library(nycflights13)
library(tidyverse)
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View(flights) View(airlines) View(weather) View(planes) View(airports)

1. Airplanes flying from LGA to XNA

planes_lga_xna <- flights %>%
  filter(origin == "LGA" & dest == "XNA") %>%
  select(tailnum) %>%
  distinct()

planes_lga_xna
## # A tibble: 70 × 1
##    tailnum
##    <chr>  
##  1 N722MQ 
##  2 N719MQ 
##  3 N739MQ 
##  4 N711MQ 
##  5 N723MQ 
##  6 N730MQ 
##  7 N734MQ 
##  8 N725MQ 
##  9 N736MQ 
## 10 N737MQ 
## # ℹ 60 more rows

2. Add airline name to flights table

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

flights_with_airline %>%
  select(carrier, name, everything()) # Displaying with airline name
## # A tibble: 336,776 × 20
##    carrier name      year month   day dep_time sched_dep_time dep_delay arr_time
##    <chr>   <chr>    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1 UA      United …  2013     1     1      517            515         2      830
##  2 UA      United …  2013     1     1      533            529         4      850
##  3 AA      America…  2013     1     1      542            540         2      923
##  4 B6      JetBlue…  2013     1     1      544            545        -1     1004
##  5 DL      Delta A…  2013     1     1      554            600        -6      812
##  6 UA      United …  2013     1     1      554            558        -4      740
##  7 B6      JetBlue…  2013     1     1      555            600        -5      913
##  8 EV      Express…  2013     1     1      557            600        -3      709
##  9 B6      JetBlue…  2013     1     1      557            600        -3      838
## 10 AA      America…  2013     1     1      558            600        -2      753
## # ℹ 336,766 more rows
## # ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>

3. Airports with no commercial flights

airports_no_flights <- airports %>%
  anti_join(flights, by = c("faa" = "origin")) %>%
  anti_join(flights, by = c("faa" = "dest")) %>%
  select(faa, name) %>%
  distinct()

airports_no_flights
## # A tibble: 1,355 × 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
##  7 0G6   Williams County Airport       
##  8 0G7   Finger Lakes Regional Airport 
##  9 0P2   Shoestring Aviation Airfield  
## 10 0S9   Jefferson County Intl         
## # ℹ 1,345 more rows

4. Airports with wind speed > 30

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

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