Untitled

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)

library(nycflights13)
library(tidyverse)

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

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

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

head(flights_with_airlines)

## # A tibble: 6 × 20
##    year 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      517            515         2      830            819
## 2  2013     1     1      533            529         4      850            830
## 3  2013     1     1      542            540         2      923            850
## 4  2013     1     1      544            545        -1     1004           1022
## 5  2013     1     1      554            600        -6      812            837
## 6  2013     1     1      554            558        -4      740            728
## # ℹ 12 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>, name <chr>

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

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

airports_no_flights <- airports %>%
  anti_join(airports_with_flights, by = "faa") %>%
  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. 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 %>%
  inner_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()

high_wind_airport_names

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