Team Members: Srinivas Havanur; Erika Siregar; Hung Do
Assignment: ICW2: Football data analysis
Course: Information Visualization
Semester: Spring 2016
###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("PassingYards","PassingTD","Rate","RushingYards","RushingTD")]
#Pairing data into matrix with three different colors
pairs(data,main = "Title: Performance of Football Player in Scatter Plot Matrix",pch = 21, bg = c("red", "green3", "blue") )
###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$Conf)) + geom_bar(stat="identity") + coord_flip() + labs(x="Player Names",y="Passing Yards",fill="Conference") + ggtitle("Title: Passing yards per player with conference as color") + scale_fill_brewer(palette="Paired")###3. Bar chart of the average of one of the statistics for each conference
a) Without Color Code:
Embedding R code in Markdown:
#Load the csv file using file browse option.
sports <- read.csv(file.choose(), header=TRUE, sep=",")
# This library is loaded to perform groupby summary statistics.
library(doBy)
#It is a package that includes set of functions that attempt to streamline the process for creating predictive models. ggplot is one of the functions in that package.
library(caret)
#This is used to calculate average of PassingAttempts per conference.
sports_filtered_avg=summaryBy(PassingAttempts ~ Conf, data = sports,FUN = list(mean))
#Graph is plotted using ggplot to represent a bar graph having average of PassingAttempts per conference.
ggplot(sports_filtered_avg, aes(sports_filtered_avg$Conf,sports_filtered_avg$PassingAttempts.mean)) + geom_bar(stat="identity", fill="steelblue") + labs(x="Conference", y="Avg Value of Passing Attempts") + ggtitle("Title: Avg of PassingAttempts statistics for each Conference")
b) With Color Code:
Embedding R code in Markdown:
#Load the csv file using file browse option.
sports <- read.csv(file.choose(), header=TRUE, sep=",")
# This library is loaded to perform groupby summary statistics.
library(doBy)
#It is a package that includes set of functions that attempt to streamline the process for creating predictive models. ggplot is one of the functions in that package.
library(caret)
#This is used to calculate average of PassingAttempts per conference.
sports_filtered_avg=summaryBy(PassingAttempts ~ Conf, data = sports,FUN = list(mean))
#Graph is plotted using ggplot to represent a bar graph having average of PassingAttempts per conference with different color code.
ggplot(sports_filtered_avg, aes(sports_filtered_avg$Conf,sports_filtered_avg$PassingAttempts.mean, fill=sports_filtered_avg$Conf)) + geom_bar(stat="identity") + labs(x="Conference", y="Avg Value of Passing Attempts", fill="Conference") + ggtitle("Title: Avg of PassingAttempts statistics for each Conference") + scale_fill_brewer(palette="Paired")