Classwork 6

Data Transformation with R - filter(), arrange(), and distinct()

Author

Byeong-Hak Choe

Published

October 5, 2026

Note

Complete the questions in this Classwork and submit your responses on the corresponding Brightspace page.

Fill in every ____ below. In the code, a blank may stand for a function, variable, operator, or value. Copy each completed code example into your R script and run it to check your answer.

Consider the nycflights13::flights data.frame.

library(tidyverse)
flights <- nycflights13::flights

For detailed descriptions of the variables in this data frame, load its help documentation using the following command:

# Documentation for the `flights` data.frame from the `nycflights13` package
?flights   # displays the description of variables in the `flights` data.frame

Question 1

The delay variables are measured in minutes. Complete each filter() expression. Pay attention to whether a boundary value should be included.

(a) Find all flights that had an arrival delay of two or more hours.

flights |>
  ____(arr_delay ____)

Example answer

Two hours is 120 minutes. >= includes flights delayed by exactly 120 minutes as well as longer delays.

late_arrivals <- flights |>
  filter(arr_delay >= 120)
nrow(late_arrivals)

[1] 10200

There are 10,200 such flights.

(b) Find all flights that flew to Houston (IAH or HOU).

flights |>
  filter(dest == "IAH" ____ dest == ____)

Example answer

The | operator means or, so a flight qualifies if its destination is either Houston airport.

houston_flights <- flights |>
  filter(dest == "IAH" | dest == "HOU")
nrow(houston_flights)

[1] 9313

There are 9,313 flights to IAH or HOU.

(c) Find all flights that departed in summer (July, August, and September).

flights |>
  filter(month == 7 ____ month == 8 ____  month == 9)

Example answer

Join the three month comparisons with | so that any one of the months qualifies.

summer_flights <- flights |>
  filter(month == 7 | month == 8 | month == 9)
nrow(summer_flights)

[1] 86326

There are 86,326 flights in the three specified months.

(d) Find all flights that arrived more than two hours late, but did not leave late. An on-time or early departure has a departure delay of zero or less.

flights |>
  filter(arr_delay ____ 120 ____ dep_delay ____ 0)

Example answer

Use > because “more than” excludes exactly 120 minutes. Use & to require both conditions; <= 0 includes on-time and early departures.

late_arrival_on_time_departure <- flights |>
  filter(arr_delay > 120 & dep_delay <= 0)
nrow(late_arrival_on_time_departure)

[1] 29

There are 29 flights meeting both conditions.

Question 2

How many flights have a missing dep_time?

Complete the code to keep flights with a missing departure time, then count their rows.

flights |>
  filter(____(dep_time)) |>
  ____()

Number of flights with missing dep_time: ____

Example answer

is.na(dep_time) identifies missing departure times, and nrow() counts the matching flights.

flights |>
  filter(is.na(dep_time)) |>
  nrow()

[1] 8255

The count is 8,255.

Question 3

Sort flights to find the most delayed flights by departure delay (dep_delay), with the largest delay first.

How head() works

head(data, n) shows the first n rows of a data frame in their current order. For example, this data frame has four rows, and head(example_flights, 2) displays only the first two:

example_flights <- data.frame(
  flight = c("A", "B", "C", "D"),
  dep_delay = c(10, 30, 0, 60)
)

head(example_flights, 2)

  flight dep_delay
1      A        10
2      B        30

With no second argument, head(data) shows the first six rows. In the exercise below, head(5) shows the first five rows from the preceding sorting step.

Complete the code to sort in descending order, then inspect the first few rows.

flights |>
  ____(____(dep_delay)) |>
  head(5)

Example answer

arrange() sorts the rows, and desc() puts the largest departure delays first. head(5) then shows the first five flights in that order.

flights |>
  arrange(desc(dep_delay)) |>
  head(5)

# A tibble: 5 × 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     9      641            900      1301     1242           1530
2  2013     6    15     1432           1935      1137     1607           2120
3  2013     1    10     1121           1635      1126     1239           1810
4  2013     9    20     1139           1845      1014     1457           2210
5  2013     7    22      845           1600      1005     1044           1815
# 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>

The largest departure delay is 1,301 minutes, on flight HA 51 from JFK to HNL on January 9, 2013.

Question 4

Was there a flight on every day of 2013?

Count unique calendar dates. A date needs its year, month, and day; the day-of-month alone repeats across months. Compare the count with the number of days in 2013.

flight_dates <- flights |>
  ____(year, ____, ____)
  
number_of_days <- ____(flight_dates)
number_of_days

number_of_days == ____

Number of distinct dates: ____

Was there a flight on every day of 2013? ____

Example answer

distinct(year, month, day) keeps one row for each calendar date. Compare the number of dates with 365, the number of days in 2013.

flight_dates <- flights |>
  distinct(year, month, day)

number_of_days <- nrow(flight_dates)
number_of_days

[1] 365

number_of_days == 365

[1] TRUE

There are 365 distinct dates, and the comparison returns TRUE. The data contain a flight record for every day of 2013.