Project 1

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(knitr)

url <- "https://raw.githubusercontent.com/Yedzinovich/Data-607/main/assignment-4.csv"
flights <- read_csv(url, show_col_types = FALSE)
print(flights)

## # A tibble: 4 × 7
##   Airline Status  `Los Angeles` Phoenix `San Diego` `San Francisco` Seattle
##   <chr>   <chr>           <dbl>   <dbl>       <dbl>           <dbl>   <dbl>
## 1 Alaska  On Time           497     221         212             503    1841
## 2 Alaska  Delay              62      12          20             102     305
## 3 AM West On Time           694    4840         383             320     201
## 4 AM West Delay             117     415          65             129      61

kable(flights)

Airline	Status	Los Angeles	Phoenix	San Diego	San Francisco	Seattle
Alaska	On Time	497	221	212	503	1841
Alaska	Delay	62	12	20	102	305
AM West	On Time	694	4840	383	320	201
AM West	Delay	117	415	65	129	61

flights_long <- flights %>%
  pivot_longer(cols = starts_with("Los Angeles"):starts_with("Seattle"), names_to = "City", values_to = "Flights")
print(flights_long)

## # A tibble: 20 × 4
##    Airline Status  City          Flights
##    <chr>   <chr>   <chr>           <dbl>
##  1 Alaska  On Time Los Angeles       497
##  2 Alaska  On Time Phoenix           221
##  3 Alaska  On Time San Diego         212
##  4 Alaska  On Time San Francisco     503
##  5 Alaska  On Time Seattle          1841
##  6 Alaska  Delay   Los Angeles        62
##  7 Alaska  Delay   Phoenix            12
##  8 Alaska  Delay   San Diego          20
##  9 Alaska  Delay   San Francisco     102
## 10 Alaska  Delay   Seattle           305
## 11 AM West On Time Los Angeles       694
## 12 AM West On Time Phoenix          4840
## 13 AM West On Time San Diego         383
## 14 AM West On Time San Francisco     320
## 15 AM West On Time Seattle           201
## 16 AM West Delay   Los Angeles       117
## 17 AM West Delay   Phoenix           415
## 18 AM West Delay   San Diego          65
## 19 AM West Delay   San Francisco     129
## 20 AM West Delay   Seattle            61

kable(flights_long)

Airline	Status	City	Flights
Alaska	On Time	Los Angeles	497
Alaska	On Time	Phoenix	221
Alaska	On Time	San Diego	212
Alaska	On Time	San Francisco	503
Alaska	On Time	Seattle	1841
Alaska	Delay	Los Angeles	62
Alaska	Delay	Phoenix	12
Alaska	Delay	San Diego	20
Alaska	Delay	San Francisco	102
Alaska	Delay	Seattle	305
AM West	On Time	Los Angeles	694
AM West	On Time	Phoenix	4840
AM West	On Time	San Diego	383
AM West	On Time	San Francisco	320
AM West	On Time	Seattle	201
AM West	Delay	Los Angeles	117
AM West	Delay	Phoenix	415
AM West	Delay	San Diego	65
AM West	Delay	San Francisco	129
AM West	Delay	Seattle	61

flights_final_transformation = flights_long %>% pivot_wider(names_from = Status, values_from = Flights)
print(flights_final_transformation)

## # A tibble: 10 × 4
##    Airline City          `On Time` Delay
##    <chr>   <chr>             <dbl> <dbl>
##  1 Alaska  Los Angeles         497    62
##  2 Alaska  Phoenix             221    12
##  3 Alaska  San Diego           212    20
##  4 Alaska  San Francisco       503   102
##  5 Alaska  Seattle            1841   305
##  6 AM West Los Angeles         694   117
##  7 AM West Phoenix            4840   415
##  8 AM West San Diego           383    65
##  9 AM West San Francisco       320   129
## 10 AM West Seattle             201    61

kable(flights_final_transformation)

Airline	City	On Time	Delay
Alaska	Los Angeles	497	62
Alaska	Phoenix	221	12
Alaska	San Diego	212	20
Alaska	San Francisco	503	102
Alaska	Seattle	1841	305
AM West	Los Angeles	694	117
AM West	Phoenix	4840	415
AM West	San Diego	383	65
AM West	San Francisco	320	129
AM West	Seattle	201	61

flights_summary <- flights_final_transformation %>%
  mutate(Delay = as.numeric(Delay)) %>%
  group_by(Airline) %>%
  summarize(Total_Delays = sum(Delay, na.rm = TRUE), Total_On_Time = sum(`On Time`, na.rm = TRUE)) %>%
  mutate(Total_Flights = Total_Delays + Total_On_Time,
         Delay_Percentage = (Total_Delays / Total_Flights) * 100,
         On_Time_Percentage = (Total_On_Time / Total_Flights) * 100)
print(flights_summary)

## # A tibble: 2 × 6
##   Airline Total_Delays Total_On_Time Total_Flights Delay_Percentage
##   <chr>          <dbl>         <dbl>         <dbl>            <dbl>
## 1 AM West          787          6438          7225             10.9
## 2 Alaska           501          3274          3775             13.3
## # ℹ 1 more variable: On_Time_Percentage <dbl>

kable(flights_summary)

Airline	Total_Delays	Total_On_Time	Total_Flights	Delay_Percentage	On_Time_Percentage
AM West	787	6438	7225	10.89273	89.10727
Alaska	501	3274	3775	13.27152	86.72848

Conclusion

AM West has a higher total number of flights and a slightly better on-time performance compared to Alaska.
Alaska has a higher percentage of delays compared to AM West.

Based on the provided information, AM West seems to be the better choice for me the following reasons:

Higher Total Number of Flights which means that AM West has a more extensive network and more frequent flights.
Better On-Time Performance means that AM West is more reliable.
Lower Percentage of Delays since Alaska tends to have a higher percentage of delays compare to AM West, so flying with AM West reduces the possibility of having delays.

Project 1

Inna Yedzinovich

2024-09-25

Conclusion