R Tutorial: An Introduction to plotly

Key Takeaways

hi I'm Adam LOI a statistician our developer and professor welcome to my course on interactive data visualization using plotly in our the plot lay our package provides an interface to the plot lead javascript graphing the library allowing you to create interactive web-based graphics entirely in our plot li 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 our bouquet and hi Chartres 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 scatter plot of prolene 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 wine data set from the UCI machine learning repository containing the results of a chemical analysis of 178 wines all grown in the same region in Italy but derived from three different cultivars we'll begin by converting the static scatter plot of prolene against flavonoids we saw earlier to a plotly interactive graphic remember that there are three parts to a ggplot graphic first we have the data set here we pass the wine data set into the ggplot command using the pipe operator second we map the variables in the data set to aesthetics in the graph here we specify the mappings with AES parentheses x equals flavonoids y equals prolene color equals type telling the plot which variable defines each aesthetic third we specify the plot type by adding a layer to create a scatter plot we add a plus sign after the ggplot base layer and specify geom underscore point finally we store this plot in the static object the command ggplot Li allows you to convert a ggplot graphic to a plot Li interactive graphic in a single line of code after loading the plot Li package past static ggplot object to the ggplot Li command and an interactive version is created before moving on there are two important points to note first while interactive graphics are wonderful tools for exploring your data and communicating your findings interactivity does not ensure that you have created a good graphic it's important to review best practices of data visualization so be sure to think about both the syntax and design principles as you complete the course second not all ggplot objects can be converted to plot Li objects so it's important to know how to create interactive graphics directly we'll focus on this ground-up approach in all subsequent lessons it's time to practice converting ggplot2 graphics in Duke