Linking scatterplots

In this exercise, you'll use plotly and crosstalk to link a scatterplot of the U.S. Housing Price Index in 2017 (HPI) against the percentage of homeowners to a scatterplot of the 2017 HPI against the employment rate. Both packages have already been loaded, and the data are stored in us2017.

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Intermediate Interactive Data Visualization with plotly in R

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# Create a SharedData object from us2017
shared_us <- ___(___)

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Este ejercicio forma parte del curso

Intermediate Interactive Data Visualization with plotly in R

IntermedioNivel de habilidad

4.9+

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A review of key plotly commands. You will review how to create multiple plot types in plotly and how to polish your charts. Additionally, you will create static versions of the bubble and line charts that you will animate in the next chapter.

Exercise 1: Interactive and dynamic graphics Exercise 2: Which is the interactive graphic?Exercise 3: A simple time series plot Exercise 4: A simple scatterplot Exercise 5: Adding aesthetics to represent a variable Exercise 6: Color and size Exercise 7: Plotting symbols Exercise 8: Polishing your graphics Exercise 9: Moving Beyond Simple Interactivity Exercise 10: Bubble charts Exercise 11: Many time series

In this chapter, you will learn how to implement keyframe animation in plotly. You will explore how to create animations, such as Hans Rosling's bubble charts, as well as cumulative animations, such as an animation of a stock's valuation over time.

Exercise 1: Introduction to animated graphics Exercise 2: What's the frame?Exercise 3: Why do we need ids?Exercise 4: Animating a scatterplot Exercise 5: Factors as frames Exercise 6: Polishing animations Exercise 7: Polishing your regional animation Exercise 8: Polishing your HPI animation Exercise 9: Layering in plotly Exercise 10: Adding background text Exercise 11: Plotting the baseline Exercise 12: Cumulative animations Exercise 13: How many rows?Exercise 14: Median life expectancies Exercise 15: Animating median life expectancies

When you are exploring unexpected structure in your graphics, it's useful to have selections made on one chart update the other. For example, if you are exploring clusters observed on a scatterplot, it is useful to have the selected cluster update some chart of group membership, such as a jittered scatterplot or sets of bar charts. In this chapter, you will learn how to link your plotly charts to enable linked brushing. Along the way, you will also learn how to add dropdown menus, checkboxes, and sliders to your plotly charts, without the need for Shiny.

Exercise 1: Linking two charts Exercise 2: sharedData objects Exercise 3: Linking scatterplots

Ejercicio actual

Exercise 4: highlighting() charts Exercise 5: Brushing groups Exercise 6: Highlighting time series data Exercise 7: Linking a dotplot and a time series plot Exercise 8: Linking a bar chart to a scatterplot Exercise 9: Selection strategies Exercise 10: Searching for clusters Exercise 11: Adding dropdown menus Exercise 12: Making shinier charts Exercise 13: Arranging views with bscols()Exercise 14: Adding checkboxes Exercise 15: Adding a slider

In the final chapter, you will use your expanded plotly toolkit to explore orbital space launches between 1957 and 2018. Along the way, you'll learn how to wrangle data to enable cumulative animations without common starting points, and hone your understanding of the crosstalk package.

Exercise 1: Introduction to the space launches data Exercise 2: Launches over time Exercise 3: Space race timeline Exercise 4: State vs. private launches Exercise 5: Recap: Animation Exercise 6: Animating the space race Exercise 7: Animating the private space race Exercise 8: Recap: linked views and selector widgets Exercise 9: Linking for group selection Exercise 10: Linked brushing Exercise 11: Linked views for summarization Exercise 12: Adding a slider for time Exercise 13: Wrap-up