Assignment5

library(nycflights13)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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

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

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

flights_with_airlines <- flights %>%
  left_join(airlines, by = "carrier") %>%
  select(year:day, carrier, name, everything()) 

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

airports_with_flights <- flights %>%
  select(origin) %>%
  distinct() %>%
  rename(faa = origin)

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

4. Create a table with the names of the airports with the most

airports_with_high_winds <- weather %>%
  filter(wind_speed > 30) %>%             
  select(origin) %>%
  distinct() %>%
  left_join(airports, by = c("origin" = "faa")) %>%  
  select(name) %>%
  distinct() %>%
  rename(`airports$name` = name)