byu football

Goal of this post. Answer some interesting questions about BYU football. Dive into different modeling approaches. I don’t explain my thinking below, but some of the charts might be cool. Some questions of interest How is Kilani Sitake doing in his second season compared to past BYU coaches? More challenging: how’s he doing relative to all second-season coaches? df_in <- read.csv(file.path(fp_data, 'byu_seasons.csv')) %>% distinct() Some basic questions: * How many years do we have data on?
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R users fall in love with ggplot2, the growing standard for data visualization in R. The ability to quickly vizualize trends, and customize just about anything you’d want, make it a powerful tool. Yet this week, I made a discvoery that may reduce how much I used ggplot2. Enter plot_ly(). For this post, I assume that you have a working knowledge of the dplyr (or magrittr) and ggplot2 packages.
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Bryan Whiting

father, innovator, data scientist

Data Scientist

Washington, D.C.