The filter() function is used to subset a data frame,
retaining all rows that satisfy your conditions.
To be retained, the row must produce a value of TRUE for all conditions.
Note that when a condition evaluates to NA
the row will be dropped, unlike base subsetting with [.
Arguments
- .data
A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
- ...
<
data-masking> Expressions that return a logical value, and are defined in terms of the variables in.data. If multiple expressions are included, they are combined with the&operator. Only rows for which all conditions evaluate toTRUEare kept.- .by
![[Experimental]](figures/lifecycle-experimental.svg)
<
tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative togroup_by(). For details and examples, see ?dplyr_by.- .preserve
Relevant when the
.datainput is grouped. If.preserve = FALSE(the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.
Value
An object of the same type as .data. The output has the following properties:
Rows are a subset of the input, but appear in the same order.
Columns are not modified.
The number of groups may be reduced (if
.preserveis notTRUE).Data frame attributes are preserved.
Details
The filter() function is used to subset the rows of
.data, applying the expressions in ... to the column values to determine which
rows should be retained. It can be applied to both grouped and ungrouped data (see group_by() and
ungroup()). However, dplyr is not yet smart enough to optimise the filtering
operation on grouped datasets that do not need grouped calculations. For this
reason, filtering is often considerably faster on ungrouped data.
Useful filter functions
There are many functions and operators that are useful when constructing the expressions used to filter the data:
Grouped tibbles
Because filtering expressions are computed within groups, they may yield different results on grouped tibbles. This will be the case as soon as an aggregating, lagging, or ranking function is involved. Compare this ungrouped filtering:
With the grouped equivalent:
In the ungrouped version, filter() compares the value of mass in each row to
the global average (taken over the whole data set), keeping only the rows with
mass greater than this global average. In contrast, the grouped version calculates
the average mass separately for each gender group, and keeps rows with mass greater
than the relevant within-gender average.
Methods
This function is a generic, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
The following methods are currently available in loaded packages:
dbplyr (tbl_lazy), dplyr (data.frame, ts)
.
Examples
# Filtering by one criterion
filter(starwars, species == "Human")
#> # A tibble: 35 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Luke … 172 77 blond fair blue 19 male
#> 2 Darth… 202 136 none white yellow 41.9 male
#> 3 Leia … 150 49 brown light brown 19 fema…
#> 4 Owen … 178 120 brown, gr… light blue 52 male
#> 5 Beru … 165 75 brown light blue 47 fema…
#> 6 Biggs… 183 84 black light brown 24 male
#> 7 Obi-W… 182 77 auburn, w… fair blue-gray 57 male
#> 8 Anaki… 188 84 blond fair blue 41.9 male
#> 9 Wilhu… 180 NA auburn, g… fair blue 64 male
#> 10 Han S… 180 80 brown fair brown 29 male
#> # ℹ 25 more rows
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
filter(starwars, mass > 1000)
#> # A tibble: 1 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Jabba … 175 1358 NA green-tan… orange 600 herm…
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# Filtering by multiple criteria within a single logical expression
filter(starwars, hair_color == "none" & eye_color == "black")
#> # A tibble: 9 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Nien N… 160 68 none grey black NA male
#> 2 Gasgano 122 NA none white, bl… black NA male
#> 3 Kit Fi… 196 87 none green black NA male
#> 4 Plo Ko… 188 80 none orange black 22 male
#> 5 Lama Su 229 88 none grey black NA male
#> 6 Taun We 213 NA none grey black NA fema…
#> 7 Shaak … 178 57 none red, blue… black NA fema…
#> 8 Tion M… 206 80 none grey black NA male
#> 9 BB8 NA NA none none black NA none
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
filter(starwars, hair_color == "none" | eye_color == "black")
#> # A tibble: 39 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Darth… 202 136 none white yellow 41.9 male
#> 2 Greedo 173 74 NA green black 44 male
#> 3 IG-88 200 140 none metal red 15 none
#> 4 Bossk 190 113 none green red 53 male
#> 5 Lobot 175 79 none light blue 37 male
#> 6 Ackbar 180 83 none brown mot… orange 41 male
#> 7 Nien … 160 68 none grey black NA male
#> 8 Nute … 191 90 none mottled g… red NA male
#> 9 Jar J… 196 66 none orange orange 52 male
#> 10 Roos … 224 82 none grey orange NA male
#> # ℹ 29 more rows
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# When multiple expressions are used, they are combined using &
filter(starwars, hair_color == "none", eye_color == "black")
#> # A tibble: 9 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Nien N… 160 68 none grey black NA male
#> 2 Gasgano 122 NA none white, bl… black NA male
#> 3 Kit Fi… 196 87 none green black NA male
#> 4 Plo Ko… 188 80 none orange black 22 male
#> 5 Lama Su 229 88 none grey black NA male
#> 6 Taun We 213 NA none grey black NA fema…
#> 7 Shaak … 178 57 none red, blue… black NA fema…
#> 8 Tion M… 206 80 none grey black NA male
#> 9 BB8 NA NA none none black NA none
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# The filtering operation may yield different results on grouped
# tibbles because the expressions are computed within groups.
#
# The following filters rows where `mass` is greater than the
# global average:
starwars %>% filter(mass > mean(mass, na.rm = TRUE))
#> # A tibble: 10 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Darth… 202 136 none white yellow 41.9 male
#> 2 Owen … 178 120 brown, gr… light blue 52 male
#> 3 Chewb… 228 112 brown unknown blue 200 male
#> 4 Jabba… 175 1358 NA green-tan… orange 600 herm…
#> 5 Jek T… 180 110 brown fair blue NA NA
#> 6 IG-88 200 140 none metal red 15 none
#> 7 Bossk 190 113 none green red 53 male
#> 8 Dexte… 198 102 none brown yellow NA male
#> 9 Griev… 216 159 none brown, wh… green, y… NA male
#> 10 Tarff… 234 136 brown brown blue NA male
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# Whereas this keeps rows with `mass` greater than the gender
# average:
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))
#> # A tibble: 15 × 14
#> # Groups: gender [3]
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Dart… 202 136 none white yellow 41.9 male
#> 2 Owen… 178 120 brown, gr… light blue 52 male
#> 3 Beru… 165 75 brown light blue 47 fema…
#> 4 Chew… 228 112 brown unknown blue 200 male
#> 5 Jabb… 175 1358 NA green-tan… orange 600 herm…
#> 6 Jek … 180 110 brown fair blue NA NA
#> 7 IG-88 200 140 none metal red 15 none
#> 8 Bossk 190 113 none green red 53 male
#> 9 Ayla… 178 55 none blue hazel 48 fema…
#> 10 Greg… 185 85 black dark brown NA NA
#> 11 Lumi… 170 56.2 black yellow blue 58 fema…
#> 12 Zam … 168 55 blonde fair, gre… yellow NA fema…
#> 13 Shaa… 178 57 none red, blue… black NA fema…
#> 14 Grie… 216 159 none brown, wh… green, y… NA male
#> 15 Tarf… 234 136 brown brown blue NA male
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# To refer to column names that are stored as strings, use the `.data` pronoun:
vars <- c("mass", "height")
cond <- c(80, 150)
starwars %>%
filter(
.data[[vars[[1]]]] > cond[[1]],
.data[[vars[[2]]]] > cond[[2]]
)
#> # A tibble: 21 × 14
#> name height mass hair_color skin_color eye_color birth_year sex
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 Darth… 202 136 none white yellow 41.9 male
#> 2 Owen … 178 120 brown, gr… light blue 52 male
#> 3 Biggs… 183 84 black light brown 24 male
#> 4 Anaki… 188 84 blond fair blue 41.9 male
#> 5 Chewb… 228 112 brown unknown blue 200 male
#> 6 Jabba… 175 1358 NA green-tan… orange 600 herm…
#> 7 Jek T… 180 110 brown fair blue NA NA
#> 8 IG-88 200 140 none metal red 15 none
#> 9 Bossk 190 113 none green red 53 male
#> 10 Ackbar 180 83 none brown mot… orange 41 male
#> # ℹ 11 more rows
#> # ℹ 6 more variables: gender <chr>, homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# Learn more in ?rlang::args_data_masking