactive()) print(x)

# set the keys separately
p3 <- p + scale_fill_viridis_c_interactive(
  data_id = function(breaks) {
    as.character(breaks)
  },
  tooltip = function(breaks) {
    as.character(breaks)
  },
  guide = "legend"
)
x <- girafe(ggobj = p3)
if (interactive()) print(x)


# make the title and labels interactive
p4 <- p + scale_fill_viridis_c_interactive(
  data_id = function(breaks) {
    as.character(breaks)
  },
  tooltip = function(breaks) {
    as.character(breaks)
  },
  guide = "legend",
  name = label_interactive("nlevel", data_id = "nlevel",
                           tooltip = "nlevel"),
  labels = function(breaks) {
    label_interactive(
      as.character(breaks),
      data_id = as.character(breaks),
      onclick = paste0("alert(\"", as.character(breaks), "\")"),
      tooltip = as.character(breaks)
    )
  }
)
x <- girafe(ggobj = p4)
x <- girafe_options(x,
                    opts_hover_key(girafe_css("stroke:red", text="stroke:none;fill:red")))
if (interactive()) print(x)

Modify defaults girafe animation options

Description

girafe animation options (see girafe_defaults()) are automatically applied to every girafe you produce. Use set_girafe_defaults() to override them. Use init_girafe_defaults() to re-init all values with the package defaults.

Usage

set_girafe_defaults(
  fonts = NULL,
  opts_sizing = NULL,
  opts_tooltip = NULL,
  opts_hover = NULL,
  opts_hover_key = NULL,
  opts_hover_inv = NULL,
  opts_hover_theme = NULL,
  opts_selection = NULL,
  opts_selection_inv = NULL,
  opts_selection_key = NULL,
  opts_selection_theme = NULL,
  opts_zoom = NULL,
  opts_toolbar = NULL
)

Arguments

See Also

Other girafe animation options: girafe_defaults(), girafe_options(), init_girafe_defaults(), opts_hover(), opts_selection(), opts_sizing(), opts_toolbar(), opts_tooltip(), opts_zoom()

Examples

library(ggplot2)

set_girafe_defaults(
  opts_hover = opts_hover(css = "r:10px;"),
  opts_hover_inv = opts_hover_inv(),
  opts_sizing = opts_sizing(rescale = FALSE, width = .8),
  opts_tooltip = opts_tooltip(opacity = .7,
                              offx = 20, offy = -10,
                              use_fill = TRUE, use_stroke = TRUE,
                              delay_mouseout = 1000),
  opts_toolbar = opts_toolbar(position = "top", saveaspng = FALSE),
  opts_zoom = opts_zoom(min = .8, max = 7)
)

init_girafe_defaults()

List of validated default fonts

Description

Validates and possibly modifies the fonts to be used as default value in a graphic according to the fonts available on the machine. It process elements named "sans", "serif", "mono" and "symbol".

Usage

validated_fonts(fonts = list())

Arguments

Value

a named list of validated font family names

See Also

girafe(), dsvg()

Other functions for font management: font_family_exists(), match_family()

Examples

validated_fonts()