1. join + filter - Which airplanes fly LGA to XNA (1 POINT)

LGA_to_XNA <- flights %>%
  inner_join(planes, join_by(tailnum)) %>%
  filter(origin == "LGA", dest == "XNA") %>%
  select(tailnum, manufacturer, model, flight, year.y, origin, dest) %>%
  distinct()
head(LGA_to_XNA)
## # A tibble: 6 × 7
##   tailnum manufacturer         model  flight year.y origin dest 
##   <chr>   <chr>                <chr>   <int>  <int> <chr>  <chr>
## 1 N711MQ  GULFSTREAM AEROSPACE G1159B   4525   1976 LGA    XNA  
## 2 N711MQ  GULFSTREAM AEROSPACE G1159B   4534   1976 LGA    XNA  
## 3 N711MQ  GULFSTREAM AEROSPACE G1159B   4413   1976 LGA    XNA  
## 4 N737MQ  CESSNA               172N     4525   1977 LGA    XNA  
## 5 N737MQ  CESSNA               172N     4413   1977 LGA    XNA  
## 6 N840MQ  CANADAIR LTD         CF-5D    4525   1974 LGA    XNA

2. join - Add the airline name to the flights table (1 POINT)

flights_with_airline <- flights %>%
  left_join(airlines, by = c("carrier" = "carrier"))
head(flights_with_airline)
## # 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)

airports_no_flights <- airports %>%
  anti_join(flights, by = c("faa" = "origin")) %>%
  anti_join(flights, by = c("faa" = "dest")) %>%
  select(faa, name) %>%
  distinct()
head(airports_no_flights)
## # A tibble: 6 × 2
##   faa   name                          
##   <chr> <chr>                         
## 1 04G   Lansdowne Airport             
## 2 06A   Moton Field Municipal Airport 
## 3 06C   Schaumburg Regional           
## 4 06N   Randall Airport               
## 5 09J   Jekyll Island Airport         
## 6 0A9   Elizabethton Municipal Airport

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

high_winds <- weather %>%
  filter(wind_speed > 30) %>%
  select(origin) %>%
  distinct() %>%
  inner_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()
print(high_winds)
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia