Hands-on Exercise 03a - Programming Interactive Data Visualisation with R

Author

Ong Chae Hui

1. Getting Started

1.1. Install and launch R packages

In this exercise, beside tidyverse, four R packages will be used. They are:

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.
patchwork for combining multiple ggplot2 graphs into one figure.
tidyverse, a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs.

Code

pacman::p_load(ggiraph, plotly, DT, patchwork, tidyverse)

2. Importing Data

In this section, Exam_data.csv provided will be used. Using read_csv() of readr package, import Exam_data.csv into R.

Code

exam_data <- read_csv("data/Exam_data.csv")

3. 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 tool tips 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.

3.1. Tooltip effect with tooltip aesthetic

Below shows a typical code chunk to plot an interactive statistical graph by using ggiraph package.

Notice that 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.

Code

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
)

Notice that two steps are involved.

First, an interactive version of ggplot2 geom (i.e. geom_dotplot_interactive()) will be used to create the basic graph.
Then, girafe() will be used to generate an svg object to be displayed on an html page.

NOTE : By hovering the mouse pointer on an data point of interest, the student’s ID will be displayed.

3.2. Displaying multiple information on tooltip

We can also customise the content of the tooltip by including a list object as shown in the code chunk below.

Code

# 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)) 

# Next, this newly created field is used as tooltip field.
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
)

NOTE : By hovering the mouse pointer on an data point of interest, the student’s ID and Class will be displayed.

3.3. Customising Tooltip Style

Example below uses opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations.

Code

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)) #<<
)

Notice that the background colour of the tooltip is black and the font colour is white and bold.

Refer to Customizing girafe objects to learn more about how to customise ggiraph objects.

3.4. Displaying statistics on 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. Code chunk below shows an advanced way to customise tooltip.

Code

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)

3.5. Hover effect with data_id aesthetic

Code chunk below shows the second interactive feature of ggiraph, namely data_id.

Code

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                      
)

NOTE :

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.
Note that the default value of the hover css is hover_css = “fill:orange;”.

3.6. Styling hover effect

In the example below, css codes are used to change the highlighting effect.

Code

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;") 
  )                                        
)

NOTE:

Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.
Different from previous example, in this example the ccs customisation request are encoded directly.

3.7. Combining tooltip and hover effect

There are times that we want to combine tooltip and hover effect on the interactive statistical graph as shown in the code chunk below.

Code

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;") 
  )                                        
)

NOTE : 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.

3.8. Click effect with onclick

An example of onclick argument of ggiraph provides hotlink interactivity on the web.

Code

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)

NOTE :

Web document link with a data object will be displayed on the web browser upon mouse click.
WARNING Note that click actions must be a string column in the dataset containing valid javascript instructions.

3.9. Coordinated Multiple Views with ggiraph

Example below shows coordinated multiple views methods has been implemented in the data visualisation below.

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.

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.

Code

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;")
       )
      )

4. 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()

4.1. Creating an interactive scatter plot: `plot_ly()` method

The example below shows a basic interactive plot created by using plot_ly().

Code

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

4.2. Working with visual variable: `plot_ly()` method

The example below uses the color argument to map to a qualitative visual variable (i.e. RACE).

NOTE : Click on the colour symbol at the legend for filtering of the data by respective RACE.

Code

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE,
        type="scatter")

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

4.3. Creating an interactive scatter plot: `ggplotly()` method

The example below plots an interactive scatter plot by using ggplotly().

NOTE : Notice that the only extra line you need to include in the code chunk is ggplotly().

Code

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)

4.4. 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.

NOTE : Click on a data point of one of the scatterplot and see how the corresponding point on the other scatterplot is selected.

Code

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))

Thing to learn from the code chunk:

highlight_key() simply creates an object of class crosstalk::SharedData.
Visit this link to learn more about crosstalk,

5. 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).

5.1. 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).

Code

DT::datatable(exam_data, class= "compact")

5.2. Linked brushing: crosstalk method

Example below is used to implement the coordinated brushing shown above.

Code

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)

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

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. These options are primarily designed for linking multiple plotly graphs, and may not behave as expected when linking plotly to another htmlwidget package via crosstalk. In some cases, other htmlwidgets will respect these options, such as persistent selection in leaflet.
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!

1. Getting Started

1.1. Install and launch R packages

2. Importing Data

3. Interactive Data Visualisation - ggiraph methods

3.1. Tooltip effect with tooltip aesthetic

3.2. Displaying multiple information on tooltip

3.3. Customising Tooltip Style

3.4. Displaying statistics on tooltip

3.5. Hover effect with data_id aesthetic

3.6. Styling hover effect

3.7. Combining tooltip and hover effect

3.8. Click effect with onclick

3.9. Coordinated Multiple Views with ggiraph

4. Interactive Data Visualisation - plotly methods!

4.1. Creating an interactive scatter plot: plot_ly() method

4.2. Working with visual variable: plot_ly() method

4.3. Creating an interactive scatter plot: ggplotly() method

4.4. Coordinated Multiple Views with plotly

5. Interactive Data Visualisation - crosstalk methods!

5.1. Interactive Data Table: DT package

5.2. Linked brushing: crosstalk method

4.1. Creating an interactive scatter plot: `plot_ly()` method

4.2. Working with visual variable: `plot_ly()` method

4.3. Creating an interactive scatter plot: `ggplotly()` method