Add header rows variables with several terms

For variables with several terms (usually categorical variables but could also be the case of continuous variables with polynomial terms or splines), tidy_add_header_rows() will add an additional row per variable, where label will be equal to var_label. These additional rows could be identified with header_row column.

Usage

tidy_add_header_rows(
  x,
  show_single_row = NULL,
  model = tidy_get_model(x),
  quiet = FALSE,
  strict = FALSE
)

Arguments

x

(data.frame)
A tidy tibble as produced by tidy_*() functions.

show_single_row

(tidy-select)
Names of dichotomous variables that should be displayed on a single row. See also all_dichotomous().

model

(a model object, e.g. glm)
The corresponding model, if not attached to x.

quiet

(logical)
Whether broom.helpers should not return a message when requested output cannot be generated. Default is FALSE.

strict

(logical)
Whether broom.helpers should return an error when requested output cannot be generated. Default is FALSE.

Details

The show_single_row argument allows to specify a list of dichotomous variables that should be displayed on a single row instead of two rows.

The added header_row column will be equal to:

• TRUE for an header row;

• FALSE for a normal row of a variable with an header row;

• NA for variables without an header row.

If the label column is not yet available in x, tidy_add_term_labels() will be automatically applied.

See also

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_rows(), tidy_add_n(), tidy_add_pairwise_contrasts(), tidy_add_reference_rows(), tidy_add_term_labels(), tidy_add_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

if (FALSE) { # interactive()
if (.assert_package("gtsummary", boolean = TRUE)) {
  df <- Titanic |>
    dplyr::as_tibble() |>
    dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

  res <-
    glm(
      Survived ~ Class + Age + Sex,
      data = df, weights = df$n, family = binomial,
      contrasts = list(Age = contr.sum, Class = "contr.SAS")
    ) |>
    tidy_and_attach() |>
    tidy_add_variable_labels(labels = list(Class = "Custom label for Class")) |>
    tidy_add_reference_rows()
  res |> tidy_add_header_rows()
  res |> tidy_add_header_rows(show_single_row = all_dichotomous())

  glm(
    response ~ stage + grade * trt,
    gtsummary::trial,
    family = binomial,
    contrasts = list(
      stage = contr.treatment(4, base = 3),
      grade = contr.treatment(3, base = 2),
      trt = contr.treatment(2, base = 2)
    )
  ) |>
    tidy_and_attach() |>
    tidy_add_reference_rows() |>
    tidy_add_header_rows()
}
}