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.

Getting started

Load packages

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 data

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

#{r names} #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 Airways
- DL: Delta Air Lines Inc.
- EV: ExpressJet Airlines Inc.
- F9: Frontier Airlines Inc.
- FL: AirTran Airways Corporation
- HA: Hawaiian Airlines Inc.
- MQ: Envoy Air
- OO: SkyWest Airlines Inc.
- UA: United Air Lines Inc.
- US: US Airways Inc.
- VX: Virgin America
- WN: 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)

## Rows: 32,735
## Columns: 16
## $ year      <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
## $ month     <int> 6, 5, 12, 5, 7, 1, 12, 8, 9, 4, 6, 11, 4, 3, 10, 1, 2, 8, 10…
## $ day       <int> 30, 7, 8, 14, 21, 1, 9, 13, 26, 30, 17, 22, 26, 25, 21, 23, …
## $ dep_time  <int> 940, 1657, 859, 1841, 1102, 1817, 1259, 1920, 725, 1323, 940…
## $ dep_delay <dbl> 15, -3, -1, -4, -3, -3, 14, 85, -10, 62, 5, 5, -2, 115, -4, …
## $ arr_time  <int> 1216, 2104, 1238, 2122, 1230, 2008, 1617, 2032, 1027, 1549, …
## $ arr_delay <dbl> -4, 10, 11, -34, -8, 3, 22, 71, -8, 60, -4, -2, 22, 91, -6, …
## $ carrier   <chr> "VX", "DL", "DL", "DL", "9E", "AA", "WN", "B6", "AA", "EV", …
## $ tailnum   <chr> "N626VA", "N3760C", "N712TW", "N914DL", "N823AY", "N3AXAA", …
## $ flight    <int> 407, 329, 422, 2391, 3652, 353, 1428, 1407, 2279, 4162, 20, …
## $ origin    <chr> "JFK", "JFK", "JFK", "JFK", "LGA", "LGA", "EWR", "JFK", "LGA…
## $ dest      <chr> "LAX", "SJU", "LAX", "TPA", "ORF", "ORD", "HOU", "IAD", "MIA…
## $ air_time  <dbl> 313, 216, 376, 135, 50, 138, 240, 48, 148, 110, 50, 161, 87,…
## $ distance  <dbl> 2475, 1598, 2475, 1005, 296, 733, 1411, 228, 1096, 820, 264,…
## $ hour      <dbl> 9, 16, 8, 18, 11, 18, 12, 19, 7, 13, 9, 13, 8, 20, 12, 20, 6…
## $ minute    <dbl> 40, 57, 59, 41, 2, 17, 59, 20, 25, 23, 40, 20, 9, 54, 17, 24…

The nycflights data frame is a massive trove of information. Let’s think about some questions we might want to answer with these data:

How delayed were flights that were headed to Los Angeles? # Filter for flights headed to Los Angeles (LAX) lax_flights <- nycflights %>% filter(dest == “LAX”)

Summarize delay statistics for LAX flights

lax_delay_summary <- lax_flights %>% summarise( avg_dep_delay = mean(dep_delay, na.rm = TRUE), avg_arr_delay = mean(arr_delay, na.rm = TRUE), max_dep_delay = max(dep_delay, na.rm = TRUE), max_arr_delay = max(arr_delay, na.rm = TRUE) )

lax_delay_summary

How do departure delays vary by month? # Calculate average departure delay by month monthly_dep_delay <- nycflights %>% group_by(month) %>% summarise(avg_dep_delay = mean(dep_delay, na.rm = TRUE))

monthly_dep_delay

Plot the average departure delay by month

ggplot(monthly_dep_delay, aes(x = month, y = avg_dep_delay)) + geom_line() + geom_point() + labs(title = “Average Departure Delay by Month”, x = “Month”, y = “Average Departure Delay (minutes)”)

Which of the three major NYC airports has the best on time percentage for departing flights? # Filter for the three major NYC airports nyc_airports <- nycflights %>% filter(origin %in% c(“JFK”, “LGA”, “EWR”))

Calculate the on-time departure percentage for each airport

airport_ontime <- nyc_airports %>% mutate(on_time = ifelse(dep_delay <= 0, 1, 0)) %>% group_by(origin) %>% summarise(on_time_percentage = mean(on_time, na.rm = TRUE) * 100)

airport_ontime

Analysis

Departure delays

Let’s start by examing the distribution of departure delays of all flights with a histogram.

#{r hist-dep-delay} #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:

#{r hist-dep-delay-bins} #ggplot(data = nycflights, aes(x = dep_delay)) + # geom_histogram(binwidth = 15) #ggplot(data = nycflights, aes(x = dep_delay)) + # geom_histogram(binwidth = 150) #

Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?

Insert your answer here By features the Small binwidth (15): Reveals fine detail and clustering patterns, particularly for smaller delays. Large binwidth (150): Smooths out the data, revealing broader trends but hiding small variations. Default binwidth: Somewhere in between, but potentially obscures both fine detail and broader trends.

In essence, smaller binwidths highlight fine patterns and clustering, while larger binwidths emphasize broad trends and smooth out small variations. The right binwidth depends on whether if we are looking for detailed insights or general trends in the data.

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.

#{r lax-flights-hist} #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).

Command 1: Take the 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.
Command 2: Basically the same 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.

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:

#{r lax-flights-summ} #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.

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:

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.

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?

Insert your answer here # Count the number of flights n_flights <- nrow(sfo_feb_flights)

Print the number of flights

n_flights #Response: 68 flights

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.

Insert your answer here # Plot a histogram of arrival delays for SFO flights in February ggplot(data = sfo_feb_flights, aes(x = arr_delay)) + geom_histogram(binwidth = 10, fill = “blue”, color = “black”) + labs(title = “Distribution of Arrival Delays for SFO Flights in February”, x = “Arrival Delay (minutes)”, y = “Frequency”)

Calculate summary statistics for arrival delays

summary_stats <- sfo_feb_flights %>% summarise( mean_arr_delay = mean(arr_delay, na.rm = TRUE), median_arr_delay = median(arr_delay, na.rm = TRUE), sd_arr_delay = sd(arr_delay, na.rm = TRUE), min_arr_delay = min(arr_delay, na.rm = TRUE), max_arr_delay = max(arr_delay, na.rm = TRUE), iqr_arr_delay = IQR(arr_delay, na.rm = TRUE) # Interquartile range )

summary_stats

#response: Most flights to San Francisco in February arrived early or on time, as indicated by the negative median and mean. While there is significant variability in arrival times, with some flights arriving very early and others experiencing severe delays, the majority experienced only moderate delays or were slightly ahead of schedule. The maximum delay of 196 minutes highlights a few extreme cases, but overall, the flights performed well, with many arriving early.

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())

## # A tibble: 2 × 4
##   origin median_dd iqr_dd n_flights
##   <chr>      <dbl>  <dbl>     <int>
## 1 EWR          0.5   5.75         8
## 2 JFK         -2.5  15.2         60

Here, we first grouped the data by origin and then calculated the summary statistics.

Calculate the median and interquartile range for arr_delays 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 # Calculate median and IQR for arrival delays, grouped by carrier sfo_feb_flights %>% group_by(carrier) %>% summarise( median_arr_delay = median(arr_delay, na.rm = TRUE), iqr_arr_delay = IQR(arr_delay, na.rm = TRUE), n_flights = n() )

While most carriers had early arrivals on average, Delta and United displayed the highest variability in arrival delays, suggesting that passengers flying with these carriers might experience more inconsistent arrival times. In contrast, JetBlue showed both early arrivals and lower variability, indicating a more reliable performance.

Departure delays by month

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:

First, calculate monthly averages for departure delays. With the new language you are learning, you could
- group_by months, then
- summarise mean departure delays.
Then, you could to arrange these average delays in descending order

nycflights %>%
  group_by(month) %>%
  summarise(mean_dd = mean(dep_delay)) %>%
  arrange(desc(mean_dd))

## # A tibble: 12 × 2
##    month mean_dd
##    <int>   <dbl>
##  1     7   20.8 
##  2     6   20.4 
##  3    12   17.4 
##  4     4   14.6 
##  5     3   13.5 
##  6     5   13.3 
##  7     8   12.6 
##  8     2   10.7 
##  9     1   10.2 
## 10     9    6.87
## 11    11    6.10
## 12    10    5.88

Suppose you really dislike departure delays and you want to schedule your travel in a month that minimizes your potential departure delay leaving NYC. One option is to choose the month with the lowest mean departure delay. Another option is to choose the month with the lowest median departure delay. What are the pros and cons of these two choices?

Insert your answer here #Option 1 By using Lowest Mean Departure Delay - Pros: - Provides an overall average delay, reflecting performance across all flights. - Includes all delays, offering a comprehensive view of typical performance. - Cons: - Sensitive to outliers, which can skew the average higher and give a misleading impression of typical delays.

#Option 2 By using the Lowest Median Departure Delay - Pros: - More robust to outliers, offering a reliable measure of central tendency. - Better reflects what most travelers experience, as it indicates the point below which half of the flights fall. - Cons: - Ignores the severity of delays, potentially underestimating the risk of significant delays if many flights have small delays alongside a few extreme ones.

#Conclusion The mean provides an overall view of performance but can be distorted by extreme delays, while the median offers a more stable estimate of typical experiences for travelers. A combined analysis of both metrics is advisable for making a well-informed decision.

On time departure rate for NYC airports

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

first classify each flight as “on time” or “delayed”,
then group flights by origin airport,
then calculate on time departure rates for each origin airport,
and finally arrange the airports in descending order for on time departure percentage.

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))

## # A tibble: 3 × 2
##   origin ot_dep_rate
##   <chr>        <dbl>
## 1 LGA          0.728
## 2 JFK          0.694
## 3 EWR          0.637

If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of?

You can also visualize the distribution of on on time departure rate across the three airports using a segmented bar plot.

#{r viz-origin-dep-type} #ggplot(data = nycflights, aes(x = origin, fill = dep_type)) + # geom_bar() Insert your answer here According to the histogram we can see JFK and EWR are slightly departing on time comparing that LGA airport but if we compare delay in departing from EWR has the biggest rate, comparing than JFK and LGA respectively, I would prefer travel from LGA or JFK airport than EWR.

More Practice

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. library(dplyr)

Insert your answer here # Mutate the data frame to include avg_speed nycflights <- nycflights %>% mutate(avg_speed = distance / (air_time / 60)) # Convert air_time from minutes to hours

Make a scatterplot of avg_speed vs. distance. Describe the relationship between average speed and distance. Hint: Use geom_point().

Insert your answer here library(ggplot2)

Scatterplot of avg_speed vs. distance

ggplot(data = nycflights, aes(x = distance, y = avg_speed)) + geom_point() + labs(title = “Average Speed vs. Distance”, x = “Distance (miles)”, y = “Average Speed (mph)”) + theme_minimal()

Replicate the following plot. Hint: The data frame plotted only contains flights from American Airlines, Delta Airlines, and United Airlines, and the points are colored 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.

Insert your answer here

library(dplyr) library(ggplot2)

Filter the data frame for the specified carriers

filtered_flights <- nycflights %>% filter(carrier %in% c(“AA”, “DL”, “UA”))

Create the scatterplot

ggplot(data = filtered_flights, aes(x = distance, y = avg_speed, color = carrier)) + geom_point(alpha = 0.7) + # Adjust transparency for better visibility labs(title = “Average Speed vs. Distance by Carrier”, x = “Distance (miles)”, y = “Average Speed (mph)”) + theme_minimal() + scale_color_manual(values = c(“AA” = “blue”, “DL” = “red”, “UA” = “green”)) # Customize colors

Introduction to data

Jose Fuentes