# Visualizing NFL Data

2016-08-31

Lets look at how teams played on offense depending on where they were on the field (their yardline) and the down they were on.  The fields in our dataframe that we will care about here are yfog (yards from own goal), type (rush or pass), dwn (current down number: 1,2,3, or 4).  We will want a table with each of these columns as well as a sum column.  That way, we can see how many times a pass attempt was done on the 4th down when a team was X yards from their own goal.

To do this, we will use a package called plyr.  The Internet says that this package makes it easy for us to split data, mess with it, and then put it back together.  I am not convinced the tool is easy, but I haven’t spent too much time with it.

Check it out for some ideas on what you can do with R.

## The Intuition Behind Principal Component Analysis

2018-12-07

Holger von Jouanne-Diedrich gives us an intuition behind how principal component analysis (PCA) works: Principal component analysis (PCA) is a dimension-reduction method that can be used to reduce a large set of (often correlated) variables into a smaller set of (uncorrelated) variables, called principal components, which still contain most of the information.PCA is a concept […]

## Plotting Diagrams In R With nest() And map()

2018-12-06

Sebastian Sauer shows how to display multiple ggplot2 diagrams together using facets as well as a combination of the nest() and map() functions: One simple way is to plot several facets according to the grouping variable: d %>% ggplot() + aes(x = hp, y = mpg) + geom_point() + facet_wrap(~ cyl) Faceting is great, but it’s good to know […]