On what day did the flight with the shortest airtime take place?
Hint: Use head() to restrict your result to one row only.
flights %>%
arrange(___) %>%
head(1)► Solution:
flights %>%
arrange(air_time) %>%
head(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 16 1355 1315 40 1442
## # ... with 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>Which flights had the heaviest delays? Can you use the tail() verb to obtain this information?
flights %>%
arrange(___) %>%
tail(1)
flights %>%
arrange(desc(___)) %>%
___(1)► Solution:
flights %>%
arrange(arr_delay) %>%
tail(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 9 30 NA 840 NA NA
## # ... with 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>Why doesn’t this give the result we’re looking for? Can we use a filter?
flights %>%
filter(!is.na(arr_delay)) %>%
arrange(arr_delay) %>%
tail(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## # ... with 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>Or the pattern below?
flights %>%
arrange(!is.na(arr_delay), arr_delay) %>%
tail(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## # ... with 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>Usually it’s easiest to sort in descending order:
flights %>%
arrange(-arr_delay) %>%
head(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## # ... with 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 %>%
arrange(desc(arr_delay)) %>%
head(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## # ... with 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>On what day did the flight with the longest airtime take place?
flights %>%
arrange(___ - ___) %>%
head(1)► Solution:
flights %>%
arrange(desc(air_time)) %>%
head(1)## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 3 17 1337 1335 2 1937
## # ... with 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 two equivalent ways to select the six "UA" flights with the lowest delay. Which is faster? Why?
Hint: RStudio has shortcuts for swapping the current line with the next or previous line.
flights %>%
filter(___) %>%
arrange(___)
flights %>%
arrange(___) %>%
filter(___)► Solution:
If we filter first, fewer observations need to be sorted.
flights %>%
filter(carrier == "UA") %>%
arrange(arr_delay)## # A tibble: 58,665 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 5 2 1947 1949 -2 2209
## 2 2013 5 2 1926 1929 -3 2157
## 3 2013 5 7 2054 2055 -1 2317
## 4 2013 2 26 1335 1335 0 1819
## 5 2013 2 26 1721 1725 -4 1936
## 6 2013 2 28 702 705 -3 924
## 7 2013 5 13 1624 1629 -5 1831
## 8 2013 5 4 1914 1915 -1 2107
## 9 2013 12 27 853 856 -3 1052
## 10 2013 3 1 629 632 -3 844
## # ... with 58,655 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 %>%
arrange(arr_delay) %>%
filter(carrier == "UA")## # A tibble: 58,665 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 5 2 1947 1949 -2 2209
## 2 2013 5 2 1926 1929 -3 2157
## 3 2013 5 7 2054 2055 -1 2317
## 4 2013 2 26 1335 1335 0 1819
## 5 2013 2 26 1721 1725 -4 1936
## 6 2013 2 28 702 705 -3 924
## 7 2013 5 13 1624 1629 -5 1831
## 8 2013 5 4 1914 1915 -1 2107
## 9 2013 12 27 853 856 -3 1052
## 10 2013 3 1 629 632 -3 844
## # ... with 58,655 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>Which flights were best in recovering from delay in the air?
► Solution:
flights %>%
arrange(dep_delay - arr_delay)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 11 1 658 700 -2 1329
## 2 2013 4 18 558 600 -2 1149
## 3 2013 8 8 1819 1519 180 5
## 4 2013 7 10 1916 1900 16 137
## 5 2013 6 27 1608 1525 43 2045
## 6 2013 7 22 1606 1615 -9 2056
## 7 2013 7 1 811 800 11 1344
## 8 2013 7 10 2011 1520 291 2357
## 9 2013 7 22 1626 1545 41 2051
## 10 2013 4 18 655 700 -5 1213
## # ... with 336,766 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 © 2018 Kirill Müller. Licensed under CC BY-NC 4.0.