View the flights dataset in RStudio’s data pane. Look up the meaning of the variables in the help.
Hint: You need to load the nycflights13 package.
view(___)Find all flights that departed today 6 years ago.
flights %>%
filter(month ___, day ___)► Solution:
Be careful with the equality operator ==:
flights %>%
filter(month = 6, day = 2)## `month` (`month = 6`), `day` (`day = 2`) must not be named, do you need
## `==`?flights %>%
filter(month == 6, day == 2)## # A tibble: 911 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 6 2 14 2359 15 339
## 2 2013 6 2 20 2155 145 222
## 3 2013 6 2 24 2245 99 133
## 4 2013 6 2 33 2059 214 150
## 5 2013 6 2 35 2130 185 332
## 6 2013 6 2 36 1914 322 223
## 7 2013 6 2 44 2359 45 420
## 8 2013 6 2 128 2159 209 325
## 9 2013 6 2 131 2146 225 229
## 10 2013 6 2 219 2055 324 322
## # … with 901 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Can we make a dynamic query?
flights %>%
filter(
month == lubridate::month(Sys.Date()),
day == lubridate::day(Sys.Date())
)## # A tibble: 970 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 4 1 454 500 -6 636
## 2 2013 4 1 509 515 -6 743
## 3 2013 4 1 526 530 -4 812
## 4 2013 4 1 534 540 -6 833
## 5 2013 4 1 542 545 -3 914
## 6 2013 4 1 543 545 -2 921
## 7 2013 4 1 551 600 -9 748
## 8 2013 4 1 552 600 -8 641
## 9 2013 4 1 553 600 -7 725
## 10 2013 4 1 554 600 -6 752
## # … with 960 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Can we use arguments?
month_ <- lubridate::month(Sys.Date())
day_ <- lubridate::day(Sys.Date())
flights %>%
filter(
month == month_,
day == day_
)## # A tibble: 970 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 4 1 454 500 -6 636
## 2 2013 4 1 509 515 -6 743
## 3 2013 4 1 526 530 -4 812
## 4 2013 4 1 534 540 -6 833
## 5 2013 4 1 542 545 -3 914
## 6 2013 4 1 543 545 -2 921
## 7 2013 4 1 551 600 -9 748
## 8 2013 4 1 552 600 -8 641
## 9 2013 4 1 553 600 -7 725
## 10 2013 4 1 554 600 -6 752
## # … with 960 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Find all flights that departed between 8:00 AM and 10:00 PM.
flights %>%
filter(between(dep_time, ___, ___))► Solution:
flights %>%
filter(dep_time >= 800, dep_time <= 2200)## # A tibble: 267,608 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 800 800 0 1022
## 2 2013 1 1 800 810 -10 949
## 3 2013 1 1 801 805 -4 900
## 4 2013 1 1 803 810 -7 903
## 5 2013 1 1 803 800 3 1132
## 6 2013 1 1 804 810 -6 1103
## 7 2013 1 1 805 805 0 1015
## 8 2013 1 1 805 800 5 1118
## 9 2013 1 1 805 815 -10 1006
## 10 2013 1 1 807 810 -3 1043
## # … with 267,598 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>flights %>%
filter(between(dep_time, 800, 2200))## # A tibble: 267,608 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 800 800 0 1022
## 2 2013 1 1 800 810 -10 949
## 3 2013 1 1 801 805 -4 900
## 4 2013 1 1 803 810 -7 903
## 5 2013 1 1 803 800 3 1132
## 6 2013 1 1 804 810 -6 1103
## 7 2013 1 1 805 805 0 1015
## 8 2013 1 1 805 800 5 1118
## 9 2013 1 1 805 815 -10 1006
## 10 2013 1 1 807 810 -3 1043
## # … with 267,598 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Find all flights that departed in the three winter months.
flights %>%
filter(month ___ c(___)))► Solution:
flights %>%
filter(month %in% c(12, 1, 2))## # A tibble: 80,090 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # … with 80,080 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>winter_months <- c(12, 1, 2)
flights %>%
filter(month %in% winter_months)## # A tibble: 80,090 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # … with 80,080 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Are there any flights where departure time is later than arrival time? What does this mean?
flights %>%
filter(_____)► Solution:
flights %>%
filter(dep_time > arr_time)## # A tibble: 10,633 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 1929 1920 9 3
## 2 2013 1 1 1939 1840 59 29
## 3 2013 1 1 2058 2100 -2 8
## 4 2013 1 1 2102 2108 -6 146
## 5 2013 1 1 2108 2057 11 25
## 6 2013 1 1 2120 2130 -10 16
## 7 2013 1 1 2121 2040 41 6
## 8 2013 1 1 2128 2135 -7 26
## 9 2013 1 1 2134 2045 49 20
## 10 2013 1 1 2136 2145 -9 25
## # … with 10,623 more rows, and 12 more variables: sched_arr_time <int>,
## # 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>Copyright © 2019 Kirill Müller. Licensed under CC BY-NC 4.0.