Data for Assignment 5

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

Question 1: Which airplanes fly LGA to XNA

library(nycflights13)
library(tidyverse)
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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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lga_to_xna_planes <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>%
  left_join(planes, by = "tailnum")

lga_to_xna_planes
## # A tibble: 745 × 27
##    year.x month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##     <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
##  1   2013     1     1      656            705        -9     1007            940
##  2   2013     1     1     1525           1530        -5     1934           1805
##  3   2013     1     1     1740           1745        -5     2158           2020
##  4   2013     1     2      656            705        -9     1014            940
##  5   2013     1     2     1531           1530         1     1846           1805
##  6   2013     1     2     1740           1745        -5     2035           2020
##  7   2013     1     3      703            705        -2     1014            940
##  8   2013     1     3     1525           1530        -5     1802           1805
##  9   2013     1     3     1737           1745        -8     1953           2020
## 10   2013     1     4      701            705        -4      934            940
## # ℹ 735 more rows
## # ℹ 19 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>, year.y <int>, type <chr>,
## #   manufacturer <chr>, model <chr>, engines <int>, seats <int>, speed <int>,
## #   engine <chr>

Question 2: Add the airline name to the flights table

library(nycflights13)
library(tidyverse)

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


flights_with_airline %>%
  select(flight, carrier, name) %>%
  head()
## # A tibble: 6 × 3
##   flight carrier name                  
##    <int> <chr>   <chr>                 
## 1   1545 UA      United Air Lines Inc. 
## 2   1714 UA      United Air Lines Inc. 
## 3   1141 AA      American Airlines Inc.
## 4    725 B6      JetBlue Airways       
## 5    461 DL      Delta Air Lines Inc.  
## 6   1696 UA      United Air Lines Inc.

Question 3: Which airports have no commercial flights

library(nycflights13)
library(tidyverse)

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

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

airports_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

Question 4: Finding high wind speeds in Airports

library(nycflights13)
library(tidyverse)

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

high_wind_airports <- high_wind_weather %>%
  inner_join(airports, by = c("origin" = "faa"))


airport_names_high_wind <- high_wind_airports %>%
  select(name) %>%
  distinct()

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