JavaScript Scatter Plot in R Studio

In this article, we will explore how to create an interactive JavaScript scatter plot using R Studio and a popular library called D3.js. This type of visualization is particularly useful when dealing with large datasets, as it allows users to filter down the data and identify general trends.

To create our scatter plot, we will start by loading the necessary libraries, including `plotly` and `dplyr`. We will also need to load our dataset, which in this case is a Excel file called `database_test.xlsx`.

The code below generates a scatter plot that depends on the date chosen by the user. Three lines are also generated, referring to the mean, mean+standard deviation, and mean-standard deviation based on the day of the week (Week) chosen.

```r

library(plotly)

library(dplyr)

pivot_longer(everything(), names_pattern = 'DR(.+)', values_to = 'val') %>%

summarize(across(starts_with('DR'), sum)) %>%

mutate(across(starts_with('date'), as.Date))

```

The resulting scatter plot uses Scalar Vector Graphics (SVG) coupled with HTML and CSS to display the data. This allows for an interactive visualization that can be zoomed in and out, and filtered based on the user's selection.

One of the key advantages of using D3.js is its ability to handle large datasets. In our case, we typically deal with datasets ranging from 15,000 to 50,000 points. While this may seem daunting, D3.js is designed to handle such large datasets and provide an interactive visualization that is easy to use.

However, there are some limitations to using D3.js in R Studio. For example, if you have too many points (more than 10,000), the scatter plot may crash your Chrome or Edge browser. To optimize this, we can filter down the data based on the user's selection and only display the relevant points.

In addition to its interactive capabilities, D3.js also provides a range of customization options for the scatter plot. For example, you can change the color scheme, add labels and titles, and customize the appearance of the lines and points.

Overall, using JavaScript scatter plots in R Studio with D3.js is a powerful way to visualize large datasets and provide an interactive experience for your users. With its ability to handle large datasets and provide customization options, D3.js is a popular choice among data analysts and scientists.

In this article, we have explored how to create an interactive JavaScript scatter plot using R Studio and D3.js. We have also discussed the limitations of using D3.js in R Studio and provided some tips for optimizing performance. Whether you are working with small or large datasets, D3.js is a powerful tool that can help you visualize your data and provide insights into general trends.

References

* Cookbook for R: Graphs (O'Reilly Media)

* D3.js Documentation (https://d3js.org/)

* R Studio Documentation (https://www.rstudio.com/)

Javascript scatter plot r studio