Assignment5

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 %>%
  filter(origin == "LGA", dest == "XNA") %>%
  select(tailnum) %>%
  distinct()

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

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

## # A tibble: 6 × 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
## # 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<-airports %>%
  anti_join(flights, by = c("faa" = "dest")) %>%  # airports never used as destination
  anti_join(flights, by = c("faa" = "origin")) %>% # airports never used as origin
  select(name) %>%
  distinct()
head(q3)

## # A tibble: 6 × 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

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

q4<-weather %>%
  filter(wind_speed > 30) %>%           # only keep rows with strong winds
  distinct(origin) %>%                  # keep each airport code only once
  left_join(airports, by = c("origin" = "faa")) %>%  # get airport names
  select('airport$name'=name)  
# only show airport names
head(q4)

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