R Tutorial : Stats with geoms

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-data-visualization-with-ggplot2 at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to the second ggplot2 course on data visualization! Here, we're going to build on the skills you learned in the first course to develop a wide variety of plots that are not only appealing but also meaningful. We'll examine the following three layers in detail: statistics, coordinates, and facets, plus, we'll review some data viz tips so that you can make the most of your new skill-set. Let's get started with the stats layer. There are two broad categories of functions in this family: those that are called from within a geom and those that are called independently. As you may have guessed, all the statistical functions begin with "stats", followed by an underscore. Even those called from within the geom layer can be accessed independently in this way. We already saw a stats function when we used geom_histogram. Recall that under the hood, this called stat_bin to summarize the total count in each group. You may also remember that when we discussed geom_bar, I mentioned that it's default stat is set to "bin" -- so we could have produced the same result if we use geom_bar! The same thing happens with geom_bar, which just calls stat_count under the hood. If we called stat_count directly, we'd get the same plot since it would call geom_bar. So we can see that specific geoms and stat functions are related. stat_smooth can accessed with geom_smooth, shown here. The standard error, which is shown as a gray ribbon behind our smooth, is by default, a 95% confidence interval. We can remove this by setting the se argument to FALSE. We know we are calling stat_smooth because of another warning message: "geom_smooth is using method equal to loess, and formula y dependent on x". LOESS is a non-parametric smoothing algorithm that