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Convert an ltsummary table to a data frame

Source: R/as_lt.R
as.data.frame.ltsummary.Rd

Returns the formatted table as a plain data frame, the counterpart of gtsummary::as_tibble(). Formatting functions, merged columns and hidden columns are applied; bold and italic cells are wrapped in markdown markers (__bold__, _italic_); and the column names are the header labels.

Usage

# S3 method for class 'ltsummary'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  ...,
  col_labels = TRUE,
  fmt_missing = FALSE
)

Arguments

x

(ltsummary)
An ltsummary object.

row.names, optional

Not used.

...

Not used.

col_labels

(scalar logical)
Whether to name the columns with their header labels. Default is TRUE; FALSE keeps the column names of x$table_body.

fmt_missing

(scalar logical)
Whether to replace missing values with the symbols set by modify_missing_symbol(). Default is FALSE, which leaves them NA.

Value

A data frame.

See also

Other output: as_lt(), inline_text(), print.ltsummary()

Examples

tbl <- trial |>
  tbl_summary(by = trt, include = c(age, grade, response)) |>
  bold_labels()

# Example 1 ----------------------------------
as.data.frame(tbl)
#>   **Characteristic** **Drug A**  \nN = 95 **Drug B**  \nN = 105
#> 1            __Age__          59 (51, 66)           62 (54, 71)
#> 2            Unknown                    4                     7
#> 3          __Grade__                 <NA>                  <NA>
#> 4                  I             25 (26%)              37 (35%)
#> 5                 II             40 (42%)              43 (41%)
#> 6                III             30 (32%)              25 (24%)
#> 7 __Tumor Response__             22 (24%)              24 (24%)
#> 8            Unknown                    2                     5

# Example 2 ----------------------------------
# without the header labels, and with the missing-value symbols
tbl |>
  modify_missing_symbol("n/a", columns = all_stat_cols(), rows = row_type == "label") |>
  as.data.frame(col_labels = FALSE, fmt_missing = TRUE)
#>                label      stat_1      stat_2
#> 1            __Age__ 59 (51, 66) 62 (54, 71)
#> 2            Unknown           4           7
#> 3          __Grade__         n/a         n/a
#> 4                  I    25 (26%)    37 (35%)
#> 5                 II    40 (42%)    43 (41%)
#> 6                III    30 (32%)    25 (24%)
#> 7 __Tumor Response__    22 (24%)    24 (24%)
#> 8            Unknown           2           5