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).

# Look at the structure of the flights dataset
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" ...
# Display the first six rows
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, min = 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 a datetime column using the year, month, day,and departure
flights <- flights %>%
  mutate(
    dep_datetime = make_datetime(
      year = year,
      month = month,
      day = day,
      hour = dep_time %/% 100,
      min = dep_time %% 100
    )
  )

# Show the first five rows with the requested columns
head(
  flights %>%
    select(year, month, day, dep_time, dep_datetime),
  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.

# Create a weekday column from departure datetime
flights <- flights %>%
  mutate(weekday = wday(dep_datetime, label = TRUE))

# Count flights on each weekday
table(flights$weekday, useNA = "ifany")
## 
##   Sun   Mon   Tue   Wed   Thu   Fri   Sat  <NA> 
## 45643 49468 49273 48858 48654 48703 37922  8255

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 using `slice_head().)

Output: The time series plot.

# select JFK flights that have a departure datetime
jfk_flights <- flights %>%
  filter(origin == "JFK", !is.na(dep_datetime)) %>%
  slice_head(n = 1000)
# Create a zoo time series of departure delays
jfk_zoo <- zoo(
  x = jfk_flights$dep_delay,
  order.by = jfk_flights$dep_datetime
)
## Warning in zoo(x = jfk_flights$dep_delay, order.by = jfk_flights$dep_datetime):
## some methods for "zoo" objects do not work if the index entries in 'order.by'
## are not unique
# Plot the departure delays over time
plot(
  jfk_zoo,
  main = "Departure Delays for JFK Flights",
  xlab = "Departure Date and 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 the origin column into a factor
flights <- flights %>%
  mutate(origin_factor = factor(origin))

# Display the airport factor levels
levels(flights$origin_factor)
## [1] "EWR" "JFK" "LGA"
# Count flights from each airport
table(flights$origin_factor)
## 
##    EWR    JFK    LGA 
## 120835 111279 104662
# Create a bar plot of flights by airport
barplot(
  table(flights$origin_factor),
  main = "Number of Flights by Origin Airport",
  xlab = "Origin Airport",
  ylab = "Number of Flights",
  col = "skyblue"
)

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.

# Replace airport abbreviations with their full name
flights <- flights %>%
  mutate(
    origin_recoded = fct_recode(
      origin_factor,
      "Kennedy" = "JFK",
      "LaGuardia" = "LGA",
      "Newark" = "EWR"
    )
  )

# Display the new factor levels
levels(flights$origin_recoded)
## [1] "Newark"    "Kennedy"   "LaGuardia"
# Create a bar plot using the recoded airport names
barplot(
  table(flights$origin_recoded),
  main = "Flights by Airport Name",
  xlab = "airport",
  ylab = "Number of Flights",
  col = "lightgreen"
)

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.

# Count missing values in every column
colSums(is.na(flights))
##           year          month            day       dep_time sched_dep_time 
##              0              0              0           8255              0 
##      dep_delay       arr_time sched_arr_time      arr_delay        carrier 
##           8255           8713              0           9430              0 
##         flight        tailnum         origin           dest       air_time 
##              0           2512              0              0           9430 
##       distance           hour         minute      time_hour   dep_datetime 
##              0              0              0              0           8255 
##        weekday  origin_factor origin_recoded 
##           8255              0              0
# Display missing-value counts for the two delay columns
colSums(is.na(flights))[c("dep_delay", "arr_delay")]
## dep_delay arr_delay 
##      8255      9430
# Replace missing departure delay with zero in a new column
flights <- flights %>%
  mutate(dep_delay_imputed = ifelse(is.na(dep_delay), 0, dep_delay))

# Keep flights with imputed departure delays between -20 and 20 minutes
delays_near_zero <- flights %>%
  filter(between(dep_delay_imputed, -20, 20))

# Display the frequency table of the imputed delays
table(delays_near_zero$dep_delay_imputed)
## 
##   -20   -19   -18   -17   -16   -15   -14   -13   -12   -11   -10    -9    -8 
##    37    19    81   110   162   408   498   901  1594  2727  5891  7875 11791 
##    -7    -6    -5    -4    -3    -2    -1     0     1     2     3     4     5 
## 16752 20701 24821 24619 24218 21516 18813 24769  8050  6233  5450  4807  4447 
##     6     7     8     9    10    11    12    13    14    15    16    17    18 
##  3789  3520  3381  3062  2859  2756  2494  2414  2256  2140  2085  1873  1749 
##    19    20 
##  1730  1704

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)

Woring with the NYC flights dateset help me learn how to use R to organize date. Making tables and bar graphs was easy because I could compare flights by airport and day. The hardest part was putting dates and time together because they were in separate columns. Finding missing data was also a little difficult.

Replacing missing delays with zero can make flights look like they were on time when we do not know if that is true. This can make airlines seem more on time than they really are.

I learned that the dataset includes flights from JFK, LaGuardia, and Newark airport in 2013. I also learned how to use R to find flight delays, compare airports, and check for missing information. Overall, this assignment helped me understand how data can be used to learn more about real-world problems.