The filter function is one of the ‘big five’ functions that makes dplyr such a
powerful data wrangling package. In essence, filter() returns rows that
match specific conditions. Let’s check it out!
library('tidyverse')
library('nycflights13')
Filter
filter() is a simple function that finds, or ‘filters’ observations
that match true to a declared condition. In this example filter()
first’s argument is the flights dataset, the following arguments declare
the conditions to be met. Retain observations (rows) with the months
variable equal to 12, and the day variable equal to 25. The result is a
data frame with 719 flights that departed New York City on Christmas
Day, 2013.
filter(flights, month == 12 & day == 25)
## # A tibble: 719 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 12 25 456 500 -4 649
## 2 2013 12 25 524 515 9 805
## 3 2013 12 25 542 540 2 832
## 4 2013 12 25 546 550 -4 1022
## 5 2013 12 25 556 600 -4 730
## 6 2013 12 25 557 600 -3 743
## 7 2013 12 25 557 600 -3 818
## 8 2013 12 25 559 600 -1 855
## 9 2013 12 25 559 600 -1 849
## 10 2013 12 25 600 600 0 850
## # … with 709 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>
There you go! Filter is really the easiest of dplyr functions to understand. Simple, but highly useful.
Thanks for reading!
- Fisher
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