3 Programming Interactive Data Visualisation with R
3.1 Purpose
To learn how to create interactive data visualisation by using functions provided by ggiraph and plotlyr packages.
After following this page, you can create this cool chart by yourself!
3.2 Getting Started
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.
pacman::p_load(ggiraph, plotly,
patchwork, DT, tidyverse) 3.3 Importing Data
Using read_csv() of readr package, import Exam_data.csv into R.
The code chunk below read_csv() of readr package is used to 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")head(exam_data, n = 10)# A tibble: 10 × 7
ID CLASS GENDER RACE ENGLISH MATHS SCIENCE
<chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Student321 3I Male Malay 21 9 15
2 Student305 3I Female Malay 24 22 16
3 Student289 3H Male Chinese 26 16 16
4 Student227 3F Male Chinese 27 77 31
5 Student318 3I Male Malay 27 11 25
6 Student306 3I Female Malay 31 16 16
7 Student313 3I Male Chinese 31 21 25
8 Student316 3I Male Malay 31 18 27
9 Student312 3I Male Malay 33 19 15
10 Student297 3H Male Indian 34 49 373.4 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.
3.4.1 Tooltip effect with tooltip aesthetic
Below shows a typical code chunk to plot 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
)3.5 Interactivity
3.5.1 Displaying multiple information on tooltip
The content of the tooltip can be customised by including a list object.
The first three lines of codes in the code chunk create a new field called tooltip. At the same time, it populates text in ID and CLASS fields into the newly created field.
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
)3.5.2 Customising Tooltip style
Use opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations.
Notice that the background colour of the tooltip is pink (#d8118c), color of hovered point turns to yellow.
tooltip_css <-
"background-color:#d8118c;
color:white;
font-style:bold;
padding:5px;
border-radius:3px;" #<<
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,
data_id = 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),
opts_hover(css = "fill:yellow;stroke:orange;")
) #<<
) - Refer to Customizing girafe objects to learn more about how to customise ggiraph objects.
3.5.3 Displaying statistics on tooltip
Code chunk below shows an advanced way to customise tooltip. In this example, a function is used to compute 90% confident interval of the mean. The derived statistics are then displayed in the tooltip.
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, size = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)3.5.4 Hover effect with data_id aesthetic
Code chunk below shows the second interactive feature of ggiraph, namely data_id.
Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.
When we use data_id in geom_dotplot_interactive(),ggiraph automatically applies the default value of the hover css which is is hover_css = “fill:orange;”.
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
) 3.5.5 Combining tooltip and hover effect
we can combine both tooltip and hover effect on the interactive statistical graph as shown in the code chunk below.
Interactivity: Elements associated with a data_id (i.e 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;")
)
) 3.5.6 Click effect with onclick
onclick argument of ggiraph provides hotlink interactivity on the web.
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) 3.5.7 Coordinated Multiple Views with ggiraph
Notice that 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.
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;")
)
) 3.6 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()
3.6.1 Creating an interactive scatter plot: plot_ly() method
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)3.6.2 Working with visual variable: plot_ly() method
TipInteractive
Click on the colour symbol at the legend.
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)3.6.3 Creating an interactive scatter plot: ggplotly() method
Notice that the only extra line we need to include in the code chunk is 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)3.6.4 Coordinated Multiple Views with
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))3.7 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).
Steps:
- 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).
- Linked brushing: crosstalk method
DT::datatable(exam_data, class= "compact")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,
options = list(
pageLength = 5, # Show 5 rows
dom = 'ftp', # Simplified controls: filter, table, pagination
scrollY = "300px", # Fixed height with scroll
scrollCollapse = TRUE
),
class = 'compact'), # Compact styling
widths = c(6, 6)
)