Assignment 5

Overview

First we create a csv file to represent this chart:

And then load it into R. The goal is to then transform the data from wide to long, and then perform some analysis on the data.

airline_data <- read.csv('airlinedata.csv')
airline_data

##   Airline On_time_delayed LosAngeles Phoenix SanDiego SanFrancisco Seattle
## 1  Alaska         on time        497     221      212          503    1841
## 2  Alaska         delayed         62      12       20          102     305
## 3  AMWest         on time        694    4840      383          320     201
## 4  AMWest         delayed        117     415       65          129      61

pacman::p_load(dplyr, tidyr, magrittr)

#Transform from a wide format to long by grouping the locations into a 'Destination' column
long_data <- gather(airline_data, 'Destination', 'Num_Flights', 3:7)
long_data

##    Airline On_time_delayed  Destination Num_Flights
## 1   Alaska         on time   LosAngeles         497
## 2   Alaska         delayed   LosAngeles          62
## 3   AMWest         on time   LosAngeles         694
## 4   AMWest         delayed   LosAngeles         117
## 5   Alaska         on time      Phoenix         221
## 6   Alaska         delayed      Phoenix          12
## 7   AMWest         on time      Phoenix        4840
## 8   AMWest         delayed      Phoenix         415
## 9   Alaska         on time     SanDiego         212
## 10  Alaska         delayed     SanDiego          20
## 11  AMWest         on time     SanDiego         383
## 12  AMWest         delayed     SanDiego          65
## 13  Alaska         on time SanFrancisco         503
## 14  Alaska         delayed SanFrancisco         102
## 15  AMWest         on time SanFrancisco         320
## 16  AMWest         delayed SanFrancisco         129
## 17  Alaska         on time      Seattle        1841
## 18  Alaska         delayed      Seattle         305
## 19  AMWest         on time      Seattle         201
## 20  AMWest         delayed      Seattle          61

Alaska_Delayed <- long_data %>%
  select(Num_Flights) %>%
  filter(long_data$Airline == 'Alaska', long_data$On_time_delayed == 'delayed')

Alaska_ontime <- long_data %>%
  select(Num_Flights) %>%
  filter(long_data$Airline == 'Alaska', long_data$On_time_delayed == 'on time')

AMWest_Delayed <- long_data %>%
  select(Num_Flights) %>%
  filter(long_data$Airline == 'AMWest', long_data$On_time_delayed == 'delayed')

AMWest_ontime <- long_data %>%
  select(Num_Flights) %>%
  filter(long_data$Airline == 'AMWest', long_data$On_time_delayed == 'on time')

Destination <- distinct(long_data, Destination)

#Re-organize the data so that we can see the number of flights delayed or on time for each airline.  Each row represents a different destination.  Also added columns of total on time flights, delayed flights, and total flights for each destination.


totals <- data.frame(c(Destination, Alaska_Delayed, Alaska_ontime, AMWest_Delayed, AMWest_ontime))
colnames(totals) <- c('Destination', 'Alaska_Delayed', 'Alaska_ontime', 'AMWest_Delayed', 'AMWest_ontime')

totals %<>% 
  mutate(On_time = Alaska_ontime + AMWest_ontime) %>%
  mutate(Delayed = Alaska_Delayed + AMWest_Delayed) %>%
  mutate(Flights = Alaska_Delayed + Alaska_ontime + AMWest_Delayed + AMWest_ontime)

totals

##    Destination Alaska_Delayed Alaska_ontime AMWest_Delayed AMWest_ontime
## 1   LosAngeles             62           497            117           694
## 2      Phoenix             12           221            415          4840
## 3     SanDiego             20           212             65           383
## 4 SanFrancisco            102           503            129           320
## 5      Seattle            305          1841             61           201
##   On_time Delayed Flights
## 1    1191     179    1370
## 2    5061     427    5488
## 3     595      85     680
## 4     823     231    1054
## 5    2042     366    2408

Alaska_Delayed <- sum(totals$Alaska_Delayed)
Alaska_ontime <- sum(totals$Alaska_ontime)
Alaska_flights <- Alaska_Delayed + Alaska_ontime
AMWest_Delayed <- sum(totals$AMWest_Delayed)
AMWest_ontime <- sum(totals$AMWest_ontime)
AMWest_flights <- AMWest_Delayed + AMWest_ontime

#Delayed Percentages by Airline
Alaska_p <- (Alaska_Delayed / Alaska_flights) * 100
AMWest_p <- (AMWest_Delayed / AMWest_flights) * 100

cat('The percentage of flights delayed for Alaska Airlines is:', Alaska_p, '%\n')

## The percentage of flights delayed for Alaska Airlines is: 13.27152 %

cat('The percentage of flights delayed for AMWest Airlines is:', AMWest_p, '%')

## The percentage of flights delayed for AMWest Airlines is: 10.89273 %

#Delayed Percentages by Destination
LA_p <- (totals[1, 7] / totals[1,8]) * 100
PHO_p <- (totals[2, 7] / totals[2,8]) * 100
SD_p <- (totals[3, 7] / totals[3,8]) * 100
SF_p <- (totals[4, 7] / totals[4,8]) * 100
SEA_p <- (totals[5, 7] / totals[5,8]) * 100

cat('The percentage of flights delayed heading to Los Angeles is:', LA_p, '%\n')

## The percentage of flights delayed heading to Los Angeles is: 13.06569 %

cat('The percentage of flights delayed heading to Phoenix is:', PHO_p, '%\n')

## The percentage of flights delayed heading to Phoenix is: 7.780612 %

cat('The percentage of flights delayed heading to San Diego is:', SD_p, '%\n')

## The percentage of flights delayed heading to San Diego is: 12.5 %

cat('The percentage of flights delayed heading to San Francisco is:', SF_p, '%\n')

## The percentage of flights delayed heading to San Francisco is: 21.91651 %

cat('The percentage of flights delayed heading to Seattle is:', SEA_p, '%')

## The percentage of flights delayed heading to Seattle is: 15.19934 %

Assignment 5

Vincent Miceli

2/26/2020

Overview

Conclusion