Exercise 13
Soojin Kim
11/26/2021
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.6
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(nycflights13)13.2.1 Exercises
1. Imagine you wanted to draw (approximately) the route each plane flies from its origin to its destination. What variables would you need? What tables would you need to combine?
Answer) I need the flights and the airports tables. And I need the keys which are (origin, dest) from the flights table, and (faa, lat, lon) from the airports table and combine with inner_join().
variable.names(airports)## [1] "faa" "name" "lat" "lon" "alt" "tz" "dst" "tzone"flights_route <- flights %>%
inner_join(select(airports,
origin = faa,
origin_lat = lat,
origin_lon = lon),
by = "origin") %>%
inner_join(select(airports,
dest = faa,
dest_lat = lat,
dest_lon = lon),
by = "dest") %>%
select("origin", "dest", "origin_lat", "origin_lon", "dest_lat", "dest_lon")
flights_route## # A tibble: 329,174 x 6
## origin dest origin_lat origin_lon dest_lat dest_lon
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 EWR IAH 40.7 -74.2 30.0 -95.3
## 2 LGA IAH 40.8 -73.9 30.0 -95.3
## 3 JFK MIA 40.6 -73.8 25.8 -80.3
## 4 LGA ATL 40.8 -73.9 33.6 -84.4
## 5 EWR ORD 40.7 -74.2 42.0 -87.9
## 6 EWR FLL 40.7 -74.2 26.1 -80.2
## 7 LGA IAD 40.8 -73.9 38.9 -77.5
## 8 JFK MCO 40.6 -73.8 28.4 -81.3
## 9 LGA ORD 40.8 -73.9 42.0 -87.9
## 10 JFK PBI 40.6 -73.8 26.7 -80.1
## # … with 329,164 more rows2. I forgot to draw the relationship between weather and airports. What is the relationship and how should it appear in the diagram?
Answer) There is connection between origin in the weather table and origin that can be linked to faa(foreign key) in the airports table.
variable.names(airports)## [1] "faa" "name" "lat" "lon" "alt" "tz" "dst" "tzone"variable.names(weather)## [1] "origin" "year" "month" "day" "hour"
## [6] "temp" "dewp" "humid" "wind_dir" "wind_speed"
## [11] "wind_gust" "precip" "pressure" "visib" "time_hour"3. weather only contains information for the origin (NYC) airports. If it contained weather records for all airports in the USA, what additional relation would it define with flights?
Answer) The keys which are year, month, day, hour, origin from the weather table are the foreign keys, and the keys which are year, month, day, hour, dest from the flights table are the primary keys. That would also match the key connecting weather with destinations.
4. We know that some days of the year are “special”, and fewer people than usual fly on them. How might you represent that data as a data frame? What would be the primary keys of that table? How would it connect to the existing tables?
Answer) There are off-seasons and peak seasons for flight trips. Usually, people travel a lot on summer break or special holidays. On the contrary, not many people travel during the school semester. The off-season is usually from February to May, and among them, only weekdays are grouped to assign a new table, and the new table and the existing table are joined.
13.3.1 Exercises
1. Add a surrogate key to flights.
flights %>%
arrange(year, month, day, sched_dep_time, carrier, flight) %>%
mutate(flight_sur = row_number()) %>%
glimpse()## Rows: 336,776
## Columns: 20
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time <int> 517, 533, 542, 544, 554, 559, 558, 559, 558, 558, 557, …
## $ sched_dep_time <int> 515, 529, 540, 545, 558, 559, 600, 600, 600, 600, 600, …
## $ dep_delay <dbl> 2, 4, 2, -1, -4, 0, -2, -1, -2, -2, -3, NA, 1, 0, -5, -…
## $ arr_time <int> 830, 850, 923, 1004, 740, 702, 753, 941, 849, 853, 838,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 728, 706, 745, 910, 851, 856, 846,…
## $ arr_delay <dbl> 11, 20, 33, -18, 12, -4, 8, 31, -2, -3, -8, NA, -6, -7,…
## $ carrier <chr> "UA", "UA", "AA", "B6", "UA", "B6", "AA", "AA", "B6", "…
## $ flight <int> 1545, 1714, 1141, 725, 1696, 1806, 301, 707, 49, 71, 79…
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N39463", "N708…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "EWR", "JFK", "LGA", "LGA",…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ORD", "BOS", "ORD", "DFW",…
## $ air_time <dbl> 227, 227, 160, 183, 150, 44, 138, 257, 149, 158, 140, N…
## $ distance <dbl> 1400, 1416, 1089, 1576, 719, 187, 733, 1389, 1028, 1005…
## $ hour <dbl> 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
## $ minute <dbl> 15, 29, 40, 45, 58, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
## $ flight_sur <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …2. Identify the keys in the following datasets
* Lahman::Batting
Answer) The primary key is (playerID). Other keys are many duplicated values.
#Lahman::Batting
Lahman::Batting %>%
count(playerID) %>%
filter(n > 1) %>%
nrow()## [1] 14748* babynames::babynames
Answer) The primary keys are (year, sex, name) since it only can be distinguished the babies. There might have many duplicated values.
#babynames::babynames
babynames::babynames %>%
count(year, sex, name) %>%
filter(n > 1) %>%
nrow()## [1] 0* nasaweather::atmos
Answer) The primary keys are (lat, long, year, month). We can get the information with those keys.
#nasaweather::atmos
nasaweather::atmos %>%
count(lat, long, year, month) %>%
filter(n > 1) %>%
nrow()## [1] 0* fueleconomy::vehicles
Answer) The primary key is (id). This key is only can be distinguished each data.
#fueleconomy::vehicles
fueleconomy::vehicles %>%
count(id) %>%
filter(n > 1) %>%
nrow()## [1] 0* ggplot2::diamonds
(You might need to install some packages and read some documentation.)
Answer) The primary keys are (carat, cut, color, clarity). We can be distinguished each data with those keys.
#ggplot2::diamonds
ggplot2::diamonds %>%
count(carat, cut, color, clarity) %>%
filter(n > 1) %>%
nrow()## [1] 80803. Draw a diagram illustrating the connections between the Batting, People, and Salaries tables in the Lahman package. Draw another diagram that shows the relationship between People, Managers, AwardsManagers.
How would you characterise the relationship between the Batting, Pitching, and Fielding tables?
13.4.6 Exercises
1. Compute the average delay by destination, then join on the airports data frame so you can show the spatial distribution of delays. Here’s an easy way to draw a map of the United States:
airports %>%
semi_join(flights, c("faa" = "dest")) %>%
ggplot(aes(lon, lat)) +
borders("state") +
geom_point() +
coord_quickmap()
#### (Don’t worry if you don’t understand what semi_join() does — you’ll learn about it next.)
You might want to use the size or colour of the points to display the average delay for each airport.
avg_delay <- flights %>%
group_by(dest) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
inner_join(airports, by = c(dest = "faa"))
avg_delay %>%
ggplot(aes(lon, lat, color = delay)) +
borders("state") +
geom_point() +
coord_quickmap()
2. Add the location of the origin and destination (i.e. the lat and lon) to flights.
location <- airports %>%
select(lat, lon, faa)
location## # A tibble: 1,458 x 3
## lat lon faa
## <dbl> <dbl> <chr>
## 1 41.1 -80.6 04G
## 2 32.5 -85.7 06A
## 3 42.0 -88.1 06C
## 4 41.4 -74.4 06N
## 5 31.1 -81.4 09J
## 6 36.4 -82.2 0A9
## 7 41.5 -84.5 0G6
## 8 42.9 -76.8 0G7
## 9 39.8 -76.6 0P2
## 10 48.1 -123. 0S9
## # … with 1,448 more rowsflights %>%
select(origin, dest) %>%
inner_join(location, by = c(origin = "faa")) %>%
inner_join(location, by = c(dest = "faa"))## # A tibble: 329,174 x 6
## origin dest lat.x lon.x lat.y lon.y
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 EWR IAH 40.7 -74.2 30.0 -95.3
## 2 LGA IAH 40.8 -73.9 30.0 -95.3
## 3 JFK MIA 40.6 -73.8 25.8 -80.3
## 4 LGA ATL 40.8 -73.9 33.6 -84.4
## 5 EWR ORD 40.7 -74.2 42.0 -87.9
## 6 EWR FLL 40.7 -74.2 26.1 -80.2
## 7 LGA IAD 40.8 -73.9 38.9 -77.5
## 8 JFK MCO 40.6 -73.8 28.4 -81.3
## 9 LGA ORD 40.8 -73.9 42.0 -87.9
## 10 JFK PBI 40.6 -73.8 26.7 -80.1
## # … with 329,164 more rows3. Is there a relationship between the age of a plane and its delays?
plane_age <- planes %>%
select(year, tailnum) %>%
mutate(age = 2021 - year)
plane_age## # A tibble: 3,322 x 3
## year tailnum age
## <int> <chr> <dbl>
## 1 2004 N10156 17
## 2 1998 N102UW 23
## 3 1999 N103US 22
## 4 1999 N104UW 22
## 5 2002 N10575 19
## 6 1999 N105UW 22
## 7 1999 N107US 22
## 8 1999 N108UW 22
## 9 1999 N109UW 22
## 10 1999 N110UW 22
## # … with 3,312 more rowsrelation <- flights %>%
select(tailnum, arr_delay) %>%
group_by(tailnum) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
inner_join(plane_age, by = c(tailnum = "tailnum"))
relation## # A tibble: 3,322 x 4
## tailnum delay year age
## <chr> <dbl> <int> <dbl>
## 1 N10156 12.7 2004 17
## 2 N102UW 2.94 1998 23
## 3 N103US -6.93 1999 22
## 4 N104UW 1.80 1999 22
## 5 N10575 20.7 2002 19
## 6 N105UW -0.267 1999 22
## 7 N107US -5.73 1999 22
## 8 N108UW -1.25 1999 22
## 9 N109UW -2.52 1999 22
## 10 N110UW 2.8 1999 22
## # … with 3,312 more rowsrelation %>%
ggplot(aes(x = age, y = delay)) +
geom_point(color = "purple") +
xlim(10, 40) + #range
ylim(50, 400)## Warning: Removed 3272 rows containing missing values (geom_point).
4. What weather conditions make it more likely to see a delay?
Answer) If it is too cold or too hot, the plane tends to be delayed.
weather_relation <- flights %>%
inner_join(weather, by = c(origin = "origin",
year = "year",
month = "month",
day = "day",
hour = "hour"))
weather_relation## # A tibble: 335,220 x 29
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 335,210 more rows, and 21 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour.x <dttm>, temp <dbl>, dewp <dbl>, humid <dbl>, wind_dir <dbl>,
## # wind_speed <dbl>, wind_gust <dbl>, precip <dbl>, pressure <dbl>,
## # visib <dbl>, time_hour.y <dttm>weather_relation %>%
group_by(temp) %>%
summarise(weather_delay = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = temp, y = weather_delay)) +
geom_point(color = "red") +
geom_line(color = "pink")## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 row(s) containing missing values (geom_path).
5. What happened on June 13 2013? Display the spatial pattern of delays, and then use Google to cross-reference with the weather.
Answer) As the light sky blue stands out in the southeast, many planes in the area appear to have a delay of more than 150 minutes. When I searched on Google, it seems that there was a serious storm that day.
June_13_2013 <- flights %>%
filter(year == 2013, month == 6, day == 13) %>%
inner_join(weather, by = c(origin = "origin",
year = "year",
month = "month",
day = "day"))
June_13_2013## # A tibble: 23,736 x 30
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 6 13 3 2255 68 120 19
## 2 2013 6 13 3 2255 68 120 19
## 3 2013 6 13 3 2255 68 120 19
## 4 2013 6 13 3 2255 68 120 19
## 5 2013 6 13 3 2255 68 120 19
## 6 2013 6 13 3 2255 68 120 19
## 7 2013 6 13 3 2255 68 120 19
## 8 2013 6 13 3 2255 68 120 19
## 9 2013 6 13 3 2255 68 120 19
## 10 2013 6 13 3 2255 68 120 19
## # … with 23,726 more rows, and 22 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour.x <dbl>, minute <dbl>,
## # time_hour.x <dttm>, hour.y <int>, temp <dbl>, dewp <dbl>, humid <dbl>,
## # wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>, precip <dbl>,
## # pressure <dbl>, visib <dbl>, time_hour.y <dttm>June_13_2013 %>%
group_by(dest) %>%
summarise(June_13_delay = mean(arr_delay, na.rm = TRUE)) %>%
inner_join(airports, by = c(dest = "faa")) %>%
ggplot(aes(y = lat, x = lon)) +
borders("state") +
geom_point(aes(size = June_13_delay, color = June_13_delay)) +
coord_quickmap()## Warning: Removed 3 rows containing missing values (geom_point).
13.5.1 Exercises
1. What does it mean for a flight to have a missing tailnum? What do the tail numbers that don’t have a matching record in planes have in common? (Hint: one variable explains ~90% of the problems.)
Answer) There are 2,512 missing tailnum data in the flight table. The common thing about the missing of tailnum is that dep_time, dep_delay, arr_time, arr_delay, and air_time are all missing. This seems to mean cancellation of the flight.
flights %>%
filter(is.na(tailnum))## # A tibble: 2,512 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 2 NA 1545 NA NA 1910
## 2 2013 1 2 NA 1601 NA NA 1735
## 3 2013 1 3 NA 857 NA NA 1209
## 4 2013 1 3 NA 645 NA NA 952
## 5 2013 1 4 NA 845 NA NA 1015
## 6 2013 1 4 NA 1830 NA NA 2044
## 7 2013 1 5 NA 840 NA NA 1001
## 8 2013 1 7 NA 820 NA NA 958
## 9 2013 1 8 NA 1645 NA NA 1838
## 10 2013 1 9 NA 755 NA NA 1012
## # … with 2,502 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>2. Filter flights to only show flights with planes that have flown at least 100 flights.
flights %>%
filter(!is.na(tailnum)) %>% # !is.na means "except missing tailnum data"
group_by(tailnum) %>%
count() %>%
filter(n >= 100)## # A tibble: 1,217 x 2
## # Groups: tailnum [1,217]
## tailnum n
## <chr> <int>
## 1 N0EGMQ 371
## 2 N10156 153
## 3 N10575 289
## 4 N11106 129
## 5 N11107 148
## 6 N11109 148
## 7 N11113 138
## 8 N11119 148
## 9 N11121 154
## 10 N11127 124
## # … with 1,207 more rows3. Combine fueleconomy::vehicles and fueleconomy::common to find only the records for the most common models.
Answer) The most common model is F150 Pickup 2WD.
variable.names(fueleconomy::vehicles)## [1] "id" "make" "model" "year" "class" "trans" "drive" "cyl" "displ"
## [10] "fuel" "hwy" "cty"variable.names(fueleconomy::common)## [1] "make" "model" "n" "years"fueleconomy::vehicles %>%
inner_join(fueleconomy::common, by = c(make = "make", model = "model")) %>%
select(make, model) %>%
group_by(model) %>%
count() %>%
arrange(desc(n))## # A tibble: 342 x 2
## # Groups: model [342]
## model n
## <chr> <int>
## 1 F150 Pickup 2WD 200
## 2 Truck 2WD 187
## 3 F150 Pickup 4WD 178
## 4 Ranger Pickup 2WD 169
## 5 Mustang 153
## 6 Jetta 150
## 7 Sierra 1500 2WD 149
## 8 Sierra 1500 4WD 149
## 9 Civic 142
## 10 Eclipse 133
## # … with 332 more rows4. Find the 48 hours (over the course of the whole year) that have the worst delays. Cross-reference it with the weather data. Can you see any patterns?
Answer) Since the worst delay is 1,301 minutes which is about 21.7 hours, there is no 48 hours delay. However, I will find and visualize the worst delays related in the weather.
flights %>%
select(arr_delay, dep_delay, year:day, hour, origin) %>%
arrange(desc(dep_delay)) %>%
inner_join(weather, by = c(year = "year",
month = "month",
day = "day",
hour = "hour",
origin = "origin")) %>%
ggplot(aes(x = wind_dir, y = precip, color = temp)) +
geom_point(shape = "diamond")## Warning: Removed 8240 rows containing missing values (geom_point).
5. What does anti_join(flights, airports, by = c(“dest” = “faa”)) tell you? What does anti_join(airports, flights, by = c(“faa” = “dest”)) tell you?
Answer) anti_join(flights, airports, by = c(“dest” = “faa”)) shows only 4 destinations which are BQN, PSE, SJU, and STT. When I search on Google, it says Aguadilla, Puerto Rico (BQN), Ponce, Puerto Rico (PSE), San Juan, Puerto Rico (SJU), and St. Thomas (STT). (https://www.jetblue.com/at-the-airport/airport-information)
On the other hand, anti_join(airports, flights, by = c(“faa” = “dest”)) shows every single airports in the U.S.
anti_join(flights, airports, by = c("dest" = "faa"))%>%
count(dest) ## # A tibble: 4 x 2
## dest n
## <chr> <int>
## 1 BQN 896
## 2 PSE 365
## 3 SJU 5819
## 4 STT 522anti_join(airports, flights, by = c("faa" = "dest")) %>%
count(faa)## # A tibble: 1,357 x 2
## faa n
## <chr> <int>
## 1 04G 1
## 2 06A 1
## 3 06C 1
## 4 06N 1
## 5 09J 1
## 6 0A9 1
## 7 0G6 1
## 8 0G7 1
## 9 0P2 1
## 10 0S9 1
## # … with 1,347 more rows6. You might expect that there’s an implicit relationship between plane and airline, because each plane is flown by a single airline. Confirm or reject this hypothesis using the tools you’ve learned above.
Answer) I might expect that there’s an implicit relationship between plane and airline. Please see the below.
nycflights13::planes # related in tailnum## # A tibble: 3,322 x 9
## tailnum year type manufacturer model engines seats speed engine
## <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
## 1 N10156 2004 Fixed wing m… EMBRAER EMB-1… 2 55 NA Turbo-…
## 2 N102UW 1998 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 3 N103US 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 4 N104UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 5 N10575 2002 Fixed wing m… EMBRAER EMB-1… 2 55 NA Turbo-…
## 6 N105UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 7 N107US 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 8 N108UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 9 N109UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 10 N110UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## # … with 3,312 more rowsnycflights13::airlines # relatied in carrier## # A tibble: 16 x 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.nycflights13::flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>flights %>%
inner_join(planes, by = c(tailnum = "tailnum")) %>%
inner_join(airlines, by = c(carrier = "carrier")) %>%
select(tailnum, carrier, name)## # A tibble: 284,170 x 3
## tailnum carrier name
## <chr> <chr> <chr>
## 1 N14228 UA United Air Lines Inc.
## 2 N24211 UA United Air Lines Inc.
## 3 N619AA AA American Airlines Inc.
## 4 N804JB B6 JetBlue Airways
## 5 N668DN DL Delta Air Lines Inc.
## 6 N39463 UA United Air Lines Inc.
## 7 N516JB B6 JetBlue Airways
## 8 N829AS EV ExpressJet Airlines Inc.
## 9 N593JB B6 JetBlue Airways
## 10 N793JB B6 JetBlue Airways
## # … with 284,160 more rows