final_project.Rmd

---
title: "final_project"
author: "Diassa Diakite,Mohamed Mabizari"
date: "2/24/2020"
output: html_document
---
*link to github repository:* https://github.com/ddiakite1/final_project.git

## About 
Growing up, football has always been a big part of our lives. In an attempt to win games, teams 
use differing amounts of run and pass plays. Over time, you can see that running the ball and passing 
the ball have varying success rates year by year at each level of football. In this project we will study the trends in teams being more likely to run/pass the ball, and look to find which has had more success.

There are five major conferences in college football, often referred to as the 'Power 5'. These 
five conferences are as follows: the Southeastern Conference (SEC), the Big Ten Conference (B1G 10), 
the Big 12 Conference (BIG 12), the Pacific-12 Conference (PAC 12), and the Atlantic Coast Conference (ACC). From these five conferences come majority of the powerhouse teams in college football, as well as the most popular teams. Within each conference, teams play each other and a conference champion is 
determined by a conference championship game. As of 2014, there is a four team playoff system to 
determine the sole national champion. By exploring data from 2010 to present date in each of these 
power five conferences, I will be able to determine which play styles work best in each conference as 
well as on a countrywide scale, and I can draw comparisons and contrasts between conferences.

As for data, we collected it from multiple different sports-reference.com subpages.
The datasests that I retrieved from sports-reference.com were downloaded as excel files, read using the readxl library within r.

Creators

Diassa Diakité

My name is Diassa Diakité and I am an undergraduate at Harvard studying Government with a specialization in Data Science. I can be reached at ddiakite@college.harvard.edu

Mohamed Mabizari

My name is Mohamed Mabizari and I am an undergraduate at Harvard studying Sociology with a secondary in Computer Science. I can be reached at mmabizari@college.harvard.edu

  
## Setup Chunk
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(readxl)
library(janitor)
library(plotly)
library(rgl)
library(car)
library(gt)
library(shinythemes)
library(tidyverse)
```

## Data
### SEC Data
```{r, echo = FALSE, message = FALSE, cache=TRUE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html
# load in SEC 2019 data

sec19 <- read_excel("SEC/sec19.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2019) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# load in SEC 2018 data
sec18 <- read_excel("SEC/sec18.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2018) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec19 %>%
  full_join(sec18, by = NULL)

# load in SEC 2017 data
sec17 <- read_excel("SEC/sec17.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2017) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec17, by = NULL)

# load in SEC 2016 data
sec16 <- read_excel("SEC/sec16.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2016) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec16, by = NULL)

# load in SEC 2015 data
sec15 <- read_excel("SEC/sec15.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2015) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec15, by = NULL)

# load in SEC 2014 data
sec14 <- read_excel("SEC/sec14.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2014) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec14, by = NULL)

# load in SEC 2013 data
sec13 <- read_excel("SEC/sec13.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2013) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec13, by = NULL)

# load in SEC 2012 data
sec12 <- read_excel("SEC/sec12.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2012) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec12, by = NULL)

# load in SEC 2011 data
sec11 <- read_excel("SEC/sec11.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2011) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec11, by = NULL)

# load in SEC 2010 data
sec10 <- read_excel("SEC/sec10.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2010) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create SEC dataset
sec <- sec %>%
  full_join(sec10, by = NULL) %>% mutate(conference = "SEC")
```

### B1G 10 Data
```{r, echo = FALSE, message = FALSE, cache=TRUE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html
# load in B1G 10 2019 data

b1g19 <- read_excel("B1G10/b1g19.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2019) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# load in B1G 2018 data
b1g18 <- read_excel("B1G10/b1g18.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2018) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g19 %>%
  full_join(b1g18, by = NULL)

# load in B1G 2017 data
b1g17 <- read_excel("B1G10/b1g17.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2017) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g17, by = NULL)

# load in B1G 2016 data
b1g16 <- read_excel("B1G10/b1g16.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2016) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g16, by = NULL)

# load in B1G 2015 data
b1g15 <- read_excel("B1G10/b1g15.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2015) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g15, by = NULL)

# load in B1G 2014 data
b1g14 <- read_excel("B1G10/b1g14.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2014) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g14, by = NULL)

# load in B1G 2013 data
b1g13 <- read_excel("B1G10/b1g13.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2013) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g13, by = NULL)

# load in B1G 2012 data
b1g12 <- read_excel("B1G10/b1g12.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2012) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g12, by = NULL)

# load in B1G 2011 data
b1g11 <- read_excel("B1G10/b1g11.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2011) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g11, by = NULL)

# load in B1G 2010 data
b1g10 <- read_excel("B1G10/b1g10.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2010) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create B1G dataset
b1g <- b1g %>%
  full_join(b1g10, by = NULL) %>% mutate(conference = "B1G 10")
```

### BIG 12 Data
```{r, echo = FALSE, message = FALSE, cache=TRUE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html
# load in Big 12 2019 data

b1219 <- read_excel("BIG12/b1219.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2019) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# load in BIG12 2018 data
b1218 <- read_excel("BIG12/b1218.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2018) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b1219 %>%
  full_join(b1218, by = NULL)

# load in BIG12 2017 data
b1217 <- read_excel("BIG12/b1217.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2017) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1217, by = NULL)

# load in BIG12 2016 data
b1216 <- read_excel("BIG12/b1216.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2016) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1216, by = NULL)

# load in BIG12 2015 data
b1215 <- read_excel("BIG12/b1215.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2015) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1215, by = NULL)

# load in BIG12 2014 data
b1214 <- read_excel("BIG12/b1214.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2014) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1214, by = NULL)

# load in BIG12 2013 data
b1213 <- read_excel("BIG12/b1213.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2013) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1213, by = NULL)

# load in BIG12 2012 data
b1212 <- read_excel("BIG12/b1212.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2012) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1212, by = NULL)

# load in BIG12 2011 data
b1211 <- read_excel("BIG12/b1211.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2011) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1211, by = NULL)

# load in BIG12 2010 data
b1210 <- read_excel("BIG12/b1210.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2010) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create BIG12 dataset
b12 <- b12 %>%
  full_join(b1210, by = NULL) %>% mutate(conference = "BIG 12")
```

### PAC 12 Data
```{r, echo = FALSE, message = FALSE, cache=TRUE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html
# load in PAC12 2019 data

pac19 <- read_excel("PAC12/pac19.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2019) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# load in PAC12 2018 data
pac18 <- read_excel("PAC12/pac18.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2018) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac19 %>%
  full_join(pac18, by = NULL)

# load in PAC12 2017 data
pac17 <- read_excel("PAC12/pac17.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2017) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac17, by = NULL)

# load in PAC12 2016 data
pac16 <- read_excel("PAC12/pac16.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2016) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac16, by = NULL)

# load in PAC12 2015 data
pac15 <- read_excel("PAC12/pac15.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2015) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac15, by = NULL)

# load in PAC12 2014 data
pac14 <- read_excel("PAC12/pac14.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2014) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac14, by = NULL)

# load in PAC12 2013 data
pac13 <- read_excel("PAC12/pac13.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2013) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac13, by = NULL)

# load in PAC12 2012 data
pac12 <- read_excel("PAC12/pac12.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2012) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac12, by = NULL)

# load in PAC12 2011 data
pac11 <- read_excel("PAC12/pac11.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2011) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac11, by = NULL)

# load in PAC12 2010 data
pac10 <- read_excel("PAC12/pac10.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2010) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create PAC12 dataset
pac <- pac %>%
  full_join(pac10, by = NULL) %>% mutate(conference = "PAC 12")
```

### ACC Data
```{r, echo = FALSE, message = FALSE, cache=TRUE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html
# load in ACC 2019 data

acc19 <- read_excel("ACC/acc19.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2019) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# load in ACC 2018 data
acc18 <- read_excel("ACC/acc18.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2018) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc19 %>%
  full_join(acc18, by = NULL)

# load in ACC 2017 data
acc17 <- read_excel("ACC/acc17.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2017) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc17, by = NULL)

# load in ACC 2016 data
acc16 <- read_excel("ACC/acc16.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2016) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc16, by = NULL)

# load in ACC 2015 data
acc15 <- read_excel("ACC/acc15.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2015) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc15, by = NULL)

# load in ACC 2014 data
acc14 <- read_excel("ACC/acc14.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2014) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc14, by = NULL)

# load in ACC 2013 data
acc13 <- read_excel("ACC/acc13.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2013) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc13, by = NULL)

# load in ACC 2012 data
acc12 <- read_excel("ACC/acc12.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2012) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc12, by = NULL)

# load in ACC 2011 data
acc11 <- read_excel("ACC/acc11.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2011) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc11, by = NULL)

# load in ACC 2010 data
acc10 <- read_excel("ACC/acc10.xls",skip=1) %>%
  clean_names() %>%
  mutate(year = 2010) %>%
  mutate(w_pct = w/(w+l), c_w_pct = c_w/(c_w+c_l)) %>%
  select(year, w, l, w_pct, c_w, c_l, c_w_pct, school, p_cmp, p_att, p_pct, p_yds, p_td, r_att, r_yds, r_avg, r_td, fum, int)

# full_join to create ACC dataset
acc <- acc %>%
  full_join(acc10, by = NULL) %>% mutate(conference = "ACC")
```


## Data Fun
```{r, include = FALSE, message=FALSE}
# message=FALSE so messages on updated column names does not come up
# echo = FALSE so that all of the code is not shown in the html

# All CFB stats
p5 <- full_join(sec, 
      full_join(b1g, 
      full_join(b12, 
      full_join(pac, acc, by=NULL), by=NULL), by=NULL), by=NULL) %>%
  rename(rushATT = r_att, rushYDS = r_yds, passATT = p_att, passYDS = p_yds,
         passCMP = p_cmp, passPCT = p_pct, rushTD = r_td, passTD = p_td)

p5sum <- p5 %>% group_by(year, conference) %>% 
  summarise(rushATT = mean(rushATT), rushYDS = mean(rushYDS), passATT = mean(passATT), 
            passYDS = mean(passYDS), passCMP = mean(passCMP), passPCT = mean(passPCT),
            rushTD = mean(rushTD), passTD = mean(passTD))

saveRDS(p5, file = "p5.RDS")
saveRDS(p5sum, file = "p5sum.RDS")

#GT Tables Creation

statistic <- c("Rushing Attempts", "Rushing Yards", "Passing Attempts", "Passing Yards", "Pass Completions", "Passing Completion Percentage", "Rushing Touchdowns", "Passing Touchdowns")

p5Cor <- c(cor(p5$rushATT, p5$w_pct), cor(p5$rushYDS, p5$w_pct), cor(p5$passATT, p5$w_pct),
            cor(p5$passYDS, p5$w_pct), cor(p5$passCMP, p5$w_pct), cor(p5$passPCT, p5$w_pct),
            cor(p5$rushTD, p5$w_pct), cor(p5$passTD, p5$w_pct))

confCorfile <- p5 %>% filter(conference=="SEC")

confCorgtfile <- c(cor(confCorfile$rushATT, confCorfile$c_w_pct), 
                 cor(confCorfile$rushYDS, confCorfile$c_w_pct), 
                 cor(confCorfile$passATT, confCorfile$c_w_pct),
                 cor(confCorfile$passYDS, confCorfile$c_w_pct), 
                 cor(confCorfile$passCMP, confCorfile$c_w_pct), 
                 cor(confCorfile$passPCT, confCorfile$c_w_pct),
                 cor(confCorfile$rushTD, confCorfile$c_w_pct), 
                 cor(confCorfile$passTD, confCorfile$c_w_pct))

confCorgt <- data.frame(statistic, confCorgtfile) %>% arrange(desc(confCorgtfile)) %>% gt() %>% 
  tab_header(title = "Correlation To Conference Win Percentage", 
             subtitle = "In the Southeastern Conference") %>% 
  cols_label(statistic = "Statistical Category", confCorgtfile = "Correlation Coefficient")

p5Corgt <- data.frame(statistic, p5Cor) %>% arrange(desc(p5Cor)) %>% gt() %>% 
  tab_header(title = "Correlation To Win Percentage", 
             subtitle = "In All Power 5 Conferences") %>% 
  cols_label(statistic = "Statistical Category", p5Cor = "Correlation Coefficient")

saveRDS(p5Corgt, file = "p5Corgt.RDS")
```