Assignment 5 607

Load packages

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)

###Import Data

dirtydata <- read.csv("https://raw.githubusercontent.com/seyi116/data607/main/alaksa%20am%20west%20flight%20data.csv", )

Tidy Data

First step is to rename some values

dirtydata[1,2] <- "Alaska On Time"
dirtydata[2,2] <- "Alaska Delayed"
dirtydata[4,2] <- "Am West On Time"
dirtydata[5,2] <- "Am West Delayed"

Next Step is to remove the Rows and columns we do not need

Lessdirtydata <- dirtydata[-c(1,2)] %>%
  slice(-3)

Next we name the rows

row.names(Lessdirtydata) <- c( "Alaska On Time", "Alaska Delayed", "Am West On Time", "Am West Delayed")

Then we create a totals column to facilitate analysis later on

Lessdirtydata <- mutate(Lessdirtydata, "Totals" = Los.Angeles + Phoenix + San.Diego + San.Francisco + Seattle)

Pivot Data

TidyData <- 
   data.frame(
  t(Lessdirtydata)
)

Analysis

To better Understand the data relative to rach other we need to create some new variables

AnalysisData <- TidyData%>%
  mutate("Alaska total_flights"= `Alaska.Delayed`+`Alaska.On.Time`)%>%
  mutate("Am West total_flights"= `Am.West.Delayed`+ `Am.West.On.Time`)%>%
  mutate("Alaska delay percentage"= `Alaska.Delayed`/(`Alaska.On.Time`+`Alaska.Delayed`))%>%
  mutate("Am West delay percentage"= `Am.West.Delayed`/(`Am.West.On.Time`+`Am.West.Delayed`))%>%
  round(digits = 3)

Visualization

library(ggplot2)
AnalysisData$Destination<- c("Los Angeles","Phoenix", "San Diego", "San Francisco", "Seattle", "Totals")
ggplot(AnalysisData, aes(x=`Destination`, y=`Alaska delay percentage`)) +
  geom_bar(position='dodge', stat='identity')

ggplot(AnalysisData, aes(x=Destination, y= `Am West delay percentage`)) +
  geom_bar(position='dodge', stat='identity')

Findings

When we compare the two airlines based on the data available we see that although Am West has more delays, a smaller percentage of its flights are delayed. San francisco and seattle seem to be problem destinations for both airlines.