R Tutorial: Moving Beyond Simple Interactivity

plotly makes it easy to create clean interactive graphics entirely in r but interactive tooltips are only the beginning of plotlist capabilities in this course you'll explore how to supercharge your interactive graphics using animations in linked brushing over the next two chapters we'll explore two primary data sets the first data set contains six indicators of economic conditions in countries across the world from 1960 to 2018 these indicators include adjusted per capita gdp a measure of carbon dioxide emissions military spending as a percentage of gdp and the urban population as a percentage of the whole population the second data set you'll explore contains economic indicators for each u.s state along with washington d.c these data include the real gdp employment level home ownership rate housing price index and population in thousands in the next chapter you'll learn how to create animated plots such as this animated bubble chart of carbon dioxide emissions against per capita gdp using animation it's easy not only to see the clear relationship between carbon dioxide emissions and income but also the clear rise of china and india in both indices this rise is so clear because animation draws our attention to changes over time far better than comparing small multiples before we animate a bubble chart let's review how one is created by reproducing the 2014 snapshot of carbon dioxide against income we begin by filtering the world indicators data set to extract the rows for 2014. next we create the canvas for our plot mapping income to x co2 to y and country names to the hover info text finally we add the marker's trace mapping population to the size of the markers and six underscore regions to the color we also pass a list to the marker's argument refining the appearance of the points we allow the size of the points to be governed by the diameter rather than the area by setting size mode equal to diameter and shrink the points a touch by setting size ref to 2. you'll also learn how to create linked graphics without creating a shiny app via the crosstalk package in this example we use a bar chart to select the world region to highlight on the mobile chart allowing for easy comparison of selected regions to the rest of the world linked to brushing can also be persistent as shown here allowing us to select multiple regions to call out and compare this can be quite useful to compare clusters in this course you'll also learn how to extend hover tool tips to include dynamic highlighting via the crosstalk package here we use dynamic highlighting to focus on a single state's housing price index trajectory to see how it compares to the others before diving deeper into plotly let's create the base plots that will augment with animation dynamic highlighting and linked brushing in later chapters