DataTransformation

Introduction

In this workshop we will do some of the exercises from Chapter 5 of R4DS.

Exercises from 5.2.4

Use a separate code block for each exercise.

for example: 1. Find all flights that had an arrival delay of two or more hours.

flights %>%
  filter(arr_delay >= 120) %>%
  ggplot() +
  geom_histogram(aes(x = arr_delay),binwidth = 5)# note delays are in minutes

2. Find all United Airline flights to “IAH”. (note this is not exactly of the book exercises.)

flights %>%
  filter(dest == "IAH" & carrier == "UA")

## # A tibble: 6,924 x 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      623            627        -4      933            932
##  4  2013     1     1      728            732        -4     1041           1038
##  5  2013     1     1      739            739         0     1104           1038
##  6  2013     1     1      908            908         0     1228           1219
##  7  2013     1     1     1028           1026         2     1350           1339
##  8  2013     1     1     1044           1045        -1     1352           1351
##  9  2013     1     1     1114            900       134     1447           1222
## 10  2013     1     1     1205           1200         5     1503           1505
## # … with 6,914 more rows, and 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>

3. Find all flights that arrived more than two hours late, but didn’t leave late

4. Find all flights the departed between midnight and 6am (inclusive).

5. Why is NA ^ 0 not missing? Why is NA | TRUE not missing? Why is FALSE & NA not missing? Can you figure out the general rule? (NA * 0 is a tricky counterexample!) How does this differ with normal practice in Mathematics?

6. Find the 5 most delayed flights. (not exactly the same as book)

7. Using a helper function, extract all columns concerning time.

flights %>%
  select(ends_with("time"))

## # A tibble: 336,776 x 5
##    dep_time sched_dep_time arr_time sched_arr_time air_time
##       <int>          <int>    <int>          <int>    <dbl>
##  1      517            515      830            819      227
##  2      533            529      850            830      227
##  3      542            540      923            850      160
##  4      544            545     1004           1022      183
##  5      554            600      812            837      116
##  6      554            558      740            728      150
##  7      555            600      913            854      158
##  8      557            600      709            723       53
##  9      557            600      838            846      140
## 10      558            600      753            745      138
## # … with 336,766 more rows

8. 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. (hint: you may need to make a function and also use maodular arithmetic as I mentioned last week.)

9.Compare air_time with arr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?

10. Explain the use of the pipe function %>% How does the design of the tidyverse facillitate the use of pipes?

11. Brainstorm at least 5 different ways to assess the typical delay characteristics of a group of flights. Consider the following scenarios:

• A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time.
• A flight is always 10 minutes late.
• A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time.
• 99% of the time a flight is on time. 1% of the time it’s 2 hours late.

12. Which is more important: arrival delay or departure delay?