The materials in this box are designed for learners who have no background in data science, statistics, or programming. However, they also assume that the learners are interested in making sense of (sometimes messy) data and willing to dive into the documentation of the packages we introduce.
This course can serve as a first course in an undergraduate data science or statistics curriculum. It can also serve as an introductory course in a graduate program, and depending on the background of the students, earlier topics can be covered more quickly to make room for more content at the end of the course.
Student facing materials:
Instructor facing materials:
ghclassfor setting up your course
learnrmodules (coming soon)
blogdownto create your course website (coming soon)
Unlike most other software designed specifically for teaching statistics, R is free and open source, powerful, flexible, and relevant beyond the introductory statistics classroom. Arguments against using and teaching R at especially the introductory statistics level generally cluster around the following two points: teaching programming in addition to statistical concepts is challenging and the command line is more intimidating to beginners than the graphical user interface (GUI) most point-and-click type software offer.
One solution for these concerns is to avoid hands-on data analysis completely. If we do not ask our students to start with raw data and instead always provide them with small, tidy rectangles of data then there is never really a need for statistical software beyond spreadsheet or graphing calculator. This is not what we want in a modern statistics course and is a disservice to students.
Another solution is to use traditional point-and-click software for data analysis. The typical argument is that the GUI is easier for students to learn and so they can spend more time on statistical concepts. However, this ignores the fact that these software tools also have nontrivial learning curves. In fact, teaching specific data analysis tasks using such software often requires lengthy step-by-step instructions, with annotated screenshots, for navigating menus and other interface elements. Also, it is not uncommon that instructions for one task do not easily extend to another. Replacing such instructions with just a few lines of R code actually makes the instructional materials more concise and less intimidating.
Many in the statistics education community are in favor of teaching R (or some other programming language, like Python) in upper level statistics courses, however the value of using R in introductory statistics courses is not as widely accepted. We acknowledge that this addition can be burdensome, however we would argue that learning a tool that is applicable beyond the introductory statistics course and that enhances students’ problem solving skills is a burden worth bearing.
There are a number of other great programming tools out there that can also be used for introducing students to data science, e.g. Python. These materials are designed for teaching data science with R. A great example of a similar curriculum using Python is Data 8 designed at Universit of California, Berkeley.
The RStudio IDE includes a viewable environment, a file browser, data viewer, and a plotting pane, which makes it less intimidating than the bare R shell. Additionally, since it is a full fledged IDE, it also features integrated help, syntax highlighting, and context-aware tab completion, which are all powerful tools that help flatten the learning curve. RStudio also has direct integration with other critically important tools for teaching computing best practices and reproducible research.
Our recommendation is that students access the RStudio IDE through a centralized RStudio server instance or using RStudio Cloud. We describe this in further detail in the Infrasture section.
It should be noted that we do not want to completely dissuade students from downloading and installing R and RStudio locally, we just do not want it to be a prerequisite for getting started. We have found that teaching personal setup is best done progressively throughout a semester, usually via one-on-one interactions during office hours or after class. Our goal is that all students will be able to continue using R in any setting.