For this project, we will be exploring the Flight Delay Dataset — 2024
Source: https://www.kaggle.com/datasets/hrishitpatil/flight-data-2024.
This dataset includes a variety of variables:
Origin City/Airport Code
Destination City/Airport Code
Date and Time (including day of the week)
Length of Delay (in minutes)
Type of Delay
2025-09-28
Dataset Overview
Key Questions to Explore
How do delays vary across different origin airports?
Which destinations experience the highest frequency or length of delays?
Do delays follow a pattern based on the month?
Which delay causes (weather, mechanical, security) contribute most to total delay time?
Importing the Data
Because this dataset has over 7 million rows, I chose to use the library data.table as it is built for larger data sets. I will be using tidyverse, plotly, and ggplot2 throughout this project.
According to the Keggle post, this dataset has been cleaned already.
library(data.table)
library(tidyverse)
library(plotly)
library(ggplot2)
library(knitr)
library(dplyr)
library(kableExtra)
library(tidyr)
library(scales)
setwd("C:/Users/bmoor/Downloads/archive (1)")
flight_data <- fread("flight_data_2024.csv")
data_dictionary <-fread("flight_data_2024_data_dictionary.csv")
Data Dictionary Summary
kable(data_dictionary[1:11,]) %>% kable_styling(font_size = 10)
| column | dtype | null_pct | example_value |
|---|---|---|---|
| year | Int64 | 0 | 2024 |
| month | Int64 | 0 | 1 |
| day_of_month | Int64 | 0 | 1 |
| day_of_week | Int64 | 0 | 1 |
| fl_date | datetime64[ns] | 0 | 2024-01-01 00:00:00 |
| op_unique_carrier | object | 0 | 9E |
| op_carrier_fl_num | float64 | 0 | 4814.0 |
| origin | object | 0 | JFK |
| origin_city_name | object | 0 | New York, NY |
| origin_state_nm | object | 0 | New York |
| dest | object | 0 | DTW |
Data Dictionary Summary Continued
kable(data_dictionary[11:21,]) %>% kable_styling(font_size = 10)
| column | dtype | null_pct | example_value |
|---|---|---|---|
| dest | object | 0.00 | DTW |
| dest_city_name | object | 0.00 | Detroit, MI |
| dest_state_nm | object | 0.00 | Michigan |
| crs_dep_time | Int64 | 0.00 | 1252 |
| dep_time | float64 | 1.31 | 1247.0 |
| dep_delay | float64 | 1.31 | -5.0 |
| taxi_out | float64 | 1.35 | 31.0 |
| wheels_off | float64 | 1.35 | 1318.0 |
| wheels_on | float64 | 1.38 | 1442.0 |
| taxi_in | float64 | 1.38 | 7.0 |
| crs_arr_time | Int64 | 0.00 | 1508 |
Data Dictionary Summary Continued
kable(data_dictionary[22:32,]) %>% kable_styling(font_size = 10)
| column | dtype | null_pct | example_value |
|---|---|---|---|
| arr_time | float64 | 1.38 | 1449.0 |
| arr_delay | float64 | 1.61 | -19.0 |
| cancelled | int64 | 0.00 | 0 |
| cancellation_code | object | 98.64 | B |
| diverted | int64 | 0.00 | 0 |
| crs_elapsed_time | float64 | 0.00 | 136.0 |
| actual_elapsed_time | float64 | 1.61 | 122.0 |
| air_time | float64 | 1.61 | 84.0 |
| distance | float64 | 0.00 | 509.0 |
| carrier_delay | int64 | 0.00 | 0 |
| weather_delay | int64 | 0.00 | 0 |
Data Dictionary Summary Continued
kable(data_dictionary[33:35,]) %>% kable_styling(font_size = 10)
| column | dtype | null_pct | example_value |
|---|---|---|---|
| nas_delay | int64 | 0 | 0 |
| security_delay | int64 | 0 | 0 |
| late_aircraft_delay | int64 | 0 | 0 |
Flight Info Summary
Origin City Summary
summary(flight_data[,c("origin","origin_city_name")])
## origin origin_city_name ## Length:7079081 Length:7079081 ## Class :character Class :character ## Mode :character Mode :character
Destination City Summary
summary(flight_data[,c("dest","dest_city_name")])
## dest dest_city_name ## Length:7079081 Length:7079081 ## Class :character Class :character ## Mode :character Mode :character
Delay Measures Summary
summary(flight_data[,c("dep_delay","arr_delay")])
## dep_delay arr_delay ## Min. : -96.00 Min. :-126.000 ## 1st Qu.: -6.00 1st Qu.: -15.000 ## Median : -2.00 Median : -6.000 ## Mean : 12.68 Mean : 7.098 ## 3rd Qu.: 9.00 3rd Qu.: 9.000 ## Max. :3777.00 Max. :3803.000 ## NA's :92970 NA's :113814
Delay Causes Summary
summary(flight_data[,c("carrier_delay","weather_delay","nas_delay",
"security_delay", "late_aircraft_delay")])
## carrier_delay weather_delay nas_delay security_delay ## Min. : 0.00 Min. : 0.0000 Min. : 0.000 Min. :0.000e+00 ## 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.000e+00 ## Median : 0.00 Median : 0.0000 Median : 0.000 Median :0.000e+00 ## Mean : 5.06 Mean : 0.8752 Mean : 2.772 Mean :2.542e-02 ## 3rd Qu.: 0.00 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.:0.000e+00 ## Max. :3689.00 Max. :1804.0000 Max. :2700.000 Max. :1.164e+03 ## late_aircraft_delay ## Min. : 0.000 ## 1st Qu.: 0.000 ## Median : 0.000 ## Mean : 5.929 ## 3rd Qu.: 0.000 ## Max. :2690.000
Checking NA Values: Rows
flight_data %>% summarise(across(everything(), ~sum(is.na(.))))
## year month day_of_month day_of_week fl_date op_unique_carrier ## 1 0 0 0 0 0 0 ## op_carrier_fl_num origin origin_city_name origin_state_nm dest dest_city_name ## 1 1 0 0 0 0 0 ## dest_state_nm crs_dep_time dep_time dep_delay taxi_out wheels_off wheels_on ## 1 0 0 92659 92970 95734 95734 97856 ## taxi_in crs_arr_time arr_time arr_delay cancelled cancellation_code diverted ## 1 97856 0 97854 113814 0 0 0 ## crs_elapsed_time actual_elapsed_time air_time distance carrier_delay ## 1 1 113814 113814 0 0 ## weather_delay nas_delay security_delay late_aircraft_delay ## 1 0 0 0 0
Checking NA Values: Columns
flight_data %>% filter(if_any(everything(), is.na))
## year month day_of_month day_of_week fl_date op_unique_carrier ## <int> <int> <int> <int> <IDat> <char> ## 1: 2024 1 1 1 2024-01-01 AS ## 2: 2024 1 1 1 2024-01-01 AS ## 3: 2024 1 1 1 2024-01-01 AS ## 4: 2024 1 1 1 2024-01-01 AS ## 5: 2024 1 1 1 2024-01-01 DL ## --- ## 113811: 2024 12 31 2 2024-12-31 WN ## 113812: 2024 12 31 2 2024-12-31 WN ## 113813: 2024 12 31 2 2024-12-31 WN ## 113814: 2024 12 31 2 2024-12-31 WN ## 113815: 2024 12 31 2 2024-12-31 YX ## op_carrier_fl_num origin origin_city_name origin_state_nm ## <num> <char> <char> <char> ## 1: 1023 PSC Pasco/Kennewick/Richland, WA Washington ## 2: 30 SFO San Francisco, CA California ## 3: 41 JFK New York, NY New York ## 4: 900 SEA Seattle, WA Washington ## 5: 2348 SLC Salt Lake City, UT Utah ## --- ## 113811: 4316 BWI Baltimore, MD Maryland ## 113812: 4899 MDW Chicago, IL Illinois ## 113813: 804 IND Indianapolis, IN Indiana ## 113814: 827 DEN Denver, CO Colorado ## 113815: 4642 CMH Columbus, OH Ohio ## dest dest_city_name dest_state_nm crs_dep_time dep_time dep_delay ## <char> <char> <char> <int> <num> <num> ## 1: SEA Seattle, WA Washington 700 NA NA ## 2: JFK New York, NY New York 1038 NA NA ## 3: SFO San Francisco, CA California 2020 NA NA ## 4: RDM Bend/Redmond, OR Oregon 2040 NA NA ## 5: SFO San Francisco, CA California 2130 2133 3 ## --- ## 113811: SAN San Diego, CA California 1850 1930 40 ## 113812: CMH Columbus, OH Ohio 1450 1517 27 ## 113813: RSW Fort Myers, FL Florida 750 744 -6 ## 113814: BUR Burbank, CA California 840 839 -1 ## 113815: BOS Boston, MA Massachusetts 700 655 -5 ## taxi_out wheels_off wheels_on taxi_in crs_arr_time arr_time arr_delay ## <num> <num> <num> <num> <int> <num> <num> ## 1: NA NA NA NA 813 NA NA ## 2: NA NA NA NA 1913 NA NA ## 3: NA NA NA NA 2359 NA NA ## 4: NA NA NA NA 2149 NA NA ## 5: NA NA NA NA 2251 NA NA ## --- ## 113811: 20 1950 NA NA 2135 NA NA ## 113812: 28 1545 2039 5 1700 2044 NA ## 113813: 10 754 1230 3 1025 1233 NA ## 113814: 24 903 1216 9 1010 1225 NA ## 113815: 15 710 1301 7 902 1308 NA ## cancelled cancellation_code diverted crs_elapsed_time ## <int> <char> <int> <num> ## 1: 1 B 0 73 ## 2: 1 A 0 335 ## 3: 1 A 0 399 ## 4: 1 B 0 69 ## 5: 1 A 0 141 ## --- ## 113811: 0 1 345 ## 113812: 0 1 70 ## 113813: 0 1 155 ## 113814: 0 1 150 ## 113815: 0 1 122 ## actual_elapsed_time air_time distance carrier_delay weather_delay ## <num> <num> <num> <int> <int> ## 1: NA NA 172 0 0 ## 2: NA NA 2586 0 0 ## 3: NA NA 2586 0 0 ## 4: NA NA 228 0 0 ## 5: NA NA 599 0 0 ## --- ## 113811: NA NA 2295 0 0 ## 113812: NA NA 283 0 0 ## 113813: NA NA 945 0 0 ## 113814: NA NA 850 0 0 ## 113815: NA NA 640 0 0 ## nas_delay security_delay late_aircraft_delay ## <int> <int> <int> ## 1: 0 0 0 ## 2: 0 0 0 ## 3: 0 0 0 ## 4: 0 0 0 ## 5: 0 0 0 ## --- ## 113811: 0 0 0 ## 113812: 0 0 0 ## 113813: 0 0 0 ## 113814: 0 0 0 ## 113815: 0 0 0
Data Wrangling, Munging, and Cleaning Conclusions:
Because this data set was cleaned prior to my downloading the files, there was minimal manipulation needed to analyze this dataset. I ran a few summaries and checked for rows and columns with missing fields. The rows did not return much information; however, the columns returned a ton of information. Which makes sense given that not every instance had a delay for all categories.
Comparing Origin City vs Departure Delays (Top 10)
Comparing Origin City vs Departure Delays (Bottom 10)
Comparing Origin City vs Arrival Delays (Top 10)
Comparing Origin City vs Arrival Delays (Bottom 10)
How do delays vary across different origin airports?
I was surprised to see the big airport hubs not be listed in either the top 10 or the bottom 10 for either arrival delays or departure delays. I expected these airports to be the ones that filtered to the top. Most the airports seems to be smaller regional airports. Many are in locations where large amounts of snowfall occur.
Destination with the longest length of Arrival Delay
Destination with the longest length of Departure Delay
Which destinations experience the highest frequency or length of delays?
In comparing the longest delay a passenger could see for both arrival and departure delays for destination cities. I was not surprised that the major US airports were listed in both instances. This makes sense due to the amount of traffic that flows through these hubs.
Comparison of Arrival Delays by Month
Delays by Month
I was surprised to see that July experienced the highest average of delays. I would have expected winter months or the holidays to be the times when we would experience the most delays. That said, passengers were, on average, delayed less than 30 minutes. I was not surprised to see the arrival and departure delays mirror one another. However, consistently, departure delays were greater than arrival delays.
Cause of Delay by Month
Which delay causes (weather, mechanical, security) contribute most to total delay time?
Throughout this dataset the a late aircraft delay was the most frequent cause for a delay. It was followed closely by a carrier delay. I was surprised to see that weather delays were one of the least common causes for a flight delay. Whatever happened in July continues in this analysis as well.
Average Delays Per Airline
Average Delay per Airline Continued
American Airlines had the highest average of delay minutes for its departures. They also had the highest average for arrival delays. The question now is, is this an airport issue or an airline policy issue? This data set does not include any information regarding the policies, however, if I worked for American Airlines and looked at this data set I would question is this a process issue that we have control over or is this a series of events at an airport outside of our control.
Conclusion
While there are definitely long delays the average delay for the year 2024, after reviewing over 7 million lines of data was less than 30 minutes. There are some airlines that may want to look at their internal policies to address any areas in which they can make their processes more efficient. Also the perception of flight delays around the holidays is not what the data suggests.
Works Cited
Patil, H. (2024, March). Flight Data 2024. Retrieved September 28, 2025, from https://www.kaggle.com/datasets/hrishitpatil/flight-data-2024