Allows customization of labels, headers, and layout of regression tables created using `gtsummary`. Designed for tables from functions like `uni_reg()`, `multi_reg()`, etc.
Usage
modify_table(
gt_table,
variable_labels = NULL,
level_labels = NULL,
header_labels = NULL,
caption = NULL,
bold_labels = FALSE,
bold_levels = FALSE,
remove_N = FALSE,
remove_N_obs = FALSE,
remove_abbreviations = FALSE,
caveat = NULL
)Arguments
- gt_table
A `gtsummary` table object.
- variable_labels
A named vector for relabeling variable names.
- level_labels
A named list for relabeling levels of variables. Should be structured as `list(var1 = c(old1 = new1, old2 = new2), ...)`.
- header_labels
A named vector for relabeling column headers. Names should match internal column names (e.g., `"estimate"`, `"p.value"`).
- caption
A character string used to set the table title.
- bold_labels
Logical. If `TRUE`, bolds variable labels.
- bold_levels
Logical. If `TRUE`, bolds factor level labels.
- remove_N
Logical. If `TRUE`, hides the `N` column in univariate regression tables (`uni_reg`, `uni_reg_nbin`). Ignored for other tables.
- remove_N_obs
Logical. If `TRUE`, removes the source note showing the no of observations in multivariable models (`multi_reg`, `multi_reg_nbin`).
- remove_abbreviations
Logical. If `TRUE`, removes default footnotes for estimate abbreviations.
- caveat
A character string to add as a footnote (source note) below the table, e.g., "N may vary due to missing data."
Examples
# \donttest{
if (requireNamespace("mlbench", quietly = TRUE)) {
data("PimaIndiansDiabetes2", package = "mlbench")
library(dplyr)
library(gtregression)
# Prepare data
pima <- PimaIndiansDiabetes2 |>
mutate(
diabetes = ifelse(diabetes == "pos", 1, 0),
bmi_cat = cut(
mass,
breaks = c(-Inf, 18.5, 24.9, 29.9, Inf),
labels = c("Underweight", "Normal", "Overweight", "Obese")
)
)
# Descriptive table
desc_tbl <- descriptive_table(pima,
exposures = c("age", "bmi_cat"),
by = "diabetes")
# Univariate logistic regression
uni_rr <- uni_reg(
data = pima,
outcome = "diabetes",
exposures = c("age", "bmi_cat"),
approach = "logit"
)
}
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: collapsing to unique 'x' values
# }