Subset rows using column values

Source: R/dplyr_methods.R

`filter()` retains the rows where the conditions you provide a `TRUE`. Note that, unlike base subsetting with `[`, rows where the condition evaluates to `NA` are dropped.

Arguments

.data

A tbl. (See dplyr)

...

<[`tidy-eval`][dplyr_tidy_eval]> Logical predicates defined in terms of the variables in `.data`. Multiple conditions are combined with `&`. Only rows where the condition evaluates to `TRUE` are kept.

.preserve

when `FALSE` (the default), the grouping structure is recalculated based on the resulting data, otherwise it is kept as is.

Value

An object of the same type as `.data`.

* 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

dplyr is not yet smart enough to optimise filtering optimisation on grouped datasets that don't need grouped calculations. For this reason, filtering is often considerably faster on [ungroup()]ed data.

Useful filter functions

* [`==`], [`>`], [`>=`] etc * [`&`], [`|`], [`!`], [xor()] * [is.na()] * [between()], [near()]

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:

The former keeps rows with `mass` greater than the global average whereas the latter keeps rows with `mass` greater than the 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:

See also

[filter_all()], [filter_if()] and [filter_at()].

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

Examples


data(se)

se |> tidybulk() |> filter(dex=="untrt")
#> # A tibble: 400 × 13
#>    .feature        .sample   counts SampleName cell  dex   albut Run   avgLength
#>    <chr>           <chr>      <int> <fct>      <fct> <fct> <fct> <fct>     <int>
#>  1 LRG_239         SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  2 ENSG00000233845 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  3 ENSG00000199201 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  4 ENSG00000270278 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  5 ENSG00000234453 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  6 ENSG00000272397 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  7 ENSG00000207997 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  8 ENSG00000201884 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#>  9 ENSG00000224188 SRR10395…      0 GSM1275862 N613… untrt untrt SRR1…       126
#> 10 ENSG00000122707 SRR10395…  11225 GSM1275862 N613… untrt untrt SRR1…       126
#> # ℹ 390 more rows
#> # ℹ 4 more variables: Experiment <fct>, Sample <fct>, BioSample <fct>,
#> #   GRangesList <list>

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