Question 1: join + filter - Which airplanes fly LGA to XNA
Question1 <- planes %>%
left_join(flights , by = "tailnum") %>%
filter(dest %in% c("LGA" , "XNA")) %>%
distinct(tailnum)
head(Question1)
## # A tibble: 6 × 1
## tailnum
## <chr>
## 1 N10156
## 2 N10575
## 3 N11106
## 4 N11107
## 5 N11109
## 6 N11113
Question 2: Join - Add the airline name to the flights table
Question2 <- flights %>%
left_join(airlines)
## Joining with `by = join_by(carrier)`
head(Question2)
## # 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>
Question 3: Join + select + distinct() - Which airports have no
commercial flights
Question3 <- airports %>%
anti_join(flights, by = c("faa" = "origin")) %>%
distinct(faa)
head(Question3)
## # A tibble: 6 × 1
## faa
## <chr>
## 1 04G
## 2 06A
## 3 06C
## 4 06N
## 5 09J
## 6 0A9
Question 4: Create a table with the names of the airports with the
most winds (wind_speed > 30). The table must contain only the airport
name and no duplicate rows
Question4 <- airports %>%
left_join(weather, by = c("faa" = "origin")) %>%
filter(wind_speed > 30) %>%
distinct(name)
print(Question4)
## # A tibble: 3 × 1
## name
## <chr>
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia