LOADING MY LIBRARIES
library(readr)
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
library(tidyr)
library(ggplot2)
IMPORTING THE DATA
airline_data <- read_csv(file.choose())
## Rows: 4 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): airline, status
## dbl (5): los_angeles, phoenix, san_diego, san_francisco, seattle
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(airline_data)
## # 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 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
glimpse(airline_data)
## Rows: 4
## Columns: 7
## $ airline <chr> "Alaska", "Alaska", "AM_West", "AM_West"
## $ status <chr> "On_Time", "Delayed", "On_Time", "Delayed"
## $ los_angeles <dbl> 497, 62, 694, 117
## $ phoenix <dbl> 221, 12, 4840, 415
## $ san_diego <dbl> 212, 20, 383, 65
## $ san_francisco <dbl> 503, 102, 320, 129
## $ seattle <dbl> 1841, 305, 201, 61
CHECKING FOR MISSING DATA
colSums(is.na(airline_data))
## airline status los_angeles phoenix san_diego
## 0 0 0 0 0
## san_francisco seattle
## 0 0
CONVERTING THE DATA COLUMNS
airline_long <- airline_data %>%
pivot_longer(
cols = los_angeles:seattle,
names_to = "city",
values_to = "flights"
)
## DATA TRANSFORMED FROM WIDE TO LONG FORMAT
head(airline_long)
## # A tibble: 6 × 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 Delayed los_angeles 62
COUNT ANALYSIS
flight_counts <- airline_long %>%
group_by(airline, status) %>%
summarise(
total_flights = sum(flights),
.groups = "drop"
)
flight_counts
## # A tibble: 4 × 3
## airline status total_flights
## <chr> <chr> <dbl>
## 1 AM_West Delayed 787
## 2 AM_West On_Time 6438
## 3 Alaska Delayed 501
## 4 Alaska On_Time 3274
CALCULATE OVERALL DELAY PERCENTAGE
overall_delay <- airline_long %>%
group_by(airline) %>%
summarise(
total_flights = sum(flights),
delayed_flights = sum(flights[status == "Delayed"]),
delay_percentage = round(
delayed_flights / total_flights * 100,
2
),
.groups = "drop"
)
overall_delay
## # A tibble: 2 × 4
## airline total_flights delayed_flights delay_percentage
## <chr> <dbl> <dbl> <dbl>
## 1 AM_West 7225 787 10.9
## 2 Alaska 3775 501 13.3
## Alaksa airline has a higher overall % delay (13.27%) compared to AM_West(10.89%), however considering the fact that AM_west has had 3,450 more flights than Alaska and still manage for have a lower delay % is outstanding. Alaska has a lot to learn from AM_West
VISUALIZATION
ggplot(
overall_delay,
aes(x = airline, y = delay_percentage)
) +
geom_col() +
labs(
title = "Overall Flight Delay Percentage by Airline",
x = "Airline",
y = "Delay Percentage"
) +
theme_minimal()
## "The overall comparison shows that Alaska had a higher percentage of delayed flights than AM West. Alaska had approximately 13.27% of flights delayed, compared with approximately 10.89% for AM West. I used percentages instead of only comparing the number of delayed flights because the two airlines had different total numbers of flights."
AIRLINE COMPARISM ACROSS CITIES
city_delay <- airline_long %>%
group_by(airline, city) %>%
summarise(
total_flights = sum(flights),
delayed_flights = sum(flights[status == "Delayed"]),
delay_percentage = delayed_flights / total_flights * 100,
.groups = "drop"
)
city_delay
## # A tibble: 10 × 5
## airline city total_flights delayed_flights delay_percentage
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 AM_West los_angeles 811 117 14.4
## 2 AM_West phoenix 5255 415 7.90
## 3 AM_West san_diego 448 65 14.5
## 4 AM_West san_francisco 449 129 28.7
## 5 AM_West seattle 262 61 23.3
## 6 Alaska los_angeles 559 62 11.1
## 7 Alaska phoenix 233 12 5.15
## 8 Alaska san_diego 232 20 8.62
## 9 Alaska san_francisco 605 102 16.9
## 10 Alaska seattle 2146 305 14.2
##
CITY TO CITY COMPARISON GRAPH
ggplot(
city_delay,
aes(
x = city,
y = delay_percentage,
fill = airline
)
) +
geom_col(position = "dodge") +
labs(
title = "Flight Delay Percentage by City and Airline",
x = "City",
y = "Delay Percentage",
fill = "Airline"
) +
theme_minimal()
## Ok i have learnt here not to conclude on analysis when i havent gone indepth, the city to city comparism makes it clear alaska isnt doing very bad. the graph makes it very clear that alaska out-performs AM_West. AM_West overall better % peformance can be attributed to Phoenix total flights (5255) while alaska records (233) for phoenix. That is a (5,022) discrepancy.
FURTHER ANALYSIS ON DISCREPANCY
city_comparison <- city_delay %>%
select(airline, city, delay_percentage) %>%
pivot_wider(
names_from = airline,
values_from = delay_percentage
)
city_comparison
## # A tibble: 5 × 3
## city AM_West Alaska
## <chr> <dbl> <dbl>
## 1 los_angeles 14.4 11.1
## 2 phoenix 7.90 5.15
## 3 san_diego 14.5 8.62
## 4 san_francisco 28.7 16.9
## 5 seattle 23.3 14.2
## alaska actually outperforms AM_West in city to city comparisms.
EXPLANATION TO DISCREPANCY
## overall it was observed that AM_West had a better % delay than Alaska. And one crucial lesson i learnt today is not to conclude on general or surface analysis when you have not gone in-depth. Because Upon in-depth analysis (city to city) i discovered that i had it all wrong. Alaska Airline outperformed AM_West in every individual city. So i had one major question and that was to try to understand why Alaska airlines have an overall poor % delay. Line 103 solved the puzzle. AM West has a particularly large number of flights in Phoenix, which has a relatively lower delay percentage for AM West. This difference in flight distribution affects the overall calculation and causes the overall result to differ from the individual city comparisons.