Extract a single column

pull() is similar to $. It's mostly useful because it looks a little nicer in pipes, it also works with remote data frames, and it can optionally name the output.

Value

A vector the same size as .data.

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: dplyr (data.frame), tidySingleCellExperiment (SingleCellExperiment) .

Examples

example(read10xVisium)
#> 
#> rd10xV> dir <- system.file(
#> rd10xV+   file.path("extdata", "10xVisium"), 
#> rd10xV+   package = "SpatialExperiment")
#> 
#> rd10xV> sample_ids <- c("section1", "section2")
#> 
#> rd10xV> samples <- file.path(dir, sample_ids, "outs")
#> 
#> rd10xV> list.files(samples[1])
#> [1] "raw_feature_bc_matrix" "spatial"              
#> 
#> rd10xV> list.files(file.path(samples[1], "spatial"))
#> [1] "scalefactors_json.json"    "tissue_lowres_image.png"  
#> [3] "tissue_positions_list.csv"
#> 
#> rd10xV> file.path(samples[1], "raw_feature_bc_matrix")
#> [1] "/__w/_temp/Library/SpatialExperiment/extdata/10xVisium/section1/outs/raw_feature_bc_matrix"
#> 
#> rd10xV> (spe <- read10xVisium(samples, sample_ids, 
#> rd10xV+   type = "sparse", data = "raw", 
#> rd10xV+   images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#>    .cell              in_tissue array_row array_col sample_id pxl_col_in_fullres
#>    <chr>              <lgl>         <int>     <int> <chr>                  <int>
#>  1 AAACAACGAATAGTTC-1 FALSE             0        16 section1                2312
#>  2 AAACAAGTATCTCCCA-1 TRUE             50       102 section1                8230
#>  3 AAACAATCTACTAGCA-1 TRUE              3        43 section1                4170
#>  4 AAACACCAATAACTGC-1 TRUE             59        19 section1                2519
#>  5 AAACAGAGCGACTCCT-1 TRUE             14        94 section1                7679
#>  6 AAACAGCTTTCAGAAG-1 FALSE            43         9 section1                1831
#>  7 AAACAGGGTCTATATT-1 FALSE            47        13 section1                2106
#>  8 AAACAGTGTTCCTGGG-1 FALSE            73        43 section1                4170
#>  9 AAACATGGTGAGAGGA-1 FALSE            62         0 section1                1212
#> 10 AAACATTTCCCGGATT-1 FALSE            61        97 section1                7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#> 
#> rd10xV> # base directory 'outs/' from Space Ranger can also be omitted
#> rd10xV> samples2 <- file.path(dir, sample_ids)
#> 
#> rd10xV> (spe2 <- read10xVisium(samples2, sample_ids, 
#> rd10xV+   type = "sparse", data = "raw", 
#> rd10xV+   images = "lowres", load = FALSE))
#> # A SpatialExperiment-tibble abstraction: 99 × 7
#> # Features = 50 | Cells = 99 | Assays = counts
#>    .cell              in_tissue array_row array_col sample_id pxl_col_in_fullres
#>    <chr>              <lgl>         <int>     <int> <chr>                  <int>
#>  1 AAACAACGAATAGTTC-1 FALSE             0        16 section1                2312
#>  2 AAACAAGTATCTCCCA-1 TRUE             50       102 section1                8230
#>  3 AAACAATCTACTAGCA-1 TRUE              3        43 section1                4170
#>  4 AAACACCAATAACTGC-1 TRUE             59        19 section1                2519
#>  5 AAACAGAGCGACTCCT-1 TRUE             14        94 section1                7679
#>  6 AAACAGCTTTCAGAAG-1 FALSE            43         9 section1                1831
#>  7 AAACAGGGTCTATATT-1 FALSE            47        13 section1                2106
#>  8 AAACAGTGTTCCTGGG-1 FALSE            73        43 section1                4170
#>  9 AAACATGGTGAGAGGA-1 FALSE            62         0 section1                1212
#> 10 AAACATTTCCCGGATT-1 FALSE            61        97 section1                7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>
#> 
#> rd10xV> # tabulate number of spots mapped to tissue
#> rd10xV> cd <- colData(spe)
#> 
#> rd10xV> table(
#> rd10xV+   in_tissue = cd$in_tissue, 
#> rd10xV+   sample_id = cd$sample_id)
#>          sample_id
#> in_tissue section1 section2
#>     FALSE       28       27
#>     TRUE        22       22
#> 
#> rd10xV> # view available images
#> rd10xV> imgData(spe)
#> DataFrame with 2 rows and 4 columns
#>     sample_id    image_id   data scaleFactor
#>   <character> <character> <list>   <numeric>
#> 1    section1      lowres   ####   0.0510334
#> 2    section2      lowres   ####   0.0510334
spe |>
    pull(in_tissue)
#>  [1] FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> [13] FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE
#> [25] FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
#> [37]  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
#> [49]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#> [61] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
#> [73] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE
#> [85] FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE
#> [97]  TRUE  TRUE FALSE