Assignment #5

R Markdown

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") %>% 
  left_join(planes, by = "tailnum") %>% 
  select(origin, dest, tailnum, model, manufacturer) %>% 
  distinct(tailnum, .keep_all = TRUE) %>% 
  arrange(tailnum)

q1

## # A tibble: 70 × 5
##    origin dest  tailnum model manufacturer
##    <chr>  <chr> <chr>   <chr> <chr>       
##  1 LGA    XNA   N0EGMQ  <NA>  <NA>        
##  2 LGA    XNA   N501MQ  <NA>  <NA>        
##  3 LGA    XNA   N507MQ  <NA>  <NA>        
##  4 LGA    XNA   N510MQ  <NA>  <NA>        
##  5 LGA    XNA   N511MQ  <NA>  <NA>        
##  6 LGA    XNA   N512MQ  <NA>  <NA>        
##  7 LGA    XNA   N514MQ  <NA>  <NA>        
##  8 LGA    XNA   N518MQ  <NA>  <NA>        
##  9 LGA    XNA   N520MQ  <NA>  <NA>        
## 10 LGA    XNA   N521MQ  <NA>  <NA>        
## # ℹ 60 more rows

2. Join - Add the airline name to the flights table

(1 POINT)

q2 <- airlines %>% 
  right_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" = "dest")) %>% 
  anti_join(flights, by = c("faa" = "origin")) %>%                   
  select(faa, name, lat, lon, alt, tz, dst) %>%
  distinct(faa, .keep_all = TRUE)

q3

## # A tibble: 1,355 × 7
##    faa   name                             lat    lon   alt    tz dst  
##    <chr> <chr>                          <dbl>  <dbl> <dbl> <dbl> <chr>
##  1 04G   Lansdowne Airport               41.1  -80.6  1044    -5 A    
##  2 06A   Moton Field Municipal Airport   32.5  -85.7   264    -6 A    
##  3 06C   Schaumburg Regional             42.0  -88.1   801    -6 A    
##  4 06N   Randall Airport                 41.4  -74.4   523    -5 A    
##  5 09J   Jekyll Island Airport           31.1  -81.4    11    -5 A    
##  6 0A9   Elizabethton Municipal Airport  36.4  -82.2  1593    -5 A    
##  7 0G6   Williams County Airport         41.5  -84.5   730    -5 A    
##  8 0G7   Finger Lakes Regional Airport   42.9  -76.8   492    -5 A    
##  9 0P2   Shoestring Aviation Airfield    39.8  -76.6  1000    -5 U    
## 10 0S9   Jefferson County Intl           48.1 -123.    108    -8 A    
## # ℹ 1,345 more rows

4. EXTRA CREDIT - 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

(2 POINTS)

extra_credit <- flights %>% 
  left_join(airports, by =c("origin"="faa")) %>% 
  arrange(desc(name)) %>% 
  full_join(weather) %>% 
  filter(wind_speed>30) %>% 
  distinct(name, .keep_all = TRUE) %>% 
  select(name) %>% 
  filter(!is.na(name))

## Joining with `by = join_by(year, month, day, origin, hour, time_hour)`

extra_credit

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