1. Wrap-up
Congratulations - you've finished the course! Now that you've reached the end, you understand how to create a variety of plotly charts, polish them, and combine traces to create new charts.
2. Chapter 1: Displaying distributions
In chapter 1 you learned the basics of plotly, including what the plot.ly javascript library is, how to convert static ggplot2 graphics to interactive plotly graphics, and how to create basic univariate and bivariate charts entirely in plotly. Bar charts, histograms, boxplots, and scatterplots are powerful, so don't forget about them as you create more advanced charts.
3. Chapter 2: Customizing your charts
In chapter 2, you learned how to customize plotly charts.
To reduce the impact of overplotting, you saw how to make points more transparent, and how to change the plotting symbol.
You explored how to thoughtfully use color to represent a third variable on a scatterplot, that not all color palettes are created equal, and know how to manually define color palettes, in case no built-in palette suits your needs.
You learned that there are often times where additional hover information or formatting greatly improves the insight provided by a chart, and hopefully won't hesitate to customize your hover info.
Finally, you saw how to customize the layout of a plotly chart. Before you share your chart with the world, be sure to include all necessary labels, add or remove gridlines, and transform axes as appropriate.
4. Chapter 3: Advanced charts
In chapter 3, you learned how to create more complex charts.
You began by layering traces to compare models. Then you explored how to facet plotly charts to provide insight across groups. You also explored the use of scatterplot matrices to explore pairwise associations.
Both subplots and scatterplot matrices introduced you to the concept of linking, a powerful idea that I encourage you can to explore further.
Finally, you explored how binned scatterplots can provide insight about the association between variables in large datasets with a great deal of overplotting.
5. Chapter 4: Exploring the 2018 U.S. election
In chapter 4, you reviewed the concepts from the first three chapters while exploring results from the 2018 midterm elections in the United States.
Along the way, you learned how to create maps in plotly. You learned two ways to create choropleth maps: using the native choropleth trace, and building them up from polygons. Building your own maps takes more thought on the data-wrangling side, but is a far more powerful and general tool.
Finally, you learned how to customize the appearance of your maps.
6. Going further
Throughout this course, you have expanded your understanding of plotly and interactive graphics by leaps and bounds. If you're eager to learn more, there are many great resources.
One starting point is Plotly for R. It's written by Carson Sievert, the author of the plotly R package. There are also more-general books on interactive graphics if you're interested in the underlying principles.
As you explore the functionality of plotly, don't hesitate to open the full documentation online or consult the cheatsheet. You can also post questions on the plotly community forum. The large user community is one of the key strengths of plotly.
Before forging ahead, be sure to review how to put your charts online, so you can share your work with the world.
Finally, if you're looking to super-charge your interactive graphics, then consider exploring linked views or combining shiny and plotly to create interactive dashboards.
7. Thank you!
Thanks for taking this course with me, I hope that you've had as much fun as I have. Now go forth and make interactive graphics!