bigglm() gtsummary example, step by step

bigglm.R

library(gtsummary)
library(broom.helpers)
library(tidyverse)
packageVersion("gtsummary")
#> [1] '1.6.0'

# build model
mod <- biglm::bigglm(response ~ age + trt, data = trial, family = binomial()) 

# build a fancy tidy data frame one step at a time
# remove any steps you don't need OR ones that are too much computation
mod_tidied <-
  mod %>%
  tidy_and_attach(tidy_fun = tidy_parameters, conf.int = TRUE, exponentiate = TRUE) %>%
  tidy_identify_variables() %>%
  tidy_add_contrasts() %>%
  tidy_add_reference_rows() %>% # this does not work for bigglm(), filed an issue to get support
  tidy_add_estimate_to_reference_rows() %>%
  tidy_add_variable_labels() %>%
  tidy_add_term_labels() %>%
  tidy_add_header_rows() %>%
  tidy_add_n() %>%
  tidy_remove_intercept() %>%
  tidy_select_variables() %>%
  tidy_add_coefficients_type() %>%
  tidy_detach_model()

# convert tidy data frame to a gtsummary object
# all the styling needs to be added manually
tbl <-
  mod_tidied %>%
  # add a column that is used in gtsummary
  mutate(row_type = ifelse(header_row, "label", "level")) %>%
  .create_gtsummary_object() %>%
  modify_cols_merge(pattern = "{conf.low}, {conf.high}", rows = !is.na(conf.low)) %>%
  modify_header(
    label = "**Characteristic**", estimate = "**OR**", 
    conf.low = "**95% CI**", p.value = "**p-value**"
  ) %>%
  modify_fmt_fun(list(c(estimate, conf.low, conf.high) ~ style_ratio,
                      p.value ~ style_pvalue)) %>%
  modify_footnote(
    estimate = "OR = Odds Ratio",
    conf.low = "CI = Confidence Interval",
    abbreviation = TRUE
  )