One of the key changes accompanying the COVID-19 pandemic has been a desire to consume readily available data in a somewhat understandable format. From the Johns Hopkins University dashboard (Dong et al., 2020) to the various visualizations created by the COVID Tracking Project, 2020 saw a large outbreak of platforms to present data to the public in an interactive way. The book Interactive web-based data visualization with R, Plotly, and shiny (Sievert, 2020), by Carson Sievert, is a great chance to hop on the train of interactive visualizations by leveraging Plotly. js (a JavaScript graphing library) in R. The book is divided in six sections: creating, publishing, combining, linking views, custom behaviors with JavaScript, and a last section with an assortment of special topics. The book does not assume any knowledge of web programming, and focuses on interfacing R with Plotly. js, mostly through the plotly package, assuming a working knowledge of R. The first section, from chapters 2–8, focuses on the creation of views. These (data) views refer to anything that provides insights and allow for the examination of data (Willis, 2008). The author mostly focuses on the plot_ly () function of the plotly package, which acts as the main interface between R and Plotly. js. This function uses several concepts from the Grammar of Graphics (Wilkinson et al., 2013), making it straightforward to implement for users with experience with ggplot2. The plotly package offers a functional implementation of this Grammar, which fits very well with pipe operators (%>%) and the rest of the tidyverse (Wickham and Grolemund, 2016). The integration with ggplot2 is made evident by the ggplotly () function, which allows users familiar with ggplot2 to create Plotly views without much effort. Chapters 2 and 3 provide the basic foundations for using the plot_ly () and ggplotly () functions, followed by a more detailed introduction to different types of views (maps, bar graphs, etc.) in chapters 4–8.