NYC Flights 2023

Load libraries and nycflights23 dataset

# Sun Oct  6 19:15:27 2024 ------------------------------
library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.4.1

Warning: package 'ggplot2' was built under R version 4.4.1

Warning: package 'tibble' was built under R version 4.4.1

Warning: package 'tidyr' was built under R version 4.4.1

Warning: package 'readr' was built under R version 4.4.1

Warning: package 'purrr' was built under R version 4.4.1

Warning: package 'dplyr' was built under R version 4.4.1

Warning: package 'stringr' was built under R version 4.4.1

Warning: package 'forcats' was built under R version 4.4.1

Warning: package 'lubridate' was built under R version 4.4.1

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(nycflights23)

Warning: package 'nycflights23' was built under R version 4.4.1

Load and take a glimpse at the flights dataset

data(flights)
glimpse(flights)

Rows: 435,352
Columns: 19
$ year           <int> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2…
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time       <int> 1, 18, 31, 33, 36, 503, 520, 524, 537, 547, 549, 551, 5…
$ sched_dep_time <int> 2038, 2300, 2344, 2140, 2048, 500, 510, 530, 520, 545, …
$ dep_delay      <dbl> 203, 78, 47, 173, 228, 3, 10, -6, 17, 2, -10, -9, -7, -…
$ arr_time       <int> 328, 228, 500, 238, 223, 808, 948, 645, 926, 845, 905, …
$ sched_arr_time <int> 3, 135, 426, 2352, 2252, 815, 949, 710, 818, 852, 901, …
$ arr_delay      <dbl> 205, 53, 34, 166, 211, -7, -1, -25, 68, -7, 4, -13, -14…
$ carrier        <chr> "UA", "DL", "B6", "B6", "UA", "AA", "B6", "AA", "UA", "…
$ flight         <int> 628, 393, 371, 1053, 219, 499, 996, 981, 206, 225, 800,…
$ tailnum        <chr> "N25201", "N830DN", "N807JB", "N265JB", "N17730", "N925…
$ origin         <chr> "EWR", "JFK", "JFK", "JFK", "EWR", "EWR", "JFK", "EWR",…
$ dest           <chr> "SMF", "ATL", "BQN", "CHS", "DTW", "MIA", "BQN", "ORD",…
$ air_time       <dbl> 367, 108, 190, 108, 80, 154, 192, 119, 258, 157, 164, 1…
$ distance       <dbl> 2500, 760, 1576, 636, 488, 1085, 1576, 719, 1400, 1065,…
$ hour           <dbl> 20, 23, 23, 21, 20, 5, 5, 5, 5, 5, 5, 6, 5, 6, 6, 6, 6,…
$ minute         <dbl> 38, 0, 44, 40, 48, 0, 10, 30, 20, 45, 59, 0, 59, 0, 0, …
$ time_hour      <dttm> 2023-01-01 20:00:00, 2023-01-01 23:00:00, 2023-01-01 2…

Data wrangling: Clean data and glimpse at cleaned data

flights_clean <- flights %>%
  filter(!is.na(sched_dep_time), !is.na(arr_delay), !is.na(dep_delay))
head(flights_clean)

# A tibble: 6 × 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  2023     1     1        1           2038       203      328              3
2  2023     1     1       18           2300        78      228            135
3  2023     1     1       31           2344        47      500            426
4  2023     1     1       33           2140       173      238           2352
5  2023     1     1       36           2048       228      223           2252
6  2023     1     1      503            500         3      808            815
# 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>

Create scatterplot

ggplot(data = flights_clean, aes(x = sched_dep_time, y = arr_delay, color = dep_delay)) +
  geom_point(alpha = 0.4) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(
    title = "When You Leave Matters: Departure Time and Arrival Delays",
    x = "Scheduled Departure Time (HH:MM)",
    y = "Arrival Delay (Minutes)",
    color = "Departure Delay (Minutes)",
    caption = "Source: nycflights23"
  ) +
  theme_minimal()

Description

The scatterplot I created shows the relationship between scheduled departure times and arrival delays for flights in the nycflights23 dataset. In the dataset, flights originated from John F. Kennedy International (JFK), LaGuardia (LGA), or Newark Liberty International (EWR) airports. The x-axis shows the scheduled departure time, and the y-axis represents arrival delays in minutes. Each point is color-coded by departure delay, with blue for shorter delays and red for longer ones. I noticed a few trends: many flights with minimal departure delays (in blue) also have minimal arrival delays, but there are quite a few late departures (in red) that lead to significant arrival delays. Flights scheduled later in the day seem to have a wider range of delays, suggesting that earlier flights may face fewer issues, while later flights could be dealing with congestion or other factors that cause delays to stack up. Overall, the plot clearly shows how departure time and delays interact, giving insight into how flight performance varies throughout the day.