Some define statistics as the field that focuses on turning information into knowledge. The first step in that process is to summarize and describe the raw information – the data. In this lab we explore flights, specifically a random sample of domestic flights that departed from the three major New York City airports in 2013. We will generate simple graphical and numerical summaries of data on these flights and explore delay times. Since this is a large data set, along the way you’ll also learn the indispensable skills of data processing and subsetting.
In this lab, we will explore and visualize the data using the tidyverse suite of packages. The data can be found in the companion package for these labs, openintro.
Let’s load the packages.
Remember that we will be using R Markdown to create reproducible lab
reports. Just like last time, create a new markdown file using the
“Openintro Lab Report” template (ask me or your group mates if you’re
not sure where to find it). You should change the name of this file to
Lab2_Your_Name.Rmd
, as well as adding your name and lab
number to the document itself. Make sure to knit often to make sure
everything is working!
The Bureau of Transportation Statistics (BTS) is a statistical agency that is a part of the Research and Innovative Technology Administration (RITA). As its name implies, BTS collects and makes transportation data available, such as the flights data we will be working with in this lab.
First, we’ll view the nycflights
data frame. Type the
following in your console to load the data:
The data set nycflights
that shows up in your workspace
is a data matrix, with each row representing an
observation and each column representing a variable. R
calls this data format a data frame, which is a term
that will be used throughout the labs. For this data set, each
observation is a single flight.
To view the names of the variables, type the command
This returns the names of the variables in this data frame. The codebook (description of the variables) can be accessed by pulling up the help file:
One of the variables refers to the carrier (i.e. airline) of the flight, which is coded according to the following system.
carrier
: Two letter carrier abbreviation.
9E
: Endeavor Air Inc.AA
: American Airlines Inc.AS
: Alaska Airlines Inc.B6
: JetBlue AirwaysDL
: Delta Air Lines Inc.EV
: ExpressJet Airlines Inc.F9
: Frontier Airlines Inc.FL
: AirTran Airways CorporationHA
: Hawaiian Airlines Inc.MQ
: Envoy AirOO
: SkyWest Airlines Inc.UA
: United Air Lines Inc.US
: US Airways Inc.VX
: Virgin AmericaWN
: Southwest Airlines Co.YV
: Mesa Airlines Inc.Remember that you can use glimpse
to take a quick peek
at your data to understand its contents better.
The nycflights
data frame is a massive trove of
information. Let’s think about some questions we might want to answer
with these data:
Let’s start by examing the distribution of departure delays of all flights with a histogram.
This function says to plot the dep_delay
variable from
the nycflights
data frame on the x-axis. It also defines a
geom
(short for geometric object), which describes the type
of plot you will produce.
Histograms are generally a very good way to see the shape of a single distribution of numerical data, but that shape can change depending on how the data is split between the different bins. You can easily define the binwidth you want to use:
If you want to visualize only on delays of flights headed to Chicago
O’Hare, you need to first filter
the data for flights with
that destination (dest == "ORD"
) and then make a histogram
of the departure delays of only those flights.
ord_flights <- nycflights %>%
filter(dest == "LAX")
ggplot(data = ord_flights, aes(x = dep_delay)) +
geom_histogram()
Let’s decipher these two commands (OK, so it might look like four
lines, but the first two physical lines of code are actually part of the
same command. It’s common to add a break to a new line after
%>%
to help readability).
nycflights
data frame,
filter
for flights headed to ORD, and save the result as a
new data frame called ord_flights
.
==
means “if it’s equal to”.ORD
is in quotation marks since it is a character
string.ggplot
call from earlier
for making a histogram, except that it uses the smaller data frame for
flights headed to ORD instead of all flights.Logical operators: Filtering for certain
observations (e.g. flights from a particular airport) is often of
interest in data frames where we might want to examine observations with
certain characteristics separately from the rest of the data. To do so,
you can use the filter
function and a series of
logical operators. The most commonly used logical
operators for data analysis are as follows:
==
means “equal to”!=
means “not equal to”>
or <
means “greater than” or “less
than”>=
or <=
means “greater than or
equal to” or “less than or equal to”You can also obtain numerical summaries for these flights:
Note that in the summarise
function you created a list
of three different numerical summaries that you were interested in. The
names of these elements are user defined, like mean_dd
,
median_dd
, n
, and you can customize these
names as you like (just don’t use spaces in your names). Calculating
these summary statistics also requires that you know the function calls.
Note that n()
reports the sample size.
Summary statistics: Some useful function calls for summary statistics for a single numerical variable are as follows:
mean
median
sd
var
IQR
min
max
Note that each of these functions takes a single vector as an argument and returns a single value.
You can also filter based on multiple criteria. Suppose you are interested in flights headed to San Francisco (SFO) in February:
Note that you can separate the conditions using commas if you want
flights that are both headed to SFO and in February. If
you are interested in either flights headed to SFO or
in February, you can use |
instead of the comma. (Make note
of this because you will need it for a later question)
Create a new data frame that includes flights headed to SFO in
February, and save this data frame as sfo_feb_flights
. How
many flights meet these criteria?
Describe the distribution of the arrival delays of these flights using a histogram and appropriate summary statistics. Hint: The summary statistics you use should depend on the shape of the distribution.
Another useful technique is quickly calculating summary statistics
for various groups in your data frame. For example, we can modify the
above command using the group_by
function to get the same
summary stats for each origin airport:
sfo_feb_flights %>%
group_by(origin) %>%
summarise(median_dd = median(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n())
Here, we first grouped the data by origin
and then
calculated the summary statistics.
arr_delay
s of flights in in the
sfo_feb_flights
data frame, grouped by carrier. Which
carrier has the most variable arrival delays?Which month would you expect to have the highest average delay departing from an NYC airport?
Let’s think about how you could answer this question:
group_by
months, thensummarise
mean departure delays.arrange
these average delays in
desc
ending orderSuppose you will be flying out of NYC and want to know which of the three major NYC airports has the best on time departure rate of departing flights. Also supposed that for you, a flight that is delayed for less than 5 minutes is basically “on time.”” You consider any flight delayed for 5 minutes of more to be “delayed”.
In order to determine which airport has the best on time departure rate, you can
Let’s start with classifying each flight as “on time” or “delayed” by
creating a new variable with the mutate
function.
The first argument in the mutate
function is the name of
the new variable we want to create, in this case dep_type
.
Then if dep_delay < 5
, we classify the flight as
"on time"
and "delayed"
if not, i.e. if the
flight is delayed for 5 or more minutes.
Note that we are also overwriting the nycflights
data
frame with the new version of this data frame that includes the new
dep_type
variable.
We can handle all of the remaining steps in one code chunk:
nycflights %>%
group_by(origin) %>%
summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>%
arrange(desc(ot_dep_rate))
You can also visualize the distribution of on on time departure rate across the three airports using a segmented bar plot.
Mutate the data frame so that it includes a new variable that
contains the average speed, avg_speed
traveled by the plane
for each flight (in mph). Hint: Average speed can be
calculated as distance divided by number of hours of travel, and note
that air_time
is given in minutes.
Make a scatterplot of avg_speed
vs. distance
. Describe the relationship between average
speed and distance. Hint: Use
geom_point()
.
Replicate the following plot. (Hint: There is
some code below to help you. The data plotted only contains flights from
American Airlines, Delta Airlines, and United Airlines, and the points
are color
ed by carrier
). Use the plot to
(roughly) determine the maximum departure delay for which a flight might
still be on time.
After you finish answering all the questions, you should knit the document to create an .html file of your lab. You can then export this file to download it to your computer, and submit this file to Canvas under the appropriate tab.
Make sure to look at your final report and ensure it is readable! In your final report, you should only include code necessary to answer the questions.
This
work is licensed under a
Creative
Commons Attribution-ShareAlike 4.0 International License.