challengeTime.Rmd

title: "Challenge time!"
author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr), Hélène Polvèche [hpolveche@istem.fr](mailto:hpolveche@istem.fr)"
date: "Mars 2020"
output:
  html_document: default
  pdf_document: default

h3 { /* Header 3 */ position: relative ; color: #729FCF ; left: 5%; } h2 { /* Header 2 */ color: darkblue ; left: 10%; } h1 { /* Header 1 */ color: #034b6f ; } #pencadre{ border:1px; border-style:solid; border-color: #034b6f; background-color: #EEF3F9; padding: 1em; text-align: center ; border-radius : 5px 4px 3px 2px; } legend{ color: #034b6f ; } #pquestion { color: darkgreen; font-weight: bold; }

knitr::opts_chunk$set(echo = TRUE)

Filter challenges :

Find all flights that:

• Had an arrival delay of two or more hours
• Were operated by United, American, or Delta
• Departed between midnight and 6am (inclusive)

Another useful dplyr filtering helper is between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?

How many flights have a missing dep_time? What other variables are missing? What might these rows represent?

Why is NA ^ 0 not NA? Why is NA | TRUE not NA? Why is FALSE & NA not NA? Can you figure out the general rule? (NA * 0 is a tricky counter-example!)

Arrange challenges :

• Sort flights to find the most delayed flights. Find the flights that left earliest.
• Sort flights to find the fastest flights.
• Which flights traveled the longest? Which traveled the shortest?

Select challenges :

• Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.
• What does the one_of() function do? Why might it be helpful in conjunction with this vector?

vars <- c("year", "month", "day", "dep_delay", "arr_delay")

• Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?

select(flights, contains("TIME"))

Mutate challenges :

• Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they’re not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.

mutate(
  flights,
  dep_time = (dep_time %/% 100) * 60 +
    dep_time %% 100,
  sched_dep_time = (sched_dep_time %/% 100) * 60 +
    sched_dep_time %% 100
)

\

• Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?

mutate(
  flights,
  dep_time = (dep_time %/% 100) * 60 + 
    dep_time %% 100,
  sched_dep_time = (sched_dep_time %/% 100) * 60 +
    sched_dep_time %% 100
)

\

Challenge with summarise() and group_by()

Imagine that we want to explore the relationship between the distance and average delay for each location. here are three steps to prepare this data:

• Group flights by destination.
• Summarise to compute distance, average delay, and number of flights.
• Filter to remove noisy points and Honolulu airport, which is almost twice as far away as the next closest airport.

flights %>% 
  group_by(dest)

\

Imagine that we want to explore the relationship between the distance and average delay for each location.

• Filter to remove noisy points and Honolulu airport, which is almost twice as far away as the next closest airport.

flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  )