Assignment 5

R Markdown

library(nycflights13)
library(tidyverse)

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1. Which airplanes fly LGA to XNA

flights_lga_xna <- flights %>%
  filter(origin == "LGA" & dest == "XNA") %>% 
  left_join(planes, by = 'tailnum')
head(flights_lga_xna)

## # A tibble: 6 × 27
##   year.x 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      656            705        -9     1007            940
## 2   2013     1     1     1525           1530        -5     1934           1805
## 3   2013     1     1     1740           1745        -5     2158           2020
## 4   2013     1     2      656            705        -9     1014            940
## 5   2013     1     2     1531           1530         1     1846           1805
## 6   2013     1     2     1740           1745        -5     2035           2020
## # ℹ 19 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>, year.y <int>, type <chr>,
## #   manufacturer <chr>, model <chr>, engines <int>, seats <int>, speed <int>,
## #   engine <chr>

2. Add the airline name to the flights table

flight_with_airline <- left_join(flights, airlines, by = 'carrier')
head(flight_with_airline)

## # 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. Which airports have no commercial flights

airports_with_flights <- flights %>%
  select(origin) %>%
  distinct() %>%
  rename(airport = origin)
airports_with_no_commercial <- airports %>%
  filter(!faa %in% airports_with_flights$airport)
head(airports_with_no_commercial)

## # A tibble: 6 × 8
##   faa   name                             lat   lon   alt    tz dst   tzone      
##   <chr> <chr>                          <dbl> <dbl> <dbl> <dbl> <chr> <chr>      
## 1 04G   Lansdowne Airport               41.1 -80.6  1044    -5 A     America/Ne…
## 2 06A   Moton Field Municipal Airport   32.5 -85.7   264    -6 A     America/Ch…
## 3 06C   Schaumburg Regional             42.0 -88.1   801    -6 A     America/Ch…
## 4 06N   Randall Airport                 41.4 -74.4   523    -5 A     America/Ne…
## 5 09J   Jekyll Island Airport           31.1 -81.4    11    -5 A     America/Ne…
## 6 0A9   Elizabethton Municipal Airport  36.4 -82.2  1593    -5 A     America/Ne…