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Team Members: Srinivas Havanur Erika Siregar Hung Do Assignment: ICW2: Football data analysis

Course: Information Visualization

Semester: Spring 2016

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Image from http://www.cs.odu.edu/~shavanur/infovis/

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1.Scatterplot matrix of passing yards, passing TDs, passer rating, rushing yards, and rushing TDs

Embedding R code in Markdown:

# Scatterplot Matrices
sn <- read.csv("passing-stats-2014.csv", header =T)

# subset data
# Should clean spaces in headings of data
data <- sn[,c("PassingAttempts","PassingYards","PassingTD","Rate","RushingYards","RushingTD")]

#Pairing data into matrix with three different colors
pairs(data,main = "Scatterplot Matrix",pch = 21, bg = c("red", "green3", "blue")[unclass(iris$Species)])

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2.Bar chart of passing yards per player (best displayed as a horizontal bar chart), with conference as color

Embedding R code in Markdown:

#Load the csv file using file browse option.
sports <- read.csv(file.choose(), header=TRUE, sep=",")

#It is a package that includes set of functions that attempt to streamline the process for creating predictive models. ggplot is one of the functions in that package. 
library(caret)

#Graph is plotted using ggplot having horizontal bar chart option to represent PassingYards per player.
ggplot(sports, aes(x=sports$Player, y=sports$PassingYards, fill=sports$Conf)) + geom_bar(stat="identity") + coord_flip() + labs(x="Player Names",y="Passing Yards",fill="Conference") + ggtitle("Title: Passing yards per player with conference as color")

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3.Bar chart of the average of one of the statistics for each conference

Embedding R code in Markdown:

#Load the csv file using file browse option.
sports <- read.csv(file.choose(), header=TRUE, sep=",")

# This library is loaded to perform groupby summary statistics.
library(doBy)

#It is a package that includes set of functions that attempt to streamline the process for creating predictive models. ggplot is one of the functions in that package. 
library(caret)

#This is used to calculate average of PassingAttempts per conference.
sports_filtered_avg=summaryBy(PassingAttempts ~ Conf, data = sports,FUN = list(mean))

#Graph is plotted using ggplot to represent a bar graph having average of PassingAttempts per conference.
ggplot(sports_filtered_avg, aes(sports_filtered_avg$Conf,sports_filtered_avg$PassingAttempts.mean)) + geom_bar(stat="identity", fill="steelblue") + labs(x="Conference", y="Avg Value") + ggtitle("Title: Avg of PassingAttempts statistics for each Conference")