Assignment5

Assignment 5: Joins and more

Kevin Hanson & Patrick O’Connell

Starter Code

library(nycflights13)

## Warning: package 'nycflights13' was built under R version 4.4.3

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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

flights %>% 
  inner_join(planes, by = "tailnum") %>% 
  filter(origin == "LGA", dest == "XNA") %>% 
  select(tailnum) %>% 
  distinct()

## # A tibble: 4 × 1
##   tailnum
##   <chr>  
## 1 N711MQ 
## 2 N737MQ 
## 3 N840MQ 
## 4 N713EV

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

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

## # A tibble: 336,776 × 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
##  7  2013     1     1      555            600        -5      913            854
##  8  2013     1     1      557            600        -3      709            723
##  9  2013     1     1      557            600        -3      838            846
## 10  2013     1     1      558            600        -2      753            745
## # ℹ 336,766 more rows
## # 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 %>% 
  anti_join(flights, by = c("faa" = "dest")) %>% 
  anti_join(flights, by = c("faa" = "origin")) %>%
  select(faa) %>%
  distinct()

## # A tibble: 1,355 × 1
##    faa  
##    <chr>
##  1 04G  
##  2 06A  
##  3 06C  
##  4 06N  
##  5 09J  
##  6 0A9  
##  7 0G6  
##  8 0G7  
##  9 0P2  
## 10 0S9  
## # 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

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

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