knitr::opts_chunk$set(eval = TRUE, message = FALSE, warning = FALSE) library(tidyverse) library(openintro)
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 OpenIntro labs, openintro.
Let's load the packages.
library(tidyverse) library(openintro)
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:
data(nycflights)
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
names(nycflights)
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:
?nycflights
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.
glimpse(nycflights)
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.
ggplot(data = nycflights, aes(x = dep_delay)) + geom_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:
ggplot(data = nycflights, aes(x = dep_delay)) + geom_histogram(binwidth = 15) ggplot(data = nycflights, aes(x = dep_delay)) + geom_histogram(binwidth = 150)
Insert your answer here
If you want to visualize only on delays of flights headed to Los Angeles, you need to first filter
the data for flights with that destination (dest == "LAX"
) and then make a histogram of the departure delays of only those flights.
lax_flights <- nycflights %>% filter(dest == "LAX") ggplot(data = lax_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 LAX, and save the result as a new data frame called lax_flights
.==
means "if it's equal to".LAX
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 LAX instead of all flights.You can also obtain numerical summaries for these flights:
lax_flights %>% summarise(mean_dd = mean(dep_delay), median_dd = median(dep_delay), n = n())
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.
You can also filter based on multiple criteria. Suppose you are interested in flights headed to San Francisco (SFO) in February:
sfo_feb_flights <- nycflights %>% filter(dest == "SFO", month == 2)
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 the |
instead of the comma.
sfo_feb_flights
. How many flights
meet these criteria? Insert your answer here
Insert your answer here
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?Insert your answer here
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 ordernycflights %>% group_by(month) %>% summarise(mean_dd = mean(dep_delay)) %>% arrange(desc(mean_dd))
Insert your answer here
Suppose 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.
nycflights <- nycflights %>% mutate(dep_type = ifelse(dep_delay < 5, "on time", "delayed"))
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.
ggplot(data = nycflights, aes(x = origin, fill = dep_type)) + geom_bar()
Insert your answer here
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.Insert your answer here
avg_speed
vs. distance
. Describe the relationship
between average speed and distance.
Hint: Use geom_point()
.Insert your answer here
color
ed by carrier
. Once you replicate
the plot, determine (roughly) what the cutoff point is for departure
delays where you can still expect to get to your destination on time.knitr::include_graphics('Question9Plot.png')
Insert your answer here
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