assignment5

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 %>%
  filter(origin == "LGA", dest == "XNA") %>%
  inner_join(planes, by = "tailnum") %>%
  select(origin, dest, tailnum, model, manufacturer)
q1

## # A tibble: 66 × 5
##    origin dest  tailnum model  manufacturer        
##    <chr>  <chr> <chr>   <chr>  <chr>               
##  1 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  2 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  3 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  4 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  5 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  6 LGA    XNA   N737MQ  172N   CESSNA              
##  7 LGA    XNA   N737MQ  172N   CESSNA              
##  8 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
##  9 LGA    XNA   N711MQ  G1159B GULFSTREAM AEROSPACE
## 10 LGA    XNA   N840MQ  CF-5D  CANADAIR LTD        
## # ℹ 56 more rows

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

q2 <- airlines %>% 
  inner_join(flights, by = "carrier")
q2

## # A tibble: 336,776 × 20
##    carrier name      year month   day dep_time sched_dep_time dep_delay arr_time
##    <chr>   <chr>    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1 9E      Endeavo…  2013     1     1      810            810         0     1048
##  2 9E      Endeavo…  2013     1     1     1451           1500        -9     1634
##  3 9E      Endeavo…  2013     1     1     1452           1455        -3     1637
##  4 9E      Endeavo…  2013     1     1     1454           1500        -6     1635
##  5 9E      Endeavo…  2013     1     1     1507           1515        -8     1651
##  6 9E      Endeavo…  2013     1     1     1530           1530         0     1650
##  7 9E      Endeavo…  2013     1     1     1546           1540         6     1753
##  8 9E      Endeavo…  2013     1     1     1550           1550         0     1844
##  9 9E      Endeavo…  2013     1     1     1552           1600        -8     1749
## 10 9E      Endeavo…  2013     1     1     1554           1600        -6     1701
## # ℹ 336,766 more rows
## # ℹ 11 more variables: sched_arr_time <int>, arr_delay <dbl>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>

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

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

## # A tibble: 1,337 × 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 1,327 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_credit <- weather %>% 
  inner_join(airports, by = c("origin" = "faa")) %>%  
  filter(wind_speed>30) %>% 
  select(name) %>% 
  distinct()
extra_credit

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