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 

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) :

• subset can be used on matrices
• filter can be used on databases
• filter drops row names
• subset drop attributes other than class, names and row names.
• subset has a select argument
• subset recycles its condition argument
• filter supports conditions as separate arguments
• filter preserves the class of the column
• filter supports the .data pronoun
• filter supports some rlang features
• filter supports grouping
• filter supports n() and row_number()
• filter is stricter
• filter is a bit faster when it counts
• subset has 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.

Interesting. I was trying to see the difference in terms of the resulting dataset and I coulnd't get an explanation to why the "[" operator behaved differently (i.e., to why it also returned NAs):

# Subset for year=2013 sub<-brfss2013 %>% filter(iyear == "2013") dim(sub) #[1] 486088 330 length(which(is.na(sub$iyear))==T) #[1] 0 sub2<-filter(brfss2013, iyear == "2013") dim(sub2) #[1] 486088 330 length(which(is.na(sub2$iyear))==T) #[1] 0 sub3<-brfss2013[brfss2013$iyear=="2013", ] dim(sub3) #[1] 486093 330 length(which(is.na(sub3$iyear))==T) #[1] 5 sub4<-subset(brfss2013, iyear=="2013") dim(sub4) #[1] 486088 330 length(which(is.na(sub4$iyear))==T) #[1] 0 

A difference is also that subset does more things than filter you can also select and drop while you have two different functions in dplyr

subset(df, select=c("varA", "varD")) dplyr::select(df,varA, varD) 

An additional advantage of filter is that it plays nice with grouped data. subset ignores groupings.

So when the data is grouped, subset will still make reference to the whole data, but filter will only reference the group.

# setup library(tidyverse) data.frame(a = 1:2) %>% group_by(a) %>% subset(length(a) == 1) # returns empty table data.frame(a = 1:2) %>% group_by(a) %>% filter(length(a) == 1) # returns all rows