## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  rows.print = 25
)

# one of the functions below needs emmeans, so dont evaluate code check in vignette
# on old R versions where emmeans is not available
if (!rlang::is_installed("emmeans")) {
  knitr::opts_chunk$set(eval = FALSE)
}

## ----setup, warning=FALSE, message=FALSE--------------------------------------
library(broom.helpers)
library(gtsummary)
library(ggplot2)
library(dplyr)

# paged_table() was introduced only in rmarkdwon v1.2
print_table <- function(tab) {
  if (packageVersion("rmarkdown") >= "1.2") {
    rmarkdown::paged_table(tab)
  } else {
    knitr::kable(tab)
  }
}

## -----------------------------------------------------------------------------
model_logit <- glm(response ~ trt + grade, trial, family = binomial)
broom::tidy(model_logit)

## -----------------------------------------------------------------------------
tidy_forest <-
  model_logit |>
  # perform initial tidying of the model
  tidy_and_attach(exponentiate = TRUE, conf.int = TRUE) |>
  # adding in the reference row for categorical variables
  tidy_add_reference_rows() |>
  # adding a reference value to appear in plot
  tidy_add_estimate_to_reference_rows() |>
  # adding the variable labels
  tidy_add_term_labels() |>
  # removing intercept estimate from model
  tidy_remove_intercept()
tidy_forest

## ----warning=FALSE------------------------------------------------------------
tidy_forest |>
  mutate(
    plot_label = paste(var_label, label, sep = ":") |>
      forcats::fct_inorder() |>
      forcats::fct_rev()
  ) |>
  ggplot(aes(x = plot_label, y = estimate, ymin = conf.low, ymax = conf.high, color = variable)) +
  geom_hline(yintercept = 1, linetype = 2) +
  geom_pointrange() +
  coord_flip() +
  theme(legend.position = "none") +
  labs(
    y = "Odds Ratio",
    x = " ",
    title = "Forest Plot using broom.helpers"
  )

## -----------------------------------------------------------------------------
tidy_table <-
  model_logit |>
  # perform initial tidying of the model
  tidy_and_attach(exponentiate = TRUE, conf.int = TRUE) |>
  # adding in the reference row for categorical variables
  tidy_add_reference_rows() |>
  # adding the variable labels
  tidy_add_term_labels() |>
  # add header row
  tidy_add_header_rows() |>
  # removing intercept estimate from model
  tidy_remove_intercept()

# print summary table
options(knitr.kable.NA = "")
tidy_table |>
  # format model estimates
  select(label, estimate, conf.low, conf.high, p.value) |>
  mutate(across(all_of(c("estimate", "conf.low", "conf.high")), style_ratio)) |>
  mutate(across(p.value, style_pvalue)) |>
  print_table()

## -----------------------------------------------------------------------------
model_logit |>
  tidy_plus_plus(exponentiate = TRUE)

## -----------------------------------------------------------------------------
model_logit |>
  tidy_plus_plus(exponentiate = TRUE) |>
  print_table()

## -----------------------------------------------------------------------------
model_poly <- glm(response ~ poly(age, 3) + ttdeath, na.omit(trial), family = binomial)

model_poly |>
  tidy_plus_plus(
    exponentiate = TRUE,
    add_header_rows = TRUE,
    variable_labels = c(age = "Age in years")
  ) |>
  print_table()

## -----------------------------------------------------------------------------
model_1 <- glm(
  response ~ stage + grade * trt,
  gtsummary::trial,
  family = binomial
)

model_1 |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_reference_rows() |>
  tidy_add_estimate_to_reference_rows(exponentiate = TRUE) |>
  tidy_add_term_labels() |>
  print_table()

## -----------------------------------------------------------------------------
model_2 <- 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)
  )
)

model_2 |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_reference_rows() |>
  tidy_add_estimate_to_reference_rows(exponentiate = TRUE) |>
  tidy_add_term_labels() |>
  print_table()

## -----------------------------------------------------------------------------
model_3 <- glm(
  response ~ stage + grade * trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(
    stage = contr.sum,
    grade = contr.sum,
    trt = contr.sum
  )
)

model_3 |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_reference_rows() |>
  tidy_add_estimate_to_reference_rows(exponentiate = TRUE) |>
  tidy_add_term_labels() |>
  print_table()

## -----------------------------------------------------------------------------
model_4 <- glm(
  response ~ stage + grade * trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(
    stage = contr.poly,
    grade = contr.helmert,
    trt = contr.poly
  )
)

model_4 |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_reference_rows() |>
  tidy_add_estimate_to_reference_rows(exponentiate = TRUE) |>
  tidy_add_term_labels() |>
  print_table()

## -----------------------------------------------------------------------------
model_logit <- glm(response ~ age + trt + grade, trial, family = binomial)

model_logit |>
  tidy_and_attach() |>
  tidy_add_pairwise_contrasts() |>
  print_table()

model_logit |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_pairwise_contrasts() |>
  print_table()

model_logit |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_pairwise_contrasts(pairwise_reverse = FALSE) |>
  print_table()

model_logit |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_pairwise_contrasts(keep_model_terms = TRUE) |>
  print_table()

## ----echo=FALSE---------------------------------------------------------------
# nolint start
tibble::tribble(
  ~Column, ~Function, ~Description,
  "original_term", "`tidy_disambiguate_terms()`, `tidy_multgee()` or `tidy_zeroinfl()`", "Original term before disambiguation. This columns is added only when disambiguation is needed (i.e. for mixed models). Also used for \"multgee\", \"zeroinfl\" and \"hurdle\" models.",
  "variable", "`tidy_identify_variables()`", "String of variable names from the model. For categorical variables and polynomial terms defined with `stats::poly()`, terms belonging to the variable are identified.",
  "var_class", "`tidy_identify_variables()`", "Class of the variable.",
  "var_type", "`tidy_identify_variables()`", "One of \"intercept\", \"continuous\", \"dichotomous\", \"categorical\", \"interaction\", \"ran_pars\" or \"ran_vals\"",
  "var_nlevels", "`tidy_identify_variables()`", "Number of original levels for categorical variables",
  "contrasts", "`tidy_add_contrasts()`", "Contrasts used for categorical variables.<br /><em>Require \"variable\" column. If needed, will automatically apply `tidy_identify_variables()`.</em>",
  "contrasts_type", "`tidy_add_contrasts()`", "Type of contrasts (\"treatment\", \"sum\", \"poly\", \"helmert\", \"sdif\", \"other\" or \"no.contrast\"). \"pairwise\ is used for pairwise contrasts computed with `tidy_add_pairwise_contrasts()`.",
  "reference_row", "`tidy_add_reference_rows()`", "Logical indicating if a row is a reference row for categorical variables using a treatment or a sum contrast. Is equal to `NA` for variables who do not have a reference row.<br /><em>Require \"contrasts\" column. If needed, will automatically apply `tidy_add_contrasts()`.<br />`tidy_add_reference_rows()` will not populate the label of the reference term. It is therefore better to apply `tidy_add_term_labels()` after `tidy_add_reference_rows()` rather than before.</em>",
  "var_label", "`tidy_add_variable_labels()`", "String of variable labels from the model. Columns labelled with the `labelled` package are retained. It is possible to pass a custom label for an interaction term with the `labels` argument. <br /><em>Require \"variable\" column. If needed, will automatically apply `tidy_identify_variables()`.",
  "label", "`tidy_add_term_labels()`", "String of term labels based on (1) labels provided in `labels` argument if provided; (2) factor levels for categorical variables coded with treatment, SAS or sum contrasts; (3) variable labels when there is only one term per variable; and (4) term name otherwise.<br /><em>Require \"variable_label\" column. If needed, will automatically apply `tidy_add_variable_labels()`.<br />Require \"contrasts\" column. If needed, will automatically apply `tidy_add_contrasts()`.</em>",
  "header_row", "`tidy_add_header_rows()`", "Logical indicating if a row is a header row for variables with several terms. Is equal to `NA` for variables who do not have an header row.</br><em>Require \"label\" column. If needed, will automatically apply `tidy_add_term_labels()`.<br />It is better to apply `tidy_add_header_rows()` after other `tidy_*` functions</em>",
  "n_obs", "`tidy_add_n()`", "Number of observations",
  "n_ind", "`tidy_add_n()`", "Number of individuals (for Cox models)",
  "n_event", "`tidy_add_n()`", "Number of events (for binomial and multinomial logistic models, Poisson and Cox models)",
  "exposure", "`tidy_add_n()`", "Exposure time (for Poisson and Cox models)"
) |>
  gt::gt() |>
  gt::fmt_markdown(columns = everything()) |>
  gt::tab_options(
    column_labels.font.weight = "bold"
  ) |>
  gt::opt_row_striping() |>
  gt::tab_style("vertical-align:top; font-size: 12px;", gt::cells_body())
# nolint end

## ----echo=FALSE---------------------------------------------------------------
tibble::tribble(
  ~Attribute, ~Function, ~Description,
  "exponentiate", "`tidy_and_attach()`", "Indicates if estimates were exponentiated",
  "conf.level", "`tidy_and_attach()`", "Level of confidence used for confidence intervals",
  "coefficients_type", "`tidy_add_coefficients_type()`", "Type of coefficients",
  "coefficients_label", "`tidy_add_coefficients_type()`", "Coefficients label",
  "variable_labels", "`tidy_add_variable_labels()`",
  "Custom variable labels passed to `tidy_add_variable_labels()`",
  "term_labels", "`tidy_add_term_labels()`",
  "Custom term labels passed to `tidy_add_term_labels()`",
  "N_obs", "`tidy_add_n()`", "Total number of observations",
  "N_event", "`tidy_add_n()`", "Total number of events",
  "N_ind", "`tidy_add_n()`", "Total number of individuals (for Cox models)",
  "Exposure", "`tidy_add_n()`", "Total of exposure time",
  "component", "`tidy_zeroinfl()`", "`component` argument passed to `tidy_zeroinfl()`"
) |>
  gt::gt() |>
  gt::fmt_markdown(columns = everything()) |>
  gt::tab_options(column_labels.font.weight = "bold") |>
  gt::opt_row_striping() |>
  gt::tab_style("vertical-align:top; font-size: 12px;", gt::cells_body())

## ----echo=FALSE---------------------------------------------------------------
supported_models |>
  dplyr::rename_with(stringr::str_to_title) |>
  gt::gt() |>
  gt::fmt_markdown(columns = everything()) |>
  gt::tab_options(column_labels.font.weight = "bold") |>
  gt::opt_row_striping() |>
  gt::tab_style("vertical-align:top; font-size: 12px;", gt::cells_body())