Below you’ll find the complete code and resources used to create the graphs in my talk The Good, the Bad and the Ugly: how to visualize Machine Learning data at this year’s Minds Mastering machines conference. You can find the German slides here:
You can find Part 1: The Good, the Bad and the Ugly: how (not) to visualize data here.
If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):

Editor’s note: This is a guest post by Nathaniel Schmucker. He is the founder of The Analyst Code, a blog that provides tools to instill a love of data in individuals of all backgrounds and to empower aspiring analysts.
Introduction In this post, we will look at:
What is a k-Means analysis? How does the k-Means algorithm work? How do we implement k-Means in R?

Below you’ll find the complete code used to create the ggplot2 graphs in my talk The Good, the Bad and the Ugly: how (not) to visualize data at this year’s data2day conference. You can find the German slides here:
You can also find a German blog article accompanying my talk on codecentric’s blog.
If you have questions or would like to talk about this article (or something else data-related), you can now book 15-minute timeslots with me (it’s free - one slot available per weekday):

In a recent project, I was looking to plot data from different variables along the same time axis. The difficulty was, that some of these variables I wanted to have as point plots, while others I wanted as box-plots.
Because I work with the tidyverse, I wanted to produce these plots with ggplot2. Faceting was the obvious first step but it took me quite a while to figure out how to best combine facets with point plots (where I have one value per time point) with and box-plots (where I have multiple values per time point).