R Markdown

library(nycflights13)
library(tidyverse)
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1. join + filter - Which airplanes fly LGA to XNA (1 POINT)

lga_to_xna_flights <- flights %>% 
  filter(origin == "LGA", dest == "XNA") %>% 
  inner_join(planes, by = "tailnum")  %>% 
  select(tailnum , manufacturer , model) %>% 
  distinct()

4 planes fly LGA to XNA.

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

airline_name <- flights %>% 
  left_join(airlines, by = "carrier") %>% 
  select(flight, carrier, name, everything())

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

airports_with_flights <- flights %>%
  select(origin, dest) %>%
  pivot_longer(cols = c(origin, dest), names_to = "type", values_to = "faa") %>%
  distinct(faa)

airports_no_commercial_flights <- airports %>%
  anti_join(airports_with_flights, by = "faa") %>%
  select(faa, name, lat, lon)

head(airports_no_commercial_flights)
## # A tibble: 6 × 4
##   faa   name                             lat   lon
##   <chr> <chr>                          <dbl> <dbl>
## 1 04G   Lansdowne Airport               41.1 -80.6
## 2 06A   Moton Field Municipal Airport   32.5 -85.7
## 3 06C   Schaumburg Regional             42.0 -88.1
## 4 06N   Randall Airport                 41.4 -74.4
## 5 09J   Jekyll Island Airport           31.1 -81.4
## 6 0A9   Elizabethton Municipal Airport  36.4 -82.2

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 airportname (airports$name) and no duplicate rows

high_wind_weather <- weather %>%
    filter(wind_speed > 30)
 
high_wind_airports <- high_wind_weather %>%
    left_join(airports, by = c("origin" = "faa")) %>%
    select(name,) %>%
    distinct() 

head(high_wind_airports)  
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia