An interactive Jupyter Notebook version of this tutorial can be found at https://github.com/rlbarter/ggplot2-thw. Feel free to download it and use for your own learning or teaching adventures! Useful resources for learning ggplot2 ggplot2 book (https://www.amazon.com/dp/0387981403/ref=cm_sw_su_dp?tag=ggplot2-20) by Hadley Wickham The layered grammar of graphics (http://vita.had.co.nz/papers/layered-grammar.pdf) by Hadley Wickham Materials outline I will begin by providing an overview of the layered grammar of graphics upon which ggplot2 is built. I will then teach ggplot2 by layering examples on top of one another.
Materials prepared by Rebecca Barter. Package developed by Max Kuhn. An interactive Jupyter Notebook version of this tutorial can be found at https://github.com/rlbarter/STAT-215A-Fall-2017/tree/master/week11. Feel free to download it and use for your own learning or teaching adventures! R has a wide number of packages for machine learning (ML), which is great, but also quite frustrating since each package was designed independently and has very different syntax, inputs and outputs. This means that if you want to do machine learning in R, you have to learn a large number of separate methods.
Developing a seamless, clean workflow for data analysis is harder than it sounds, especially because this is something that is almost never explicitly taught. Apparently we are all just supposed to “figure it out for ourselves”. For most of us, when we start our first few analysis projects, we basically have no idea how we are going to structure all of our files, or even what files we will need to make.