NCAA College Bowl Pick'em Predictions

Thomas Roh

*Please read the Terms of Use if you are going to use the data This is the code from my presentation last week at the Omaha R User Group Meeting. A fun competition every year is to pick the winners for each college football bowl. I wanted to build a data-driven model to help me make this year’s picks. This post will walk through the process of obtaining data for predictions, building a couple predictive models, and then compare the results of the models. [Read More]


Garth Highland

Visualizing Data Using the Functional Boxplot library(fda) data(growth) girls=growth$hgtf age=growth$age The Berkley growth data found in the fda package is an excellent example of functional data. Here we consider each growth curve to be one observation. With data of this type there are a couple of plotting avenues we might take: over plotting or sequential boxplots. These methods aren’t inherently “incorrect”, but limitations to their interpretability exist especially when encountered with larger data sets. [Read More]

Submitting a Post with Blogdown

Thomas Roh

Set up You will first need to set up an account on github and install git. Next, make a copy of the repository into your desired workspace. I like to keep all of my version controlled projects under one directory. Open up a command terminal of your choosing and use the following: cd #[working directory] git clone git branch #[new branch] git checkout #[new branch] git push --set-upstream origin #[new branch] After the initial set up you can open the R Project in Rstudio and use the IDE to handle most of the work you will need to do in git. [Read More]