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 … Continue reading →
It is time for the NCAA Basketball Tournament. Sixty-four teams dream big (er…I mean 68…well actually by now, 64) and schools like Iona and Florida Gulf Coast University (go Eagles!) are hoping that Robert Morris astounding victory in the N.I.T. … Continue reading →
One of my graduate students worked some ggplot magic and created an almost Light Bright-esqe plot of our very own Goldy Gopher. She also, thoughtfully, published a tutorial on her blog. Read and enjoy!
As a celebration of Florence Henderson’s 79th birthday (on February 14), I have created this scatterplot to use in my regression course. The plot depicts the relationship between time spent on mathematics homework outside of school (expressed as z-scores) and … Continue reading →
I don’t understand the website Pinterest, but it looks pretty (especially on the iPad), and an undergraduate student said it was the greatest thing since Facebook, so I thought I would give it a shot. The idea is that Pinterest … Continue reading →
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), … Continue reading →
Apparently I have issues with my reading comprehension and with Textmate (initially) when it comes to using the sqldf package. G. pointed out in the previous comments, I could have just used options(gsubfn.engine = "R") instead of going through the trouble of installing the tcltk binaries. If you’ve got a happy distribution of R that
Of course, a few days before I leave for a much needed vacation, USA Today released their updated NCAA coaching salary database. For sports junkies, there’s an unlimited number of analysis and visualizations that can be done on the data. I took a quick break from packing to condense the data to a csv and
Time Series as calendar heat maps + All of my running data since April 1, 2009 = Generated by the following code: #Sample Code based on example program at: source(file = "calendarHeat.R") run<- read.csv("log.csv", header = TRUE, sep=",") sum(run$Distance) date <- c() for (i in 1: dim(run)){ if(run$DistanceUnit== 'Kilometer'){ miles <- c(miles,run$Distance * 0.62) }
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
