as_tibble() turns an existing object, such as a data frame or
matrix, into a so-called tibble, a data frame with class tbl_df. This is
in contrast with tibble(), which builds a tibble from individual columns.
as_tibble() is to tibble() as base::as.data.frame() is to
base::data.frame().
as_tibble() is an S3 generic, with methods for:
data.frame: Thin wrapper around the list method
that implements tibble's treatment of rownames.
Default: Other inputs are first coerced with base::as.data.frame().
as_tibble_row() converts a vector to a tibble with one row.
If the input is a list, all elements must have size one.
as_tibble_col() converts a vector to a tibble with one column.
# S3 method for class 'SpatialExperiment'
as_tibble(
x,
...,
.name_repair = c("check_unique", "unique", "universal", "minimal"),
rownames = pkgconfig::get_config("tibble::rownames", NULL)
)A data frame, list, matrix, or other object that could reasonably be coerced to a tibble.
Unused, for extensibility.
Treatment of problematic column names:
"minimal": No name repair or checks, beyond basic existence,
"unique": Make sure names are unique and not empty,
"check_unique": (default value), no name repair, but check they are
unique,
"universal": Make the names unique and syntactic
a function: apply custom name repair (e.g., .name_repair = make.names
for names in the style of base R).
A purrr-style anonymous function, see rlang::as_function()
This argument is passed on as repair to vctrs::vec_as_names().
See there for more details on these terms and the strategies used
to enforce them.
How to treat existing row names of a data frame or matrix:
NULL: remove row names. This is the default.
NA: keep row names.
A string: the name of a new column. Existing rownames are transferred
into this column and the row.names attribute is deleted.
No name repair is applied to the new column name, even if x already contains
a column of that name.
Use as_tibble(rownames_to_column(...)) to safeguard against this case.
Read more in rownames.
tibble
The default behavior is to silently remove row names.
New code should explicitly convert row names to a new column using the
rownames argument.
For existing code that relies on the retention of row names, call
pkgconfig::set_config("tibble::rownames" = NA) in your script or in your
package's .onLoad() function.
Using as_tibble() for vectors is superseded as of version 3.0.0,
prefer the more expressive as_tibble_row() and
as_tibble_col() variants for new code.
tibble() constructs a tibble from individual columns. enframe()
converts a named vector to a tibble with a column of names and column of
values. Name repair is implemented using vctrs::vec_as_names().
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 |>
as_tibble()
#> # A tibble: 99 × 7
#> .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>