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Build Interactive Data Visualizations in plotlyInteractive graphics allow you to manipulate plotted data to gain further insights. As an example, an interactive graphic would allow you to zoom in on a subset of your data without the need to create a new plot. In this course, you will learn how to create and customize interactive graphics in plotly using R.
Get Started Using plotlyYou’ll start the course with an introduction to plotly and a view of different plots you can make using this R package, including histograms, bar charts, bivariate graphics, scatterplots, and boxplots. You’ll also learn how to convert a ggplot2 scatterplot into plotly so that you can enhance your graphics and dashboards.
Explore Creating plotly Plots and DashesThe next two chapters of the course show you how you can customize your graphics to build the perfect dashboard, and even add hover-over information to add detail and depth. Then you’ll move on to advanced charts that visualize complex relationships and larger datasets. By completing this course, you’ll be able to create manual and automated faceting, binned scatterplots, and your first scatter plot matrix (SPLOM).
Create Visualizations with Real-World DataThe final chapter of this course uses your new-found plotly skills to visualize the results of the 2018 US elections. You’ll create the first interactive plotly dash in your portfolio and learn how to create maps using this valuable data visualization tool.
Introduction to plotlyFree
In this chapter, you will receive an introduction to basic graphics with plotly. You will create your first interactive graphics, displaying both univariate and bivariate distributions. Additionally, you will discover how to easily convert ggplot2 graphics to interactive plotly graphics.
Styling and customizing your graphics
In this chapter, you will learn how to customize the appearance of your graphics and use opacity, symbol, and color to clarify your message. You will also learn how to transform axes, label your axes, and customize the hover information of your graphs.Customize your traces50 xpColor and opacity100 xpAlternative color formats100 xpSize and symbol100 xpThoughtful use of color50 xpAdding a third variable100 xpBeyond color: Symbols100 xpTransforming a color scale100 xpHover info50 xpRemoving a piece of hover info100 xpAdding to hoverinfo100 xpCustom hoverinfo100 xpCustomizing your layout50 xpPolishing a scatterplot100 xpMatching a theme100 xp
In this chapter, you move past basic plotly charts to explore more-complex relationships and larger datasets. You will learn how to layer traces, create faceted charts and scatterplot matrices, and create binned scatterplots.Layering traces50 xpAdding a linear smoother100 xpOverlayed density plots100 xpSubplots50 xpManual faceting100 xpAutomated faceting100 xpPlot and axis titles100 xpPolishing axis titles100 xpScatterplot matrices50 xpYour first SPLOM100 xpCustomizing color100 xpTweaking the appearance100 xpBinned scatterplots50 xpBinning a scatterplot100 xp
In the final chapter, you use your plotly toolkit to explore the results of the 2018 United States midterm elections, learning how to create maps in plotly along the way.Introduction to the 2018 election data50 xpDid voters turn out?100 xpAdding a reference line100 xpWhich state had the highest turnout?100 xpHow much was spent on Senate races?100 xpWhich candidate spent the most?100 xpChoropleth maps50 xpMapping change in voter turnout100 xpMapping Senate winners100 xpAdding points to a map100 xpGeo layout100 xpFrom polygons to maps50 xpMapping Senate winners, redux100 xpA county-level choropleth map100 xpWrap-up50 xp
In the following tracksInteractive Data Visualization in R
PrerequisitesIntroduction to the Tidyverse
Assistant Professor of Statistics at Carleton College
Adam is an assistant professor of statistics at Carleton College where he teaches courses in statistics in data science. His research interests lie in statistical graphics and computing, R development, and statistics/data science education. Find out more on Adam's webpage.