Tidying Data:

Create a csv and load it from github:

library(tidyr)
library(plyr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)

##Read in wide data from github
u_data<-read.csv("https://raw.githubusercontent.com/scottogden10/607-Assignment2/master/untidy2.csv")

##Now clean up and label data.
u_data[2, 1] <- "ALASKA"
u_data[5, 1] <- "AMWEST"
clean<-u_data[-c(3),]
clean1<-plyr::rename(clean,c("X"="Airline","X.1"="Status"))

#Use the package to turn wide data into long, usable for analysis, with the only columns being for measures (dimensions collapse)

t<-gather(clean1,"City","Number",3:7)
t<-spread(t,"Status","Number")
head(t)
##   Airline          City delayed on_time
## 1  ALASKA   Los.Angeles      62     497
## 2  ALASKA       Phoenix      12     221
## 3  ALASKA     San.Diego      20     212
## 4  ALASKA San.Francisco     102     503
## 5  ALASKA       Seattle     305    1841
## 6  AMWEST   Los.Angeles     117     694
alaska<-subset(t,t$Airline=="ALASKA")
AMWEST<-subset(t,t$Airline=="AMWEST")

##Now Plot the on time percentage for each city with Alaska in Blue and AMWEST in red.

ggplot()+geom_point(data=alaska,aes(x=alaska$City,y=alaska$on_time/(alaska$delayed+alaska$on_time)),colour='blue',size=4)+geom_point(data=AMWEST,aes(x=AMWEST$City,y=AMWEST$on_time/(AMWEST$delayed+AMWEST$on_time)),colour='red',size=4)+labs(x="City",y="Percent on time")+ggtitle("Alaska vs AMWEST On Time Percentage")

##You can see quickly that AMWEST Always underperforms Alaska

##We'd like to see if this is a significant difference, a fairly reasonable test may be the Chi Square test for independence for each city (adding Yates' correction for continuity when needed)  

##Make tables for each city Where the rows are the airline and the columns are on time and delayed.

 LA<-rbind(subset(alaska,alaska$City=="Los.Angeles",select = c(delayed,on_time)),subset(AMWEST,AMWEST$City=="Los.Angeles",select = c(delayed,on_time)))
 
 Ph<-rbind(subset(alaska,alaska$City=="Phoenix",select = c(delayed,on_time)),subset(AMWEST,AMWEST$City=="Phoenix",select = c(delayed,on_time)))
 
 SD<-rbind(subset(alaska,alaska$City=="San.Diego",select = c(delayed,on_time)),subset(AMWEST,AMWEST$City=="San.Diego",select = c(delayed,on_time)))
 
 SF<-rbind(subset(alaska,alaska$City=="San.Francisco",select = c(delayed,on_time)),subset(AMWEST,AMWEST$City=="San.Francisco",select = c(delayed,on_time)))
 
 Se<-rbind(subset(alaska,alaska$City=="Seattle",select = c(delayed,on_time)),subset(AMWEST,AMWEST$City=="Seattle",select = c(delayed,on_time)))

##Summarize
 df<-data.frame(City=alaska$City,P_Value=alaska$on_time)
 
options(scipen = 999)
 
#Calculate chi-square test for independence on these variables and display with a summary column.

df[1,2]<-chisq.test(LA)$p.value
df[2,2]<-chisq.test(Ph)$p.value
df[3,2]<-chisq.test(SD)$p.value
df[4,2]<-chisq.test(SF)$p.value
df[5,2]<-chisq.test(Se)$p.value
 summ<-ifelse(df$P_Value<0.05,"Significant","Not-Significant")
 
dff<-data.frame(City=df$City,P_Value=df$P_Value,Summary=summ)

dff
##            City        P_Value         Summary
## 1   Los.Angeles 0.085662990091 Not-Significant
## 2       Phoenix 0.159476871459 Not-Significant
## 3     San.Diego 0.037627532723     Significant
## 4 San.Francisco 0.000005855264     Significant
## 5       Seattle 0.000163744783     Significant

So we see for San Diego, San Francisco and Seattle we have significant differences among the rows (Airline)

In Conclusions, the plot shows alaska outperforms AMWEST for all cities, but the (Airline X Outcome) observed minus expected values are signficant only for 3 out of 5.