Assignment 5

library(nycflights13)
library(tidyverse)

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Create the code makes a table for each of the below questions.

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

q1 <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>%
  left_join(planes, by = "tailnum")
head(q1)

## # 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     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
## # 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)

q2 <- flights %>%
  left_join(airlines, by = "carrier")
head(q2)

## # 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)

q3 <- airports %>%
  left_join(flights, by = c("faa" = "dest")) %>%
  filter(is.na(flight)) %>%
  select(faa, name) %>%
  distinct()
head(q3)

## # A tibble: 6 × 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

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

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

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