Difference between subset and filter from dplyr
In the main use cases they behave the same :
library(dplyr)
identical(
filter(starwars, species == "Wookiee"),
subset(starwars, species == "Wookiee"))
# [1] TRUE
But they have a quite a few differences, including (I was as exhaustive as possible but might have missed some) :
subsetcan be used on matricesfiltercan be used on databasesfilterdrops row namessubsetdrop attributes other than class, names and row names.subsethas aselectargumentsubsetrecycles its condition argumentfiltersupports conditions as separate argumentsfilterpreserves the class of the columnfiltersupports the.datapronounfiltersupports somerlangfeaturesfiltersupports groupingfiltersupportsn()androw_number()filteris stricterfilteris a bit faster when it countssubsethas methods in other packages
subset can be used on matrices
subset(state.x77, state.x77[,"Population"] < 400)
# Population Income Illiteracy Life Exp Murder HS Grad Frost Area
# Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
# Wyoming 376 4566 0.6 70.29 6.9 62.9 173 97203
Though columns can't be used directly as variables in the subset argument
subset(state.x77, Population < 400)
Error in subset.matrix(state.x77, Population < 400) : object 'Population' not found
Neither works with filter
filter(state.x77, state.x77[,"Population"] < 400)
Error in UseMethod("filter_") : no applicable method for 'filter_' applied to an object of class "c('matrix', 'double', 'numeric')"
filter(state.x77, Population < 400)
Error in UseMethod("filter_") : no applicable method for 'filter_' applied to an object of class "c('matrix', 'double', 'numeric')"
filter can be used on databases
library(DBI)
con <- dbConnect(RSQLite::SQLite(), ":memory:")
dbWriteTable(con, "mtcars", mtcars)
tbl(con,"mtcars") %>%
filter(hp < 65)
# # Source: lazy query [?? x 11]
# # Database: sqlite 3.19.3 [:memory:]
# mpg cyl disp hp drat wt qsec vs am gear carb
# <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
subset can't
tbl(con,"mtcars") %>%
subset(hp < 65)
Error in subset.default(., hp < 65) : object 'hp' not found
filter drops row names
filter(mtcars, hp < 65)
# mpg cyl disp hp drat wt qsec vs am gear carb
# 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
subset doesn't
subset(mtcars, hp < 65)
# mpg cyl disp hp drat wt qsec vs am gear carb
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
subset drop attributes other than class, names and row names.
cars_head <- head(cars)
attr(cars_head, "info") <- "head of cars dataset"
attributes(subset(cars_head, speed > 0))
#> $names
#> [1] "speed" "dist"
#>
#> $row.names
#> [1] 1 2 3 4 5 6
#>
#> $class
#> [1] "data.frame"
attributes(filter(cars_head, speed > 0))
#> $names
#> [1] "speed" "dist"
#>
#> $row.names
#> [1] 1 2 3 4 5 6
#>
#> $class
#> [1] "data.frame"
#>
#> $info
#> [1] "head of cars dataset"
subset has a select argument
While dplyr follows tidyverse principles which aim at having each function doing one thing, so select is a separate function.
identical(
subset(starwars, species == "Wookiee", select = c("name", "height")),
filter(starwars, species == "Wookiee") %>% select(name, height)
)
# [1] TRUE
It also has a drop argument, that makes mostly sense in the context of using the select argument.
subset recycles its condition argument
half_iris <- subset(iris,c(TRUE,FALSE))
dim(iris) # [1] 150 5
dim(half_iris) # [1] 75 5
filter doesn't
half_iris <- filter(iris,c(TRUE,FALSE))
Error in filter_impl(.data, quo) : Result must have length 150, not 2
filter supports conditions as separate arguments
Conditions are fed to ... so we can have several conditions as different arguments, which is the same as using & but might be more readable sometimes due to logical operator precedence and automatic identation.
identical(
subset(starwars,
(species == "Wookiee" | eye_color == "blue") &
mass > 120),
filter(starwars,
species == "Wookiee" | eye_color == "blue",
mass > 120)
)
filter preserves the class of the column
df <- data.frame(a=1:2, b = 3:4, c= 5:6)
class(df$a) <- "foo"
class(df$b) <- "Date"
# subset preserves the Date, but strips the "foo" class
str(subset(df,TRUE))
#> 'data.frame': 2 obs. of 3 variables:
#> $ a: int 1 2
#> $ b: Date, format: "1970-01-04" "1970-01-05"
#> $ c: int 5 6
# filter keeps both
str(dplyr::filter(df,TRUE))
#> 'data.frame': 2 obs. of 3 variables:
#> $ a: 'foo' int 1 2
#> $ b: Date, format: "1970-01-04" "1970-01-05"
#> $ c: int 5 6
filter supports the use use of the .data pronoun
mtcars %>% filter(.data[["hp"]] < 65)
# mpg cyl disp hp drat wt qsec vs am gear carb
# 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
filter supports some rlang features
x <- "hp"
library(rlang)
mtcars %>% filter(!!sym(x) < 65)
# m pg cyl disp hp drat wt qsec vs am gear carb
# 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
filter65 <- function(data,var){
data %>% filter(!!enquo(var) < 65)
}
mtcars %>% filter65(hp)
# mpg cyl disp hp drat wt qsec vs am gear carb
# 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
# 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
filter supports grouping
iris %>%
group_by(Species) %>%
filter(Petal.Length < quantile(Petal.Length,0.01))
# # A tibble: 3 x 5
# # Groups: Species [3]
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <dbl> <dbl> <dbl> <dbl> <fctr>
# 1 4.6 3.6 1.0 0.2 setosa
# 2 5.1 2.5 3.0 1.1 versicolor
# 3 4.9 2.5 4.5 1.7 virginica
iris %>%
group_by(Species) %>%
subset(Petal.Length < quantile(Petal.Length,0.01))
# # A tibble: 2 x 5
# # Groups: Species [1]
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <dbl> <dbl> <dbl> <dbl> <fctr>
# 1 4.3 3.0 1.1 0.1 setosa
# 2 4.6 3.6 1.0 0.2 setosa
filter supports n() and row_number()
filter(iris, row_number() < n()/30)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 3.5 1.4 0.2 setosa
# 2 4.9 3.0 1.4 0.2 setosa
# 3 4.7 3.2 1.3 0.2 setosa
# 4 4.6 3.1 1.5 0.2 setosa
filter is stricter
It trigger errors if the input is suspicious.
filter(iris, Species = "setosa")
# Error: `Species` (`Species = "setosa"`) must not be named, do you need `==`?
identical(subset(iris, Species = "setosa"), iris)
# [1] TRUE
df1 <- setNames(data.frame(a = 1:3, b=5:7),c("a","a"))
# df1
# a a
# 1 1 5
# 2 2 6
# 3 3 7
filter(df1, a > 2)
#Error: Column `a` must have a unique name
subset(df1, a > 2)
# a a.1
# 3 3 7
filter is a bit faster when it counts
Borrowing the dataset that Benjamin built in his answer (153 k rows), it's twice faster, though it should rarely be a bottleneck.
air <- lapply(1:1000, function(x) airquality) %>% bind_rows
microbenchmark::microbenchmark(
subset = subset(air, Temp>80 & Month > 5),
filter = filter(air, Temp>80 & Month > 5)
)
# Unit: milliseconds
# expr min lq mean median uq max neval cld
# subset 8.771962 11.551255 19.942501 12.576245 13.933290 108.0552 100 b
# filter 4.144336 4.686189 8.024461 6.424492 7.499894 101.7827 100 a
subset has methods in other packages
subset is an S3 generic, just as dplyr::filter is, but subset as a base function is more likely to have methods developed in other packages, one prominent example is zoo:::subset.zoo.
One additional difference not yet mentioned is that filter discards rownames, while subset doesn't:
filter(mtcars, gear == 5)
mpg cyl disp hp drat wt qsec vs am gear carb
1 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
2 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
3 15.8 4 351.0 264 4.22 3.170 14.5 0 1 5 4
4 19.7 4 145.0 175 3.62 2.770 15.5 0 1 5 6
5 15.0 4 301.0 335 3.54 3.570 14.6 0 1 5 8
subset(mtcars, gear == 5)
mpg cyl disp hp drat wt qsec vs am gear carb
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
Ford Pantera L 15.8 4 351.0 264 4.22 3.170 14.5 0 1 5 4
Ferrari Dino 19.7 4 145.0 175 3.62 2.770 15.5 0 1 5 6
Maserati Bora 15.0 4 301.0 335 3.54 3.570 14.6 0 1 5 8
They are, indeed, producing the same result, and they are very similar in concept.
The advantage of subset is that it is part of base R and doesn't require any additional packages. With small sample sizes, it seems to be a bit faster than filter (6 times faster in your example, but that's measured in microseconds).
As the data sets grow, filter seems gains the upper hand in efficiency. At 15,000 records, filter outpaces subset by about 300 microseconds. And at 153,000 records, filter is three times faster (measured in milliseconds).
So in terms of human time, I don't think there's much difference between the two.
The other advantage (and this is a bit of a niche advantage) is that filter can operate on SQL databases without pulling the data into memory. subset simply doesn't do that.
Personally, I tend to use filter, but only because I'm already using the dplyr framework. If you aren't working with out-of-memory data, it won't make much of a difference.
library(dplyr)
library(microbenchmark)
# Original example
microbenchmark(
df1<-subset(airquality, Temp>80 & Month > 5),
df2<-filter(airquality, Temp>80 & Month > 5)
)
Unit: microseconds
expr min lq mean median uq max neval cld
subset 95.598 107.7670 118.5236 119.9370 125.949 167.443 100 a
filter 551.886 564.7885 599.4972 571.5335 594.993 2074.997 100 b
# 15,300 rows
air <- lapply(1:100, function(x) airquality) %>% bind_rows
microbenchmark(
df1<-subset(air, Temp>80 & Month > 5),
df2<-filter(air, Temp>80 & Month > 5)
)
Unit: microseconds
expr min lq mean median uq max neval cld
subset 1187.054 1207.5800 1293.718 1216.671 1257.725 2574.392 100 b
filter 968.586 985.4475 1056.686 1023.862 1036.765 2489.644 100 a
# 153,000 rows
air <- lapply(1:1000, function(x) airquality) %>% bind_rows
microbenchmark(
df1<-subset(air, Temp>80 & Month > 5),
df2<-filter(air, Temp>80 & Month > 5)
)
Unit: milliseconds
expr min lq mean median uq max neval cld
subset 11.841792 13.292618 16.21771 13.521935 13.867083 68.59659 100 b
filter 5.046148 5.169164 10.27829 5.387484 6.738167 65.38937 100 a