Stat. 450 Section 1 or 2: Homework 3

Prof. Eric A. Suess

So how should you complete your homework for this class?

• First thing to do is type all of your information about the problems you do in the text part of your R Notebook.
• Second thing to do is type all of your R code into R chunks that can be run.
• If you load the tidyverse in an R Notebook chunk, be sure to include the “message = FALSE” in the {r}, so {r message = FALSE}.
• Last thing is to spell check your R Notebook. Edit > Check Spelling… or hit the F7 key.

Homework 3:

 Read: Chapter 5
 Do 5.2.4 Exercises 1, 2,
 Do 5.3.1 Exericise 2

library(tidyverse)

5.2.4

1.

This problem looks at the nycflights13 data set.

library(nycflights13)
flights

These questions relate to finding the flights that meet the conditions specified.

Note that dep_delay is in minutes.

1. Had an arrival delay of two or more hours. So more than 2*60 minutes.

flights %>% filter(dep_delay >= 120)

2. Flew to Houston (IAH or HOU)

flights %>% filter(dest == "IAH" | dest == "HOU")

or

flights %>% filter(dest %in% c("IAH", "HOU"))

3. Were operated by United, American, or Delta

Lets check the airlines dataframe for the codes for these airlines.

airlines

flights %>% filter(carrier %in% c("DL", "AA", "UA"))

4. Departed in summer (July, August, and September)

flights %>% filter(month %in% c(7,8,9))

5. Arrived more than two hours late, but didn’t leave late

flights %>% filter(dep_delay <= 0 & arr_delay > 2*60)

6. Were delayed by at least an hour, but made up over 30 minutes in flight

flights %>% filter(dep_delay >= 60 & dep_delay - arr_delay > 30  )

7. Departed between midnight and 6am (inclusive)

Note that midnight is 2400, not 0.

flights %>% filter(dep_time <= 600 | dep_time == 2400 )

2.

The between() function can be used like %in%.

The between() can be used with the months to filter the rows from July, August, September.

flights %>% filter(between(month, 7, 9))

5.3.1

2.

Sort flights to find the most delayed flights. Find the flights that left earliest.

The five most delayed flights.

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

The five flights that left the earliest.

flights %>% arrange(dep_delay) %>%
  head(5)