R News / Shiny

Boost your shiny app with sparkling data visualizations: a deep dive into Chart.js JavaScript library

by Yohann Mansiaux · September 4, 2024

This article is originally published at https://rtask.thinkr.fr

You can read the original post in its original format on Rtask website by ThinkR here: Boost your shiny app with sparkling data visualizations: a deep dive into Chart.js JavaScript library

Let’s continue our exploration of integrating JavaScript code into a {shiny} application! We will show how to move beyond the classic graphs produced in base R or with {ggplot2} to explore the interactive dataviz production libraries of JavaScript, particularly the Chart.js library.

If you missed our first article on integrating JavaScript libraries into a {shiny} application I invite you to read it before diving into this one.

Crucial concepts are covered and will not be repeated here. We particularly think about:

How to add a JavaScript library’s dependencies to a {shiny} application
How to call JavaScript code from R

TL;DR

Creating interactive charts that go beyond the usual dataviz produced in R is possible by integrating a JavaScript library!
- We use the example of Chart.js, a very popular JavaScript dataviz library
- Specificities related to integrating Chart.js into a {shiny} application are addressed, including passing data from R to JavaScript and the differences in expected data formats.
- We’ll see how to make sure our JavaScript code is working properly by using the web browser’s console.

Importing Chart.js into a `{shiny}` app created with `{golem}`

Chart.js is a JavaScript library that allows you to create many types of charts (bars, lines, radar, etc.) and customize them as you wish
It is very well documented
It is the most popular JavaScript dataviz library on GitHub (over 60,000 “stars” at the time of this article’s publication)

To get an overview of the possibilities offered by Chart.js, visit the official page: https://www.chartjs.org/docs/latest/samples/information.html

Add the dependencies to Chart.js in your {shiny} app

The following sections assume that you have already created a {shiny} app with {golem}.

If this is not the case and you want to learn more about {golem}, I invite you to consult the official documentation.

To add Chart.js to your {shiny} app, you will need to find a way to incorporate the necessary files for its operation into your application. As we saw in our previous article, two solutions are possible.

Use a “CDN” (Content Delivery Network) to load the files from a third-party server.
Download the necessary files and integrate them directly into the application.

We will use the “CDN” method here.

Go to the “Getting Started” section of Chart.js documentation .

We retrieve the CDN URL and store this information for later use.

After creating the skeleton of an application via {golem}, we will add the Chart.js dependency.

Let’s open the R/app_ui.R file of our application and add the link we copied earlier into the body of the golem_add_external_resources() function.

golem_add_external_resources <- function() {
  add_resource_path(
    "www",
    app_sys("app/www")
  )
  tags$head(
    favicon(),
    bundle_resources(
      path = app_sys("app/www"),
      app_title = "chartJS"
    ),
    # Add here other external resources
    # for example, you can add shinyalert::useShinyalert()
    # Chart.js
    tags$script(src = "https://cdn.jsdelivr.net/npm/chart.js")
  )
}

How to know if Chart.js is properly imported ?

The “Getting Started” section previously consulted to retrieve the CDN link indicates that it is necessary to incorporate the HTML <canvas> tag into our application to display a Chart.js chart. We add this element to the R/app_ui.R file of our application.

app_ui <- function(request) {
  tagList(
    # Leave this function for adding external resources
    golem_add_external_resources(),
    # Your application UI logic
    fluidPage(
      h1("golemchartjs"),
      tags$div(
        tags$canvas(id="myChart")
      )
    )
  )
}

To verify that Chart.js is properly imported into our application, we run our app with golem::run_dev(), and the rest will take place in the web browser.

NB: The following screenshots were taken using the Google Chrome browser.

In the window of our application, right-click and then select “Inspect”. In the new window that opens, choose the “Console” tab and type the command to generate a Chart.js chart, as indicated once again in the “Getting Started” section between the HTML script tags.

The code to copy and paste into the console is the following:

  const ctx = document.getElementById('myChart');
  new Chart(ctx, {
    type: 'bar',
    data: {
      labels: ['Red', 'Blue', 'Yellow', 'Green', 'Purple', 'Orange'],
      datasets: [{
        label: '# of Votes',
        data: [12, 19, 3, 5, 2, 3],
        borderWidth: 1
      }]
    },
    options: {
      scales: {
        y: {
          beginAtZero: true
        }
      }
    }
  });

The chart from the demo page appears as expected! 🎉

We can move on! 😊

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step1

Creating a Bar Chart with Chart.js

The code used previously allowed us to verify that Chart.js was properly imported into our application. Now, we will see how to create a Chart.js chart from our {shiny} application. The goal is to produce bar charts for various datasets with customizable options based on user choices.

Let’s revisit the code executed previously:

  const ctx = document.getElementById('myChart');
  new Chart(ctx, {
    type: 'bar',
    data: {
      labels: ['Red', 'Blue', 'Yellow', 'Green', 'Purple', 'Orange'],
      datasets: [{
        label: '# of Votes',
        data: [12, 19, 3, 5, 2, 3],
        borderWidth: 1
      }]
    },
    options: {
      scales: {
        y: {
          beginAtZero: true
        }
      }
    }
  });

We could imagine passing the labels, label, data, and borderWidth elements as function parameters.

Let’s go! 🚀

Creating a JavaScript code usable from R

We saw in our previous article that the way to call JavaScript code from R is to use a “JS handler”. To do this, go to the dev/02_dev.R file! We add the following line in the “External Resources” section:

golem::add_js_handler("barchartJS")

We fill in the skeleton by indicating “barchartJS” as the name of our handler and adding the JavaScript code we saw previously.

$(document).ready(function () {
  Shiny.addCustomMessageHandler("barchartJS", function (arg) {
    const ctx = document.getElementById("myChart");
    new Chart(ctx, {
      type: "bar",
      data: {
        labels: ["Red", "Blue", "Yellow", "Green", "Purple", "Orange"],
        datasets: [
          {
            label: "# of Votes",
            data: [12, 19, 3, 5, 2, 3],
            borderWidth: 1,
          },
        ],
      },
      options: {
        scales: {
          y: {
            beginAtZero: true,
          },
        },
      },
    });
  });
});

We replace the labels, label, data, and borderWidth parameters, which are hardcoded here, with the future elements passed as arguments to our handler. The notation to use here will be arg.param_name to access the values passed by our {shiny} application. The . notation is a JavaScript convention for accessing properties of an object. To draw a parallel with R, it’s somewhat like using arg$param_name.

At the beginning of our handler, we add a call to the console.log() function to check the contents of the arg element from the JS console. This will allow us to verify that the elements passed from R are correctly transmitted to our handler.

$( document ).ready(function() {
  Shiny.addCustomMessageHandler('barchartJS', function(arg) {
    console.log(arg);    
    const ctx = document.getElementById('myChart');
    new Chart(ctx, {
      type: 'bar',
      data: {
        labels: arg.labels,
        datasets: [{
          label: arg.label,
          data: arg.data,
          borderWidth: arg.borderWidth
        }]
      },
      options: {
        scales: {
          y: {
            beginAtZero: true
          }
        }
      }
    }); 
  })
});

We will add elements to the R/app_ui.R file to generate the parameters to be passed to our handler:

arg.labels will be a vector of 5 character strings, randomly chosen from the letters of the latin alphabet.
arg.label will be a character string, randomly chosen from the letters of the latin alphabet.
arg.data will be a vector of 5 integer numbers, randomly chosen between 1 and 100.
arg.borderWidth will be an integer, randomly chosen between 1 and 5.

The display of the chart will be triggered by clicking a “Show Barplot” button.

Here is the content of our R/app_ui.R file:

app_ui <- function(request) {
    tagList(
        # Leave this function for adding external resources
        golem_add_external_resources(),
        # Your application UI logic
        fluidPage(
            h1("golemchartjs"),
            actionButton(
                inputId = "showbarplot",
                label = "Show Barplot"
            ),
            tags$div(
                tags$canvas(id = "myChart")
            )
        )
    )
}

And the content of the R/app_server.R file :

app_server <- function(input, output, session) {
    observeEvent(input$showbarplot, {
        app_labels <- sample(letters, 5)
        app_label <- paste0(sample(letters, 10), collapse = "")
        app_data <- sample(1:100, 5)
        app_borderWidth <- sample(1:5, 1)
        golem::invoke_js(
            "barchartJS",
            list(
                labels = app_labels,
                label = app_label,
                data = app_data,
                borderWidth = app_borderWidth
            )
        )
    })
}

Here are the key points to remember:

The first parameter in the call to golem::invoke_js() is the name of the JavaScript handler.
The following parameters are the elements to be passed as arguments to our handler. They should be passed in a named list where the names correspond to the elements in the arg object of our handler.

Let’s run our application with golem::run_dev() and verify that everything works as expected!

Congratulations! 👏

In addition to the displayed chart, we can see that the JavaScript console in the browser correctly shows the content of the arg object, including its 4 sub-elements: labels, label, data, and borderWidth.

And if you click the button again, what happens?

The chart does not update; it remains stuck on the first chart! 😮

The JavaScript console indicates that the arg object has indeed been updated, but the chart does not refresh. Additionally, an error message appears in the JavaScript console: “Error: Canvas is already in use. Chart with ID ‘0’ must be destroyed before the canvas with ID ‘myChart’ can be reused.”

Let’s try to understand what’s happening: in the R/app_ui.R file, we added a canvas element with the ID “myChart” (with tags$canvas(id = "myChart")). This element is used to display the chart. When we click the “Show Barplot” button, a new chart is generated and displayed in this element. However, the previous chart is not destroyed, and the error message indicates that the “canvas” is already in use.

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step2

Why isn’t the chart updating?

To find the answer, we need to refer back to the Chart.js documentation. We can read in the “.destroy()” section that in order to reuse the HTML “canvas” element for displaying a new chart, it is necessary to destroy the previous chart.

There is also a command “.update()” for updating an existing chart. This method seems more appropriate here, as we are using the same type of chart, with only a few parameters changing. The .update() method allows updating an existing chart without having to destroy and recreate it, which will be less “brutal” visually (with a chart disappearing and then reappearing). However, the .destroy() method should be kept in mind for cases where we want to radically change the type of chart, for example.

Updating a chart implies that a chart has already been generated once. Therefore, we need to modify our JavaScript handler to account for this and find a way to detect the existence of a chart on our page. For this, we will refer again to the Chart.js documentation, particularly the getChart method: https://www.chartjs.org/docs/latest/developers/api.html#static-getchart-key.

The command to use is in the following form: const chart = Chart.getChart("canvas-id");. According to the documentation, if the chart exists, the variable chart will contain the Chart.js object associated with the HTML “canvas” element. If the chart does not exist, the variable chart will be undefined.

For this command to work, we need to replace “canvas-id” with the ID of our “canvas”, which is “myChart” here: const chart = Chart.getChart("myChart");

Let’s restart our application. We indeed find that the chart object is undefined as long as the chart has not been created, and it correctly reflects this status afterwards.

We can adapt our code as follows:

If chart is undefined, we create a new chart.
If chart is not undefined, we update the existing chart.

We adapt our handler by referring to the documentation for the .update() method: https://www.chartjs.org/docs/latest/developers/api.html#update-mode

$(document).ready(function () {
  Shiny.addCustomMessageHandler("barchartJS", function (arg) {
    console.log(arg);
    const ctx = document.getElementById("myChart");
    const chart = Chart.getChart("myChart");
    if (chart == undefined) {
      console.log("Creating a new chart");
      new Chart(ctx, {
        type: "bar",
        data: {
          labels: arg.labels,
          datasets: [
            {
              label: arg.label,
              data: arg.data,
              borderWidth: arg.borderWidth,
            },
          ],
        },
        options: {
          scales: {
            y: {
              beginAtZero: true,
            },
          },
        },
      });
    } else {
      console.log("Updating an existing chart");
      chart.data.labels = arg.labels;
      chart.data.datasets[0].label = arg.label;
      chart.data.datasets[0].data = arg.data;
      chart.data.datasets[0].borderWidth = arg.borderWidth;
      chart.update();
    }
  });
});

This example is a bit more complex than those seen so far:

Retrieve the Chart.js object associated with the HTML “canvas” element using the method Chart.getChart("myChart").
Check if this object is undefined: if it is, use the code that has been working until now to create a new chart.
If it is not undefined, overwrite the configuration elements you want to update and then use the .update() method. Note the specifics of handling configuration elements: chart.data.labels = arg.labels for the labels, chart.data.datasets[0].label = arg.label for the label, etc. Use . to access object properties, with each . allowing access to a deeper level of “depth”. It is also important to note that array indexing starts at 0 in JavaScript, not at 1 like in R.

After all these efforts, let’s see if everything is back in order 😄!

Phew, everything is OK this time! 🥲

We’ve touched on a more complex case of using a JavaScript library in a {shiny} application. It is crucial to understand the library’s functioning by delving into the depths of its documentation. Moreover, one of the advantages of using a very popular library is that you can often find help on StackOverflow 😊 (here is an example of using the .destroy() method).

Feel free to go further in customizing your chart, such as changing the bar colors: https://www.chartjs.org/docs/latest/general/colors.html and https://www.chartjs.org/docs/latest/charts/bar.html.

The best way to learn is to try reproducing examples from the documentation.

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step3

Creating a Scatter Plot with Chart.js

We will attempt to create a scatter plot with Chart.js. To develop our code, we will rely on the Chart.js documentation: https://www.chartjs.org/docs/latest/charts/scatter.html.

As before, our code will be stored in a JS handler. Therefore, we will add a new handler in the dev/02_dev.R file:

golem::add_js_handler("scatterplotJS")

The documentation is slightly different from that provided for bar charts. We will need to adapt our handler accordingly. We identify an element config, which will include the type, data, and options elements we have already seen. There is also a data element containing datasets and labels.

We will fill in the skeleton of our handler with the JavaScript code from the Chart.js documentation. Initially, we will leave out the “update” part.

$(document).ready(function () {
  Shiny.addCustomMessageHandler("scatterplotJS", function (arg) {
    const ctx = document.getElementById("myChart2");
    const data = {
      datasets: [
        {
          label: "Scatter Dataset",
          data: [
            {
              x: -10,
              y: 0,
            },
            {
              x: 0,
              y: 10,
            },
            {
              x: 10,
              y: 5,
            },
            {
              x: 0.5,
              y: 5.5,
            },
          ],
          backgroundColor: "rgb(255, 99, 132)",
        },
      ],
    };
    const config = {
      type: "scatter",
      data: data,
      options: {
        scales: {
          x: {
            type: "linear",
            position: "bottom",
          },
        },
      },
    };
    new Chart(ctx, config);
  });
});

Our JS handler “scatterplotJS” is ready! We need to add the “div” and “canvas” to the UI to display the generated chart. We need to modify the HTML ID of our “canvas” to avoid any conflict with the bar chart. It will be named “myChart2” here.

Note that there is a slightly different syntax compared to the code used for the bar chart, where the call to “new Chart” was made directly with the data and options elements. Here, we store these elements in data and config variables before passing them to new Chart.

Next, we add the following to the R/app_ui.R file:

h1("Scatterplot"),
actionButton(
    inputId = "showscatterplot",
    label = "Show Scatterplot"
),
tags$div(
    tags$canvas(id = "myChart2")
)

We add the following to the R/app_server.R file:

  observeEvent(input$showscatterplot, {
    golem::invoke_js(
      "scatterplotJS",
      list(
      )
    )
  })

Our handler does not use any elements passed from R. However, it is necessary to pass an empty list as an argument to ensure the proper functioning of golem::invoke_js().

Let’s run your application with golem::run_dev() and verify that everything works as expected!

The chart from the documentation works! 🎉

Now, let’s go further by passing our own data as input.

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step4

An example with the iris dataset

We will use the iris dataset to generate a scatter plot. We will pass as arguments to our JS handler the data from the Sepal.Length and Sepal.Width columns.

As with the bar chart, we will use elements passed from R through the arg object in JavaScript.

We modify the data object to include a legend title and, most importantly, the data. To observe the elements passed from R, we add a call to console.log().

console.log(arg);
const data = {
  datasets: [
    {
      label: arg.label,
      data: arg.data,
      backgroundColor: "rgb(255, 99, 132)",
    },
  ],
};

As a reminder, in the example from the documentation, the data is passed in the form of an “array of dictionaries”. Each dictionary contains the keys x and y for the point coordinates.

data: [{
    x: -10,
    y: 0
  }, {
    x: 0,
    y: 10
  }, {
    x: 10,
    y: 5
  }, {
    x: 0.5,
    y: 5.5
}]

Let’s try to pass the contents of the Sepal.Length and Sepal.Width columns via a list. We make the following modification in R/app_server.R:

observeEvent(input$showscatterplot, {
    golem::invoke_js(
        "scatterplotJS",
        list(
            label = "My scatterplot",
            data = list(
                x = iris$Sepal.Length,
                y = iris$Sepal.Width
            )
        )
    )
})

We restart our application, and unfortunately, nothing shows up!

Thanks to the console.log() call in our handler, we can observe the content of the arg object in the JavaScript console of the browser. We notice that the data passed is not in the correct format. Here, we get an array of two elements, the first containing the values of Sepal.Length and the second containing the values of Sepal.Width, which is not the expected format.

Here, we need to do some work on the R side to transform our data into the expected format.

If we display a JSON preview of the data we passed as input, indeed the rendering is incorrect.

jsonlite::toJSON(
  list(
    x = iris$Sepal.Length,
    y = iris$Sepal.Width
  )
)

#> {"x":[5.1,4.9,4.7,4.6,5,5.4,4.6,5,4.4,4.9],"y":[3.5,3,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1]}

For manipulating lists, the {purrr} package is a top choice.

new_data <- purrr::transpose(
  list(
    x = iris$Sepal.Length,
    y = iris$Sepal.Width
  )
)
jsonlite::toJSON(
  new_data,
  auto_unbox = TRUE
)

#> [{"x":5.1,"y":3.5},{"x":4.9,"y":3},{"x":4.7,"y":3.2},{"x":4.6,"y":3.1},{"x":5,"y":3.6},{"x":5.4,"y":3.9},{"x":4.6,"y":3.4},{"x":5,"y":3.4},{"x":4.4,"y":2.9},{"x":4.9,"y":3.1}]

The rendering seems to be more in line with what is expected by Chart.js. Therefore, we will modify our code to pass the data in this manner.

observeEvent(input$showscatterplot, {
    golem::invoke_js(
        "scatterplotJS",
        list(
            label = "My scatterplot",
            data = purrr::transpose(
                list(
                    x = iris$Sepal.Length,
                    y = iris$Sepal.Width
                )
            )
        )
    )
})

Let’s observe the result:

This time it’s good! 😊 We can see in the JavaScript console that the data has indeed been passed in the correct format.

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step5

A Little Extra Polish

Our chart still lacks titles for the axes! To find out how to do this, the documentation comes to our rescue once again: https://www.chartjs.org/docs/latest/axes/labelling.html#scale-title-configuration.

We need to add a title object to our existing scales object. Each axis, “x” and “y”, is an object within the scales object and can have a title along with its associated parameters (color, font, etc.).

We will add a title element to the x object within our scales object. Several parameters are customizable, and we will need to modify the text parameter to set the title for each axis and the display parameter to show them, as this parameter is set to false by default (note the different boolean notation between JavaScript and R: true/false VS TRUE/FALSE).

The documentation sometimes lacks examples, so we can also rely on StackOverflow: https://stackoverflow.com/questions/27910719/in-chart-js-set-chart-title-name-of-x-axis-and-y-axis. However, be careful with the version of Chart.js used, as parameters may vary.

In our JS handler, we will add an xAxisTitle parameter and a yAxisTitle parameter.

const config = {
    type: 'scatter',
    data: data,
    options: {
      scales: {
        x: {
          type: 'linear',
          position: 'bottom',
          title: {
            display: true,
            text: arg.xAxisTitle
            }
          },
        y: {
          title: {
            display: true,
            text: arg.yAxisTitle
          }
        }
      }
    }
  };

Be cautious once again about the syntax difference between JavaScript and R. Parameters are passed in the form display: true rather than display = TRUE, for example. Confusing : with = can easily occur and result in non-functional code.

In our R/app_server.R file, we will add the xAxisTitle and yAxisTitle elements to the list passed as an argument to our handler.

observeEvent(input$showscatterplot, {
    golem::invoke_js(
        "scatterplotJS",
        list(
            label = "My scatterplot",
            data = purrr::transpose(
                list(
                    x = iris$Sepal.Length,
                    y = iris$Sepal.Width
                )
            ),
            xAxisTitle = "Sepal Length",
            yAxisTitle = "Sepal Width"
        )
    )
})

And here’s the result in our application:

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step6

Going further with the scatter plot

Additional modifications can be made to enhance the chart:

Modify the title
Change the fill color of the points and their border color

Here are the resources used to produce the code that we will present shortly:

Title: https://www.chartjs.org/docs/latest/configuration/title.htmlThe title should be included in a plugins object, which in turn is included in the options object.
Point Colors: https://www.chartjs.org/docs/latest/charts/line.html#point-stylesThe color of the points will be managed within the datasets object.

We will offer users the ability to set the chart title, its color, and the color of the points through shiny inputs (which will be a good way to revisit the “update”-related issues 😉).

Below is a preview of the chart created here (without functional “update” for now):

The handler code has been completed to account for these new elements:

$(document).ready(function () {
  Shiny.addCustomMessageHandler("scatterplotJS", function (arg) {
    const ctx = document.getElementById("myChart2");
    console.log(arg);
    const data = {
      datasets: [
        {
          label: arg.label,
          data: arg.data,
          borderColor: arg.pointBorderColor,
          backgroundColor: arg.pointBackGroundColor,
        },
      ],
    };
    const plugins = {
      title: {
        display: true,
        text: arg.mainTitle,
        color: arg.mainTitleColor,
      },
    };
    const config = {
      type: "scatter",
      data: data,
      options: {
        plugins: plugins,
        scales: {
          x: {
            type: "linear",
            position: "bottom",
            title: {
              display: true,
              text: arg.xAxisTitle,
            },
          },
          y: {
            title: {
              display: true,
              text: arg.yAxisTitle,
            },
          },
        },
      },
    };
    new Chart(ctx, config);
  });
});

In R/app_ui.R, elements have been added to allow the user to pass the necessary parameters:

h1("Scatterplot"),
textInput(
    inputId = "scatterplot_title",
    label = "Scatterplot Title",
    value = "ChartJS rocks !"
),
selectInput(
    inputId = "title_color",
    label = "Title Color",
    choices = c("brown", "orange", "purple"),
    selected = "brown"
),
selectInput(
  inputId = "points_background_color",
    label = "Points Background Color",
    choices = c("red", "blue", "green"),
    selected = "red"
),
actionButton(
  inputId = "showscatterplot",
    label = "Show Scatterplot"
),
tags$div(
    tags$canvas(id = "myChart2")
)

Finally, in R/app_server.R, we add the necessary elements to pass the parameters to our handler:

observeEvent(input$showscatterplot, {
        golem::invoke_js(
            "scatterplotJS",
            list(
                label = "My scatterplot",
                data = purrr::transpose(
                    list(
                        x = iris$Sepal.Length,
                        y = iris$Sepal.Width
                    )
                ),
                xAxisTitle = "Sepal Length",
                yAxisTitle = "Sepal Width",
                mainTitle = input$scatterplot_title,
                mainTitleColor = input$title_color,
                pointBorderColor = "black",
                pointBackGroundColor = input$points_background_color
            )
        )
    })

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step7

We still need to include the .update() method to account for updates to shiny inputs related to the title and the color of the points.

We will use the approach from the previous chart to modify our JS handler accordingly.

$(document).ready(function () {
  Shiny.addCustomMessageHandler("scatterplotJS", function (arg) {
    const ctx = document.getElementById("myChart2");
    console.log(arg);
    const chart2 = Chart.getChart("myChart2");
    if (chart2 == undefined) {
      console.log("Creating a new chart");
      const data = {
        datasets: [
          {
            label: arg.label,
            data: arg.data,
            borderColor: arg.pointBorderColor,
            backgroundColor: arg.pointBackGroundColor,
          },
        ],
      };
      const plugins = {
        title: {
          display: true,
          text: arg.mainTitle,
          color: arg.mainTitleColor,
        },
      };
      const config = {
        type: "scatter",
        data: data,
        options: {
          plugins: plugins,
          scales: {
            x: {
              type: "linear",
              position: "bottom",
              title: {
                display: true,
                text: arg.xAxisTitle,
              },
            },
            y: {
              title: {
                display: true,
                text: arg.yAxisTitle,
              },
            },
          },
        },
      };
      new Chart(ctx, config);
    } else {
      console.log("Updating an existing chart");
      chart2.data.datasets[0].backgroundColor = arg.pointBackGroundColor;
      chart2.options.plugins.title.text = arg.mainTitle;
      chart2.options.plugins.title.color = arg.mainTitleColor;
      chart2.update();
    }
  });
});

Let’s observe the result:

Well done! 🎉

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step8

Modifying the tooltip (advanced level)

We will look to modify the tooltip that appears when hovering over a point on the chart. In addition to changing its title, we want to display the row number from the dataset corresponding to the hovered point, as well as the corresponding values of Sepal.Length and Sepal.Width.

Here are the resources used:

Tooltip title: https://www.chartjs.org/docs/latest/configuration/tooltip.html#tooltip-callbacks
Tooltip content: https://www.chartjs.org/docs/latest/configuration/tooltip.html#tooltip-callbacks and https://www.youtube.com/watch?v=anseX1ePfUw

This part will be more complex than the previous ones. But we will manage it! 💪

The plugins object, used previously to manage the chart title, contains a tooltip element, which in turn contains a callbacks element. It is within this element that we can modify the title and content of the tooltip. Most tooltip elements can be configured via a function call that takes a context element as input. This is a JavaScript object that contains several items related to the hovered point. We will explore the content of this object to extract the information we need later when customizing the tooltip content.

We modify our JS handler by including a fixed title (we could also have passed it as a parameter):

const tooltip = {
  callbacks: {
    title: function (context) {
      return "Tooltip title";
    },
  },
};
const plugins = {
  title: {
    display: true,
    text: arg.mainTitle,
    color: arg.mainTitleColor,
  },
  tooltip: tooltip
};

Let’s see if it works:

The application code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step9

Let’s proceed with customizing the tooltip content!

In this step, we will modify the label parameter in the tooltip object. To refine our code, we will use the debugger function, which we haven’t used so far! If you are familiar with using browser() in R, debugger is its JavaScript equivalent. It allows you to pause the code execution and open the browser console to explore the arguments passed to a function.

Let’s modify our handler:

const tooltip = {
  callbacks: {
    title: function (context) {
      return "Tooltip title";
    },
    label: function(context) {
      debugger;
    }
  },
};
const plugins = {
  title: {
    display: true,
    text: arg.mainTitle,
    color: arg.mainTitleColor,
  },
  tooltip: tooltip,
};

We add a call to the JavaScript debugger in the label function of the callbacks object. We restart our application:

When hovering over a point on the chart, code execution is paused and the browser console opens. We can then explore the content of the context object passed to the label function.

We can identify the information that will be useful:

The row number in the dataset: context.dataIndex
The values of the point: context.formattedValue

We can then construct a customized tooltip (remembering to remove the debugger call 😉):

const tooltip = {
  callbacks: {
    title: function (context) {
      return "Tooltip title";
    },
    label: function (context) {
      lab =
        "Line number: " +
        context.dataIndex +
        " values: " +
        context.formattedValue;
      return lab;
      },
  },
};
const plugins = {
  title: {
    display: true,
    text: arg.mainTitle,
    color: arg.mainTitleColor,
  },
  tooltip: tooltip,
};

Mission accomplished! 🚀

The code for this step is available here: https://github.com/ymansiaux/golemchartjs/tree/step10

Conclusion

After our initial foray into calling JavaScript code from R with the sweetalert2 library, we have now explored using a data visualization library.

Key takeaways:

Always try to get the documentation examples working before adapting them to your application.
Use jsonlite::toJSON() to verify that the data passed is in the format expected by the library.
Keep in mind that sometimes you need to “update” or “destroy” objects on a web page.
Use console.log() or debugger to see the contents of a JavaScript object passed as an argument to a function.

After overcoming some challenging moments, we can see the possibilities offered by JavaScript data visualization libraries. You can achieve a high degree of customization for your charts, and Chart.js offers many features. Documentation, combined with research on discussion forums, can help solve problems that may seem insurmountable at first.

Feel free to dive into integrating JavaScript libraries into your {shiny} applications. It can be an excellent way to break new ground and offer interactive and customized charts to your users.

See you soon for new adventures! 🚀

This post is better presented on its original ThinkR website here: Boost your shiny app with sparkling data visualizations: a deep dive into Chart.js JavaScript library

Thanks for visiting r-craft.org
This article is originally published at https://rtask.thinkr.fr
Please visit source website for post related comments.

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Boost your shiny app with sparkling data visualizations: a deep dive into Chart.js JavaScript library

You may also like...

Categories

Boost your shiny app with sparkling data visualizations: a deep dive into Chart.js JavaScript library

TL;DR

Importing Chart.js into a {shiny} app created with {golem}

Add the dependencies to Chart.js in your {shiny} app

How to know if Chart.js is properly imported ?

Creating a Bar Chart with Chart.js

Creating a JavaScript code usable from R

Why isn’t the chart updating?

Creating a Scatter Plot with Chart.js

An example with the iris dataset

A Little Extra Polish

Going further with the scatter plot

Modifying the tooltip (advanced level)

Conclusion

You may also like...

Numpy Ravel, Explained

Call for talks deadline extended!

Advent of 2021, Day 7 – Starting Spark with R and Python

Categories

Importing Chart.js into a `{shiny}` app created with `{golem}`