library(tidyverse)
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## ✔ purrr 1.0.4
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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(nycflights13)
Create the code makes a table for each of the below questions.
1. join + filter - Which airplanes fly LGA to XNA (1 POINT)
LGA_to_XNA <- flights %>%
filter(origin == "LGA", dest == "XNA") %>%
left_join(planes, by = "tailnum")
head(LGA_to_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. join - Add the airline name to the flights table (1 POINT)
flights_with_airlines <- flights %>%
left_join(airlines, by = "carrier")
head(flights_with_airlines)
## # 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)
no_commercial_flights <- 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)
head(no_commercial_flights)
## # A tibble: 6 × 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