Read data from github

The data file is hosted in github.
Airline

url <- getURL("https://raw.githubusercontent.com/binishkurian/DATA-607/master/week-04/flights.csv")
df <- read.csv(text = url, header = TRUE, check.names = FALSE)

Convert data frame to tibble

flight_schedule <- as.tibble(df) 
flight_schedule
## # A tibble: 4 x 7
##   carrier arrival `Los Angeles` Phoenix `San Diego` `San Francisco` Seatt…
##   <fctr>  <fctr>          <int>   <int>       <int>           <int>  <int>
## 1 ALASKA  on time           497     221         212             503   1841
## 2 ALASKA  delayed            62      12          20             102    305
## 3 AM WEST on time           694    4840         383             320    201
## 4 AM WEST delayed           117     415          65             129     61

Tidy data

flight_schedule_tidy <-flight_schedule %>%
        gather('Los Angeles':Seattle, key = "destination", value = "flight_count") %>%
        spread(key = arrival, value = flight_count) %>%
        rename(on_time = `on time`)
flight_schedule_tidy
## # A tibble: 10 x 4
##    carrier destination   delayed on_time
##    <fctr>  <chr>           <int>   <int>
##  1 ALASKA  Los Angeles        62     497
##  2 ALASKA  Phoenix            12     221
##  3 ALASKA  San Diego          20     212
##  4 ALASKA  San Francisco     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 Francisco     129     320
## 10 AM WEST Seattle            61     201

Rank delays from least to maximum delayed across carriers and destination

flight_schedule_tidy %>%
        mutate(
                perc_delayed = delayed / (delayed + on_time),
                delay_score = min_rank(perc_delayed)
        ) %>%
        arrange(delay_score)
## # A tibble: 10 x 6
##    carrier destination   delayed on_time perc_delayed delay_score
##    <fctr>  <chr>           <int>   <int>        <dbl>       <int>
##  1 ALASKA  Phoenix            12     221       0.0515           1
##  2 AM WEST Phoenix           415    4840       0.0790           2
##  3 ALASKA  San Diego          20     212       0.0862           3
##  4 ALASKA  Los Angeles        62     497       0.111            4
##  5 ALASKA  Seattle           305    1841       0.142            5
##  6 AM WEST Los Angeles       117     694       0.144            6
##  7 AM WEST San Diego          65     383       0.145            7
##  8 ALASKA  San Francisco     102     503       0.169            8
##  9 AM WEST Seattle            61     201       0.233            9
## 10 AM WEST San Francisco     129     320       0.287           10
AM WEST's service to San Francisco has most delayed flights.

Box plots of delays to check outliers

boxplot(flight_schedule_tidy[flight_schedule_tidy$carrier == "ALASKA", ]$delayed)      

boxplot(flight_schedule_tidy[flight_schedule_tidy$carrier == "AM WEST", ]$delayed)

Compare delays between airlines

flight_schedule_tidy %>%
        group_by(carrier) %>%
        summarise(
                count = n(),
                mean = mean(delayed, na.rm = TRUE), 
                sd = sd(delayed, na.rm = TRUE),
                median = median(delayed, na.rm = TRUE),
                iqr = IQR(delayed, na.rm = TRUE),
                abs_dev = mad(delayed, na.rm = TRUE),
                min = min(delayed, na.rm = TRUE),
                max = max(delayed, na.rm = TRUE)
                )
## # A tibble: 2 x 9
##   carrier count  mean    sd median   iqr abs_dev   min   max
##   <fctr>  <int> <dbl> <dbl>  <int> <dbl>   <dbl> <dbl> <dbl>
## 1 ALASKA      5   100   120     62  82.0    62.3  12.0   305
## 2 AM WEST     5   157   147    117  64.0    77.1  61.0   415

Conclusion

Since both airline data have a outlier, median and IQR are more robust than
mean and sd. Following the table above, ALASKA has median of 62 flights and 
IQR of 82 and AM WEST has median of 117 flights and IQR of 64. This makes me to 
conclude that ALASKA has better control on their flight delays.