count() lets you quickly count the unique values of one or more variables:
df %>% count(a, b) is roughly equivalent to
df %>% group_by(a, b) %>% summarise(n = n()).
count() is paired with tally(), a lower-level helper that is equivalent
to df %>% summarise(n = n()). Supply wt to perform weighted counts,
switching the summary from n = n() to n = sum(wt).
add_count() and add_tally() are equivalents to count() and tally()
but use mutate() instead of summarise() so that they add a new column
with group-wise counts.
# S3 method for class 'SpatialExperiment'
add_count(x, ..., wt = NULL, sort = FALSE, name = NULL)A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).
<data-masking> Variables to group
by.
<data-masking> Frequency weights.
Can be NULL or a variable:
If NULL (the default), counts the number of rows in each group.
If a variable, computes sum(wt) for each group.
If TRUE, will show the largest groups at the top.
The name of the new column in the output.
If omitted, it will default to n. If there's already a column called n,
it will use nn. If there's a column called n and nn, it'll use
nnn, and so on, adding ns until it gets a new name.
An object of the same type as .data. count() and add_count()
group transiently, so the output has the same groups as the input.
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 |>
count()
#> tidySingleCellExperiment says: A data frame is returned for independent data analysis.
#> # A tibble: 1 × 1
#> n
#> <int>
#> 1 99
spe |>
add_count()
#> # A SpatialExperiment-tibble abstraction: 99 × 8
#> # Features = 50 | Cells = 99 | Assays = counts
#> .cell in_tissue array_row array_col sample_id n pxl_col_in_fullres
#> <chr> <lgl> <int> <int> <chr> <int> <int>
#> 1 AAACAACGAAT… FALSE 0 16 section1 99 2312
#> 2 AAACAAGTATC… TRUE 50 102 section1 99 8230
#> 3 AAACAATCTAC… TRUE 3 43 section1 99 4170
#> 4 AAACACCAATA… TRUE 59 19 section1 99 2519
#> 5 AAACAGAGCGA… TRUE 14 94 section1 99 7679
#> 6 AAACAGCTTTC… FALSE 43 9 section1 99 1831
#> 7 AAACAGGGTCT… FALSE 47 13 section1 99 2106
#> 8 AAACAGTGTTC… FALSE 73 43 section1 99 4170
#> 9 AAACATGGTGA… FALSE 62 0 section1 99 1212
#> 10 AAACATTTCCC… FALSE 61 97 section1 99 7886
#> # ℹ 89 more rows
#> # ℹ 1 more variable: pxl_row_in_fullres <int>