library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

1. Creating the CSV, exporting it to Github

initial_csv <- rbind(c(NA, NA, "Los Angeles", "Phoenix", "San Diego", "San Fransisco", "Seattle"),
                     c("ALASKA", "on time", 497, 221, 212, 503, 1841),
                     c(NA, "delayed", 62, 12, 20, 102, 305),
                     c("AM WEST", "on time", 694, 4840, 383, 320, 201),
                     c(NA, "delayed", 117, 415, 65, 129, 61))
write.table(initial_csv, file = "Initial_csv_Data", sep = ",", col.names = F, row.names = F)

2. Here I will transform the data using tidyr and dplyr. I will start this by going from wide to long data. Note, I do not need to load any extra packages because tidyr and dplyr are located within tidyverse. Importing CSV from GitHub into a usable table

csv_week5 <- read.csv("https://raw.githubusercontent.com/jonburns2454/DATA607/main/Initial_csv_Data")

Start by renaming the first two columns

csv_week5 <- csv_week5 %>% 
    rename("Airline" = 1, "Status" = 2)

Dealing with NAs | This replaces the NA figs under ‘Airline’ with their respective airline classifications to avoid issues throuhout the rest of the problem

csv_week5 <- csv_week5 %>% 
    fill(Airline, .direction = c("down"))

shift to long data format This takes the data and utilized the ‘pivot_longer’ funtion in tidyR to change the wide data into ‘Arrival.City’ and ‘Number.Of.Flights’. After this I selected the entire df using Airline:Number.Of.Flights and then arranged these figures by status in decending order by the airline. The next funtion this uses is spread, which separates number of flights into ontime and delayed. Lastly, the rename function changes the finiky ‘on time’ to on.time, to make analysis easier.

long_airline_data <- csv_week5 %>% 
    pivot_longer(cols =  3:7, names_to = "Arrival.City",
                 values_to = "Number.Of.Flights") %>% 
    select(Airline:Number.Of.Flights) %>% 
    arrange(Airline, desc(Status), 'Arrival City') %>% 
    spread(Status, Number.Of.Flights) %>% 
    rename(on.time = 'on time')
data.table::data.table(long_airline_data)
##     Airline  Arrival.City delayed on.time
##  1:  ALASKA   Los.Angeles      62     497
##  2:  ALASKA       Phoenix      12     221
##  3:  ALASKA     San.Diego      20     212
##  4:  ALASKA San.Fransisco     102     503
##  5:  ALASKA       Seattle     305    1841
##  6: AM WEST   Los.Angeles     117     694
##  7: AM WEST       Phoenix     415    4840
##  8: AM WEST     San.Diego      65     383
##  9: AM WEST San.Fransisco     129     320
## 10: AM WEST       Seattle      61     201

3. Comparing general mean of “ontime flights” and “delayed flights” bewteen the two

On_time_data <- long_airline_data %>% 
    group_by(Airline) %>% 
    summarize(mean_ontime = mean(on.time))
delayed_data <- long_airline_data %>% 
    group_by(Airline) %>% 
    summarize(mean_delayed = mean(delayed))

Plotting and comparing on.time flights to delayed flights at the two airports

On time

ggplot(On_time_data, aes(x = Airline, y = mean_ontime, color = Airline)) +
    geom_bar(stat = "identity")

Delayed

ggplot(delayed_data, aes(x = Airline, y = mean_delayed, color = Airline)) +
    geom_bar(stat = "identity")

Conclusion The data shows that AM West has both more On time flights and delayed arrivals than compared with ALASKA. One thing to note however, is that AM west appears to run many more flights than Alaska but the difference between the mean delayed flight of the airlines is fairly small in comparison to how many more flights AM west runs.