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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 [.

Usage

filter(.data, ..., .preserve = FALSE)

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 to TRUE are kept.

.preserve

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.

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 .preserve is not TRUE).

  • 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:

starwars %>% filter(mass > mean(mass, na.rm = TRUE))

With the grouped equivalent:

starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))

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) .

See also

Other single table verbs: arrange(), mutate(), rename(), select(), slice(), summarise()

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 Sky…    172    77 blond      fair       blue            19   male 
#>  2 Darth Va…    202   136 none       white      yellow          41.9 male 
#>  3 Leia Org…    150    49 brown      light      brown           19   fema…
#>  4 Owen Lars    178   120 brown, gr… light      blue            52   male 
#>  5 Beru Whi…    165    75 brown      light      blue            47   fema…
#>  6 Biggs Da…    183    84 black      light      brown           24   male 
#>  7 Obi-Wan …    182    77 auburn, w… fair       blue-gray       57   male 
#>  8 Anakin S…    188    84 blond      fair       blue            41.9 male 
#>  9 Wilhuff …    180    NA auburn, g… fair       blue            64   male 
#> 10 Han Solo     180    80 brown      fair       brown           29   male 
#> # … with 25 more rows, and 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 Des…    175  1358 NA         green-tan… orange           600 herm…
#> # … with 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 Nunb     160    68 none       grey       black             NA male 
#> 2 Gasgano       122    NA none       white, bl… black             NA male 
#> 3 Kit Fisto     196    87 none       green      black             NA male 
#> 4 Plo Koon      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 Ti      178    57 none       red, blue… black             NA fema…
#> 8 Tion Medon    206    80 none       grey       black             NA male 
#> 9 BB8            NA    NA none       none       black             NA none 
#> # … with 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: 38 × 14
#>    name      height  mass hair_color skin_color eye_color birth_year sex  
#>    <chr>      <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>
#>  1 Darth Va…    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 Nunb    160    68 none       grey       black           NA   male 
#>  8 Nute Gun…    191    90 none       mottled g… red             NA   male 
#>  9 Jar Jar …    196    66 none       orange     orange          52   male 
#> 10 Roos Tar…    224    82 none       grey       orange          NA   male 
#> # … with 28 more rows, and 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 Nunb     160    68 none       grey       black             NA male 
#> 2 Gasgano       122    NA none       white, bl… black             NA male 
#> 3 Kit Fisto     196    87 none       green      black             NA male 
#> 4 Plo Koon      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 Ti      178    57 none       red, blue… black             NA fema…
#> 8 Tion Medon    206    80 none       grey       black             NA male 
#> 9 BB8            NA    NA none       none       black             NA none 
#> # … with 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 Va…    202   136 none       white      yellow          41.9 male 
#>  2 Owen Lars    178   120 brown, gr… light      blue            52   male 
#>  3 Chewbacca    228   112 brown      unknown    blue           200   male 
#>  4 Jabba De…    175  1358 NA         green-tan… orange         600   herm…
#>  5 Jek Tono…    180   110 brown      fair       blue            NA   male 
#>  6 IG-88        200   140 none       metal      red             15   none 
#>  7 Bossk        190   113 none       green      red             53   male 
#>  8 Dexter J…    198   102 none       brown      yellow          NA   male 
#>  9 Grievous     216   159 none       brown, wh… green, y…       NA   male 
#> 10 Tarfful      234   136 brown      brown      blue            NA   male 
#> # … with 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: 14 × 14
#> # Groups:   gender [2]
#>    name     height   mass hair_color skin_color eye_color birth_year sex  
#>    <chr>     <int>  <dbl> <chr>      <chr>      <chr>          <dbl> <chr>
#>  1 Darth V…    202  136   none       white      yellow          41.9 male 
#>  2 Owen La…    178  120   brown, gr… light      blue            52   male 
#>  3 Beru Wh…    165   75   brown      light      blue            47   fema…
#>  4 Chewbac…    228  112   brown      unknown    blue           200   male 
#>  5 Jabba D…    175 1358   NA         green-tan… orange         600   herm…
#>  6 Jek Ton…    180  110   brown      fair       blue            NA   male 
#>  7 IG-88       200  140   none       metal      red             15   none 
#>  8 Bossk       190  113   none       green      red             53   male 
#>  9 Ayla Se…    178   55   none       blue       hazel           48   fema…
#> 10 Luminar…    170   56.2 black      yellow     blue            58   fema…
#> 11 Zam Wes…    168   55   blonde     fair, gre… yellow          NA   fema…
#> 12 Shaak Ti    178   57   none       red, blue… black           NA   fema…
#> 13 Grievous    216  159   none       brown, wh… green, y…       NA   male 
#> 14 Tarfful     234  136   brown      brown      blue            NA   male 
#> # … with 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 Va…    202   136 none       white      yellow          41.9 male 
#>  2 Owen Lars    178   120 brown, gr… light      blue            52   male 
#>  3 Biggs Da…    183    84 black      light      brown           24   male 
#>  4 Anakin S…    188    84 blond      fair       blue            41.9 male 
#>  5 Chewbacca    228   112 brown      unknown    blue           200   male 
#>  6 Jabba De…    175  1358 NA         green-tan… orange         600   herm…
#>  7 Jek Tono…    180   110 brown      fair       blue            NA   male 
#>  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 
#> # … with 11 more rows, and 6 more variables: gender <chr>,
#> #   homeworld <chr>, species <chr>, films <list>, vehicles <list>,
#> #   starships <list>
# Learn more in ?dplyr_data_masking