glimpse() is like a transposed version of print(): columns run down the page, and data runs across. This makes it possible to see every column in a data frame. It's a little like str() applied to a data frame but it tries to show you as much data as possible. (And it always shows the underlying data, even when applied to a remote data source.)

See format_glimpse() for details on the formatting.

Value

x original x is (invisibly) returned, allowing glimpse() to be used within a data pipe line.

S3 methods

glimpse is an S3 generic with a customised method for tbls and data.frames, and a default method that calls str().

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 |>
    glimpse()
#> Formal class 'SpatialExperiment' [package "SpatialExperiment"] with 9 slots
#>   ..@ int_elementMetadata:Formal class 'DFrame' [package "S4Vectors"] with 6 slots
#>   ..@ int_colData        :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
#>   ..@ int_metadata       :List of 5
#>   .. ..$ version         :Classes 'package_version', 'numeric_version'  hidden list of 1
#>   .. ..$ spatialDataNames: chr(0) 
#>   .. ..$ version         :Classes 'package_version', 'numeric_version'  hidden list of 1
#>   .. ..$ spatialDataNames: chr(0) 
#>   .. ..$ imgData         :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
#>   ..@ rowRanges          :Formal class 'CompressedGRangesList' [package "GenomicRanges"] with 5 slots
#>   ..@ colData            :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
#>   ..@ assays             :Formal class 'SimpleAssays' [package "SummarizedExperiment"] with 1 slot
#>   ..@ NAMES              : NULL
#>   ..@ elementMetadata    :Formal class 'DFrame' [package "S4Vectors"] with 6 slots
#>   ..@ metadata           : list()