I don’t find arrange as useful as the other dplyr functions, but in the interest of posterity I’m including an example here!
library('tidyverse')
library('nycflights13')
Arrange
Often observations within a data frame are ordered arbitrarily, or
unproductively. arrange() reorders observations according to its user
specified arguments. In this example, the flights data frame is called
upon as the first argument once again; this is common syntax within
dplyr functions and I won’t be referencing it henceforth. The second
argument is desc(dep_time). desc() is one of the secondary functions
listed previously; it simply transforms a vector into a format that will
be sorted in descending order. dep_time is the flights variable
departure time, a number from 0000 to 2400, indicating the actual
departure time of an individual aircraft. The resulting data frame has
all 336,776 observations sorted from latest (highest) to earliest
(lowest) departure time.
arrange(flights, desc(dep_time))
## # 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 10 30 2400 2359 1 327
## 2 2013 11 27 2400 2359 1 515
## 3 2013 12 5 2400 2359 1 427
## 4 2013 12 9 2400 2359 1 432
## 5 2013 12 9 2400 2250 70 59
## 6 2013 12 13 2400 2359 1 432
## 7 2013 12 19 2400 2359 1 434
## 8 2013 12 29 2400 1700 420 302
## 9 2013 2 7 2400 2359 1 432
## 10 2013 2 7 2400 2359 1 443
## # … 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>
That’s all for now!
- Fisher
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