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

q1 <- flights %>%
  filter(origin == 'LGA', dest == 'XNA') %>%
  left_join(planes, by = 'tailnum') %>%
  select(type, manufacturer) %>%
  distinct()
head(q1)
## # A tibble: 5 × 2
##   type                     manufacturer        
##   <chr>                    <chr>               
## 1 <NA>                     <NA>                
## 2 Fixed wing multi engine  GULFSTREAM AEROSPACE
## 3 Fixed wing single engine CESSNA              
## 4 Fixed wing multi engine  CANADAIR LTD        
## 5 Fixed wing multi engine  BOMBARDIER INC

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

q2 <- flights %>%
  inner_join(airlines, by = "carrier") %>% # Join using the 'carrier' column
  select(flight, carrier, name)
head(q2)
## # A tibble: 6 × 3
##   flight carrier name                  
##    <int> <chr>   <chr>                 
## 1   1545 UA      United Air Lines Inc. 
## 2   1714 UA      United Air Lines Inc. 
## 3   1141 AA      American Airlines Inc.
## 4    725 B6      JetBlue Airways       
## 5    461 DL      Delta Air Lines Inc.  
## 6   1696 UA      United Air Lines Inc.

3. join + select + distinct() - Which airports have no commercial flights (1 POINT)

q3 <- anti_join(airports, 
                flights %>%
                  select(origin, dest) %>%
                  pivot_longer(cols = c(origin, dest), values_to = "faa") %>%
                  distinct(faa), 
                by = "faa")%>%
  select(faa, name)
head(q3)
## # 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 POINT) - 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

extra_credit <- weather %>%
  filter(wind_speed > 30) %>%  # Filter for high wind speeds
  select(origin) %>%  # Select origin (airport code) and wind speed
  distinct() %>%  # Remove duplicate rows
  inner_join(airports, by = c("origin" = "faa")) %>%  # Join with airports dataset
  select(name) %>%  # Select airport name and wind speed
  distinct()  # Remove duplicate rows
head(extra_credit)
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia