Assignment 4

library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.3     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.2     
── 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)
data(flights)
library(ggplot2)
library(dplyr)
library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine
head(flights)
# 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>
str(flights)
tibble [435,352 × 19] (S3: tbl_df/tbl/data.frame)
 $ year          : int [1:435352] 2023 2023 2023 2023 2023 2023 2023 2023 2023 2023 ...
 $ month         : int [1:435352] 1 1 1 1 1 1 1 1 1 1 ...
 $ day           : int [1:435352] 1 1 1 1 1 1 1 1 1 1 ...
 $ dep_time      : int [1:435352] 1 18 31 33 36 503 520 524 537 547 ...
 $ sched_dep_time: int [1:435352] 2038 2300 2344 2140 2048 500 510 530 520 545 ...
 $ dep_delay     : num [1:435352] 203 78 47 173 228 3 10 -6 17 2 ...
 $ arr_time      : int [1:435352] 328 228 500 238 223 808 948 645 926 845 ...
 $ sched_arr_time: int [1:435352] 3 135 426 2352 2252 815 949 710 818 852 ...
 $ arr_delay     : num [1:435352] 205 53 34 166 211 -7 -1 -25 68 -7 ...
 $ carrier       : chr [1:435352] "UA" "DL" "B6" "B6" ...
 $ flight        : int [1:435352] 628 393 371 1053 219 499 996 981 206 225 ...
 $ tailnum       : chr [1:435352] "N25201" "N830DN" "N807JB" "N265JB" ...
 $ origin        : chr [1:435352] "EWR" "JFK" "JFK" "JFK" ...
 $ dest          : chr [1:435352] "SMF" "ATL" "BQN" "CHS" ...
 $ air_time      : num [1:435352] 367 108 190 108 80 154 192 119 258 157 ...
 $ distance      : num [1:435352] 2500 760 1576 636 488 ...
 $ hour          : num [1:435352] 20 23 23 21 20 5 5 5 5 5 ...
 $ minute        : num [1:435352] 38 0 44 40 48 0 10 30 20 45 ...
 $ time_hour     : POSIXct[1:435352], format: "2023-01-01 20:00:00" "2023-01-01 23:00:00" ...
tab1 <- flights %>% select(dep_time,sched_dep_time, dep_delay,carrier)
tab1
# A tibble: 435,352 × 4
   dep_time sched_dep_time dep_delay carrier
      <int>          <int>     <dbl> <chr>  
 1        1           2038       203 UA     
 2       18           2300        78 DL     
 3       31           2344        47 B6     
 4       33           2140       173 B6     
 5       36           2048       228 UA     
 6      503            500         3 AA     
 7      520            510        10 B6     
 8      524            530        -6 AA     
 9      537            520        17 UA     
10      547            545         2 NK     
# ℹ 435,342 more rows
p1 <- ggplot(tab1, aes(x = sched_dep_time, y = dep_delay, color = carrier)) +
  labs(title = "Departure Delay by Carrier",
       caption = "NYC Flights Dataset 2023") +
  xlab("Scheduled Departure Time") +
  ylab ("Departure Delay") +
  theme_minimal(base_size = 12) 
p1 + geom_point()
Warning: Removed 10738 rows containing missing values or values outside the scale range
(`geom_point()`).

Paragraph

I have created a scatter plot of the flight departure delay. It can be seen in the graph that the x-axis is the scheduled departure time and the y-axis is the departure delay. The higher on the y-axis that the dot on the graph is, the greater the departure delay. The airline carriers are distinguished by color on the graph. It can be observed on the graph that the airline carrier that appears to have the longest departure delays is the airline carrier AA. No particular correlation pertaining to the scheduled departure time and departure delay can be seen.