Startup

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 %>%
  inner_join(planes, by = "tailnum") %>%
  filter(origin == "LGA", dest == "XNA") %>%
  select(tailnum, model) %>%
  distinct()
head(q1)
## # A tibble: 4 × 2
##   tailnum model      
##   <chr>   <chr>      
## 1 N711MQ  G1159B     
## 2 N737MQ  172N       
## 3 N840MQ  CF-5D      
## 4 N713EV  CL-600-2C10

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

q2 <- flights %>%
  left_join(airlines, by = "carrier") %>%
  select(flight, tailnum, carrier, name)
head(q2)
## # A tibble: 6 × 4
##   flight tailnum carrier name                  
##    <int> <chr>   <chr>   <chr>                 
## 1   1545 N14228  UA      United Air Lines Inc. 
## 2   1714 N24211  UA      United Air Lines Inc. 
## 3   1141 N619AA  AA      American Airlines Inc.
## 4    725 N804JB  B6      JetBlue Airways       
## 5    461 N668DN  DL      Delta Air Lines Inc.  
## 6   1696 N39463  UA      United Air Lines Inc.

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

q3 <- airports %>%
  anti_join(flights, by = c("faa" = "origin")) %>%
  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

EC <- weather %>%
  filter(wind_speed > 30) %>%
  select(origin) %>%
  distinct() %>%
  left_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()
head(EC)
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia