index.Rmd

---
title       : NFL Player Data, 12/18/2016
subtitle    : Coursera Developing Data Products Course Project
author      : Harrison Hassig
job         : 
framework   : io2012        # {io2012, html5slides, shower, dzslides, ...}
highlighter : highlight.js  # {highlight.js, prettify, highlight}
hitheme     : tomorrow      # 
widgets     : []            # {mathjax, quiz, bootstrap}
mode        : selfcontained # {standalone, draft}
knit        : slidify::knit2slides
---

## Overview

This project is designed to give an graphical representation of NFL player's daily fantasy salary compared to their respective average daily fantasy points. This is broken down by position, according to FanDuel.
After plotting the players, a regression line is drawn. This is great, as visual learners can see which players are above average for their position, and which aren't.

---

## Data Sources
The data comes from FanDuel's website for the "Main" entry pool of NFL games on Sunday, December 18th 2016.
FanDuel is nice enough to provide a CSV file for this data, which is unlinkable, but is included in the github repo and hosted here:

(https://www.dropbox.com/s/px1yv5ioqj79i2l/playerData.csv)

```{r echo = FALSE}
library(shiny)
library(plotly)
library(ggplot2)
mydata = read.csv("playerData.csv")
```
```{r}
summary(mydata$Position)
```

---

## Calculations
Here, we plot the players Salary (x-axis) against their Average Fantasy Points (FFPG) (y-axis) and then add our regression line. "input$position" allows for user input, part of the assignment. The code is shown below:

```{r echo = TRUE, eval = FALSE}
mydata <- subset(mydata, Position == input$position)
ggplot(data = mydata, aes(x = Salary, y = FPPG, label = LastName)) + 
  geom_point(aes(colour = Position)) +
  geom_smooth(size = .5, method = loess) +
  xlab("Salary") +
  ylab("Average Points per Game") + 
  ggtitle("NFL Data 12/18/2016")
```

---

## Output
The final output will be a shiny app that will include a brief explanation, a radio input block for the user to select which position they would like to see charted. This will then show the different players of that position graphed with their salary and average points per game. You can see a working version here:

(https://hhassig.shinyapps.io/project_week_4/)

```{r echo = FALSE}
mydata <- subset(mydata, Position == "WR")
ggplot(data = mydata, aes(x = Salary, y = FPPG)) + 
  geom_point(aes(colour = Position)) +
  geom_smooth(size = .5, method = loess) +
  xlab("Salary") +
  ylab("Average Points per Game") + 
  ggtitle("NFL Data 12/18/2016")
```