Title: | Make 'ggplot2' Graphics Interactive |
---|---|
Description: | Create interactive 'ggplot2' graphics using 'htmlwidgets'. |
Authors: | David Gohel [aut, cre], Panagiotis Skintzos [aut], Mike Bostock [cph] (d3.js), Speros Kokenes [cph] (d3-lasso), Eric Shull [cph] (saveSvgAsPng js library), Lee Thomason [cph] (TinyXML2), Vladimir Agafonkin [cph] (Flatbush), Eric Book [ctb] (hline and vline geoms) |
Maintainer: | David Gohel <[email protected]> |
License: | GPL-3 |
Version: | 0.8.11 |
Built: | 2024-11-19 14:23:09 UTC |
Source: | https://github.com/davidgohel/ggiraph |
The layer is based on ggplot2::annotate()
.
See the documentation for that function for more details.
annotate_interactive(...)
annotate_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
girafe()
, interactive_parameters, annotation_raster_interactive()
# 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)
# 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)
The layer is based on ggplot2::annotation_raster()
.
See the documentation for that function for more details.
annotation_raster_interactive(...)
annotation_raster_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
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.
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() )
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() )
file |
the file where output will appear. |
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height , width
|
Height and width in inches. |
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bg |
Default background color for the plot (defaults to "white"). |
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pointsize |
default point size. |
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standalone |
Produce a stand alone svg file? If |
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setdims |
If |
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canvas_id |
svg id within HTML page. |
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title |
A label for accessibility purposes (aria-label/aria-labelledby). Be aware that when using this, the browser will use it as a tooltip for the whole svg and it may class with the interactive elements' tooltip. |
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desc |
A longer description for accessibility purposes (aria-description/aria-describedby). |
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fonts |
Named list of font names to be aliased with
fonts installed on your system. If unspecified, the R default
families "sans", "serif", "mono" and "symbol"
are aliased to the family returned by If fonts are available, the default mapping will use these values:
As an example, using You can also use theme_minimal(base_family="Roboto"). |
fileout <- tempfile(fileext = ".svg") dsvg(file = fileout) plot(rnorm(10), main="Simple Example", xlab = "", ylab = "") dev.off()
fileout <- tempfile(fileext = ".svg") dsvg(file = fileout) plot(rnorm(10), main="Simple Example", xlab = "", ylab = "") dev.off()
This is useful primarily for testing. Requires the htmltools
package.
dsvg_view(code, ...)
dsvg_view(code, ...)
code |
Plotting code to execute. |
... |
Other arguments passed on to |
dsvg_view(plot(1:10)) dsvg_view(hist(rnorm(100)))
dsvg_view(plot(1:10)) dsvg_view(hist(rnorm(100)))
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.
element_line_interactive(...) element_rect_interactive(...) element_text_interactive(...)
element_line_interactive(...) element_rect_interactive(...) element_text_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
These facets are based on
ggplot2::facet_grid()
.
To make a facet interactive, it is mandatory to use
labeller_interactive()
for argument labeller
.
facet_grid_interactive(..., interactive_on = "text")
facet_grid_interactive(..., interactive_on = "text")
... |
arguments passed to base function and
|
interactive_on |
one of 'text' (only strip text are made interactive), 'rect' (only strip rectangles are made interactive) or 'both' (strip text and rectangles are made interactive). |
An interactive facetting object.
These facets are based on
ggplot2::facet_wrap()
.
To make a facet interactive, it is mandatory to use
labeller_interactive()
for argument labeller
.
facet_wrap_interactive(..., interactive_on = "text")
facet_wrap_interactive(..., interactive_on = "text")
... |
arguments passed to base function and
|
interactive_on |
one of 'text' (only strip text are made interactive), 'rect' (only strip rectangles are made interactive) or 'both' (strip text and rectangles are made interactive). |
An interactive facetting object.
Check if a font family exists in system fonts.
font_family_exists(font_family = "sans")
font_family_exists(font_family = "sans")
font_family |
font family name (case sensitive) |
A logical value
Other functions for font management:
match_family()
,
validated_fonts()
font_family_exists("sans") font_family_exists("Arial") font_family_exists("Courier")
font_family_exists("sans") font_family_exists("Arial") font_family_exists("Courier")
These geometries are based on ggplot2::geom_abline()
,
ggplot2::geom_hline()
and ggplot2::geom_vline()
.
geom_abline_interactive(...) geom_hline_interactive(...) geom_vline_interactive(...)
geom_abline_interactive(...) geom_hline_interactive(...) geom_vline_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometries are based on ggplot2::geom_bar()
and ggplot2::geom_col()
.
See the documentation for those functions for more details.
geom_bar_interactive(...) geom_col_interactive(...)
geom_bar_interactive(...) geom_col_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_bin_2d()
.
See the documentation for those functions for more details.
geom_bin_2d_interactive(...)
geom_bin_2d_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_boxplot()
.
See the documentation for that function for more details.
geom_boxplot_interactive(...)
geom_boxplot_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
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.
# 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)
# 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)
These geometries are based on ggplot2::geom_contour()
and ggplot2::geom_contour_filled()
.
See the documentation for those functions for more details.
geom_contour_interactive(...) geom_contour_filled_interactive(...)
geom_contour_interactive(...) geom_contour_filled_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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) }
# 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) }
The geometry is based on ggplot2::geom_bin2d()
.
See the documentation for those functions for more details.
geom_count_interactive(...)
geom_count_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
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.
geom_crossbar_interactive(...) geom_errorbar_interactive(...) geom_linerange_interactive(...) geom_pointrange_interactive(...)
geom_crossbar_interactive(...) geom_errorbar_interactive(...) geom_linerange_interactive(...) geom_pointrange_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometries are based on ggplot2::geom_segment()
and ggplot2::geom_curve()
.
See the documentation for those functions for more details.
geom_curve_interactive(...) geom_segment_interactive(...)
geom_curve_interactive(...) geom_segment_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometries are based on ggplot2::geom_density_2d()
and ggplot2::geom_density_2d_filled()
.
See the documentation for those functions for more details.
geom_density_2d_interactive(...) geom_density_2d_filled_interactive(...)
geom_density_2d_interactive(...) geom_density_2d_filled_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_density()
.
See the documentation for those functions for more details.
geom_density_interactive(...)
geom_density_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
This geometry is based on ggplot2::geom_dotplot()
.
See the documentation for those functions for more details.
geom_dotplot_interactive(...)
geom_dotplot_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
This geometry is based on ggplot2::geom_errorbarh()
.
See the documentation for those functions for more details.
geom_errorbarh_interactive(...)
geom_errorbarh_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
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.
geom_freqpoly_interactive(...) geom_histogram_interactive(...)
geom_freqpoly_interactive(...) geom_histogram_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_hex()
.
See the documentation for those functions for more details.
geom_hex_interactive(...)
geom_hex_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_jitter()
.
See the documentation for those functions for more details.
geom_jitter_interactive(...)
geom_jitter_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometries are based on ggplot2::geom_text()
and ggplot2::geom_label()
.
See the documentation for those functions for more details.
geom_label_interactive(...) geom_text_interactive(...)
geom_label_interactive(...) geom_text_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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$carb) ## the plot gg_text = ggplot(dataset, aes(x = mpg, y = wt, label = label, color = qsec, tooltip = tooltip, data_id = label)) + geom_text_interactive(check_overlap = TRUE) + coord_cartesian(xlim = c(0,50)) ## display the plot x <- girafe(ggobj = gg_text) x <- girafe_options(x = x, opts_hover(css = "fill:#FF4C3B;font-style:italic;") ) if( interactive() ) print(x)
# add interactive labels to a ggplot ------- library(ggplot2) library(ggiraph) p <- ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars))) + geom_label_interactive(aes(tooltip = paste(rownames(mtcars), mpg, sep = "\n"))) x <- girafe(ggobj = p) if( interactive() ) print(x) p <- ggplot(mtcars, aes(wt, mpg, label = rownames(mtcars))) + geom_label_interactive(aes(fill = factor(cyl), tooltip = paste(rownames(mtcars), mpg, sep = "\n")), colour = "white", fontface = "bold") x <- girafe(ggobj = p) if( interactive() ) print(x) # add interactive texts to a ggplot ------- library(ggplot2) library(ggiraph) ## the data dataset = mtcars dataset$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)
The geometry is based on ggplot2::geom_map()
.
See the documentation for those functions for more details.
geom_map_interactive(...)
geom_map_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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) }
# 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) }
These geometries are based on ggplot2::geom_path()
,
ggplot2::geom_line()
and ggplot2::geom_step()
.
See the documentation for those functions for more details.
geom_path_interactive(...) geom_line_interactive(...) geom_step_interactive(...)
geom_path_interactive(...) geom_line_interactive(...) geom_step_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_point()
.
See the documentation for those functions for more details.
geom_point_interactive(...)
geom_point_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
The following shapes id 3, 4 and 7 to 14 are composite symbols and should not be used.
# 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)
# 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)
The geometry is based on ggplot2::geom_polygon()
.
See the documentation for those functions for more details.
geom_polygon_interactive(...)
geom_polygon_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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) }
# 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) }
The geometry is based on ggplot2::geom_quantile()
.
See the documentation for those functions for more details.
geom_quantile_interactive(...)
geom_quantile_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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) }
# 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) }
The geometry is based on ggplot2::geom_raster()
.
See the documentation for those functions for more details.
geom_raster_interactive(...)
geom_raster_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# add interactive raster to a ggplot ------- library(ggplot2) library(ggiraph) df <- expand.grid(x = 0:5, y = 0:5) df$z <- runif(nrow(df)) gg <- ggplot(df, aes(x, y, fill = z, tooltip = "tooltip")) + geom_raster_interactive() + scale_fill_gradient_interactive( data_id = "coco", onclick = "cici", tooltip = "cucu" ) x <- girafe(ggobj = gg) if( interactive() ) print(x)
# add interactive raster to a ggplot ------- library(ggplot2) library(ggiraph) df <- expand.grid(x = 0:5, y = 0:5) df$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)
These geometries are based on ggplot2::geom_rect()
and ggplot2::geom_tile()
.
See the documentation for those functions for more details.
geom_rect_interactive(...) geom_tile_interactive(...)
geom_rect_interactive(...) geom_tile_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
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.
# 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)
# 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)
The geometries are based on ggplot2::geom_ribbon()
and ggplot2::geom_area()
.
See the documentation for those functions for more details.
geom_ribbon_interactive(...) geom_area_interactive(...)
geom_ribbon_interactive(...) geom_area_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
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.
geom_sf_interactive(...) geom_sf_label_interactive(...) geom_sf_text_interactive(...)
geom_sf_interactive(...) geom_sf_label_interactive(...) geom_sf_text_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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) }
# 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) }
The geometry is based on ggplot2::geom_smooth()
.
See the documentation for those functions for more details.
geom_smooth_interactive(...)
geom_smooth_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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)
The geometry is based on ggplot2::geom_spoke()
.
See the documentation for those functions for more details.
geom_spoke_interactive(...)
geom_spoke_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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$x)) p <- ggplot(df, aes(x, y)) + geom_point() + geom_spoke_interactive(aes(angle = angle, tooltip=round(angle, 2)), radius = 0.5) x <- girafe(ggobj = p) if( interactive() ) print(x) p2 <- ggplot(df, aes(x, y)) + geom_point() + geom_spoke_interactive(aes(angle = angle, radius = speed, tooltip=paste(round(angle, 2), round(speed, 2), sep="\n"))) x2 <- girafe(ggobj = p2) if( interactive() ) print(x2)
# add interactive line segments parameterised by location, # direction and distance to a ggplot ------- library(ggplot2) library(ggiraph) df <- expand.grid(x = 1:10, y=1:10) df$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)
The geometries are based on ggrepel::geom_text_repel()
and ggrepel::geom_label_repel()
.
See the documentation for those functions for more details.
geom_text_repel_interactive(...) geom_label_repel_interactive(...)
geom_text_repel_interactive(...) geom_label_repel_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
The ggrepel
package is required for these geometries
# 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) }
# 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) }
The geometry is based on ggplot2::geom_violin()
.
See the documentation for those functions for more details.
geom_violin_interactive(...)
geom_violin_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
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.
# 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)
# 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 an interactive graphic with a ggplot object
to be used in a web browser. The function should replace function
ggiraph
.
girafe( code, ggobj = NULL, pointsize = 12, width_svg = NULL, height_svg = NULL, options = list(), dependencies = NULL, ... )
girafe( code, ggobj = NULL, pointsize = 12, width_svg = NULL, height_svg = NULL, options = list(), dependencies = NULL, ... )
code |
Plotting code to execute |
ggobj |
ggplot object to print. Argument |
pointsize |
the default pointsize of plotted text in pixels, default to 12. |
width_svg , height_svg
|
The width and height of the graphics region in inches. The default values are 6 and 5 inches. This will define the aspect ratio of the graphic as it will be used to define viewbox attribute of the SVG result. If you use |
options |
a list of options for girafe rendering, see
|
dependencies |
Additional widget HTML dependencies, see |
... |
arguments passed on to |
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.
girafe animations can be customized with function girafe_options()
.
Options are available to customize tooltips, hover effects, zoom effects
selection effects and toolbar.
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.
girafe_options()
, validated_fonts()
, dsvg()
library(ggplot2) dataset <- mtcars dataset$carname <- row.names(mtcars) gg_point <- ggplot( data = dataset, mapping = aes( x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname ) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg_point) if (interactive()) { print(x) }
library(ggplot2) dataset <- mtcars dataset$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) }
It allows specifying individual styles for various SVG elements.
girafe_css( css, text = NULL, point = NULL, line = NULL, area = NULL, image = NULL )
girafe_css( css, text = NULL, point = NULL, line = NULL, area = NULL, image = NULL )
css |
The generic css style |
text |
Override style for text elements (svg:text) |
point |
Override style for point elements (svg:circle) |
line |
Override style for line elements (svg:line, svg:polyline) |
area |
Override style for area elements (svg:rect, svg:polygon, svg:path) |
image |
Override style for image elements (svg:image) |
css as scalar character
girafe_css_bicolor()
, girafe()
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" )
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" )
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.
girafe_css_bicolor(primary = "orange", secondary = "gray")
girafe_css_bicolor(primary = "orange", secondary = "gray")
primary , secondary
|
colors used to define animations of fill and stroke properties with text, lines, areas, points and images in 'girafe' outputs. |
library(ggplot2) library(ggiraph) dat <- mtcars dat$id <- "id" dat$label <- "a line" dat <- dat[order(dat$wt), ] p <- ggplot( data = dat, mapping = aes( x = wt, y = mpg, data_id = id, tooltip = label)) + geom_line_interactive(color = "white", size = .75, hover_nearest = TRUE) + theme_dark() + theme(plot.background = element_rect(fill="black"), panel.background = element_rect(fill="black"), text = element_text(colour = "white"), axis.text = element_text(colour = "white") ) x <- girafe( ggobj = p, options = list( opts_hover( css = girafe_css_bicolor( primary = "yellow", secondary = "black")) )) if (interactive()) print(x)
library(ggplot2) library(ggiraph) dat <- mtcars dat$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)
The current formatting properties are automatically applied to every girafe you produce. These default values are returned by this function.
girafe_defaults(name = NULL)
girafe_defaults(name = NULL)
name |
optional, option's name to return, one of 'fonts', 'opts_sizing', 'opts_tooltip', 'opts_hover', 'opts_hover_key', 'opts_hover_inv', 'opts_hover_theme', 'opts_selection', 'opts_selection_inv', 'opts_selection_key', 'opts_selection_theme', 'opts_zoom', 'opts_toolbar'. |
a list containing default values or an
element selected with argument name
.
Other girafe animation options:
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_toolbar()
,
opts_tooltip()
,
opts_zoom()
,
set_girafe_defaults()
girafe_defaults()
girafe_defaults()
Defines the animation options related to
a girafe()
object.
girafe_options(x, ...)
girafe_options(x, ...)
x |
girafe object. |
... |
set of options defined by calls to |
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()
library(ggplot2) library(htmlwidgets) dataset <- mtcars dataset$carname = row.names(mtcars) gg_point = ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg_point) x <- girafe_options(x = x, opts_tooltip(opacity = .7), opts_zoom(min = .5, max = 4), sizingPolicy(defaultWidth = "100%", defaultHeight = "300px"), opts_hover(css = "fill:red;stroke:orange;r:5pt;") ) if(interactive()){ print(x) }
library(ggplot2) library(htmlwidgets) dataset <- mtcars dataset$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) }
Render a girafe within an application page.
girafeOutput(outputId, width = "100%", height = NULL)
girafeOutput(outputId, width = "100%", height = NULL)
outputId |
output variable to read the girafe from. Do not use special JavaScript
characters such as a period |
width |
widget width, its default value is set so that the graphic can cover the entire available horizontal space. |
height |
widget height, its default value is NULL so that width adaptation is not restricted. The height will then be defined according to the width used and the aspect ratio. Only use a value for the height if you have a specific reason and want to strictly control the size. |
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.
The guide is based on ggplot2::guide_bins()
.
See the documentation for that function for more details.
guide_bins_interactive(...)
guide_bins_interactive(...)
... |
arguments passed to base function. |
An interactive guide object.
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.
interactive_parameters, girafe()
# 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)
# 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)
The guide is based on ggplot2::guide_colourbar()
.
See the documentation for that function for more details.
guide_colourbar_interactive(...) guide_colorbar_interactive(...)
guide_colourbar_interactive(...) guide_colorbar_interactive(...)
... |
arguments passed to base function. |
An interactive guide object.
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.
interactive_parameters, girafe()
# 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)
# 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)
The guide is based on ggplot2::guide_coloursteps()
.
See the documentation for that function for more details.
guide_coloursteps_interactive(...) guide_colorsteps_interactive(...)
guide_coloursteps_interactive(...) guide_colorsteps_interactive(...)
... |
arguments passed to base function. |
An interactive guide object.
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.
interactive_parameters, girafe()
# 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)
# 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)
The guide is based on ggplot2::guide_legend()
.
See the documentation for that function for more details.
guide_legend_interactive(...)
guide_legend_interactive(...)
... |
arguments passed to base function. |
An interactive guide object.
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.
interactive_parameters, girafe()
# 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)
# 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 all defaults options with the package defaults.
init_girafe_defaults()
init_girafe_defaults()
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_toolbar()
,
opts_tooltip()
,
opts_zoom()
,
set_girafe_defaults()
The grob is based on circleGrob()
.
See the documentation for that function for more details.
interactive_circle_grob(...)
interactive_circle_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on curveGrob()
.
See the documentation for that function for more details.
interactive_curve_grob(...)
interactive_curve_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
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.
tooltip |
Tooltip text to associate with one or more elements. If this is supplied a tooltip is shown when the element is hovered. Plain text or html is supported. To use html markup it is advised to use |
onclick |
Javascript code to associate with one or more elements. This code will be executed when the element is clicked. |
hover_css |
Individual css style associate with one or more elements.
This css style is applied when the element is hovered and overrides the default style,
set via |
selected_css |
Individual css style associate with one or more elements.
This css style is applied when the element is selected and overrides the default style,
set via |
data_id |
Identifier to associate with one or more elements. This is mandatory parameter if hover and selection interactivity is desired. Identifiers are available as reactive input values in Shiny applications. |
tooltip_fill |
Color to use for tooltip background when |
hover_nearest |
Set to TRUE to apply the hover effect on the nearest element
while moving the mouse. In this case it is mandatory to also set the |
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.
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.
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.
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.
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.
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.
The grob is based on pathGrob()
.
See the documentation for that function for more details.
interactive_path_grob(...)
interactive_path_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on pointsGrob()
.
See the documentation for that function for more details.
interactive_points_grob(...)
interactive_points_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on polygonGrob()
.
See the documentation for that function for more details.
interactive_polygon_grob(...)
interactive_polygon_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
These grobs are based on polylineGrob()
and linesGrob()
.
See the documentation for those functions for more details.
interactive_polyline_grob(...) interactive_lines_grob(...)
interactive_polyline_grob(...) interactive_lines_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on rasterGrob()
.
See the documentation for that function for more details.
interactive_raster_grob(...)
interactive_raster_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
interactive_parameters, girafe()
The grob is based on rectGrob()
.
See the documentation for that function for more details.
interactive_rect_grob(...)
interactive_rect_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on roundrectGrob()
.
See the documentation for that function for more details.
interactive_roundrect_grob(...)
interactive_roundrect_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on segmentsGrob. See the documentation for that function for more details.
interactive_segments_grob(...)
interactive_segments_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
The grob is based on textGrob. See the documentation for that function for more details.
interactive_text_grob(...)
interactive_text_grob(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive grob object.
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.
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.
label_interactive(label, ...)
label_interactive(label, ...)
label |
The text for the label (scalar character) |
... |
any of the interactive_parameters. |
an interactive label object
interactive_parameters, labeller_interactive()
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)
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)
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.
labeller_interactive(.mapping = NULL, ...)
labeller_interactive(.mapping = NULL, ...)
.mapping |
set of aesthetic mappings created by |
... |
arguments passed to base function |
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())
ggplot2::labeller()
, label_interactive()
, ggplot2::labellers
# 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)
# 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)
match_family()
returns the best font family match.
match_family(font = "sans", bold = TRUE, italic = TRUE, debug = NULL)
match_family(font = "sans", bold = TRUE, italic = TRUE, debug = NULL)
font |
family or face to match. |
bold |
Wheter to match a font featuring a |
italic |
Wheter to match a font featuring an |
debug |
deprecated |
Other functions for font management:
font_family_exists()
,
validated_fonts()
match_family("sans") match_family("serif")
match_family("sans") match_family("serif")
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).
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)
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)
css |
css to associate with elements when they are hovered.
It must be a scalar character. It can also be constructed with
|
reactive |
if TRUE, in Shiny context, hovering will set Shiny input values. |
nearest_distance |
a scalar positive number defining the maximum distance to use
when using the |
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.
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()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_hover(css = "fill:wheat;stroke:orange;r:5pt;") ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
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).
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) )
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) )
css |
css to associate with elements when they are selected.
It must be a scalar character. It can also be constructed with
|
type |
selection mode ("single", "multiple", "none") when widget is in a Shiny application. |
only_shiny |
disable selections if not in a shiny context. |
selected |
character vector, id to be selected when the graph will be initialized. |
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.
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()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_selection(type = "multiple", only_shiny = FALSE, css = "fill:red;stroke:gray;r:5pt;") ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
Allows customization of the svg style sizing
opts_sizing(rescale = TRUE, width = 1)
opts_sizing(rescale = TRUE, width = 1)
rescale |
If FALSE, graphic will not be resized and the dimensions are exactly those of the svg. If TRUE the graphic will be resize to fit its container |
width |
widget width ratio (0 < width <= 1). |
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_toolbar()
,
opts_tooltip()
,
opts_zoom()
,
set_girafe_defaults()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_sizing(rescale = FALSE) ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
Allows customization of the toolbar
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 )
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 )
position |
Position of the toolbar relative to the plot. One of 'top', 'bottom', 'topleft', 'topright', 'bottomleft', 'bottomright' |
saveaspng |
Show (TRUE) or hide (FALSE) the 'download png' button. |
pngname |
The default basename (without .png extension) to use for the png file. |
tooltips |
A named list with tooltip labels for the buttons, for adapting to other language. Passing NULL will use the default tooltips: list( lasso_select = 'lasso selection', lasso_deselect = 'lasso deselection', zoom_on = 'activate pan/zoom', zoom_off = 'deactivate pan/zoom', zoom_rect = 'zoom with rectangle', zoom_reset = 'reset pan/zoom', saveaspng = 'download png' ) |
A character vector with the names of the buttons or button groups to be hidden from the toolbar. Valid button groups: selection, zoom, misc Valid button names: lasso_select, lasso_deselect, zoom_onoff, zoom_rect, zoom_reset, saveaspng |
|
fixed |
if FALSE (default), the toolbar will float above the graphic, if TRUE, the toolbar will be fixed and always visible. |
delay_mouseover |
The duration in milliseconds of the transition associated with toolbar display. |
delay_mouseout |
The duration in milliseconds of the transition associated with toolbar end of display. |
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).
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_tooltip()
,
opts_zoom()
,
set_girafe_defaults()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_toolbar(position = "top") ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
Settings to be used with girafe()
for tooltip customisation.
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 )
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 )
css |
extra css (added to |
offx , offy
|
tooltip x and y offset |
use_cursor_pos |
should the cursor position be used to position tooltip (in addition to offx and offy). Setting to TRUE will have no effect in the RStudio browser windows. |
opacity |
tooltip background opacity |
use_fill , use_stroke
|
logical, use fill and stroke properties to color tooltip. |
delay_mouseover |
The duration in milliseconds of the transition associated with tooltip display. |
delay_mouseout |
The duration in milliseconds of the transition associated with tooltip end of display. |
placement |
Defines the container used for the tooltip element. It can be one of "auto" (default), "doc" or "container".
|
zindex |
tooltip css z-index, default to 999. |
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_toolbar()
,
opts_zoom()
,
set_girafe_defaults()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_tooltip(opacity = .7, offx = 20, offy = -10, use_fill = TRUE, use_stroke = TRUE, delay_mouseout = 1000) ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
Allows customization of the zoom.
opts_zoom(min = 1, max = 1, duration = 300)
opts_zoom(min = 1, max = 1, duration = 300)
min |
minimum zoom factor |
max |
maximum zoom factor |
duration |
duration of the zoom transitions, in milliseconds |
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_toolbar()
,
opts_tooltip()
,
set_girafe_defaults()
library(ggplot2) dataset <- mtcars dataset$carname = row.names(mtcars) gg <- ggplot( data = dataset, mapping = aes(x = wt, y = qsec, color = disp, tooltip = carname, data_id = carname) ) + geom_point_interactive() + theme_minimal() x <- girafe(ggobj = gg) x <- girafe_options(x, opts_zoom(min = .7, max = 2) ) if( interactive() ) print(x)
library(ggplot2) dataset <- mtcars dataset$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)
Makes a reactive version of girafe object for use in Shiny.
renderGirafe(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())
renderGirafe(expr, env = parent.frame(), quoted = FALSE, outputArgs = list())
expr |
An expression that returns a |
env |
The environment in which to evaluate expr. |
quoted |
Is |
outputArgs |
A list of arguments to be passed through to the implicit call to |
Run shiny examples and see corresponding code
run_girafe_example(name = "crimes")
run_girafe_example(name = "crimes")
name |
an application name, one of cars, click_scale, crimes, DT, dynamic_ui, iris, maps and modal. |
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
# 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)
# 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)
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.
scale_linetype_interactive(...) scale_linetype_continuous_interactive(...) scale_linetype_discrete_interactive(...) scale_linetype_binned_interactive(...)
scale_linetype_interactive(...) scale_linetype_continuous_interactive(...) scale_linetype_discrete_interactive(...) scale_linetype_binned_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
# 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)
# 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)
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.
scale_shape_interactive(...) scale_shape_continuous_interactive(...) scale_shape_discrete_interactive(...) scale_shape_binned_interactive(...) scale_shape_ordinal_interactive(...)
scale_shape_interactive(...) scale_shape_continuous_interactive(...) scale_shape_discrete_interactive(...) scale_shape_binned_interactive(...) scale_shape_ordinal_interactive(...)
... |
arguments passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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
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.
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(...)
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 passed to base function, plus any of the interactive_parameters. |
An interactive scale object.
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.
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()
# 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)
# 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)
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.
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 )
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 )
fonts |
default values for |
opts_sizing |
default values for |
opts_tooltip |
default values for |
opts_hover |
default values for |
opts_hover_key |
default values for |
opts_hover_inv |
default values for |
opts_hover_theme |
default values for |
opts_selection |
default values for |
opts_selection_inv |
default values for |
opts_selection_key |
default values for |
opts_selection_theme |
default values for |
opts_zoom |
default values for |
opts_toolbar |
default values for |
Other girafe animation options:
girafe_defaults()
,
girafe_options()
,
init_girafe_defaults()
,
opts_hover()
,
opts_selection()
,
opts_sizing()
,
opts_toolbar()
,
opts_tooltip()
,
opts_zoom()
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()
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()
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".
validated_fonts(fonts = list())
validated_fonts(fonts = list())
fonts |
Named list of font names to be aliased with
fonts installed on your system. If unspecified, the R default
families "sans", "serif", "mono" and "symbol"
are aliased to the family returned by If fonts are available, the default mapping will use these values:
|
a named list of validated font family names
Other functions for font management:
font_family_exists()
,
match_family()
validated_fonts()
validated_fonts()