Create the code makes a table for each of the below questions.
1. join + filter - Which airplanes fly LGA to XNA (1 POINT)
q1 <- flights %>%
filter(origin == "LGA", dest == "XNA") %>%
left_join(planes , by = "tailnum")
head(q1)
## # A tibble: 6 × 27
## year.x 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 1 656 705 -9 1007 940
## 2 2013 1 1 1525 1530 -5 1934 1805
## 3 2013 1 1 1740 1745 -5 2158 2020
## 4 2013 1 2 656 705 -9 1014 940
## 5 2013 1 2 1531 1530 1 1846 1805
## 6 2013 1 2 1740 1745 -5 2035 2020
## # ℹ 19 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>, year.y <int>, type <chr>,
## # manufacturer <chr>, model <chr>, engines <int>, seats <int>, speed <int>,
## # engine <chr>
2. join - Add the airline name to the flights table (1 POINT)
q2 <- flights %>%
left_join(airlines , by = "carrier")
head(q2)
## # A tibble: 6 × 20
## 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
## # ℹ 12 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>, name <chr>
3. join + select + distinct() - Which airports have no commercial
flights (1 POINT)
q3 <- flights %>%
group_by(carrier) %>%
tally() %>%
left_join(airlines)
head(q3)
## # A tibble: 6 × 3
## carrier n name
## <chr> <int> <chr>
## 1 9E 18460 Endeavor Air Inc.
## 2 AA 32729 American Airlines Inc.
## 3 AS 714 Alaska Airlines Inc.
## 4 B6 54635 JetBlue Airways
## 5 DL 48110 Delta Air Lines Inc.
## 6 EV 54173 ExpressJet Airlines Inc.
4. EXTRA CREDIT - (2 POINT2) - NO HELP - NO PARTIAL CREDIT Create a
table with the names of the airports with the most winds (wind_speed
> 30). The table must contain only the airport name (airports$name)
and no duplicate rows
q4 <- weather %>%
filter(wind_speed > 30) %>%
left_join(airports , by = c("origin" = "faa")) %>%
select(name) %>%
distinct()
head(q4)
## # A tibble: 3 × 1
## name
## <chr>
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia