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Sample n rows from a table

Source: R/dplyr_methods.R
sample_n.Rd

[Superseded] sample_n() and sample_frac() have been superseded in favour of slice_sample(). While they will not be deprecated in the near future, retirement means that we will only perform critical bug fixes, so we recommend moving to the newer alternative.

These functions were superseded because we realised it was more convenient to have two mutually exclusive arguments to one function, rather than two separate functions. This also made it to clean up a few other smaller design issues with sample_n()/sample_frac:

  • The connection to slice() was not obvious.

  • The name of the first argument, tbl, is inconsistent with other single table verbs which use .data.

  • The size argument uses tidy evaluation, which is surprising and undocumented.

  • It was easier to remove the deprecated .env argument.

  • ... was in a suboptimal position.

Usage

# S3 method for Seurat
sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL, ...)

# S3 method for Seurat
sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL, ...)

Arguments

tbl

A data.frame.

size

<tidy-select> For sample_n(), the number of rows to select. For sample_frac(), the fraction of rows to select. If tbl is grouped, size applies to each group.

replace

Sample with or without replacement?

weight

<tidy-select> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

.env

DEPRECATED.

...

ignored

Examples

data(pbmc_small)
pbmc_small |> sample_n(50)
#> # A Seurat-tibble abstraction: 50 × 15
#> # Features=230 | Cells=50 | Active assay=RNA | Assays=RNA
#>    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
#>    <chr> <fct>           <dbl>        <int> <fct>           <fct>         <chr> 
#>  1 GATA… SeuratPro…        328           72 1               B             g1    
#>  2 GGCA… SeuratPro…        126           53 0               A             g1    
#>  3 ATGC… SeuratPro…         70           47 0               A             g2    
#>  4 AGAT… SeuratPro…        187           61 0               A             g2    
#>  5 TACA… SeuratPro…        108           44 0               A             g2    
#>  6 CATG… SeuratPro…         51           26 0               A             g2    
#>  7 GCAC… SeuratPro…        292           71 1               B             g2    
#>  8 CGTA… SeuratPro…        371           75 1               B             g1    
#>  9 TTAC… SeuratPro…        298           65 1               B             g1    
#> 10 ATAA… SeuratPro…         99           42 1               B             g2    
#> # ℹ 40 more rows
#> # ℹ 8 more variables: RNA_snn_res.1 <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,
#> #   PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>
pbmc_small |> sample_frac(0.1)
#> # A Seurat-tibble abstraction: 8 × 15
#> # Features=230 | Cells=8 | Active assay=RNA | Assays=RNA
#>   .cell  orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
#>   <chr>  <fct>           <dbl>        <int> <fct>           <fct>         <chr> 
#> 1 GATAG… SeuratPro…        328           72 1               B             g1    
#> 2 GGCAT… SeuratPro…        126           53 0               A             g1    
#> 3 ATGCC… SeuratPro…         70           47 0               A             g2    
#> 4 AGATA… SeuratPro…        187           61 0               A             g2    
#> 5 TACAA… SeuratPro…        108           44 0               A             g2    
#> 6 CATGA… SeuratPro…         51           26 0               A             g2    
#> 7 GCACT… SeuratPro…        292           71 1               B             g2    
#> 8 CGTAG… SeuratPro…        371           75 1               B             g1    
#> # ℹ 8 more variables: RNA_snn_res.1 <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,
#> #   PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>