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, 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.

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

head(flights_dt, 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.

weekddf <- flights_dt |> 
  mutate(weekday = wday(dep_datetime, label=TRUE)) |> 
  count(weekday)

weekddf # This is much prettier
## # A tibble: 8 × 2
##   weekday     n
##   <ord>   <int>
## 1 Sun     45643
## 2 Mon     49468
## 3 Tue     49273
## 4 Wed     48858
## 5 Thu     48654
## 6 Fri     48703
## 7 Sat     37922
## 8 <NA>     8255
table(weekddf) # This outputs a truth table which is hard to read
##        n
## weekday 8255 37922 45643 48654 48703 48858 49273 49468
##     Sun    0     0     1     0     0     0     0     0
##     Mon    0     0     0     0     0     0     0     1
##     Tue    0     0     0     0     0     0     1     0
##     Wed    0     0     0     0     0     1     0     0
##     Thu    0     0     0     1     0     0     0     0
##     Fri    0     0     0     0     1     0     0     0
##     Sat    0     1     0     0     0     0     0     0

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.

timeseries_df <- flights |> 
  filter(origin=="JFK") |> 
  slice_head(n=1000) |> 
  mutate(dep_datetime = make_datetime(year, month, day, hour=dep_time%/%100, min=dep_time%%100))

timeseries <- zoo(timeseries_df$dep_delay, timeseries_df$dep_datetime)
## Warning in zoo(timeseries_df$dep_delay, timeseries_df$dep_datetime): some
## methods for "zoo" objects do not work if the index entries in 'order.by' are
## not unique
plot(timeseries)

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.

fact_df <- flights |> 
  mutate(origin_factor = fct_recode(origin, 
                            "JFK" = "JFK",
                            "LGA" = "LGA",
                            "EWR" = "EWR")) |> 
  group_by(origin_factor)
levels(fact_df$origin_factor)
## [1] "EWR" "JFK" "LGA"
table(fact_df$origin_factor)
## 
##    EWR    JFK    LGA 
## 120835 111279 104662
barplot(table(fact_df$origin_factor))

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.

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

levels(fact_df$origin_recoded)
## [1] "Newark"    "Kennedy"   "LaGuardia"
barplot(table(fact_df$origin_recoded))

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))
##           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 
##              0              0              0              0
flights_m <- flights |> 
  mutate(dep_delay_imputed = ifelse(is.na(dep_delay), 0, dep_delay)) |> 
  filter(dep_delay_imputed>-20) |> 
  filter(dep_delay_imputed<20)

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

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)

It was a bit challenging to filter the specific delays for the final question bnut I found a good way to do it. Assuming zero delay for missing values could be too generous considering the airports’ history with being late however assuming no delay also denies that they could be early, so overall it can be a good medium point.

I learned that in 2013, flights out of the three New York City airports (John F. Kennedy, LaGuardia, and Newark) were more often early than they were late. In the final coding question you can see just by looking at the quantity of flights that had negative delay versus positive delay. There are far more flights for a delay like -4 (24000+ flights) than there are for a 4 minute delay (4800+ flights). We can also see that a pluurality of the traffic goes through Newark as opposed to the airports actually in New York state.