Daniel Kaplan and Libby Shoop have developed a one-credit class called Data Computation Fundamentals, which was offered this semester at Macalester College. This course is part of a larger research and teaching effort funded by Howard Hughes Medical Institute (HHMI) to help students understand the fundamentals and structures of data, especially big data. [Read more about the project in Macalester Magazine.]
The course introduces students to R and covers topics such as merging data sources, data formatting and cleaning, clustering and text mining.
We have just finished another semester, and before my mind completely turns to rubble, I want to share what I believe to be a fairly good assignment. What I present below was parts of two separate assignments that I gave this semester, but upon reflection I think it would be better as one.
Read the article Let’s Practice What We Preach: Turning Tables into Graphs (full reference given below). In this article, Gelman, Pascarica, & Dodhia suggest that presentations of results using graphs are more effective than results presented in tables.
I have been thinking for quite some time about the computing skills that graduate students will need as they exit our program. It is absolutely clear to me (not necessarily all of my colleagues) that students need computing skills. First, a little background…
I teach in the Quantitative Methods in Education program within the Educational Psychology Department at the University of Minnesota. After graduating, many of our students take either academic jobs, a job working in testing companies (e.
I was creating a dataset this last week in which I had to partition the observed responses to show how the ANOVA model partitions the variability. I had the observed _Y _(in this case prices for 113 bottles of wine), and a categorical predictor X (the region of France that each bottle of wine came from). I was going to add three columns to this data, the first showing the marginal mean, the second showing the effect, and the third showing the residual.
I attended useR! 2012 this past summer and one of the highlights of the conference was a presentation by Yihui Xie and JJ Allaire on knitr. As an often frustrated user of Sweave, I was very impressed with how they streamlined the process of integrating R with LaTeX and other document types, and I was excited to take advantage of the tools. It also occurred to me that these tools, especially the simpler markdown language, could be useful to the students in my introductory statistics course.
