Homework Assignment: Analyzing NYC Flight Data

This homework assignment uses the flights dataset from the nycflights13 package, which contains real-world data on over 336,000 flights departing from New York City airports (JFK, LGA, EWR) in 2013. The dataset includes variables such as departure and arrival times (with date components), airline carrier (categorical), origin and destination airports (categorical), delays (with missing values for cancelled flights), distance, and more. It is sourced from the US Bureau of Transportation Statistics.

Objectives

This assignment reinforces the Week 4 topics:

  • Parsing and manipulating dates/times using lubridate.
  • Creating and analyzing time series with zoo.
  • Working with factors, inspecting levels, and recoding them.
  • Identifying and handling missing data (e.g., removal, imputation).

All questions (except the final reflection) require you to write and run R code to solve them. Submit your URL for your RPubs. Make sure to comment your code, along with key outputs (e.g., summaries, plots, or tables). Use the provided setup code to load the data.

Setup

Install and load the necessary packages if not already done:

#install.packages(c("nycflights13", "dplyr", "lubridate", "zoo", "forcats"))  # If needed
library(nycflights13)
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(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(zoo)
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(forcats)  # For factor recoding; base R alternatives are acceptable
data(flights)  # Load the dataset

Explore the data briefly with str(flights) and head(flights) to understand the structure. Note: Dates are in separate year, month, day columns; times are in dep_time and arr_time (as integers like 517 for 5:17 AM).

#Explore your data here
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" ...
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  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
## # 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>

Question 1: Creating Dates with lubridate

Create a column dep_datetime by combining year, month, day, and dep_time into a POSIXct datetime using lubridate. (Hint: Use make_datetime function to combine: year, month, day, for hour and min use division, e.g., hour = dep_time %/% 100, minute = dep_time %% 100.)

Show the first 5 rows of flights with dep_datetime.

Output: First 5 rows showing year, month, day, dep_time, and dep_datetime.

flights <- flights %>%
  mutate(
    dep_datetime = make_datetime(
      year = year,
      month = month,
      day = day,
      hour = dep_time %/% 100,
      min = dep_time %% 100
    )
  )

flights %>%
  select(year, month, day, dep_time, dep_datetime) %>%
  head(5)
## # A tibble: 5 × 5
##    year month   day dep_time dep_datetime       
##   <int> <int> <int>    <int> <dttm>             
## 1  2013     1     1      517 2013-01-01 05:17:00
## 2  2013     1     1      533 2013-01-01 05:33:00
## 3  2013     1     1      542 2013-01-01 05:42:00
## 4  2013     1     1      544 2013-01-01 05:44:00
## 5  2013     1     1      554 2013-01-01 05:54:00

Question 2: Simple Date Manipulations with lubridate

Using dep_datetime from Question 1, create a column weekday with the day of the week (e.g., “Mon”) using wday(dep_datetime, label = TRUE). Use table() to show how many flights occur on each weekday.

Output: The table of flight counts by weekday.

flights <- flights %>%
  mutate(
    weekday = wday(dep_datetime, label = TRUE)
  )

table(flights$weekday)
## 
##   Sun   Mon   Tue   Wed   Thu   Fri   Sat 
## 45643 49468 49273 48858 48654 48703 37922

Question 3: Time Series with zoo

Filter for flights from JFK (origin == “JFK”) and create a zoo time series of departure delays (dep_delay) by dep_datetime. Plot the time series (use plot()). (Hint: Use a subset to avoid memory issues, e.g., first 1000 JFK flights.)

Output: The time series plot.

jfk_flights <- flights %>%
  filter(origin == "JFK") %>%
  select(dep_datetime, dep_delay) %>%
  slice(1:1000)

jfk_ts <- zoo(jfk_flights$dep_delay, jfk_flights$dep_datetime)
## Warning in zoo(jfk_flights$dep_delay, jfk_flights$dep_datetime): some methods
## for "zoo" objects do not work if the index entries in 'order.by' are not unique
plot(jfk_ts, main = "Departure Delays for First 1000 JFK Flights",
     xlab = "Time", ylab = "Departure Delay (minutes)")

Question 4: Working with Factors

Convert the origin column (airports: “JFK”, “LGA”, “EWR”) to a factor called origin_factor. Show the factor levels with levels() and create a frequency table with table(). Make a bar plot of flights by airport using barplot().

Output: The levels, frequency table, and bar plot.

flights <- flights %>%
  mutate(origin_factor = factor(origin))

levels(flights$origin_factor)
## [1] "EWR" "JFK" "LGA"
table(flights$origin_factor)
## 
##    EWR    JFK    LGA 
## 120835 111279 104662
barplot(
  table(flights$origin_factor),
  main = "Number of Flights by NYC Airport",
  xlab = "Airport",
  ylab = "Number of Flights"
)

Question 5: Recoding Factors

Recode origin_factor from Question 4 into a new column origin_recoded with full names: “JFK” to “Kennedy”, “LGA” to “LaGuardia”, “EWR” to “Newark” using fct_recode() or base R. Create a bar plot of the recoded factor.

Output: The new levels and bar plot.

flights <- flights %>%
  mutate(
    origin_recoded = fct_recode(
      origin_factor,
      "Kennedy" = "JFK",
      "LaGuardia" = "LGA",
      "Newark" = "EWR"
    )
  )

levels(flights$origin_recoded)
## [1] "Newark"    "Kennedy"   "LaGuardia"
barplot(
  table(flights$origin_recoded),
  main = "Flights by Airport (Recoded Names)",
  xlab = "Airport",
  ylab = "Number of Flights"
)

Question 6: Handling Missing Data

Count missing values in dep_delay and arr_delay using colSums(is.na(flights)). Impute missing dep_delay values with 0 (assuming no delay for cancelled flights) in a new column dep_delay_imputed. Create a frequency table of dep_delay_imputed for delays between -20 and 20 minutes (use filter() to subset).

Output: NA counts, and the frequency table for imputed delays.

colSums(is.na(flights[, c("dep_delay", "arr_delay")]))
## dep_delay arr_delay 
##      8255      9430
flights <- flights %>%
  mutate(
    dep_delay_imputed = ifelse(is.na(dep_delay), 0, dep_delay)
  )

flights %>%
  filter(dep_delay_imputed >= -20, dep_delay_imputed <= 20) %>%
  count(dep_delay_imputed)
## # A tibble: 41 × 2
##    dep_delay_imputed     n
##                <dbl> <int>
##  1               -20    37
##  2               -19    19
##  3               -18    81
##  4               -17   110
##  5               -16   162
##  6               -15   408
##  7               -14   498
##  8               -13   901
##  9               -12  1594
## 10               -11  2727
## # ℹ 31 more rows

Question 7: Reflection (No Coding)

Reflect on the assignment: What was easy or hard about working with flight dates or missing data? How might assuming zero delay for missing values (Question 6) affect conclusions about flight punctuality? What did you learn about NYC flights in 2013? (150-200 words)

Reflection

At first, it was difficult to work with dates and timings in the NYC flights dataset as they were kept in different columns. I was able to transform the data into a useful datetime format and make this process much simpler by using the lubridate package, particularly the make_datetime() method. After the datetime column was formed, it was easy to extract weekdays, and the results showed that flights are dispersed reasonably equally throughout the week.

More thought had to be given to how to handle missing data. Flight cancellations caused several departure delays. Although it simplifies the study, imputing missing delays with zero implies that cancelled flights incurred no delay, which may result in incorrect conclusions on punctuality. This method should always be described properly because it lowers variability and may understate actual delays.

I was able to comprehend how departure delays vary over time, particularly for JFK flights, by utilizing the zoo package to create a time series. Plots and descriptions became clearer after factors were worked with and airport names were recoded. All things considered, this assignment reaffirmed the significance of appropriate date handling, factor management, and clear missing-data judgments while examining actual transportation data.