Subset rows randomly
Usage
# S3 method for flowFrame
slice_sample(.data, ..., n, prop, by = NULL, weight_by = NULL, replace = FALSE)Arguments
- .data
- ...
Unused.
- n, prop
Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used. If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. prop will be rounded towards zero to generate an integer number of rows.
A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.
- by
Optionally, an unquoted selection of columns to group by for just this operation. An alternative to group_by.
- weight_by
Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardized to sum to 1.
- replace
Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.
Value
An object of the same type as .data. The output has the following properties:
* Each row may appear 0, 1, or many times in the output.
* Columns are not modified.
* Groups are not modified.
* A flowFrame's identifier is preserved.
Examples
my_flowframe <- simulate_cytometry_data()$flowframe
my_flowframe |>
dplyr::slice_sample(n = 5)
#> flowFrame object 'V1'
#> with 5 cells and 10 observables:
#> name desc range minRange maxRange
#> $P1 feature_1 feature_1 67 0 66
#> $P2 feature_2 feature_2 78 0 77
#> $P3 feature_3 feature_3 95 0 94
#> $P4 feature_4 feature_4 74 0 73
#> $P5 feature_5 feature_5 94 0 93
#> $P6 feature_6 feature_6 72 0 71
#> $P7 feature_7 feature_7 101 0 100
#> $P8 feature_8 feature_8 78 0 77
#> $P9 feature_9 feature_9 87 0 86
#> $P10 feature_10 feature_10 96 0 95
#> 88 keywords are stored in the 'description' slot