Introduction to data
Akeem Lawrence
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.
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:
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
## [1] "year" "month" "day" "dep_time" "dep_delay" "arr_time"
## [7] "arr_delay" "carrier" "tailnum" "flight" "origin" "dest"
## [13] "air_time" "distance" "hour" "minute"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.
## 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?
- How do departure delays vary by month?
- Which of the three major NYC airports has the best on time percentage for departing flights?
Analysis
Departure delays
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:
- Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?
A smaller bin width shows the distribution of flight data with more clarity versus a larger bin width, which shows the counts of the maxiumum values with more clarity.
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).
- Command 1: Take the
nycflightsdata frame,filterfor flights headed to LAX, and save the result as a new data frame calledlax_flights.==means “if it’s equal to”.LAXis in quotation marks since it is a character string.
- Command 2: Basically the same
ggplotcall 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:
## # A tibble: 1 × 3
## mean_dd median_dd n
## <dbl> <dbl> <int>
## 1 9.78 -1 1583Note 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:
meanmediansdvarIQRminmax
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 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?
## year month day dep_time dep_delay
## Min. :2013 Min. :2 Min. : 1.00 Min. : 613 Min. :-10.0
## 1st Qu.:2013 1st Qu.:2 1st Qu.: 7.00 1st Qu.: 943 1st Qu.: -5.0
## Median :2013 Median :2 Median :16.00 Median :1268 Median : -2.0
## Mean :2013 Mean :2 Mean :15.26 Mean :1298 Mean : 10.5
## 3rd Qu.:2013 3rd Qu.:2 3rd Qu.:22.50 3rd Qu.:1742 3rd Qu.: 9.0
## Max. :2013 Max. :2 Max. :28.00 Max. :2159 Max. :209.0
## arr_time arr_delay carrier tailnum
## Min. : 118 Min. :-66.00 Length:68 Length:68
## 1st Qu.:1233 1st Qu.:-21.25 Class :character Class :character
## Median :1497 Median :-11.00 Mode :character Mode :character
## Mean :1607 Mean : -4.50
## 3rd Qu.:2062 3rd Qu.: 2.00
## Max. :2256 Max. :196.00
## flight origin dest air_time
## Min. : 11.0 Length:68 Length:68 Min. :317.0
## 1st Qu.: 85.0 Class :character Class :character 1st Qu.:345.0
## Median : 641.0 Mode :character Mode :character Median :354.0
## Mean : 795.1 Mean :351.9
## 3rd Qu.:1487.2 3rd Qu.:360.0
## Max. :2126.0 Max. :376.0
## distance hour minute
## Min. :2565 Min. : 6.00 Min. : 1.00
## 1st Qu.:2586 1st Qu.: 9.00 1st Qu.:25.00
## Median :2586 Median :12.50 Median :33.50
## Mean :2584 Mean :12.62 Mean :36.35
## 3rd Qu.:2586 3rd Qu.:17.00 3rd Qu.:54.00
## Max. :2586 Max. :21.00 Max. :59.0068 flights meet this 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.
sfo_feb_flights %>%
summarise(iqr_dd = IQR(dep_delay, na.rm = TRUE),
median_dd = median(dep_delay, na.rm = TRUE),
n = n())## # A tibble: 1 × 3
## iqr_dd median_dd n
## <dbl> <dbl> <int>
## 1 14 -2 68
The distribution of arrival delays is left-skewed, with most flights arriving near their scheduled time or slightly late. The median arrival delay is approximately two minutes, and the interquartile range is about 14 minutes, indicating moderate spread. A few flights arrive significantly late, contributing to the skew.
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 60Here, 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 thesfo_feb_flightsdata frame, grouped by carrier. Which carrier has the most variable arrival delays?
arr_delays_feb <- sfo_feb_flights %>%
group_by(carrier) %>%
summarise(
median_dd = median(arr_delay, na.rm = TRUE),
iqr_dd = IQR(arr_delay, na.rm = TRUE),
n_flights = n()) %>%
filter(iqr_dd == max(iqr_dd))
arr_delays_feb## # A tibble: 2 × 4
## carrier median_dd iqr_dd n_flights
## <chr> <dbl> <dbl> <int>
## 1 DL -15 22 19
## 2 UA -10 22 21# arr_delays_feb2 <- sfo_feb_flights %>%
# group_by(carrier) %>%
# summarise(
# median_dd = median(arr_delay, na.rm = TRUE),
# iqr_dd = IQR(arr_delay, na.rm = TRUE),
# n_flights = n()
# ) %>%
# arrange(desc(iqr_dd))
#
# arr_delays_feb2
arr_delays_feb3 <- sfo_feb_flights %>%
group_by(carrier) %>%
summarise(
median_dd = median(arr_delay, na.rm = TRUE),
standard_dd = sd(arr_delay, na.rm = TRUE),
n_flights = n()
) %>%
arrange(desc(standard_dd))
most_variable_carrier <- arr_delays_feb3 %>%
filter(standard_dd == max(standard_dd, na.rm = TRUE))
most_variable_carrier## # A tibble: 1 × 4
## carrier median_dd standard_dd n_flights
## <chr> <dbl> <dbl> <int>
## 1 UA -10 48.3 21Both DL (Delta) and UA (United) have the most variability based on their IQR of 22. However, if we compare based on standard deviation, we see that UA has the highest standard deviation, and we can use this metric as the tiebreaker – UA is the most variable arrival delays.
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_bymonths, thensummarisemean departure delays.
- Then, you could to
arrangethese average delays indescending order
## # 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?
pros_mean <- nycflights %>%
group_by(month) %>%
summarise(mean_dd = mean(dep_delay)) %>%
arrange(desc(mean_dd))
pros_mean## # 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 5.88pros_median <- nycflights %>%
group_by(month) %>%
summarise(median_dd = median(dep_delay)) %>%
arrange(desc(median_dd))
pros_median## # A tibble: 12 × 2
## month median_dd
## <int> <dbl>
## 1 12 1
## 2 6 0
## 3 7 0
## 4 3 -1
## 5 5 -1
## 6 8 -1
## 7 1 -2
## 8 2 -2
## 9 4 -2
## 10 11 -2
## 11 9 -3
## 12 10 -3The pros and cons depend on the distribution of departure delays, and the number of outliers. We saw earlier from our histograms that we have some large outliers skewing our data, the median is likely more reliable here. However, to test both cases, I have calculated both the mean and median departure delays.
If we used the mean, I would be leaving nyc in July. If we used the median, I would be leaving nyc in October, so my travel would be quite different – in one case I’d be leaving 75F weather and likely going somewhere hotter if a tropical location, so I may choose to travel to Toronto instead of October when I’l likely want to avoid Toronto, and visit the Caribbean instead.
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.
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.
nycflights_summary <- nycflights %>%
group_by(origin) %>%
summarise(
delayed = sum(dep_type == "delayed", na.rm = TRUE),
on_time = sum(dep_type == "on time", na.rm = TRUE),
total = n(),
delay_to_on_time_ratio = delayed / on_time,
on_time_rate = on_time / total
) %>%
arrange(on_time_rate)
nycflights_summary## # A tibble: 3 × 6
## origin delayed on_time total delay_to_on_time_ratio on_time_rate
## <chr> <int> <int> <int> <dbl> <dbl>
## 1 EWR 4273 7498 11771 0.570 0.637
## 2 JFK 3339 7558 10897 0.442 0.694
## 3 LGA 2739 7328 10067 0.374 0.728At first glance, I almost chose Newark Airport, because it has the highest number of flights on time. However, this turned out to be misleading, because Newark had the highest number of total flights. I created a ratio of delayed to ontime flights, with a higher ratio being worse – more delayed flights for every on-time flight is a nuisance as a traveler.
After calculating this data, and an on-time rate, which is the number of on-time flights out of the total number of flights, I discovered that La Guardia Airport has the lowest ratio of flights delayed as a percentage of on-time flights, and has the largest number of flights that depart in a timely fashion as a percentage of the total flights taken.
Based on this, if I were selecting an airport based on time departure percentage, I’d like to take the M60 Bus to La Guardia rather that take NJ Transit and take my chances in Newark.
More Practice
- Mutate the data frame so that it includes a new variable that
contains the average speed,
avg_speedtraveled 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 thatair_timeis given in minutes.
## # A tibble: 32,735 × 18
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 6 30 940 15 1216 -4 VX N626VA
## 2 2013 5 7 1657 -3 2104 10 DL N3760C
## 3 2013 12 8 859 -1 1238 11 DL N712TW
## 4 2013 5 14 1841 -4 2122 -34 DL N914DL
## 5 2013 7 21 1102 -3 1230 -8 9E N823AY
## 6 2013 1 1 1817 -3 2008 3 AA N3AXAA
## 7 2013 12 9 1259 14 1617 22 WN N218WN
## 8 2013 8 13 1920 85 2032 71 B6 N284JB
## 9 2013 9 26 725 -10 1027 -8 AA N3FSAA
## 10 2013 4 30 1323 62 1549 60 EV N12163
## # ℹ 32,725 more rows
## # ℹ 9 more variables: flight <int>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, dep_type <chr>, avg_speed <dbl>## # A tibble: 6 × 18
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr> <int>
## 1 2013 6 30 940 15 1216 -4 VX N626VA 407
## 2 2013 5 7 1657 -3 2104 10 DL N3760C 329
## 3 2013 12 8 859 -1 1238 11 DL N712TW 422
## 4 2013 5 14 1841 -4 2122 -34 DL N914DL 2391
## 5 2013 7 21 1102 -3 1230 -8 9E N823AY 3652
## 6 2013 1 1 1817 -3 2008 3 AA N3AXAA 353
## # ℹ 8 more variables: origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, dep_type <chr>, avg_speed <dbl># Create the boxplot
ggplot(data = nycflights, aes(x = carrier, y = avg_speed, fill = carrier)) +
geom_boxplot() +
labs(
title = "Distribution of Average Flight Speeds by Carrier",
subtitle = "Box = middle 50% (IQR), line = median, points = outliers",
x = "Carrier",
y = "Average Speed (mph)"
) +
theme_minimal()
- Make a scatterplot of
avg_speedvs.distance. Describe the relationship between average speed and distance. Hint: Usegeom_point().
ggplot(data = nycflights, aes(x = distance, y = avg_speed)) +
geom_point(alpha = 0.3, color = "steelblue") +
geom_smooth(method = "lm", color = "darkred") +
labs(
title = "Average Speed vs. Distance with Linear Trend",
x = "Distance (miles)",
y = "Average Speed (mph)"
) +
theme_minimal()
As the flight total distance increases, the average speed increases.
- 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 bycarrier. 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.
library(ggplot2)
nycflights <- nycflights %>%
filter(carrier == "DL" | carrier == "UA" | carrier == "AA") %>%
group_by(carrier)
ggplot(data = nycflights, aes(x = dep_delay, y = arr_delay, color = carrier)) +
geom_point() 
ggplot(data = nycflights, aes(x = dep_delay, y = arr_delay, color = carrier)) +
geom_point(alpha = 0.4) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
labs(
title = "Arrival Delay vs. Departure Delay by Carrier",
x = "Departure Delay (min)",
y = "Arrival Delay (min)"
) +
theme_minimal()
on_time_or_early <- nycflights %>%
filter(dep_delay > 0, arr_delay <= 0) # Departed late but arrived early/on time
on_time_or_early## # A tibble: 1,923 × 18
## # Groups: carrier [3]
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 2 1 729 9 1018 -5 UA N36247
## 2 2013 7 5 920 5 1204 -6 AA N328AA
## 3 2013 9 9 1821 8 2053 -35 UA N24202
## 4 2013 4 16 1745 8 1937 -7 DL N320US
## 5 2013 3 24 2005 5 2248 -37 UA N39726
## 6 2013 10 23 816 1 1046 -9 DL N694DL
## 7 2013 10 27 1601 1 1914 -13 DL N912DL
## 8 2013 7 18 1451 5 1726 -19 UA N24729
## 9 2013 5 19 1601 11 1857 -10 DL N906DL
## 10 2013 12 18 803 18 1044 -16 AA N3EJAA
## # ℹ 1,913 more rows
## # ℹ 9 more variables: flight <int>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, dep_type <chr>, avg_speed <dbl>The cutoff line is about 5 minutes, meaning that if we face a small delay within this time window, we usually still arrive on time. I have displayed a summary of the 1923 flights in this category below.
I plotted the x-intercept of the median of flights that left late, but still arrived on time or early, which is 5 minutes. 14% of flights fell into this category – you it makes sense to text your ride from the airport that you may still arrive on time.
on_time_or_early <- nycflights %>%
filter(dep_delay > 0, arr_delay <= 0) # Departed late but arrived early/on time
on_time_or_early## # A tibble: 1,923 × 18
## # Groups: carrier [3]
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 2 1 729 9 1018 -5 UA N36247
## 2 2013 7 5 920 5 1204 -6 AA N328AA
## 3 2013 9 9 1821 8 2053 -35 UA N24202
## 4 2013 4 16 1745 8 1937 -7 DL N320US
## 5 2013 3 24 2005 5 2248 -37 UA N39726
## 6 2013 10 23 816 1 1046 -9 DL N694DL
## 7 2013 10 27 1601 1 1914 -13 DL N912DL
## 8 2013 7 18 1451 5 1726 -19 UA N24729
## 9 2013 5 19 1601 11 1857 -10 DL N906DL
## 10 2013 12 18 803 18 1044 -16 AA N3EJAA
## # ℹ 1,913 more rows
## # ℹ 9 more variables: flight <int>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, dep_type <chr>, avg_speed <dbl>## [1] 0.1402728latest_recovered_flight <- on_time_or_early %>%
filter(arr_delay == max(arr_delay, na.rm = TRUE))
latest_recovered_flight## # A tibble: 83 × 18
## # Groups: carrier [3]
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr>
## 1 2013 5 25 1440 11 1619 0 DL N721TW
## 2 2013 9 29 705 10 920 0 AA N5ENAA
## 3 2013 10 18 1701 1 1945 0 AA N5CAAA
## 4 2013 10 26 1850 15 2137 0 DL N3760C
## 5 2013 6 11 2147 2 2315 0 DL N351NB
## 6 2013 1 27 1506 6 1742 0 DL N541US
## 7 2013 12 13 1348 3 1640 0 UA N67058
## 8 2013 4 7 1923 13 2230 0 UA N77520
## 9 2013 1 7 1134 9 1617 0 UA N12225
## 10 2013 3 11 2200 25 50 0 AA N320AA
## # ℹ 73 more rows
## # ℹ 9 more variables: flight <int>, origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>, dep_type <chr>, avg_speed <dbl>## [1] 5## [1] 63# Calculate the cutoffs
cutoff_median <- median(on_time_or_early$dep_delay, na.rm = TRUE)
cutoff_max <- max(on_time_or_early$dep_delay, na.rm = TRUE)
ggplot(nycflights, aes(x = dep_delay, y = arr_delay, color = carrier)) +
geom_point(alpha = 0.4) +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_vline(xintercept = c(cutoff_median, cutoff_max),
linetype = c("dotted", "dashed"),
color = c("red", "blue")) +
annotate("text", x = cutoff_median + 5, y = 250,
label = paste("Median:", round(cutoff_median, 1)), color = "red") +
annotate("text", x = cutoff_max + 5, y = 200,
label = paste("Max:", round(cutoff_max, 1)), color = "blue") +
theme_minimal()