Extract the table body from a gt object

We can extract the body of a gt table, even at various stages of its rendering, from a gt_tbl object using extract_body(). By default, the data frame returned will have gone through all of the build stages but we can intercept the table body after a certain build stage. Here are the eight different build stages and some notes about each:

"init": the body table is initialized here, entirely with NA values. It's important to note that all columns of the are of the character type in this first stage. And all columns remain in the same order as the input data table.
"fmt_applied": Any cell values that have had formatting applied to them are migrated to the body table. All other cells remain as NA values. Depending on the output type, the formatting may also be different.
"sub_applied": Any cell values that have had substitution functions applied to them (whether or not they were previously formatted) are migrated to the body table or modified in place (if formatted). All cells that had neither been formatted nor undergone substitution remain as NA values.
"unfmt_included": All cells that either didn't have any formatting or any substitution operations applied are migrated to the body table. NA values now become the string "NA", so, there aren't any true missing values in this body table.
"cols_merged": The result of column-merging operations (through cols_merge() and related functions) is materialized here. Columns that were asked to be hidden will be present here (i.e., hiding columns doesn't remove them from the body table).
"body_reassembled": Though columns do not move positions rows can move to different positions, and this is usually due to migration to different row groups. At this stage, rows will be in the finalized order that is seen in the associated display table.
"text_transformed": Various text_*() functions in gt can operate on body cells (now fully formatted at this stage) and return transformed character values. After this stage, the effects of those functions are apparent.
"footnotes_attached": Footnote marks are attached to body cell values (either on the left or right of the content). This stage performs said attachment.

Usage

extract_body(
  data,
  build_stage = NULL,
  incl_hidden_cols = FALSE,
  incl_stub_cols = TRUE,
  ...,
  output = c("html", "latex", "rtf", "word", "grid")
)

Arguments

data

The gt table data object

obj:<gt_tbl> // required

This is the gt table object that is commonly created through use of the gt() function.

build_stage

The build stage of the formatted R data frame

scalar<character> // default: NULL (optional)

When a gt undergoes rendering, the body of the table proceeds through several build stages. Providing a single stage name will yield a data frame that has been extracted after completed that stage. Here are the build stages in order: (1) "init", (2) "fmt_applied", (3) "sub_applied", (4) "unfmt_included", (5) "cols_merged", (6) "body_reassembled", (7) "text_transformed", and (8) "footnotes_attached". If not supplying a value for build_stage then the entire build for the table body (i.e., up to and including the "footnotes_attached" stage) will be performed before returning the data frame.

incl_hidden_cols

Should hidden columns be included?

scalar<logical> // default: FALSE

Certain columns may be hidden from final display via cols_hide(). By default, those columns won't be part of the extracted data frame. However, we can choose to include them by using incl_hidden_cols = TRUE.

incl_stub_cols

Should stub columns be included?

scalar<logical> // default: TRUE

Any stub columns in the gt object (which may consist of a grouping column and a column for row labels) are included in the extracted data for clarity but clearly marked with the names "::group_id::" and "::rowname::". We can exclude them by setting incl_stub_cols = FALSE.

...

These dots are for future extensions and must be empty.

output

Output format

singl-kw:[html|latex|rtf|word] // default: "html"

The output format of the resulting data frame. This can either be "html" (the default), "latex", "rtf", or "word".

Value

A data frame or tibble object containing the table body.

Examples

Use a modified version of sp500 the dataset to create a gt table with row groups and row labels. Formatting will be applied to the date- and currency-based columns.

gt_tbl <-
  sp500 |>
  dplyr::filter(date >= "2015-01-05" & date <= "2015-01-16") |>
  dplyr::arrange(date) |>
  dplyr::mutate(week = paste0("W", strftime(date, format = "%V"))) |>
  dplyr::select(-adj_close, -volume) |>
  gt(
    rowname_col = "date",
    groupname_col = "week"
  ) |>
  fmt_date(columns = date, date_style = "day_month_year") |>
  fmt_currency(columns = c(open, high, low, close)) |>
  cols_hide(columns = c(high, low))

gt_tbl

This image of a table was generated from the first code example in the `extract_body()` help file.

Using extract_body() on the gt object (gt_tbl) will provide us with a tibble that contains the fully built data cells for the output context (in this case, "html").

extract_body(gt_tbl)
#> # A tibble: 10 x 4
#>    `::group_id::` `::rowname::`   open      close
#>    <chr>          <chr>           <chr>     <chr>
#>  1 W02            5 January 2015  $2,054.44 $2,020.58
#>  2 W02            6 January 2015  $2,022.15 $2,002.61
#>  3 W02            7 January 2015  $2,005.55 $2,025.90
#>  4 W02            8 January 2015  $2,030.61 $2,062.14
#>  5 W02            9 January 2015  $2,063.45 $2,044.81
#>  6 W03            12 January 2015 $2,046.13 $2,028.26
#>  7 W03            13 January 2015 $2,031.58 $2,023.03
#>  8 W03            14 January 2015 $2,018.40 $2,011.27
#>  9 W03            15 January 2015 $2,013.75 $1,992.67
#> 10 W03            16 January 2015 $1,992.25 $2,019.42

To provide us with a better frame of reference, the grouping and row label values are provided as the first columns in the returned output. We could suppress those in the output by setting incl_stub_cols = FALSE.

extract_body(gt_tbl, incl_stub_cols = FALSE)
#> # A tibble: 10 x 2
#>    open      close
#>    <chr>     <chr>
#>  1 $2,054.44 $2,020.58
#>  2 $2,022.15 $2,002.61
#>  3 $2,005.55 $2,025.90
#>  4 $2,030.61 $2,062.14
#>  5 $2,063.45 $2,044.81
#>  6 $2,046.13 $2,028.26
#>  7 $2,031.58 $2,023.03
#>  8 $2,018.40 $2,011.27
#>  9 $2,013.75 $1,992.67
#> 10 $1,992.25 $2,019.42

The high and low columns were hidden via cols_hide() and so they won't be shown in the returned data unless we use incl_hidden_cols = TRUE.

extract_body(
  gt_tbl,
  incl_stub_cols = FALSE,
  incl_hidden_cols = TRUE
)
#> # A tibble: 10 x 4
#>    open      high      low       close
#>    <chr>     <chr>     <chr>     <chr>
#>  1 $2,054.44 $2,054.44 $2,017.34 $2,020.58
#>  2 $2,022.15 $2,030.25 $1,992.44 $2,002.61
#>  3 $2,005.55 $2,029.61 $2,005.55 $2,025.90
#>  4 $2,030.61 $2,064.08 $2,030.61 $2,062.14
#>  5 $2,063.45 $2,064.43 $2,038.33 $2,044.81
#>  6 $2,046.13 $2,049.30 $2,022.58 $2,028.26
#>  7 $2,031.58 $2,056.93 $2,008.25 $2,023.03
#>  8 $2,018.40 $2,018.40 $1,988.44 $2,011.27
#>  9 $2,013.75 $2,021.35 $1,991.47 $1,992.67
#> 10 $1,992.25 $2,020.46 $1,988.12 $2,019.42

Function ID

13-7

Function Introduced

v0.10.0 (October 7, 2023)