Static pie-chart glyphs can only show information about the relative
proportions of the different categories. Sometimes, information about
the raw counts of categories could also be important. The geom_pie_interactive
function can be used in conjunction with the ggiraph
framework to create interactive visualisations where hovering over a
pie-glyph shows additional information about the categories shown.
All the interactive parameters from ggiraph
are supported and the plots can be fully customised. A few useful
examples are shown here. See ggiraph book for all
available options.
set.seed(737)
plot_data <- data.frame(response = rnorm(15, 100, 30),
system = 1:15,
group = sample(size = 15, x = c('G1', 'G2', 'G3'), replace = T),
A = round(runif(15, 3, 9), 2),
B = round(runif(15, 1, 5), 2),
C = round(runif(15, 3, 7), 2),
D = round(runif(15, 1, 9), 2))The data has 15 observations and seven columns. response
is a continuous variable measuring system output while
system describes the 15 individual systems of interest.
Each system is placed in one of three groups shown in
group. Columns A, B,
C, and D measure system attributes.
Creating interactive pie-chart glyphs is similar to creating their
static counterparts, we just use geom_pie_interactive
instead of geom_pie_glyph
and wrap the ggplot object in the girafe()
function.
# Same ggplot call as the static version
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# geom_pie_interactive instead of geom_pie_glyph
geom_pie_interactive(slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16)
# Pass the ggplot object to the girafe function to create the interactive plot
girafe(ggobj = gg_obj)By default, hovering over a specific pie-glyph would present a tooltip containing information about the raw values and percentages of the different categories shown in that pie-glyph.
The tooltip
aesthetic can be modified to show a custom tooltip when hovering over a
pie-glyph. This could either be set to a column in the data, a
particular character string or a combination of two.
We create a plot with the tooltip showing the group
variable in the data.
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# specify tooltip aesthetic
# wrapped in paste0 to add more descriptive text to the tooltip
geom_pie_interactive(aes(tooltip = paste0("Group: ", group)),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16)
# Pass the ggplot object to the girafe function to create the interactive plot
girafe(ggobj = gg_obj)Hovering over a pie-glyph will now show the group
that observation belongs to.
The onclick
aesthetic can be configured to execute javascript code when a pie-glyph
is clicked. This could be useful in a shiny application for populating a
container with information or for running javascript code like opening a
webpage or making an API call based on the observations selected by the
user in a plot. We show a basic example that opens up an alert box
showing the group variable of the observation clicked by the user.
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# specify onclick aesthetic
geom_pie_interactive(aes(
onclick = "alert(\"This observation belongs to group \" +
this.getAttribute(\"data-id\"))"),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16)
# Pass the ggplot object to the girafe function to create the interactive plot
girafe(ggobj = gg_obj)Click on any pie-glyph to show a dialog box containing
information about the group of the observation.
If the data has a grouping variable, the visualisation can be
configured to highlight all the pie-glyphs for observations belonging to
the same group as that of the hovered pie-glyph. This is accomplished
using the data_id
aesthetic.
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# specify data_id aesthetic
geom_pie_interactive(aes(data_id = group),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16)
# Pass the ggplot object to the girafe function to create the interactive plot
girafe(ggobj = gg_obj)Hovering over a pie-glyph would now highlight it in addition to showing the tooltip.
The options argument in the girafe()
function can be used to customise the aesthetic properties of the
tooltip (using opts_tooltip()),
hover animation(using opts_hover()
and opts_hover_inv())
and general visual aesthetics (using opts_sizing(),
opts_toolbar(),
opts_zoom(),
etc.) of the plot using CSS. We show an example modifying a few of these
properties, refer to Customising
girafe animations in the ggiraph book for more information.
# Reuse the previous gg_obj
girafe(ggobj = gg_obj,
options = list(
# Options for formatting the appearance of the tooltip
# The tooltip will have a blue background with white text
opts_tooltip = opts_tooltip(css = "background-color:blue;color:white;padding:5px"),
# Options for changing animation of observations which are hovered
# Hovered pie-glyph will be highlighted yellow with dark grey border
opts_hover(css = "fill:yellow;stroke:darkgrey;"),
# Options for changing properties of the pie-glyphs not selected
# Make non-selected pie-glyphs fade into background
opts_hover_inv(css = "opacity:0.25;"),
# Options for panning and zooming
# Set max to a number greater than 1 and tooltip will appear on
# top right giving options for panning and zooming across the plot
opts_zoom(max = 3)
))Hover over a pie-glyph to show the customised tooltip. The highlighted pie-glyph would have a different appearance while those not selected would fade into the background. The menu on the top right can also be used to pan and zoom across the plot.
The opts_selection()
function can be used to configure properties for selecting observations
by dragging the mouse across the plot. The type = "single"
allows for selecting a single value while type="multiple"
enables lasso selection for selecting multiple values. A toolbar appear
on top-right with options for selecting and deselecting
observations.
Note: It is important to specify the data_id
aesthetic (ideally a unique identifier for each observation) for
performing selection.
# Add a unique identifier for each observertion
plot_data$id <- 1:nrow(plot_data)
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# specify data_id as a unique identifier for each observation
geom_pie_interactive(aes(data_id = id),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16)
girafe(ggobj = gg_obj,
options = list(
# Options for selecting observations
# only_shiny = FALSE is needed to see selections in page
opts_selection(type = "multiple",
only_shiny = FALSE),
# Panning and zooming
opts_zoom(max = 3)
))Use the menu from the top right menu to perform lasso selection/deslection to select/deselect observations in the plot and zoom and pan across the plot.
It is also possible to arrange multiple plots beside each other and
perform selection between plots. The two plots can be arranged using cowplot or patchwork and if they
share the same data_id attribute, hovering over an
observation in one plot would highlight that observation in the other
plot.
# Lets first add two more variables to our data
set.seed(373)
plot_data <- plot_data %>%
mutate(response2 = round(rnorm(15, 100, 30), 2),
system2 = round(runif(15, 5, 20), 2))
gg_obj1 <- ggplot(data = plot_data, aes(x = system, y = response)) +
# specify data_id as a unique identifier for each observation
geom_pie_interactive(aes(data_id = id),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
theme_classic(base_size = 16) +
theme(legend.position = "top")
gg_obj2 <- ggplot(data = plot_data, aes(x = system2, y = response2)) +
# specify data_id as a unique identifier for each observation
geom_pie_interactive(aes(data_id = id),
slices = c("A", "B", "C", "D"),
colour = "black") +
scale_fill_brewer(palette = "Dark2") +
labs(x = "Another system", y = "Another response") +
theme_classic(base_size = 16) +
theme(legend.position = "top")
girafe(ggobj = cowplot::plot_grid(gg_obj1, gg_obj2,
nrow = 1),
options = list(
# Options for selecting observations
# only_shiny = FALSE is needed to see selections in page
opts_selection(type = "multiple",
only_shiny = FALSE)
))Hover over a pie-glyph in any plot to see the corresponding pie-glyph in the other plot. Use the lasso selection option from the top-right menu to select multiple observations between the plots.
ggiraphgeom_pie_interactive can be combined with other elements
of ggiraph like geoms, scales, facets, etc. to convert
every component of a plot interactive. In this example, every element of
the plot including the pie-glyphs, facet labels, plot title, axis title
and text, legend title and keys are interactive and they can be hovered
over or clicked on to present additional information or execute
javascript code in the background.
gg_obj <- ggplot(data = plot_data, aes(x = system, y = response)) +
# Simple theme for plot
theme_classic(base_size = 16) +
# Title for plot
labs(title = "This is a plot with each component being interactive") +
# Interactive pie-glyphs
geom_pie_interactive(aes(data_id = id),
slices = c("A", "B", "C", "D"),
colour = "black") +
# Interactive version of facet_wrap.
# Use labeller_interactive for the interactive strips
facet_wrap_interactive(vars(group), nrow = 1,
labeller = labeller_interactive(
aes(tooltip = paste0("Group: ", group),
data_id = group)
)
) +
# Interactive version of scale_fill_manual
# Specify he tooltip and data_id attributes for the legend keys
# Use labeller_interactive for making legend title interactive
scale_fill_manual_interactive(values = c("#1B9E77", "#D95F02",
"#7570B3", "#E7298A"),
name = label_interactive(
"Attributes",
tooltip = "Interactive legend title",
data_id = "legend.title",
onclick = "alert(\"You clicked the legend title.\")"
),
tooltip = function(x) paste0("Attribute: ", x),
data_id = function(x) x) +
# Interactive elements in the theme
# Use element_text_interactive for the axis title, axis text and plot title
theme(legend.position = "top",
axis.title = element_text_interactive(
data_id = "axis.title",
tooltip = "Axis title",
onclick = "alert(\"You clicked the axis title.\")",
hover_css = "fill:red;stroke:none;"
),
axis.text = element_text_interactive(
data_id = "axis.text",
tooltip = "Axis text",
onclick = "alert(\"You clicked the axis text\")",
hover_css = "fill:red;stroke:none;"
),
plot.title = element_text_interactive(
data_id = "plot.title",
tooltip = "Plot title",
onclick = "alert(\"You clicked the plot title\")",
hover_css = "fill:blue;stroke:black;"
))
girafe(ggobj = gg_obj)Hover and click over any element in the plot to highlight it and open a dialog box showing additional information.
ggiraph is a very powerful package and the examples
presented here in this vignette cover a small subset of features offered
by ggiraph. Please see the ggiraph book to learn
more about the wonderful plots that can be created using
ggiraph.