R Tutorial: Comparing multiple populations

Want to learn more? Take the full course at https://learn.datacamp.com/courses/visualization-best-practices-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we're experts in visualizing proportions of a single population, we may start to ask ourselves, 'what if I want to compare multiple wholes to each other'? As someone who has completed the DataCamp series on ggplot you may think to yourself, 'why not use faceting?' This good intuition but unfortunately, it's not ideal for when you are plotting with pie or waffle charts. This is because when you place a bunch of pie or waffle charts next to each other there is no easy anchoring point for you to make out-of-group comparisons amongst the different populations. Here, we are looking at the proportion of cases for different diseases across the countries in the Southeast Asia region. It's almost impossible to make meaningful comparisons across the countries. Luckily, there is a better way. We can make a stacked bar chart by un-rolling our pies into bars and putting them on the x-axis according to their group. Now to compare diseases across countries we simply need to scan across the x-axis. Compared to the previous faceted pies, we have a much more precise and information-dense chart. Note the use of the position = fill argument in geom_col. This causes the bars to stretch all the way to the top of the y-axis, making them proportion charts. If we left this argument out the bars would all be different heights due to different total numbers of classes. In this scenario, this would just obscure the point of the chart, but it can be useful in other scenarios! Unfortunately, stacked bars suffer from some caveats that prevent them from always being the correct visualization for a problem. First, like a pie chart, there is a lack of anchoring for in-group comparisons for anything other than the first and last classes. Additionally, the stre