Gist 836e6cd6c12eb7398f8b
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Aabhishekpolavarapu
Last edited Feb 2, 2016
Created on Feb 2, 2016
This example visualizes college football passing statistics from the 2014 season, showing relationships among metrics like passing yards, touchdowns, and rushing performance. It presents a scatterplot matrix of offensive variables, a horizontal bar chart of passing yards colored by conference, and a bar chart of average passing touchdowns per conference. The R code uses base plotting functions including `pairs`, `barplot`, and `legend`, with custom color mapping via nested `ifelse` statements to distinguish the 11 conferences. Data is loaded from a CSV file with `read.csv`, and aggregate means are calculated using `aggregate`.
AI-generated description1.
setwd("C:/Users/apolavar.CS/Documents/Rdocs")
mydata=read.csv("passing-stats-2014.csv")
mydata
myvars <- c("Passing.Yards", "Passing.TD", "Rate", "Rushing.Yards", "Rushing.TD")
newdata <- mydata[myvars]
pairs(newdata)
2.
setwd("C:/Users/apolavar.CS/Documents/Rdocs")
mydata=read.csv("passing-stats-2014.csv")
mydata
myvars <- c(Player", "Conf", "Passing.Yards")
newdata <- mydata[myvars]
colour<-function(color) {
ifelse(color=='ACC','white',
ifelse(color=='American','aquamarine1',
ifelse(color=='Big 12','blue3',
ifelse(color=='Big Ten','brown2',
ifelse(color=='CUSA','darkmagenta',
ifelse(color=='Ind','darkorange',
ifelse(color=='MAC','gray4',
ifelse(color=='MWC','deepskyblue',
ifelse(color=='Pac-12','chartreuse',
ifelse(color=='SEC','darkgoldenrod4',
ifelse(color=='Sun Belt','bisque1', 'yellow')))))))))))}
distinct<-unique(newdata$Conf,incomparables=FALSE)
barplot(newdata$Passing.Yards,horiz = TRUE,xlab = "Passing Yards",names.arg = newdata$Player,las = 1,cex.names = 0.45,xlim = c(0,5000), col=colour(newdata$Conf))
clrs <- c("white", "aquamarine1", "blue3", "brown2", "darkmagenta", "darkorange","gray4","deepskyblue","chartreuse" ,"darkgoldenrod4","bisque1","yellow")
legend("topright",c("ACC", "American", "Big 12", "Big Ten", "CUSA", "Ind", "MAC", "MWC", "Pac-12", "Sec", "Sun Belt"),cex=0.6,bty="n", fill=clrs)
3.
setwd("C:/Users/apolavar.CS/Documents/Rdocs")
mydata=read.csv("passing-stats-2014.csv")
mydata
myvars <- c("Passing.TD","Conf")
newdata <- mydata[myvars]
colour<-function(color) {
ifelse(color=='ACC','white',
ifelse(color=='American','aquamarine1',
ifelse(color=='Big 12','blue3',
ifelse(color=='Big Ten','brown2',
ifelse(color=='CUSA','darkmagenta',
ifelse(color=='Ind','darkorange',
ifelse(color=='MAC','gray4',
ifelse(color=='MWC','deepskyblue',
ifelse(color=='Pac-12','chartreuse',
ifelse(color=='SEC','darkgoldenrod4',
ifelse(color=='Sun Belt','bisque1', 'yellow')))))))))))}
distinct<-unique(newdata$Conf,incomparables=FALSE)
mean <- aggregate(Passing.TD ~ Conf, newdata, mean);
barplot(mean$Passing.TD,xlab = "Conf",ylab= "Passing touchdowns",las = 1,cex.names = 0.6,ylim = c(0,30), names.arg=mean$Conf,col=colour(mean$Conf))
clrs <- c("white", "aquamarine1", "blue3", "brown2", "darkmagenta", "darkorange","gray4","deepskyblue","chartreuse" ,"darkgoldenrod4","bisque1","yellow")
legend("topright",c("ACC", "American", "Big 12", "Big Ten", "CUSA", "Ind", "MAC", "MWC", "Pac-12", "Sec", "Sun Belt"),cex=0.6,bty="n", fill=clrs)
