3.1 Learning Outcome

In this hands-on exercise, you will learn how to create interactive data visualisation by using functions provided by ggiraph and plotlyr packages.

3.2 Getting Started

Check, 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.

Code

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

3.3 Importing Data

In this section, Exam_data.csv provided will be used. 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.

Code

exam_data <- read_csv("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

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.

3.5 Interactivity

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

3.5.1 Displaying multiple information on tooltip

The content of the tooltip can be customised by including a list object as shown in the code chunk below.

Code

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
)

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. Next, this newly created field is used as tooltip field as shown in the code of line 7.

3.6 Interactivity

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

3.6.1 Customising Tooltip style

Code chunk 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.

3.6.2 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.

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, size = 0.2
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

3.6.3 Hover effect with data_id aesthetic

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

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, size = 0.2
  )

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

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;”.

3.6.4 Styling hover effect

In the code chunk 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;") 
  )                                        
)

Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over.

Note: Different from previous example, in this example the ccs customisation request are encoded directly.

3.6.5 Combining tooltip and hover effect

There are time 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;") 
  )                                        
)

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.

3.6.6 Click effect with onclick

onclick argument of ggiraph provides hotlink interactivity on the web.

The code chunk below shown an example of onclick.

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)

Interactivity: Web document link with a data object will be displayed on the web browser upon mouse click.

Note that click actions must be a string column in the dataset containing valid javascript instructions.

3.6.7 Coordinated Multiple Views with ggiraph

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.

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;")
         )
       )

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.

3.7 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()

3.7.1 Creating an interactive scatter plot: plot_ly() method

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

Code

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

3.7.2 Working with visual variable: plot_ly() method

In the code chunk below, color argument is mapped to a qualitative visual variable (i.e. RACE).

Code

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

3.7.3 Creating an interactive scatter plot: ggplotly() method

The code chunk below plots an interactive scatter plot by using 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)

3.7.4 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.

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.

3.8 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).

3.8.1 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")

3.8.2 Linked brushing: crosstalk method

Code chunk 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)

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!.

--- title: "Hands On Exercise 3A" subtitle: "Programming Interactive Data Visualisation with R" format: html date: 04/25/2025 date-format: long date-modified: last-modified editor: visual execute: eval: true echo: true warning: false freeze: true --- # **3.1 Learning Outcome** In this hands-on exercise, you will learn how to create interactive data visualisation by using functions provided by **ggiraph** and **plotlyr** packages. # 3.2 Getting Started Check, install and launch the following R packages. - [**ggiraph**](https://davidgohel.github.io/ggiraph/) for making ‘ggplot’ graphics interactive. - [**plotly**](https://plotly.com/r/), R library for plotting interactive statistical graphs. - [**DT**](https://rstudio.github.io/DT/) provides an R interface to the JavaScript library [DataTables](https://datatables.net/) that create interactive table on html page. - [**tidyverse**](https://www.tidyverse.org/), a family of modern R packages specially designed to support data science, analysis and communication task including creating static statistical graphs. - [**patchwork**](https://patchwork.data-imaginist.com/) for combining multiple ggplot2 graphs into one figure. ```{r} pacman::p_load(ggiraph, plotly, patchwork, DT, tidyverse) ``` # 3.3 Importing Data In this section, *Exam_data.csv* provided will be used. Using [*read_csv()*](https://readr.tidyverse.org/reference/read_delim.html) 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`. ```{r} exam_data <- read_csv("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 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. ```{r} 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. # 3.5 Interactivity By hovering the mouse pointer on an data point of interest, the student’s ID will be displayed. ## 3.5.1 Displaying multiple information on tooltip The content of the tooltip can be customised by including a list object as shown in the code chunk below. ```{r} 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 ) ``` 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. Next, this newly created field is used as tooltip field as shown in the code of line 7. # 3.6 Interactivity By hovering the mouse pointer on an data point of interest, the student’s ID and Class will be displayed. ## 3.6.1 Customising Tooltip style Code chunk below uses opts_tooltip() of ggiraph to customize tooltip rendering by add css declarations. ```{r} 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. ## 3.6.2 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. ```{r} 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) ``` ## 3.6.3 Hover effect with data_id aesthetic Code chunk below shows the second interactive feature of ggiraph, namely data_id. ```{r} 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) ``` 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;”. 3.6.4 Styling hover effect In the code chunk below, css codes are used to change the highlighting effect. ```{r} 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;") ) ) ``` Interactivity: Elements associated with a data_id (i.e CLASS) will be highlighted upon mouse over. Note: Different from previous example, in this example the ccs customisation request are encoded directly. 3.6.5 Combining tooltip and hover effect There are time that we want to combine tooltip and hover effect on the interactive statistical graph as shown in the code chunk below. ```{r} 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;") ) ) ``` 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. ## 3.6.6 Click effect with onclick onclick argument of ggiraph provides hotlink interactivity on the web. The code chunk below shown an example of onclick. ```{r} 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) ``` Interactivity: Web document link with a data object will be displayed on the web browser upon mouse click. Note that click actions must be a string column in the dataset containing valid javascript instructions. ## 3.6.7 Coordinated Multiple Views with ggiraph 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. ```{r} 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;") ) ) ``` 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. # 3.7 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() ## 3.7.1 Creating an interactive scatter plot: plot_ly() method The tabset below shows an example a basic interactive plot created by using plot_ly(). ```{r} plot_ly(data = exam_data, x = ~MATHS, y = ~ENGLISH) ``` ## 3.7.2 Working with visual variable: plot_ly() method In the code chunk below, color argument is mapped to a qualitative visual variable (i.e. RACE). ```{r} plot_ly(data = exam_data, x = ~ENGLISH, y = ~MATHS, color = ~RACE) ``` ## 3.7.3 Creating an interactive scatter plot: ggplotly() method The code chunk below plots an interactive scatter plot by using ggplotly(). ```{r} 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) ``` ## 3.7.4 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. ```{r} 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. # 3.8 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). ## 3.8.1 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). ```{r} DT::datatable(exam_data, class= "compact") ``` ## 3.8.2 Linked brushing: crosstalk method Code chunk below is used to implement the coordinated brushing shown above. ```{r} 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. 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!.