Difference between revisions of "Discussion of Stat 370"

Revision as of 07:30, 26 February 2017

John Nolan's course description for Stat 370 left a lot of room for personalization. To decide what direction to take the course, I instructed students to propose projects of interest to them. I told students to let their passion motivate their learning. We discussed and brainstormed ideas for projects, in class, and during office hours, and I used student feedback and stated interests to guide the decisions about where to take the course.

Early on in the class, we covered reproducible research with R-Markdown and knitr. As part of the discussion, I mentioned to the class that R-Studio can easily build an entire website and I raised the possibility of students presenting their projects in this way. I explained that, with a website, students could showcase their work for family, friends, classmates, and potential employers. A good number of students were passionately interested in this opportunity, and none of the students objected to me taking the class in this direction. We decided that all graded work for this class would go onto dynamic documents that would be accessible via this website.

Here is what we have covered so far, or will cover, time permitting.

• Using R-Studio including the editor and the console.
• Variables in R, including the various types.
• Vectors, Matrices, Lists, and Data Frames.
• Programming with functions and passing arguments, and assigning default values.
• Programming with loops and conditionals.
• Reproducible research with R-Markdown and knitr.
• Loading data into R.
• Downloading data sets with R.
• Git and Github for version control, through a shell, Git's app, and R-Studio IDE.
• Committing, pushing and pulling Git repositories.
• Branching with Git.
• Creating and downloading Git repositories from Github, including cloning repositories of others.
• Git and Github for collaboration, including forking repositories, pull requests, issue tracking, using project wikis.
• Random number generation and sampling.
• Simulating Markov Chains, especially with regard to sports applications: baseball, tennis and volleyball.
• Creating websites in R-Studio with R-Markdown.
• Creating interaction in website with Shiny.
• Scraping the web for data.
• Elegant plots with ggplot2.
• Text mining twitter.
• Sentiment analysis.
• Database and SQL.

Some of the material covered, or to be covered, preserves the original intent of the course, including R programming, random number generation, sampling, simulations, graphical analysis, text mining and database/SQL. But I am also taking the course in new directions including version control and collaboration with Git and Github, reproducible research, website generation, and website interaction through Shiny. Most of the driving force behind these directions was student interest, though I admit that I am also interested. I was embolden to try these avenues by informal discussions with colleagues that suggested that this material is not taught elsewhere at American University.