CODEWITHSUNDEEP
r
aukk123
aukk123

Reputation: 323

Can we combine 2 filters in R?

recently I am thinking about that can 2 filter functions combine into 1 filter?

For example

library(tidyverse)
library(nycflights13)
a <- flights %>% filter(month == 1)
b <- flights %>% filter(day == 1)

Now, a is filtering flights that in Jan, and b on 1st. I want to combine the filter a & b, which means I want to find that flights that are 1st Jan. Except for

flights %>% filter(month == 1 & day == 1)

Can we find a and b first, and combine 2 filters in 1, because sometimes I need to filter lots of information, and I think if I can filter it separately first will be easier. Thank you!

Upvotes: 0

Views: 791

Answers (2)

G. Grothendieck
G. Grothendieck

Reputation: 269694

Is this what you are looking for where the dot notation defines a function:

library(dplyr)

a <- . %>% filter(cyl == 4)  # or a <- function(x) filter(x, cyl == 4)
b <- . %>% filter(gear == 4)

mtcars %>% a %>% b

giving those rows for which cyl is 4 and gear is 4:

                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Also note that filtering successively as we do here is more efficient than filtering each condition separately and then combining them. In the first case at each step we are reducing the size of the data but in the second we are dealing with the full data at each filtering step.

Upvotes: 2

Ronak Shah
Ronak Shah

Reputation: 389047

You can use dplyr::intersect.

a1 <- flights %>% filter(month == 1)
a2 <- flights %>% filter(day == 1)
result1 <- dplyr::intersect(a1, a2)
nrow(result1)
#[1] 842

This is same as doing :

nrow(flights %>% filter(month == 1 & day == 1))
#[1] 842

If you have more than 2 conditions use it with Reduce :

a3 <- flights %>% filter(dep_delay == 2)
result2 <- Reduce(dplyr::intersect, list(a1, a2, a3))
nrow(result2)
#[1] 23

is same as

nrow(flights %>% filter(month == 1 & day == 1 & dep_delay == 2))
#[1] 23

Upvotes: 1

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