inst/doc/tbl_regression.R

In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  comment = "#>"
)

gt_compact_fun <- function(x) {
  gt::tab_options(x,
    table.font.size = "small",
    data_row.padding = gt::px(1),
    summary_row.padding = gt::px(1),
    grand_summary_row.padding = gt::px(1),
    footnotes.padding = gt::px(1),
    source_notes.padding = gt::px(1),
    row_group.padding = gt::px(1)
  )
}

# exit if car package not installed (added to pass Cmd Checks on old R versions)
if (!requireNamespace("car")) knitr::knit_exit()

## ---- echo = FALSE, results = 'asis'------------------------------------------
# we do NOT want the vignette to build on CRAN...it's taking too long
if (!identical(Sys.getenv("IN_PKGDOWN"), "true") &&
  !tolower(as.list(Sys.info())$user) %in% c("sjobergd", "currym", "whitingk", "whiting")) {
  msg <-
    paste(
      "View this vignette on the",
      "[package website](https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html)."
    )
  cat(msg)
  knitr::knit_exit()
}

## ----message = FALSE, warning=FALSE-------------------------------------------
# install.packages("gtsummary")
library(gtsummary)

## ---- echo = FALSE------------------------------------------------------------
trial %>%
  purrr::imap_dfr(
    ~ tibble::tibble(
      Variable = glue::glue("`{.y}`"),
      Class = class(.x),
      Label = attr(.x, "label")
    )
  ) %>%
  gt::gt() %>%
  gt::tab_source_note("Includes mix of continuous, dichotomous, and categorical variables") %>%
  gt::fmt_markdown(columns = c(Variable)) %>%
  gt::cols_align("left") %>%
  gt_compact_fun()

## ---- message=FALSE-----------------------------------------------------------
# build logistic regression model
m1 <- glm(response ~ age + stage, trial, family = binomial)

# view raw model results
summary(m1)$coefficients

## ---- message=FALSE-----------------------------------------------------------
tbl_regression(m1, exponentiate = TRUE)

## ---- echo = FALSE------------------------------------------------------------
tibble::tribble(
  ~Argument, ~Description,
  "`label=`", "modify variable labels in table",
  "`exponentiate=`", "exponentiate model coefficients",
  "`include=`", "names of variables to include in output. Default is all variables",
  "`show_single_row=`", "By default, categorical variables are printed on multiple rows. If a variable is dichotomous and you wish to print the regression coefficient on a single row, include the variable name(s) here.",
  "`conf.level=`", "confidence level of confidence interval",
  "`intercept=`", "indicates whether to include the intercept",
  "`estimate_fun=`", "function to round and format coefficient estimates",
  "`pvalue_fun=`", "function to round and format p-values",
  "`tidy_fun=`", "function to specify/customize tidier function"
) %>%
  gt::gt() %>%
  gt::fmt_markdown(columns = c(Argument)) %>%
  gt_compact_fun()

## ----echo = FALSE-------------------------------------------------------------
tibble::tribble(
  ~Function, ~Description,
  "`add_global_p()`", "adds the global p-value for a categorical variables",
  "`add_glance_source_note()`", "adds statistics from `broom::glance()` as source note",
  "`add_vif()`", "adds column of the variance inflation factors (VIF)",
  "`add_q()`", "add a column of q values to control for multiple comparisons"
) %>%
  gt::gt() %>%
  gt::fmt_markdown(columns = c(Function)) %>%
  gt_compact_fun()

## ----echo = FALSE-------------------------------------------------------------
tibble::tribble(
  ~Function,                     ~Description,
  "`modify_header()`",           "update column headers",
  "`modify_footnote()`",         "update column footnote",
  "`modify_spanning_header()`",  "update spanning headers",
  "`modify_caption()`",          "update table caption/title",
  "`bold_labels()`",             "bold variable labels",
  "`bold_levels()`",             "bold variable levels",
  "`italicize_labels()`",        "italicize variable labels",
  "`italicize_levels()`",        "italicize variable levels",
  "`bold_p()`",                  "bold significant p-values"
) %>%
  gt::gt() %>%
  gt::fmt_markdown(columns = c(Function)) %>%
  gt_compact_fun()

## -----------------------------------------------------------------------------
m1 %>%
  tbl_regression(exponentiate = TRUE) %>%
  as_gt() %>%
  gt::tab_source_note(gt::md("*This data is simulated*"))

## ---- eval=TRUE---------------------------------------------------------------
# format results into data frame with global p-values
m1 %>%
  tbl_regression(
    exponentiate = TRUE,
    pvalue_fun = ~ style_pvalue(.x, digits = 2),
  ) %>%
  add_global_p() %>%
  bold_p(t = 0.10) %>%
  bold_labels() %>%
  italicize_levels()

## ----tbl_uvregression---------------------------------------------------------
trial %>%
  select(response, age, grade) %>%
  tbl_uvregression(
    method = glm,
    y = response,
    method.args = list(family = binomial),
    exponentiate = TRUE,
    pvalue_fun = ~ style_pvalue(.x, digits = 2)
  ) %>%
  add_global_p() %>% # add global p-value
  add_nevent() %>% # add number of events of the outcome
  add_q() %>% # adjusts global p-values for multiple testing
  bold_p() %>% # bold p-values under a given threshold (default 0.05)
  bold_p(t = 0.10, q = TRUE) %>% # now bold q-values under the threshold of 0.10
  bold_labels()

## ----supported_models, echo = FALSE-------------------------------------------
broom.helpers::supported_models %>%
  gt::gt() %>%
  gt::cols_label(model = gt::md("Model"), notes = gt::md("Details")) %>%
  gt::fmt_markdown(columns = everything()) %>%
  gt::tab_options(
    table.font.size = 11, data_row.padding = gt::px(1),
    summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
    footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
    row_group.padding = gt::px(1)
  )