library(nycflights13)
library(tidyverse)
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data(flights)
data(airlines)
data(weather)
data(planes)
data(airports)

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

did select so you could see the origin and desitnation

Q1 <- full_join(planes,flights, 'tailnum')%>%
  filter(origin == 'LGA', dest == 'XNA')%>%
  select(tailnum,origin,dest)
head(Q1)
## # A tibble: 6 × 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 N711MQ  LGA    XNA

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

can see the name in the variable list

Q2 <- left_join(flights,airlines, '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' = 'origin')) %>%
  anti_join(flights, by = c('faa' = 'dest')) %>%
  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

highWinds <- filter( weather, wind_speed>30)

Q4 <- left_join(highWinds, airports, by =c('origin' = 'faa'))%>%
  select('name')%>%
  distinct()

head(Q4)
## # A tibble: 3 × 1
##   name               
##   <chr>              
## 1 Newark Liberty Intl
## 2 John F Kennedy Intl
## 3 La Guardia