filter | R Documentation |
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 [
.
## S3 method for class 'Seurat'
filter(.data, ..., .preserve = FALSE)
.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. |
... |
< |
.preserve |
Relevant when the |
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.
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.
There are many functions and operators that are useful when constructing the expressions used to filter the data:
==
, >
, >=
etc
&
, |
, !
, xor()
is.na()
between()
, near()
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.
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")}.
Other single table verbs:
arrange()
,
mutate()
,
reframe()
,
rename()
,
select()
,
slice()
,
summarise()
data("pbmc_small")
pbmc_small |> filter(groups == "g1")
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