rowwise() allows you to compute on a data frame a row-at-a-time.
This is most useful when a vectorised function doesn't exist.
Most dplyr verbs preserve row-wise grouping. The exception is summarise(),
which return a grouped_df. You can explicitly ungroup with ungroup()
or as_tibble(), or convert to a grouped_df with group_by().
A row-wise data frame with class rowwise_df. Note that a
rowwise_df is implicitly grouped by row, but is not a grouped_df.
Because a rowwise has exactly one row per group it offers a small
convenience for working with list-columns. Normally, summarise() and
mutate() extract a groups worth of data with [. But when you index
a list in this way, you get back another list. When you're working with
a rowwise tibble, then dplyr will use [[ instead of [ to make your
life a little easier.
nest_by() for a convenient way of creating rowwise data frames
with nested data.
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 |>
rowwise()
#> tidySingleCellExperiment says: A data frame is returned for independent data analysis.
#> # A tibble: 99 × 7
#> # Rowwise:
#> .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>