R Tutorial : Stats outside 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. --- Let's take a look at some statistics that we call directly. In this plot of the iris dataset, sepal length is described by species. What can we do with this data? A typical way to summarize this data would be to take the mean and standard deviation or the 95% confidence interval. We can calculate these values manually, or we can do it directly in ggplot2. Let's see how it works. The function smean-dot-sdl from the Hmisc package returns the mean plus or minus one standard deviation as a named vector. By setting the mult argument to 1, we specify 1 standard deviation. In ggplot2, the function mean_sdl converts this vector to a data frame, renaming the variables to match ggplot2 aesthetics. We call mean_sdl using the fun.data argument of the stat_summary function. By default we get geom_pointrange, which requires y, ymin and ymax, exactly what is returned by mean_sdl. For errorbars, we can just calculate the mean and use "point" as the geom, then we can call mean_sdl using the "errorbar" geom, where we can also set the width of the error bars. But notice that we could have also made a typical bar plot with error bars, by simply calling the bar geom - but this is NOT RECOMMENDED! We'll learn why when we get to data viz best practices later on! The 95% CI is also straight forward. mean_cl_normal returns the mean and the upper and lower bounds of the 95% confidence interval, calculated using the t-distribution. Two other useful stat_layer functions are stat_function and stat_qq. These are particularly useful if we want to look at distributions. Statisticians typically use visual cues to get an idea of the distribution of their data instead of relying only on numbers. To see this in action let's return to the first example we