Data Exploration and Descriptive Analysis with `flights` Dataset

Author

Saihaj Singh Randhawa

Published

January 22, 2026

Loading Packages and Data

First we need to load the flights data, which comes as part of the nycflights13 package.

#Installation method

library(pacman)
p_load(nycflights13, tidyverse)

data("flights") #loads in data frame "flights"

Data Exploration and Analysis

Data Exploration

The following chunk displays an initial data inspection of the flights dataset, including a preview with the head() function, a structural overview with the str() function, and summary statistics with the summary() function.

# Display the first 7 rows of the flights dataset
head(flights, 7)

# A tibble: 7 × 19
   year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
  <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
1  2013     1     1      517            515         2      830            819
2  2013     1     1      533            529         4      850            830
3  2013     1     1      542            540         2      923            850
4  2013     1     1      544            545        -1     1004           1022
5  2013     1     1      554            600        -6      812            837
6  2013     1     1      554            558        -4      740            728
7  2013     1     1      555            600        -5      913            854
# ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
#   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
#   hour <dbl>, minute <dbl>, time_hour <dttm>

# Examine the structure of the dataset (variable types and layout)
str(flights)

tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame)
 $ year          : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
 $ month         : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
 $ day           : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
 $ dep_time      : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
 $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
 $ dep_delay     : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
 $ arr_time      : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
 $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ...
 $ arr_delay     : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ...
 $ carrier       : chr [1:336776] "UA" "UA" "AA" "B6" ...
 $ flight        : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ...
 $ tailnum       : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ...
 $ origin        : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ...
 $ dest          : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ...
 $ air_time      : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ...
 $ distance      : num [1:336776] 1400 1416 1089 1576 762 ...
 $ hour          : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ...
 $ minute        : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ...
 $ time_hour     : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...

# Generate summary statistics for all variables
summary(flights)

      year          month             day           dep_time    sched_dep_time
 Min.   :2013   Min.   : 1.000   Min.   : 1.00   Min.   :   1   Min.   : 106  
 1st Qu.:2013   1st Qu.: 4.000   1st Qu.: 8.00   1st Qu.: 907   1st Qu.: 906  
 Median :2013   Median : 7.000   Median :16.00   Median :1401   Median :1359  
 Mean   :2013   Mean   : 6.549   Mean   :15.71   Mean   :1349   Mean   :1344  
 3rd Qu.:2013   3rd Qu.:10.000   3rd Qu.:23.00   3rd Qu.:1744   3rd Qu.:1729  
 Max.   :2013   Max.   :12.000   Max.   :31.00   Max.   :2400   Max.   :2359  
                                                 NA's   :8255                 
   dep_delay          arr_time    sched_arr_time   arr_delay       
 Min.   : -43.00   Min.   :   1   Min.   :   1   Min.   : -86.000  
 1st Qu.:  -5.00   1st Qu.:1104   1st Qu.:1124   1st Qu.: -17.000  
 Median :  -2.00   Median :1535   Median :1556   Median :  -5.000  
 Mean   :  12.64   Mean   :1502   Mean   :1536   Mean   :   6.895  
 3rd Qu.:  11.00   3rd Qu.:1940   3rd Qu.:1945   3rd Qu.:  14.000  
 Max.   :1301.00   Max.   :2400   Max.   :2359   Max.   :1272.000  
 NA's   :8255      NA's   :8713                  NA's   :9430      
   carrier              flight       tailnum             origin         
 Length:336776      Min.   :   1   Length:336776      Length:336776     
 Class :character   1st Qu.: 553   Class :character   Class :character  
 Mode  :character   Median :1496   Mode  :character   Mode  :character  
                    Mean   :1972                                        
                    3rd Qu.:3465                                        
                    Max.   :8500                                        
                                                                        
     dest              air_time        distance         hour      
 Length:336776      Min.   : 20.0   Min.   :  17   Min.   : 1.00  
 Class :character   1st Qu.: 82.0   1st Qu.: 502   1st Qu.: 9.00  
 Mode  :character   Median :129.0   Median : 872   Median :13.00  
                    Mean   :150.7   Mean   :1040   Mean   :13.18  
                    3rd Qu.:192.0   3rd Qu.:1389   3rd Qu.:17.00  
                    Max.   :695.0   Max.   :4983   Max.   :23.00  
                    NA's   :9430                                  
     minute        time_hour                  
 Min.   : 0.00   Min.   :2013-01-01 05:00:00  
 1st Qu.: 8.00   1st Qu.:2013-04-04 13:00:00  
 Median :29.00   Median :2013-07-03 10:00:00  
 Mean   :26.23   Mean   :2013-07-03 05:22:54  
 3rd Qu.:44.00   3rd Qu.:2013-10-01 07:00:00  
 Max.   :59.00   Max.   :2013-12-31 23:00:00

The code below provides an initial exploration of the dataset. First,head(flights, 7) displays the first seven rows, allowing a quick look at how variables such as year, month, dep_time, arr_delay, carrier, origin, dest, and others appear in the raw data. The str(flights) command then examines the structure of the dataset by showing each variable’s type (e.g., integer, character) and a preview of its values, helping clarify how variables like sched_dep_time, flight, tailnum, air_time, and distance are stored. Finally, summary(flights) generates summary statistics for all 19 variables, including measures such as minimums, maximums, means, and counts of missing values. Together, these commands give a clear overview of the dataset’s layout, variable types, and key characteristics before deeper analysis. These observations are done for 336,776 flights in the flights data frame.

A list of the 19 variables are below:

year
month
day
departure time
schedule time
departure delay
scheduled arrival
time arrival
time
carrier flight
tail number
origin of flight
destination
air time
distance
hour amount of flight
minute amount of flight
dated time

Average Scheduled Arrival Time and Actual Arrival Time

Calculates the mean actual and scheduled arrival times while ignoring missing values, then stores those averages in the variables avg_arr_time and avg_sched_arr_time.

# Get average arrival time and scheduled arrival time, ignoring NA values
mean(flights$arr_time, na.rm = TRUE)         # Calculates mean of actual arrival times, removing NA values

[1] 1502.055

mean(flights$sched_arr_time, na.rm = TRUE)   # Calculates mean of scheduled arrival times, removing NA values

[1] 1536.38

# Store the means as variables
avg_arr_time <- mean(flights$arr_time, na.rm = TRUE)        # Saves mean arrival time into avg_arr_time
avg_sched_arr_time <- mean(flights$sched_arr_time, na.rm = TRUE)  # Saves mean scheduled arrival time into avg_sched_arr_time

The plane was scheduled to arrive at 1502.055 but actually arrived at 1536.38, meaning it landed about 34 minutes late, so if its departure delay was larger than 34 minutes, it made up some time in the air.