Motivation Like many, COVID-19 completely altered our lives as the virus spread across the country and became a mainstay in our society. Though my family and I have been spared of tragedy, we’ve had to find ways to replace kid’s activities, work routines, and social interactions with other (hopefully) enriching activities… Like family games. Ticket to Ride caused a lot of strife between my kids :) One thing that we’ve added to our new normal is watching “The Price is Right” after dinner.
A Tribute! Since the dawn of time, people have been developing human-like attachments to their transportation. I imagine Moses naming his chariot, Noah his ark, and maybe even Jesus naming his borrowed donkey. Well, maybe I’m taking too much historical license, but man’s affinity for their transportation remains. At the age of 21, my dad and I split the cost of a 2005 Ford F150.
The Goal of this Analysis One of my superpowers is being a bad fantasy football manager. My motivation for this analysis is to show how I’m not actually that bad - but rather unlucky. To show this, I created this post. Below I summarize how I got the data, the information I can glean from the data, share the link to this analysis for my league, and share the code so you can do it to.
Introduction Often times, I run simulations on my computer to help educate my students about different statistical topics. In doing this, I often end up running fairly “long” simulations that take up more time than what I’d like to use in the classrooms. Complaining about this let me to Googling, which, as usual, let me to a solution. Posted below is an example of what I learned with an accompanying ShinyApp which helps me explain bootstrapping to my students.
Inspiration At the 2019 New York R Conference, Emily Robinson gave a great talk called “Everything You Wanted to Know About Making R Packages but Were Afraid to Ask” (Note: This link will be live once the video is published). Emily Robinson is one of my ‘heros of R.’ I was honored to host her several years back a the United States Military Academy when she came to present her work on A/B Testing during her time at Esty.
Introduction In my previous post, I worked through how to get your league information from the ESPN and YAHOO APIs. The motivation for this project was to compare two leagues performances over the 2018 fantasy football season. As the ring leader of the West Point Department of Mathematical Sciences fantasy football league, we had 20 managers. This, of course, is too many for one league so we split the teams across two leagues.
A Little About Dates in R Before we launch into any analysis that contains dates, we should know a few important nuggets about how R handles date-like objects. There are 3 date/time classes are built in to R - Date - POSIXct - POSIXlt Base R First, base R can read a string of text and convert it to a date class. To help it read the date, you must tell R what date format your character string should expect.
Tracking my cadet’s summer plans with dplyr and ggplot. The interactive ShinyApp is below! % -- % -- % -- % -- % -- % -- % -- % -- % -- % --
Was honored to speak at this years 2018 New York R Conference hosted by Lander Analytics and Work-Bench. Jared Lander sure knows how to host a good party, I mean, conference. My Slides are available, and you can contact me at firstname.lastname@example.org Here is the youtube link to the video: Oh, and I got to go for a little run before the conference!
As my experience with statistics, computer science, and the all encompasing term, data science, increases, I have decided that it is time to share some of my projects. Creating this blog serves several purposes. It archives some projects I’ve created. It makes my work public so I can receive (hopefully) constructive feedback to improve my tools/analysis. It helps me share what I’ve learned with the community that has been so helpful to me.