![]() ![]() You can now include these in a geom_text() layer. X = c(round(min(gm_eu$lifeExp), 2), round(mean(gm_eu$lifeExp), 2), round(max(gm_eu$lifeExp), 2)), It should contain X and Y values, and also the labels that will be displayed: annotations <- ame( Maybe you find vertical lines too intrusive, and you just want a plain textual representation of specific values.įirst things first, you’ll need to create a ame for annotations. Annotationsįinally, let’s see how you can add annotations to your ggplot histogram. ![]() For example, if you were to embed the above chart to a dashboard, you could let the user toggle the overlay for maximum customizability.ĭo you want to build dashboards professionally? Here’s how to start a career as an R Shiny Developer. It’s somewhat of a richer data representation than if you’d’ve gone with the histogram alone. Image 7 – Adding density plots to histograms Geom_vline(aes(xintercept = mean(lifeExp) - sd(lifeExp)), color = "#000000", size = 1, linetype = "dashed") Geom_vline(aes(xintercept = mean(lifeExp) + sd(lifeExp)), color = "#000000", size = 1, linetype = "dashed") + The following code snippet draws a black line at the mean, and dashed black lines at -1 and +1 standard deviation marks: ggplot(gm_eu, aes(lifeExp)) + It’s a good idea to style the lines differently, just so your histogram isn’t confusing. For example, we sometimes like to add a vertical line representing the mean, and two surrounding lines representing the range between -1 and +1 standard deviations from the mean. You can bring more life to your ggplot histogram. How to Style and Annotate ggplot Histograms Styling Let’s dive deeper into styling and annotations next. Much better, provided you like the blue color. Image 5 – Tweaking the fill and outline color Here’s how the first couple of rows from gm_eu look like: Here’s the code you need to import libraries, load, and filter the dataset: library(dplyr) We’ll use only a subset that shows countries in Europe and discard everything else. It’s a relatively small dataset showing life expectancy, population, and GDP per capita in countries between 19. We’ll use the Gapminder dataset throughout the article to visualize histograms. Let’s see how you can use R and ggplot to visualize histograms. Keep this in mind when drawing conclusions from the shape of a histogram, alone. It’s usually skewed in either direction or has multiple peaks. In reality, you’re rarely dealing with a perfectly normal distribution. Anything outside the -3 and +3 standard deviation range is considered to be an outlier.99.72% of the data points are located between -3 and +3 standard deviations (49.86% in either direction).95.44% of the data points are located between -2 and +2 standard deviations (47.72% in either direction).68.26% of the data points are located between -1 and +1 standard deviations (34.13% in either direction).When data is distributed normally (bell curve), you can draw the following conclusions: ![]() Image 1 – Histogram of a standard normal distributionĪlthough at first glance the histogram doesn’t look like much, it actually tells you a lot. The image below shows a histogram of 10,000 numbers drawn from a standard normal distribution (mean = 0, standard deviation = 1): The easiest way to understand them is through visualization. You can change the number of bins easily. A single bar (bin) represents a range of values, and the height of the bar represents how many data points fall into the range. ![]()
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