NFL Injury Analysis

Group Members: Willow Duffell, Lauren Mock, Addison McGhee, Zach Clement, and Dan Nolte

1. Data Build/Web Scraping

We generated two csv files which underlie all of our analysis in this project. Both are saved in the Data folder, and they are called:

• all_injuries_clean.csv - This is the data on all injuries incurred by players in the NFL between 2009-2021.It contains information on player name, team, year, injury type, and injury duration.
• all_player_demographic_clean.csv - This dataset contains "demographic" data on NFL players in the 2009-2021 period. For example, height, weight, position, and birth date.

We built these datasets ourselves by web scraping a website which we assessed to be a reliable source of information on NFL injuries called pro-football-reference.com. All web scraping and data building scripts are located in the Data/data_build folder. The process for scraping and constructing these data is listed below:

Building all_player_demographic_clean.csv

1. webscraping_injuries.R (as in Rscript webscraping_injuries.R) updates the Injuries_year.Rdata file, which contains a list with dataframes with injury tables for every year/team combination. The file scrapes creates these dataframes from tables found in webpages such as this one.

2. clean_players.R takes a csv file from gridironai.com (not contained on the github because of file size, but it can be downloaded here) and writes to Data/all_player_demographic_clean.csv which contains one row per player per year.

Building all_injuries_clean.csv

1. list_to_players.R uses the Injuries_year.Rdata file to produce a csv file (injuries_2009_2021.csv) which has a row for every player who appeared in the injuries tables.

2. clean_injuries.R uses the injuries_2009_2021.csv file to produce a csv file with our definition of number of injuries per year for each player. This file is written to Data/all_injuries_clean.csv.

There are 2 other subfolders within the Data folder: Data/data_dictionary and Data/data_for_plots. The data dictionary folder contains a single file explaining each variable in our datasets. The data_for_plots folder contains datasets that were generated by adapting our two primary datasets each of our 4 various analyses. We save these intermediate datasets in this folder so that we can refer to them directly, and so minimize cleaning code in our final RMarkdown file.

2. Exploratory Data Analysis

For each of our 4 research questions, we completed exploratory data analysis. Each analysis is saved in the EDA folder for each question being analyzed. Data, plots, and regression output which appears in our final RMarkdown file are available in the question-specific EDA files.

3. Shiny App

The R Shiny App can be found in the following path: BST_260_project/EDA/Question2 and is named app.R This app is also shown on a separate tab on our project website under the RShiny Tab.

4. Final Report

Our final report RMarkdown and associated HTML files are saved in the Final Report folder.

5. Website

The Google site displaying our project objectives and analyses can be found here: Google Site.