knitr::opts_chunk$set(echo = TRUE)
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 <- (inner_join(flights, planes , by = 'tailnum')) %>%
  filter(origin == "LGA", dest == "XNA") %>% 
  select(model) %>% 
  distinct()
  print(q1)
## # A tibble: 4 × 1
##   model      
##   <chr>      
## 1 G1159B     
## 2 172N       
## 3 CF-5D      
## 4 CL-600-2C10

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

q2 <- (left_join(flights , airlines , by= 'carrier'))
print(q2)
## # 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)

q3 <- anti_join(airports, flights , by = c('faa' = 'origin')) %>% 
  anti_join(flights , by = c('faa' = 'dest')) %>% 
  select(faa) %>% 
  distinct()
print(q3)
## # 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 mostwinds (wind_speed > 30). The table must contain only the airportname (airports$name) and no duplicate rows

q4 <- weather %>%
  filter(wind_speed > 30) %>%
  select(origin) %>%
  distinct() %>%
  left_join(airports, by = c("origin" = "faa")) %>%
  select(name) %>%
  distinct()
print(q4)
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia