R Markdown

library(nycflights13)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
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## ── 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

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)

lga_to_xna_flights <- flights %>%
  filter(origin == "LGA", dest == "XNA") %>%
  select(flight, tailnum, origin, dest, carrier)

print(lga_to_xna_flights)
## # A tibble: 745 × 5
##    flight tailnum origin dest  carrier
##     <int> <chr>   <chr>  <chr> <chr>  
##  1   4534 N722MQ  LGA    XNA   MQ     
##  2   4525 N719MQ  LGA    XNA   MQ     
##  3   4413 N739MQ  LGA    XNA   MQ     
##  4   4534 N719MQ  LGA    XNA   MQ     
##  5   4525 N711MQ  LGA    XNA   MQ     
##  6   4413 N723MQ  LGA    XNA   MQ     
##  7   4534 N711MQ  LGA    XNA   MQ     
##  8   4525 N730MQ  LGA    XNA   MQ     
##  9   4413 N722MQ  LGA    XNA   MQ     
## 10   4534 N719MQ  LGA    XNA   MQ     
## # ℹ 735 more rows

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

flights_with_airline <- flights %>%
  left_join(airlines, by = "carrier") %>%
  select(flight, tailnum, origin, dest, carrier, name)  # Adding airline name

print(flights_with_airline)
## # A tibble: 336,776 × 6
##    flight tailnum origin dest  carrier name                    
##     <int> <chr>   <chr>  <chr> <chr>   <chr>                   
##  1   1545 N14228  EWR    IAH   UA      United Air Lines Inc.   
##  2   1714 N24211  LGA    IAH   UA      United Air Lines Inc.   
##  3   1141 N619AA  JFK    MIA   AA      American Airlines Inc.  
##  4    725 N804JB  JFK    BQN   B6      JetBlue Airways         
##  5    461 N668DN  LGA    ATL   DL      Delta Air Lines Inc.    
##  6   1696 N39463  EWR    ORD   UA      United Air Lines Inc.   
##  7    507 N516JB  EWR    FLL   B6      JetBlue Airways         
##  8   5708 N829AS  LGA    IAD   EV      ExpressJet Airlines Inc.
##  9     79 N593JB  JFK    MCO   B6      JetBlue Airways         
## 10    301 N3ALAA  LGA    ORD   AA      American Airlines Inc.  
## # ℹ 336,766 more rows

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

commercial_airports <- flights %>%
  select(origin) %>%
  distinct() %>%
  rename(faa = origin)

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

print(airports_no_flights)
## # A tibble: 1,437 × 1
##    name                          
##    <chr>                         
##  1 Lansdowne Airport             
##  2 Moton Field Municipal Airport 
##  3 Schaumburg Regional           
##  4 Randall Airport               
##  5 Jekyll Island Airport         
##  6 Elizabethton Municipal Airport
##  7 Williams County Airport       
##  8 Finger Lakes Regional Airport 
##  9 Shoestring Aviation Airfield  
## 10 Jefferson County Intl         
## # ℹ 1,427 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

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

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