R Tutorial: An Introduction to plotly

Want to learn more? Take the full course at https://learn.datacamp.com/courses/interactive-data-visualization-with-plotly-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I'm Adam Loy, a statistician, R developer, and professor. Welcome to my course on Interactive Data Visualization using plotly in R. The plotly R package provides an interface to the plotly JavaScript graphing library, allowing you to create interactive web-based graphics entirely in R. plotly is a great choice for creating interactive graphics because you can create a wide variety of interactive graphics in multiple formats. For example, you can execute your code in the console and interact with your graphic entirely in the viewer pane, or you could deploy your graphic to the web as a shiny app. plotly is also backed by a strong community and is still under heavy development, making it a great time to learn how to harness its power. As of November 2018, plotly downloads were an order of magnitude higher than its competitors like rbokeh and highcharter. Before you start creating graphics, it's important to think carefully about what type of graphic best suits your purpose: a static graphic, or an interactive graphic. To highlight features of each type of graphic, let's consider a scatterplot of proline against flavonoids, two chemicals found in wine. A static plot, such as one rendered in ggplot2, remains permanently fixed. This format is useful for printed materials such as reports, but can only display what you, the creator, have highlighted. On the other hand, the user can update an interactive graphic. For example, you can drill down to specific observations using hover info, or focus on subsets of your data by selecting or deselecting groups. Simple interactions can improve your ability to explore your data, and throughout this course, you'll learn how to add these to your graphics toolkit. To begin, consider the