Question

The FAA tracks average flight delays. It found that, this year, a particular airline has an average flight delay of 16.9 minutes. That is 10.6% shorter that the airline’s average for the previous year. What was its average flight delay last year?

1. thuhuong
Step-by-step explanation:editor_options:
chunk_output_type: console
# Data transformation {#transform .r4ds-section}
## Introduction {#introduction-2 .r4ds-section}
“{r setup,message=FALSE,cache=FALSE}
library(“nycflights13”)
library(“tidyverse”)
“
## Filter rows with filter() {#filter-rows-with-filter .r4ds-section}
### Exercise 5.2.1 {.unnumbered .exercise data-number=”5.2.1″}
<div class=”question”>
Find all flights that
1.  Had an arrival delay of two or more hours
1.  Flew to Houston (IAH or HOU)
1.  Were operated by United, American, or Delta
1.  Departed in summer (July, August, and September)
1.  Arrived more than two hours late, but didn’t leave late
1.  Were delayed by at least an hour, but made up over 30 minutes in flight
1.  Departed between midnight and 6 am (inclusive)
</div>
The answer to each part follows.
1.  Since the arr_delay variable is measured in minutes, find
flights with an arrival delay of 120 or more minutes.
“{r ex-5.2.1-1, indent = 4}
filter(flights, arr_delay >= 120)
“
1.  The flights that flew to Houston are those flights where the
destination (dest) is either “IAH” or “HOU”.
“{r ex-5.2.1-2, indent=4}
filter(flights, dest == “IAH” | dest == “HOU”)
“
However, using %in% is more compact and would scale to cases where
there were more than two airports we were interested in.
“{r ex-5.2.1-3, indent=4}
filter(flights, dest %in% c(“IAH”, “HOU”))
“

1.  In the flights dataset, the column carrier indicates the airline, but it uses two-character carrier codes.
We can find the carrier codes for the airlines in the airlines dataset.
Since the carrier code dataset only has r nrow(airlines) rows, and the names
of the airlines in that dataset are not exactly “United”, “American”, or “Delta”,
it is easiest to manually look up their carrier codes in that data.
“{r ex-5.2.1-4,indent=4}
airlines
“
The carrier code for Delta is ”DL”, for American is ”AA”, and for United is ”UA”.
Using these carriers codes, we check whether carrier is one of those.
“{r, indent=4}
filter(flights, carrier %in% c(“AA”, “DL”, “UA”))
“
1.  The variable month has the month, and it is numeric.
So, the summer flights are those that departed in months 7 (July), 8 (August), and 9 (September).
“{r, indent=4}
filter(flights, month >= 7, month <= 9)
“
The %in% operator is an alternative. If the : operator is used to specify
the integer range, the expression is readable and compact.
“{r, indent=4}
filter(flights, month %in% 7:9)
“
We could also use the | operator. However, the | does not scale to
many choices.
Even with only three choices, it is quite verbose.
“{r, indent=4}
filter(flights, month == 7 | month == 8 | month == 9)
“
We can also use the between() function as shown in [Exercise 5.2.2](#exercise-5.2.2).
1.  Flights that arrived more than two hours late, but didn’t leave late will
have an arrival delay of more than 120 minutes (arr_delay > 120) and
a non-positive departure delay (dep_delay <= 0).
“`{r, indent=4}
filter(flights, arr_delay > 120, dep_delay <= 0)