Assignment-5.R

library(nycflights13)
library(tidyverse)

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# use the below code to look at the data - DO NOT ADD VIEW STATEMENTS TO RMARKDOWN
View(flights)
View(airlines)
View(weather)
View(planes)
View(airports)

# Create the code makes a table for each of the below questions.

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

lga_to_xna_flights <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>%
  select(flight, tailnum, carrier, origin, dest)

lga_to_xna_flights_joined <- lga_to_xna_flights %>%
  left_join(airlines, by = "carrier")

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

flights_with_airline_name <- flights %>%
  left_join(airlines, by = "carrier") %>%
  select(year, month, day, flight, tailnum, carrier, name, origin, dest, 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), values_to = "faa") %>%
  distinct(faa)

airports_no_flights <- airports %>%
  anti_join(airports_with_flights, by = c("faa"))

# 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) %>%
  select(origin) %>%
  distinct()
high_wind_airport_names <- high_wind_airports %>%
  left_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()