Ahmad Mousavi

Ahmad Mousavi

Assistant Prof. of Data Science,
Dept. of Math. and Stat.,
American University

Lab 4: dplyr, Part 1

DATA 412/612 · Week 4 · PDF · Source (.qmd)

Filter, slice, arrange, select, mutate, and summarize flights.

Work in a Quarto document. Number the exercises. Do 4.1 through 4.3 first.

library(tidyverse)
library(nycflights13)
data("flights")

1. Filter and slice

  1. Flights in odd months, or on even days of even months.
  2. Flights to Houston (IAH or HOU), on UA, AA, or DL, in July–September, that arrived more than 15 minutes late and did not leave late.
  3. Rows 10, 100, 1000, 10000, and 100000.
  4. The last 10 of the first 30 Newark (EWR) flights.
  5. set.seed(1). A random 1% sample, and a sample of 3367 rows. Are the last 10 rows the same? Why?
  6. The 50 longest air_times, then the 10 smallest arr_delays among those. Repeat after filter(air_time < 300). What does with_ties = FALSE change?

2. Arrange, select, mutate

  1. Longest distance, ties broken by air_time.
  2. Keep every arrival column plus year, month, and day.
  3. Convert dep_time and sched_dep_time to minutes past midnight.
  4. Move columns ending in "time" to just before flight. Then put sched columns before the actual times.

3. Summarize and group

  1. Standard deviation of dep_delay.
  2. Maximum dep_delay by origin in one pipeline.
  3. For each day, the number and proportion of canceled flights (is.na(dep_delay) | is.na(arr_delay)). Plot the proportion against day, and against average departure delay.