library(tidyr)
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
# Reading the file into R from githib

rawfile <- read.csv("https://raw.githubusercontent.com/johnnyboy1287/hwflight/main/hwflight.csv")

# Placing the data into a data frame

rawfiledf <- data.frame(rawfile)

# Dropping the row with blank values

rawfiledf <- drop_na(rawfiledf)

# Renaming the columns

rawfiledf <- rename(rawfiledf, "Origin"=X, "Status"=X.1)

# Changing the row names

rawfiledf[2,1]="ALASKA"
rawfiledf[4,1]="AM WEST"

# Creating a long data set with pivot longer

tidydf = pivot_longer(rawfiledf, cols = c("Los.Angeles","Phoenix", "San.Diego", "San.Francisco", "Seattle"), values_to="Number_of_Flights")

# view data

tidydf
## # A tibble: 20 × 4
##    Origin  Status  name          Number_of_Flights
##    <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
##  7 ALASKA  delayed Phoenix                      12
##  8 ALASKA  delayed San.Diego                    20
##  9 ALASKA  delayed San.Francisco               102
## 10 ALASKA  delayed 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 delayed Los.Angeles                 117
## 17 AM WEST delayed Phoenix                     415
## 18 AM WEST delayed San.Diego                    65
## 19 AM WEST delayed San.Francisco               129
## 20 AM WEST delayed Seattle                      61
with(tidydf,sum(Number_of_Flights[Origin=="ALASKA" & Status == "delayed"]))/with(tidydf,sum(Number_of_Flights[Origin=="ALASKA"]))
## [1] 0.1327152
with(tidydf,sum(Number_of_Flights[Origin=="AM WEST" & Status == "delayed"]))/with(tidydf,sum(Number_of_Flights[Origin=="AM WEST"]))
## [1] 0.1089273

Based on my analysis, AM West had the lower percentage of delayed flights compared to Alaska.