filter: Subset rows using column values

Description Usage Arguments Details Value Useful filter functions Grouped tibbles Methods See Also Examples

View source: R/filter.R

Description

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

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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.

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.

Value

An object of the same type as .data. The output has the following properties:

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:

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

With the grouped equivalent:

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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: \Sexpr[stage=render,results=rd]{dplyr:::methods_rd("filter")}.

See Also

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

Examples

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# Filtering by one criterion
filter(starwars, species == "Human")
filter(starwars, mass > 1000)

# Filtering by multiple criteria within a single logical expression
filter(starwars, hair_color == "none" & eye_color == "black")
filter(starwars, hair_color == "none" | eye_color == "black")

# When multiple expressions are used, they are combined using &
filter(starwars, hair_color == "none", eye_color == "black")


# 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))

# Whereas this keeps rows with `mass` greater than the gender
# average:
starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))


# 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]]
  )
# Learn more in ?dplyr_data_masking

Example output

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# A tibble: 35 x 13
   name  height  mass hair_color skin_color eye_color birth_year gender
   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
 1 Luke~    172    77 blond      fair       blue            19   male  
 2 Dart~    202   136 none       white      yellow          41.9 male  
 3 Leia~    150    49 brown      light      brown           19   female
 4 Owen~    178   120 brown, gr~ light      blue            52   male  
 5 Beru~    165    75 brown      light      blue            47   female
 6 Bigg~    183    84 black      light      brown           24   male  
 7 Obi-~    182    77 auburn, w~ fair       blue-gray       57   male  
 8 Anak~    188    84 blond      fair       blue            41.9 male  
 9 Wilh~    180    NA auburn, g~ fair       blue            64   male  
10 Han ~    180    80 brown      fair       brown           29   male  
# ... with 25 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#   films <list>, vehicles <list>, starships <list>
# A tibble: 1 x 13
  name  height  mass hair_color skin_color eye_color birth_year gender homeworld
  <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>  <chr>    
1 Jabb~    175  1358 <NA>       green-tan~ orange           600 herma~ Nal Hutta
# ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
#   starships <list>
# A tibble: 9 x 13
  name  height  mass hair_color skin_color eye_color birth_year gender homeworld
  <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>  <chr>    
1 Nien~    160    68 none       grey       black             NA male   Sullust  
2 Gasg~    122    NA none       white, bl~ black             NA male   Troiken  
3 Kit ~    196    87 none       green      black             NA male   Glee Ans~
4 Plo ~    188    80 none       orange     black             22 male   Dorin    
5 Lama~    229    88 none       grey       black             NA male   Kamino   
6 Taun~    213    NA none       grey       black             NA female Kamino   
7 Shaa~    178    57 none       red, blue~ black             NA female Shili    
8 Tion~    206    80 none       grey       black             NA male   Utapau   
9 BB8       NA    NA none       none       black             NA none   <NA>     
# ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
#   starships <list>
# A tibble: 38 x 13
   name  height  mass hair_color skin_color eye_color birth_year gender
   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
 1 Dart~    202   136 none       white      yellow          41.9 male  
 2 Gree~    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 Ackb~    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 ~    196    66 none       orange     orange          52   male  
10 Roos~    224    82 none       grey       orange          NA   male  
# ... with 28 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#   films <list>, vehicles <list>, starships <list>
# A tibble: 9 x 13
  name  height  mass hair_color skin_color eye_color birth_year gender homeworld
  <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr>  <chr>    
1 Nien~    160    68 none       grey       black             NA male   Sullust  
2 Gasg~    122    NA none       white, bl~ black             NA male   Troiken  
3 Kit ~    196    87 none       green      black             NA male   Glee Ans~
4 Plo ~    188    80 none       orange     black             22 male   Dorin    
5 Lama~    229    88 none       grey       black             NA male   Kamino   
6 Taun~    213    NA none       grey       black             NA female Kamino   
7 Shaa~    178    57 none       red, blue~ black             NA female Shili    
8 Tion~    206    80 none       grey       black             NA male   Utapau   
9 BB8       NA    NA none       none       black             NA none   <NA>     
# ... with 4 more variables: species <chr>, films <list>, vehicles <list>,
#   starships <list>
# A tibble: 10 x 13
   name  height  mass hair_color skin_color eye_color birth_year gender
   <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 Chew~    228   112 brown      unknown    blue           200   male  
 4 Jabb~    175  1358 <NA>       green-tan~ orange         600   herma~
 5 Jek ~    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 Dext~    198   102 none       brown      yellow          NA   male  
 9 Grie~    216   159 none       brown, wh~ green, y~       NA   male  
10 Tarf~    234   136 brown      brown      blue            NA   male  
# ... with 5 more variables: homeworld <chr>, species <chr>, films <list>,
#   vehicles <list>, starships <list>
# A tibble: 25 x 13
# Groups:   gender [3]
   name  height  mass hair_color skin_color eye_color birth_year gender
   <chr>  <int> <dbl> <chr>      <chr>      <chr>          <dbl> <chr> 
 1 C-3PO    167    75 <NA>       gold       yellow         112   <NA>  
 2 Dart~    202   136 none       white      yellow          41.9 male  
 3 Owen~    178   120 brown, gr~ light      blue            52   male  
 4 Beru~    165    75 brown      light      blue            47   female
 5 Bigg~    183    84 black      light      brown           24   male  
 6 Anak~    188    84 blond      fair       blue            41.9 male  
 7 Chew~    228   112 brown      unknown    blue           200   male  
 8 Jek ~    180   110 brown      fair       blue            NA   male  
 9 Bossk    190   113 none       green      red             53   male  
10 Ackb~    180    83 none       brown mot~ orange          41   male  
# ... with 15 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#   films <list>, vehicles <list>, starships <list>
# A tibble: 21 x 13
   name  height  mass hair_color skin_color eye_color birth_year gender
   <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 Bigg~    183    84 black      light      brown           24   male  
 4 Anak~    188    84 blond      fair       blue            41.9 male  
 5 Chew~    228   112 brown      unknown    blue           200   male  
 6 Jabb~    175  1358 <NA>       green-tan~ orange         600   herma~
 7 Jek ~    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 Ackb~    180    83 none       brown mot~ orange          41   male  
# ... with 11 more rows, and 5 more variables: homeworld <chr>, species <chr>,
#   films <list>, vehicles <list>, starships <list>

dplyr documentation built on June 19, 2021, 1:07 a.m.