Assignment 5

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

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

q1

## # A tibble: 66 × 3
##    tailnum origin dest 
##    <chr>   <chr>  <chr>
##  1 N711MQ  LGA    XNA  
##  2 N711MQ  LGA    XNA  
##  3 N711MQ  LGA    XNA  
##  4 N711MQ  LGA    XNA  
##  5 N711MQ  LGA    XNA  
##  6 N737MQ  LGA    XNA  
##  7 N737MQ  LGA    XNA  
##  8 N711MQ  LGA    XNA  
##  9 N711MQ  LGA    XNA  
## 10 N840MQ  LGA    XNA  
## # ℹ 56 more rows

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

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

q2

## # A tibble: 336,776 × 2
##    carrier name                    
##    <chr>   <chr>                   
##  1 UA      United Air Lines Inc.   
##  2 UA      United Air Lines Inc.   
##  3 AA      American Airlines Inc.  
##  4 B6      JetBlue Airways         
##  5 DL      Delta Air Lines Inc.    
##  6 UA      United Air Lines Inc.   
##  7 B6      JetBlue Airways         
##  8 EV      ExpressJet Airlines Inc.
##  9 B6      JetBlue Airways         
## 10 AA      American Airlines Inc.  
## # ℹ 336,766 more rows

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

q3 <- airports %>%
  anti_join(
    flights %>%
      select(origin, dest) %>%
      pivot_longer(cols = c(origin, dest), values_to = "faa") %>%
      distinct(faa),
    by = "faa"
  ) %>%
  select(faa, name)
  
q3

## # 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

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

extra

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