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

library(nycflights13)
library(tidyverse)
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## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
lga_to_xna_flights <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>% 
  left_join(planes, by = "tailnum")

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

flights_with_airlines <- flights %>%
  left_join(airlines, by = "carrier")

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

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

airports_with_no_flights <- airports %>%
  anti_join(flights_airports, by = "faa")

airports_with_no_flights %>%
  select(faa, name, lat, lon)
## # A tibble: 1,355 × 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
##  7 0G6   Williams County Airport         41.5  -84.5
##  8 0G7   Finger Lakes Regional Airport   42.9  -76.8
##  9 0P2   Shoestring Aviation Airfield    39.8  -76.6
## 10 0S9   Jefferson County Intl           48.1 -123. 
## # ℹ 1,345 more rows

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_wind_airports <- weather %>%
  filter(wind_speed > 30) %>%  
  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