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 [.
filter(.data, ..., .preserve = FALSE)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 to TRUE are kept.
Relevant when the .data input is grouped.
If .preserve = FALSE (the default), the grouping structure
is recalculated based on the resulting data, otherwise the grouping is kept as is.
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 .preserve is not TRUE).
Data frame attributes are preserved.
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.
There are many functions and operators that are useful when constructing the expressions used to filter the data:
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.
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)
.
# Filtering by one criterion
filter(starwars, species == "Human")
#> # A tibble: 35 × 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Luke Sk… 172 77 blond fair blue 19 male mascu…
#> 2 Darth V… 202 136 none white yellow 41.9 male mascu…
#> 3 Leia Or… 150 49 brown light brown 19 fema… femin…
#> 4 Owen La… 178 120 brown, gr… light blue 52 male mascu…
#> 5 Beru Wh… 165 75 brown light blue 47 fema… femin…
#> 6 Biggs D… 183 84 black light brown 24 male mascu…
#> 7 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
#> 8 Anakin … 188 84 blond fair blue 41.9 male mascu…
#> 9 Wilhuff… 180 NA auburn, g… fair blue 64 male mascu…
#> 10 Han Solo 180 80 brown fair brown 29 male mascu…
#> # … with 25 more rows, and 5 more variables: 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 gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Jabba De… 175 1358 NA green-tan… orange 600 herm… mascu…
#> # … with 5 more variables: 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 gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Nien Nunb 160 68 none grey black NA male mascu…
#> 2 Gasgano 122 NA none white, bl… black NA male mascu…
#> 3 Kit Fisto 196 87 none green black NA male mascu…
#> 4 Plo Koon 188 80 none orange black 22 male mascu…
#> 5 Lama Su 229 88 none grey black NA male mascu…
#> 6 Taun We 213 NA none grey black NA fema… femin…
#> 7 Shaak Ti 178 57 none red, blue… black NA fema… femin…
#> 8 Tion Med… 206 80 none grey black NA male mascu…
#> 9 BB8 NA NA none none black NA none mascu…
#> # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> # vehicles <list>, starships <list>
filter(starwars, hair_color == "none" | eye_color == "black")
#> # A tibble: 38 × 14
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Darth V… 202 136 none white yellow 41.9 male mascu…
#> 2 Greedo 173 74 NA green black 44 male mascu…
#> 3 IG-88 200 140 none metal red 15 none mascu…
#> 4 Bossk 190 113 none green red 53 male mascu…
#> 5 Lobot 175 79 none light blue 37 male mascu…
#> 6 Ackbar 180 83 none brown mot… orange 41 male mascu…
#> 7 Nien Nu… 160 68 none grey black NA male mascu…
#> 8 Nute Gu… 191 90 none mottled g… red NA male mascu…
#> 9 Jar Jar… 196 66 none orange orange 52 male mascu…
#> 10 Roos Ta… 224 82 none grey orange NA male mascu…
#> # … with 28 more rows, and 5 more variables: 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 gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Nien Nunb 160 68 none grey black NA male mascu…
#> 2 Gasgano 122 NA none white, bl… black NA male mascu…
#> 3 Kit Fisto 196 87 none green black NA male mascu…
#> 4 Plo Koon 188 80 none orange black 22 male mascu…
#> 5 Lama Su 229 88 none grey black NA male mascu…
#> 6 Taun We 213 NA none grey black NA fema… femin…
#> 7 Shaak Ti 178 57 none red, blue… black NA fema… femin…
#> 8 Tion Med… 206 80 none grey black NA male mascu…
#> 9 BB8 NA NA none none black NA none mascu…
#> # … with 5 more variables: 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 gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Darth V… 202 136 none white yellow 41.9 male mascu…
#> 2 Owen La… 178 120 brown, gr… light blue 52 male mascu…
#> 3 Chewbac… 228 112 brown unknown blue 200 male mascu…
#> 4 Jabba D… 175 1358 NA green-tan… orange 600 herm… mascu…
#> 5 Jek Ton… 180 110 brown fair blue NA male mascu…
#> 6 IG-88 200 140 none metal red 15 none mascu…
#> 7 Bossk 190 113 none green red 53 male mascu…
#> 8 Dexter … 198 102 none brown yellow NA male mascu…
#> 9 Grievous 216 159 none brown, wh… green, y… NA male mascu…
#> 10 Tarfful 234 136 brown brown blue NA male mascu…
#> # … with 5 more variables: 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: 14 × 14
#> # Groups: gender [2]
#> name height mass hair_color skin_color eye_color birth_year sex gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Darth … 202 136 none white yellow 41.9 male mascu…
#> 2 Owen L… 178 120 brown, gr… light blue 52 male mascu…
#> 3 Beru W… 165 75 brown light blue 47 fema… femin…
#> 4 Chewba… 228 112 brown unknown blue 200 male mascu…
#> 5 Jabba … 175 1358 NA green-tan… orange 600 herm… mascu…
#> 6 Jek To… 180 110 brown fair blue NA male mascu…
#> 7 IG-88 200 140 none metal red 15 none mascu…
#> 8 Bossk 190 113 none green red 53 male mascu…
#> 9 Ayla S… 178 55 none blue hazel 48 fema… femin…
#> 10 Lumina… 170 56.2 black yellow blue 58 fema… femin…
#> 11 Zam We… 168 55 blonde fair, gre… yellow NA fema… femin…
#> 12 Shaak … 178 57 none red, blue… black NA fema… femin…
#> 13 Grievo… 216 159 none brown, wh… green, y… NA male mascu…
#> 14 Tarfful 234 136 brown brown blue NA male mascu…
#> # … with 5 more variables: 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 gender
#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Darth V… 202 136 none white yellow 41.9 male mascu…
#> 2 Owen La… 178 120 brown, gr… light blue 52 male mascu…
#> 3 Biggs D… 183 84 black light brown 24 male mascu…
#> 4 Anakin … 188 84 blond fair blue 41.9 male mascu…
#> 5 Chewbac… 228 112 brown unknown blue 200 male mascu…
#> 6 Jabba D… 175 1358 NA green-tan… orange 600 herm… mascu…
#> 7 Jek Ton… 180 110 brown fair blue NA male mascu…
#> 8 IG-88 200 140 none metal red 15 none mascu…
#> 9 Bossk 190 113 none green red 53 male mascu…
#> 10 Ackbar 180 83 none brown mot… orange 41 male mascu…
#> # … with 11 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#> # films <list>, vehicles <list>, starships <list>
# Learn more in ?dplyr_data_masking