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
# Explore structure and first few rows
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.

# Create dep_datetime column
flights <- flights %>%
  mutate(
    hour = dep_time %/% 100,
    minute = dep_time %% 100,
    dep_datetime = make_datetime(year, month, day, hour, minute)
  )

# Show first 5 rows with relevant columns
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.

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

# Count flights by weekday
weekday_counts <- table(flights$weekday)
weekday_counts
## 
##   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.

# Filter JFK flights and take first 1000 for manageable plotting
jfk_flights <- flights %>%
  filter(origin == "JFK") %>%
  arrange(dep_datetime) %>%
  slice(1:1000)  # subset for memory/plot clarity

# Create zoo time series
dep_delay_series <- 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
plot(dep_delay_series, main = "Departure Delays at JFK (First 1000 Flights)",
     xlab = "Departure 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.

# Convert origin to factor
flights$origin_factor <- factor(flights$origin)

# Show levels
levels(flights$origin_factor)
## [1] "EWR" "JFK" "LGA"
# Frequency table
origin_table <- table(flights$origin_factor)
origin_table
## 
##    EWR    JFK    LGA 
## 120835 111279 104662
# Bar plot
barplot(origin_table, main = "Flights by Airport",
        xlab = "Airport", ylab = "Number of Flights",
        col = "steelblue")

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.

# Recode factor levels
flights$origin_recoded <- fct_recode(flights$origin_factor,
                                     "Kennedy" = "JFK",
                                     "LaGuardia" = "LGA",
                                     "Newark" = "EWR")

# Show new levels
levels(flights$origin_recoded)
## [1] "Newark"    "Kennedy"   "LaGuardia"
# Bar plot for recoded factor
recoded_table <- table(flights$origin_recoded)
barplot(recoded_table, main = "Flights by Airport (Full Names)",
        xlab = "Airport", ylab = "Number of Flights",
        col = "coral")

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.

#### Question 6: Handling Missing Data 

# Count NAs in delay columns
delay_cols <- c("dep_delay", "arr_delay")
na_counts <- colSums(is.na(flights[delay_cols]))
na_counts
## dep_delay arr_delay 
##      8255      9430
# Impute missing dep_delay with 0
flights$dep_delay_imputed <- ifelse(is.na(flights$dep_delay), 0, flights$dep_delay)

# Frequency table for delays between -20 and 20 minutes
delay_freq <- flights %>%
  filter(dep_delay_imputed >= -20 & dep_delay_imputed <= 20) %>%
  mutate(delay_bin = cut(dep_delay_imputed, 
                         breaks = seq(-20, 20, by = 5),
                         include.lowest = TRUE)) %>%
  count(delay_bin) %>%
  rename(count = n)

delay_freq
## # A tibble: 8 × 2
##   delay_bin  count
##   <fct>      <int>
## 1 [-20,-15]    817
## 2 (-15,-10]  11611
## 3 (-10,-5]   81940
## 4 (-5,0]    113935
## 5 (0,5]      28987
## 6 (5,10]     16611
## 7 (10,15]    12060
## 8 (15,20]     9141

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)

Working with flight dates in lubridate was straightforward, but dealing with missing delay values created real challenges. Many of the missing entries were tied to cancelled flights. Filling them with zero looks simple, but it introduces clear bias. Doing that makes the data imply that cancelled flights arrived on time, which inflates punctuality and can lead to misleading conclusions about airline performance. Cancellations are major disruptions, so they should be examined on their own instead of being blended into regular delay measurements.

The 2013 NYC flight data also showed recognizable operational patterns. JFK handled most long-haul routes. LaGuardia concentrated on short domestic trips. Newark acted as a major hub with a mix of traffic. Delays tended to cluster during busy hours and on certain weekdays, which reflects congestion and scheduling strain. Through date manipulation, recoding variables, and building time series plots, the assignment showed how decisions in data cleaning, especially around missing values, shape the results. These choices matter, and they directly affect how we understand real transportation data.