DATA607Assignment4

Setup and load libraries and data

Install and load in the neccesary libraries

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ 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

library(openintro)

## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdata

library(dplyr)
library(ggplot2)

Load the data into a data frame and display

flights_data_wide <- read.csv('https://raw.githubusercontent.com/ErickH1/DATA607Assignment4/refs/heads/main/data607hw4data.csv')

head(flights_data_wide)

##         X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1  ALASKA on time         497     221       212           503    1841
## 2         delayed          62      12        20           102     305
## 3                          NA      NA        NA            NA      NA
## 4 AM WEST on time         694    4840       383           320     201
## 5         delayed         117     415        65           129      61

glimpse(flights_data_wide)

## Rows: 5
## Columns: 7
## $ X             <chr> "ALASKA", "", "", "AM WEST", ""
## $ X.1           <chr> "on time", "delayed", "", "on time", "delayed"
## $ Los.Angeles   <int> 497, 62, NA, 694, 117
## $ Phoenix       <int> 221, 12, NA, 4840, 415
## $ San.Diego     <int> 212, 20, NA, 383, 65
## $ San.Francisco <int> 503, 102, NA, 320, 129
## $ Seattle       <int> 1841, 305, NA, 201, 61

Tidy Data

Tidying up the data by removing na values

flights_data_wide <- flights_data_wide %>%
  drop_na() %>% 
  rename(airline = X, status = X.1)

flights_data_wide <- flights_data_wide %>%
  mutate(airline = na_if(airline, '')) %>% 
  fill(airline, .direction = c('down'))

Making the data frame long

flights_data_long <- flights_data_wide %>%
  pivot_longer(cols = c("Los.Angeles", "Phoenix", "San.Diego", "San.Francisco", "Seattle"),
               names_to = "city",
               values_to = "count")
head(flights_data_long)

## # A tibble: 6 × 4
##   airline status  city          count
##   <chr>   <chr>   <chr>         <int>
## 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  delayed Los.Angeles      62

Finally tidying the data to the desired dataframe

flights_data_tidy <- flights_data_long %>%
  pivot_wider(names_from = status,
              values_from = count)

names(flights_data_tidy)[3] <- "on_time"
head(flights_data_tidy)

## # A tibble: 6 × 4
##   airline city          on_time delayed
##   <chr>   <chr>           <int>   <int>
## 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

Data Analysis

Adding delay and on time rate as their own columns

flights_data_tidy <- flights_data_tidy %>%
  mutate(delayed_rate = delayed / (on_time + delayed))

flights_data_tidy <- flights_data_tidy %>%
  mutate(ontime_rate = on_time / (on_time + delayed))

glimpse(flights_data_tidy)

## Rows: 10
## Columns: 6
## $ airline      <chr> "ALASKA", "ALASKA", "ALASKA", "ALASKA", "ALASKA", "AM WES…
## $ city         <chr> "Los.Angeles", "Phoenix", "San.Diego", "San.Francisco", "…
## $ on_time      <int> 497, 221, 212, 503, 1841, 694, 4840, 383, 320, 201
## $ delayed      <int> 62, 12, 20, 102, 305, 117, 415, 65, 129, 61
## $ delayed_rate <dbl> 0.11091234, 0.05150215, 0.08620690, 0.16859504, 0.1421248…
## $ ontime_rate  <dbl> 0.8890877, 0.9484979, 0.9137931, 0.8314050, 0.8578751, 0.…

Create and display plots

ggplot(flights_data_tidy, aes(fill= airline, y = delayed_rate, x = city)) +
  geom_bar(position="dodge", stat = "identity")

ggplot(flights_data_tidy, aes(fill= airline, y = ontime_rate, x = city)) +
  geom_bar(position="dodge", stat = "identity")

Conclusion

In conclusion, based on the analysis on the two airlines, AM West has a higher delayed rate in every arrival in a city.