Assignment 5

library(nycflights13)
library(tidyverse)

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

result <- flights %>%
  inner_join(planes, by = "tailnum") %>%
  filter(origin == "LGA", dest == "XNA")

head(result)

## # A tibble: 6 × 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     2     1531           1530         1     1846           1805
## 2   2013     1     3      703            705        -2     1014            940
## 3   2013     1     7     1737           1745        -8     2009           2020
## 4   2013     1     9     1736           1745        -9     2008           2020
## 5   2013     1    13     1741           1745        -4     2028           2020
## 6   2013     1    14     1530           1530         0     1914           1805
## # 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>

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

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

head(flights_with_airline)

## # 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)

no_commercial_flights <- airports %>%
  anti_join(flights, by = c("faa" = "origin")) %>%
  anti_join(flights, by = c("faa" = "dest")) %>%
  select(faa, name, lat, lon) %>%  # Select relevant airport details
  distinct()  # Remove duplicates

head(no_commercial_flights)

## # A tibble: 6 × 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

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

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

head(windy_airports)

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