pacman::p_load(ggiraph, plotly,
patchwork, DT, tidyverse) Hands-on Exercise 3a: Programming Interactive Data Visualisation with R
3 Programming Interactive Data Visualisation with R
3.1 Learning Outcome
This hands-on exercise demonstrates how to create interactive data visualisation by using functions provided by ggiraph and plotlyr packages.
3.2 Getting Started
The code chunk below install and launch the following R packages:
ggiraph for making ‘ggplot’ graphics interactive.
plotly, R library for plotting interactive statistical graphs.
DT provides an R interface to the JavaScript library DataTables that create interactive table on html page.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.
patchwork for combining multiple ggplot2 graphs into one figure.
3.3 Importing Data
The code chunk below import Exam_data.csv data file into R and save it as an tibble data frame called exam_data:
exam_data <- read_csv("data/Exam_data.csv")3.4 Interactive Data Visualisation - ggiraph methods
ggiraph is an htmlwidget and a ggplot2 extension. It allows ggplot graphics to be interactive.
Interactive is made with ggplot geometries that can understand three arguments:
Tooltip: a column of data-sets that contain tooltips to be displayed when the mouse is over elements.
Onclick: a column of data-sets that contain a JavaScript function to be executed when elements are clicked.
Data_id: a column of data-sets that contain an id to be associated with elements.
If it used within a shiny application, elements associated with an id (data_id) can be selected and manipulated on client and server sides. Refer to this article for more detail explanation.
3.4.1 Tooltip effect with tooltip aesthetic
Following code chunk plots an interactive statistical graph by using ggiraph package. The code chunk consists of two parts. First, an ggplot object will be created. Next, girafe() of ggiraph will be used to create an interactive svg object. By hovering the mouse pointer on an data point of interest, the student’s ID will be displayed.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)3.4.2 Displaying multiple information on tooltip
Following code chunk demonstrates customizing tooltip contents by including a list of objects, by hovering the mouse pointer on an data point of interest, the student’s ID and Class will be displayed.
exam_data$tooltip <- c(paste0(
"Name = ", exam_data$ID,
"\n Class = ", exam_data$CLASS))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = exam_data$tooltip),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 8,
height_svg = 8*0.618
)3.4.3 Customising Tooltip style
Code chunk below uses opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations, the background color of the tooltip is changed to white and font color changed to black.
tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list( #<<
opts_tooltip( #<<
css = tooltip_css)) #<<
) - Refer to Customizing girafe objects to learn more about how to customise ggiraph objects.
3.4.5 Displaying statistics on tooltip
Code chunk below demonstrates adding a function to compute 90% confident interval of the mean. The derived statistics are then displayed in the tooltip when mouse hover above each bar.
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, linewidth = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)3.4.6 Hover effect with data_id aesthetic
Code chunk below adds the second interactive element (data_id = CLASS), when mouse hovering over or clicking a dot, all other dots belonging to the same CLASS will be highlighted.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill:red;stroke:black;cursor:pointer;")
)
) Note that the default value of the hover css is hover_css = “fill:orange;”.
3.4.7 Styling hover effect
In the code chunk below, css codes are used to change the highlighting effect.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 3.4.8 Combining tooltip and hover effect
Following code chunk combines tooltip and hover effect on the interactive statistical graph, student with same data_id (CLASS) will be highlighted upon mouse over. At the same time, the tooltip will show the CLASS.
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) 3.4.9 Click effect with onclick
onclick argument of ggiraph provides hotlink interactivity on the web, as demonstrated by the following code chunk, web document link with a data object will be displayed on the web browser upon mouse click.
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618) WARNING: Note that click actions must be a string column in the dataset containing valid javascript instructions.
3.4.10 Coordinated Multiple Views with ggiraph
Coordinated multiple views methods has been implemented in the data visualisation below.
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 + p2),
width_svg = 6,
height_svg = 3,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
) When a data point of one of the dotplot is selected, the corresponding data point ID on the second data visualisation will be highlighted too.
In order to build a coordinated multiple views as shown in the example above, the following programming strategy will be used:
Appropriate interactive functions of ggiraph will be used to create the multiple views.
patchwork function of patchwork package will be used inside girafe function to create the interactive coordinated multiple views.
The data_id aesthetic is critical to link observations between plots and the tooltip aesthetic is optional but nice to have when mouse over a point.
3.5 Interactive Data Visualisation - plotly methods!
Plotly’s R graphing library create interactive web graphics from ggplot2 graphs and/or a custom interface to the (MIT-licensed) JavaScript library plotly.js inspired by the grammar of graphics. Different from other plotly platform, plot.R is free and open source.
There are two ways to create interactive graph by using plotly, they are:
by using plot_ly(), and
by using ggplotly()
3.5.1 Creating an interactive scatter plot: plot_ly() method
The tabset below shows an example a basic interactive plot created by using plot_ly().
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)3.5.2 Working with visual variable: plot_ly() method
The code chunk below maps color argument to a qualitative visual variable (i.e. RACE).
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)3.5.3 Creating an interactive scatter plot: ggplotly() method
The code chunk below plots an interactive scatter plot by using ggplotly().
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)3.5.4 Coordinated Multiple Views with plotly
The creation of a coordinated linked plot by using plotly involves three steps:
highlight_key()of plotly package is used as shared data.two scatterplots will be created by using ggplot2 functions.
lastly, subplot() of plotly package is used to place them next to each other side-by-side.
d <- highlight_key(exam_data)
p1 <- ggplot(data=d,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=d,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))Key knowledge:
highlight_key()simply creates an object of class crosstalk::SharedData.Visit this link to learn more about crosstalk
3.6 Interactive Data Visualisation - crosstalk methods!
Crosstalk is an add-on to the htmlwidgets package. It extends htmlwidgets with a set of classes, functions, and conventions for implementing cross-widget interactions (currently, linked brushing and filtering).
3.6.1 Interactive Data Table: DT package
A wrapper of the JavaScript Library DataTables
Data objects in R can be rendered as HTML tables using the JavaScript library ‘DataTables’ (typically via R Markdown or Shiny).
DT::datatable(exam_data, class= "compact")3.6.2 Linked brushing: crosstalk method
Code chunk below is used to implement the coordinated brushing shown above.
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5) Things to learn from the code chunk:
highlight() is a function of plotly package. It sets a variety of options for brushing (i.e., highlighting) multiple plots.
bscols() is a helper function of crosstalk package. It makes it easy to put HTML elements side by side. It can be called directly from the console but is especially designed to work in an R Markdown document. Warning: This will bring in all of Bootstrap!.
3.7 Reference
3.7.1 ggiraph
This link provides online version of the reference guide and several useful articles. Use this link to download the pdf version of the reference guide.
This link provides code example on how ggiraph is used to interactive graphs for Swiss Olympians - the solo specialists.
3.7.2 plotly for R
A collection of plotly R graphs are available via this link.
Carson Sievert (2020) Interactive web-based data visualization with R, plotly, and shiny, Chapman and Hall/CRC is the best resource to learn plotly for R. The online version is available via this link
Plotly R Figure Reference provides a comprehensive discussion of each visual representations.
Plotly R Library Fundamentals is a good place to learn the fundamental features of Plotly’s R API.
Visit this link for a very interesting implementation of gganimate by your senior.