erikerhardt/Summary_tables_example.qmd

## Summary_tables_example.qmd
---
title:        Tables using `gtsummary`
subtitle:     Example workflow for Table 1 (demographics), regression, contingency table, etc.
author:       Erik Erhardt
date:         last-modified # today, now, last-modified
date-format:  long          # full, long, medium, short, iso,   https://quarto.org/docs/reference/dates.html
format:
  html:
    theme:                  litera
    highlight-style:        atom-one
    toc:                    true
    toc-depth:              4
    toc-location:           body      # left, body, right
    number-sections:        true      # true, false
    number-depth:           3
    code-fold:              show      # true (initially hidden), false, show (initially shown)
    code-tools:                       # menu top-right to show/hide all code
      toggle:               true
      caption:              "Code"    # none
      source:               false
    code-overflow:          scroll    # scroll, wrap
    code-block-bg:          true
    code-block-border-left: "#30B0E0"
    df-print:               paged     # default, kable, tibble, paged   # https://quarto.org/docs/computations/r.html
    self-contained:         false     # !!! this can cause a render error: "ERROR: The process cannot access the file because it is being used by another process. (os error 32)"
fig-width:                  6
fig-height:                 4
execute: # https://quarto.org/docs/computations/execution-options.html, https://quarto.org/docs/computations/r.html
  cache:    false   # false, true
  eval:     true    # true, false  Evaluate the code chunk (if false, just echos the code into the output).
  echo:     true    # true, false  Include the source code in output
---

Other frequency tables https://cran.r-project.org/web/packages/janitor/vignettes/tabyls.html

<!---
# Erik's compile commands in R:
  fn_qmd <- "Summary_tables_example.qmd"
  setwd("D:/Dropbox/StatAcumen/consult/Rpackages/erikmisc/data-raw/tables")
  quarto::quarto_render(input = fn_qmd)
-->

```{r chunk-01, echo=FALSE}
options(width = 80)
#options(warn  = -1)
options(str   = strOptions(list.len = 1e3))
options(knitr.kable.NA = '') # Display NAs as blanks
my_seed <- 34567
set.seed(my_seed)
```

# Data

```{r}
library(erikmisc)
library(tidyverse)

# Other packages: huxtable, gt, smd
```

```{r}
dat_mtcars_e <-
  erikmisc::dat_mtcars_e

## ### Version for Max
## dat_mtcars_e <-
##   datasets::mtcars %>%
##   tibble::as_tibble(
##     rownames = "model"
##   ) %>%
##   dplyr::mutate(
##     cyl = cyl %>% factor(levels = c(4, 6, 8), labels = c("four", "six", "eight"))
##   , vs  = vs  %>% factor(levels = c(0, 1), labels = c("V-shaped", "straight"))
##   , am  = am  %>% factor(levels = c(0, 1), labels = c("automatic", "manual"))
##   )
##
## # Label columns
## dat_labels <-
##   tibble::tribble(
##     ~var    , ~label
##   , "model" , "Model"
##   , "mpg"   , "Miles/(US) gallon"
##   , "cyl"   , "Number of cylinders"
##   , "disp"  , "Displacement (cu.in.)"
##   , "hp"    , "Gross horsepower"
##   , "drat"  , "Rear axle ratio"
##   , "wt"    , "Weight (1000 lbs)"
##   , "qsec"  , "1/4 mile time"
##   , "vs"    , "Engine"                     # (0 = V-shaped, 1 = straight)"
##   , "am"    , "Transmission"               # (0 = automatic, 1 = manual)"
##   , "gear"  , "Number of forward gears"
##   , "carb"  , "Number of carburetors"
##   )
##
## for (i_row in 1:nrow(dat_labels)) {
##   labelled::var_label(dat_mtcars_e[[dat_labels[["var"]][i_row] ]]) <- dat_labels[["label"]][i_row]
## }
##
## str(dat_mtcars_e)


dat_mtcars_e %>%
  str()

# drastically change scale for two numeric variables
dat_mtcars_e <-
  dat_mtcars_e %>%
  dplyr::mutate(
    drat = drat * 1e3
  , qsec = qsec / 1e3
  )


## Variable lists
Var <- list()

Var[["list"]][["var_ID"]] <-
  c(
    "model"
  )

Var[["list"]][["var_demo"]] <-
  c(
    "mpg"
  , "cyl"
  , "disp"
  , "hp"
  , "drat"
  , "wt"
  , "qsec"
  , "vs"
  , "am"
  , "gear"
  , "carb"
  )

Var[["list"]][["var_groups"]] <-
  c(
    "cyl"
  , "vs"
  , "am"
  )
```

# Summary Tables

## Set theme (basic is best for export to Excel)

```{r}
library(gtsummary)

# core https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html
# output https://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html

# https://www.danieldsjoberg.com/gtsummary/articles/themes.html
# set themes for compact display
#gtsummary::theme_gtsummary_journal(journal = "jama") # Setting theme `JAMA`
#gtsummary::theme_gtsummary_compact()                 # Setting theme `Compact`
gtsummary::reset_gtsummary_theme()                   # Reset theme
```

## Basic table

```{r}
table1_out <-
  dat_mtcars_e %>%
  gtsummary::tbl_summary(
    by           = Var[["list"]][["var_groups"]][1] # use gtsummary::tbl_strata for more than 1
  # , label        = NULL                          # automatically uses the label attribute
  # , statistic    = NULL
  # , digits       = NULL
  # , type         = NULL
  # , value        = NULL
  # , missing      = NULL
  # , missing_text = NULL
  # , sort         = NULL
  # , percent      = NULL
  , include      = Var[["list"]][["var_demo"]]
  ) %>%
  gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups
  gtsummary::add_q()          %>%  # add a column of q values to control for multiple comparisons
  gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
  #gtsummary::add_difference() %>%  # add column for difference between two group, confidence interval, and p-value
  #gtsummary::add_ci()         %>%  # add confidence intervals
  gtsummary::add_overall()    %>%  # add a column with overall summary statistics
  gtsummary::add_stat_label() %>%  # add label for the summary statistics shown in each row
  gtsummary::add_n()          %>%  # add a column with N (or N missing) for each variable
  gtsummary::bold_labels()    %>%  # bold variable name labels
  gtsummary::italicize_levels() %>% # italicize levels
  gtsummary::modify_caption("Table title")

# print to Viewer
table1_out

# export to Excel
table1_out %>%
  gtsummary::as_hux_xlsx(file = "Table1_out.xlsx")
```


## Table of differences, exactly two levels

```{r}
table1_out_diff <-
  dat_mtcars_e %>%
  gtsummary::tbl_summary(
    by           = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
  # , label        = NULL                          # automatically uses the label attribute
  # , statistic    = NULL
  # , digits       = NULL
  # , type         = NULL
  # , value        = NULL
  # , missing      = NULL
  # , missing_text = NULL
  # , sort         = NULL
  # , percent      = NULL
  , include      = Var[["list"]][["var_demo"]]
  ) %>%
  #gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups
  #gtsummary::add_q()          %>%  # add a column of q values to control for multiple comparisons
  #gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
  gtsummary::add_difference() %>%  # add column for difference between two group, confidence interval, and p-value
  #gtsummary::add_ci()         %>%  # add confidence intervals
  gtsummary::add_overall()    %>%  # add a column with overall summary statistics
  gtsummary::add_stat_label() %>%  # add label for the summary statistics shown in each row
  gtsummary::add_n()          %>%  # add a column with N (or N missing) for each variable
  #gtsummary::bold_labels()    %>%  # bold variable name labels
  #gtsummary::italicize_levels()   # italicize levels
  gtsummary::modify_caption("Table title")

# print to Viewer
table1_out_diff

# export to Excel
table1_out_diff %>%
  gtsummary::as_hux_xlsx(file = "Table1_out_diff.xlsx")
```

## Strata: Basic table

```{r}
# Use one "strata" and one "by", two variables works fine

table1_out_strata <-
  dat_mtcars_e %>%
  gtsummary::tbl_strata(
    strata       = Var[["list"]][["var_groups"]][3] # use gtsummary::tbl_strata for more than 1 (don't use for more than 1 here)
  , .tbl_fun =
      ~ .x %>%
        gtsummary::tbl_summary(
          by           = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
        # , label        = NULL                          # automatically uses the label attribute
        # , statistic    = NULL
        # , digits       = NULL
        # , type         = NULL
        # , value        = NULL
        # , missing      = NULL
        # , missing_text = NULL
        # , sort         = NULL
        # , percent      = NULL
        #, include      = Var[["list"]][["var_demo"]]  # not working here or in the tbl_strata() argument
        ) %>%
        gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups
        #gtsummary::add_q()          %>%  # add a column of q values to control for multiple comparisons
        gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
        #gtsummary::add_difference() %>%  # add column for difference between two group, confidence interval, and p-value
        #gtsummary::add_ci()         %>%  # add confidence intervals
        gtsummary::add_overall()    %>%  # add a column with overall summary statistics
        gtsummary::add_stat_label() %>%  # add label for the summary statistics shown in each row
        gtsummary::add_n()               # add a column with N (or N missing) for each variable
        #gtsummary::bold_labels()    %>%  # bold variable name labels
        #gtsummary::italicize_levels()   # italicize levels

  #, .header = "**{strata}**, N = {n}"
  ) %>%
  gtsummary::modify_caption("Table title")

# print to Viewer
table1_out_strata

# export to Excel
table1_out_strata %>%
  gtsummary::as_hux_xlsx(file = "Table1_out_strata.xlsx")
```


## Strata: Table of differences, exactly two levels

```{r}
table1_out_diff_strata <-
  dat_mtcars_e %>%
  gtsummary::tbl_strata(
    strata       = Var[["list"]][["var_groups"]][3] # use gtsummary::tbl_strata for more than 1 (don't use for more than 1 here)
  , .tbl_fun =
      ~ .x %>%
        gtsummary::tbl_summary(
          by           = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
        # , label        = NULL                          # automatically uses the label attribute
        # , statistic    = NULL
        # , digits       = NULL
        # , type         = NULL
        # , value        = NULL
        # , missing      = NULL
        # , missing_text = NULL
        # , sort         = NULL
        # , percent      = NULL
        #, include      = Var[["list"]][["var_demo"]]  # not working here or in the tbl_strata() argument
        ) %>%
        #gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups
        #gtsummary::add_q()          %>%  # add a column of q values to control for multiple comparisons
        #gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
        gtsummary::add_difference() %>%  # add column for difference between two group, confidence interval, and p-value
        #gtsummary::add_ci()         %>%  # add confidence intervals
        gtsummary::add_overall()    %>%  # add a column with overall summary statistics
        gtsummary::add_stat_label() %>%  # add label for the summary statistics shown in each row
        gtsummary::add_n()               # add a column with N (or N missing) for each variable
        #gtsummary::bold_labels()    %>%  # bold variable name labels
        #gtsummary::italicize_levels()   # italicize levels

  #, .header = "**{strata}**, N = {n}"
  ) %>%
  gtsummary::modify_caption("Table title")

# print to Viewer
table1_out_diff_strata

# export to Excel
table1_out_diff_strata %>%
  gtsummary::as_hux_xlsx(file = "Table1_out_diff_strata.xlsx")
```

## Other details

```{r}
##   # gtsummary::add_stat()       %>%  # generic function to add a column with user-defined values
##
##
##   ## Note at bottom of table, converts from tbl_summary to gt, which changes some functionality
##   #as_gt() %>%                      # Add a note
##   #gt::tab_source_note(gt::md("*mtcars is from the **datasets** package*")) %>%
```

```{r}
## ## strata2 doesn't seem to work very well, especially for numeric variables
##
## table1_out_strata2 <-
##   dat_mtcars_e %>%
##   gtsummary::tbl_strata2(
##     strata       = Var[["list"]][["var_groups"]][c(1, 3)] # use gtsummary::tbl_strata2 for more than 1
##   , .tbl_fun =
##       ~ .x %>%
##         gtsummary::tbl_summary(
##           by           = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata2 for more than 1
##         # , label        = NULL                          # automatically uses the label attribute
##         # , statistic    = NULL
##         # , digits       = NULL
##         # , type         = NULL
##         # , value        = NULL
##         # , missing      = NULL
##         # , missing_text = NULL
##         # , sort         = NULL
##         # , percent      = NULL
##         #, include      = Var[["list"]][["var_demo"]]  # not working here or in the tbl_strata() argument
##         ) %>%
##         gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups
##         #gtsummary::add_q()          %>%  # add a column of q values to control for multiple comparisons
##         gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
##         #gtsummary::add_difference() %>%  # add column for difference between two group, confidence interval, and p-value
##         #gtsummary::add_ci()         %>%  # add confidence intervals
##         gtsummary::add_overall()    %>%  # add a column with overall summary statistics
##         gtsummary::add_stat_label() %>%  # add label for the summary statistics shown in each row
##         gtsummary::add_n()               # add a column with N (or N missing) for each variable
##         #gtsummary::bold_labels()    %>%  # bold variable name labels
##         #gtsummary::italicize_levels()   # italicize levels
##
##   #, .header = "**{strata}**, N = {n}"
##   ) %>%
##   gtsummary::modify_caption("Table title")
##
## # print to Viewer
## table1_out_strata2
##
## # export to Excel
## table1_out_strata2 %>%
##   gtsummary::as_hux_xlsx(file = "Table1_out_strata2.xlsx")
```


# Statistics

## Regression

https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html

```{r}
fit_lm <-
  lm( mpg ~
          cyl
        + disp
        #+ hp
        #+ drat
        + wt
        #+ qsec
        + vs
        + am
        #+ gear
        #+ carb
  , data = dat_mtcars_e
  )

fit_lm %>% car::Anova(type = 3)
fit_lm %>% summary()

tab_fit_lm <-
  fit_lm %>%
  gtsummary::tbl_regression(
  #  label           = NULL
  #, exponentiate    = FALSE
  #, include         = everything()
  #, show_single_row = NULL
    conf.level      = 0.95
  , intercept       = TRUE
  #, estimate_fun    = NULL
  #, pvalue_fun      = NULL
  #, tidy_fun        = NULL
  #, add_estimate_to_reference_rows = FALSE
  , conf.int        = TRUE
  ) %>%
  #gtsummary::add_q()                  %>%  # add a column of q values to control for multiple comparisons
  gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
  gtsummary::add_global_p()           %>%  # adds the global p-value for a categorical variables, instead of difference from intercept
  gtsummary::add_glance_source_note() %>%  # adds statistics from `broom::glance()` as source note
  gtsummary::add_vif(statistic = c("VIF", "GVIF", "aGVIF", "df")[c(2, 4)])  %>%  # adds column of the variance inflation factors (VIF)
  gtsummary::bold_labels()    %>%  # bold variable name labels
  gtsummary::italicize_levels() %>% # italicize levels
  gtsummary::modify_caption("Table title")

tab_fit_lm

##   gtsummary::add_significance_stars() %>% # add significance stars to estimates with small p-values (regression table)


# export to Excel
tab_fit_lm %>%
  gtsummary::as_hux_xlsx(file = "Table_fit_lm.xlsx")
```

## Chi-square

https://www.danieldsjoberg.com/gtsummary/reference/tbl_cross.html

```{r}
## Original tables
# Tabulate by two categorical variables:
tab_xtabs <-
  xtabs(
    ~ cyl + am
  , data = dat_mtcars_e
  )
tab_xtabs

# column proportions
prop.table(tab_xtabs, margin = 2)

# Chi-square test approximation
tab_xtabs_chisq <-
  tab_xtabs %>%
  chisq.test(correct = FALSE)
tab_xtabs_chisq

# Fisher's exact test
tab_xtabs_fisher <-
  tab_xtabs %>%
  fisher.test()
tab_xtabs_fisher


## gtsummary tables

tab_cross <-
  dat_mtcars_e %>%
  gtsummary::tbl_cross(
    row          = "cyl"
  , col          = "am"
  , label        = NULL
  , statistic    = NULL
  , digits       = NULL
  , percent      = c("none", "column", "row", "cell")[2]
  , margin       = c("column", "row")
  , missing      = c("ifany", "always", "no")[1]
  , missing_text = "Unknown"
  , margin_text  = "Total"
  ) %>%
  gtsummary::add_p()          %>%  # add p-values to the output comparing values across groups (tbl_cross options: test = c("chisq.test", "fisher.test")[2])
  gtsummary::bold_labels()    %>%  # bold variable name labels
  gtsummary::italicize_levels() %>% # italicize levels
  gtsummary::modify_caption("Table title")

tab_cross

# export to Excel
tab_cross %>%
  gtsummary::as_hux_xlsx(file = "Table_cross.xlsx")

```
	---
	title: Tables using `gtsummary`
	subtitle: Example workflow for Table 1 (demographics), regression, contingency table, etc.
	author: Erik Erhardt
	date: last-modified # today, now, last-modified
	date-format: long # full, long, medium, short, iso, https://quarto.org/docs/reference/dates.html
	format:
	html:
	theme: litera
	highlight-style: atom-one
	toc: true
	toc-depth: 4
	toc-location: body # left, body, right
	number-sections: true # true, false
	number-depth: 3
	code-fold: show # true (initially hidden), false, show (initially shown)
	code-tools: # menu top-right to show/hide all code
	toggle: true
	caption: "Code" # none
	source: false
	code-overflow: scroll # scroll, wrap
	code-block-bg: true
	code-block-border-left: "#30B0E0"
	df-print: paged # default, kable, tibble, paged # https://quarto.org/docs/computations/r.html
	self-contained: false # !!! this can cause a render error: "ERROR: The process cannot access the file because it is being used by another process. (os error 32)"
	fig-width: 6
	fig-height: 4
	execute: # https://quarto.org/docs/computations/execution-options.html, https://quarto.org/docs/computations/r.html
	cache: false # false, true
	eval: true # true, false Evaluate the code chunk (if false, just echos the code into the output).
	echo: true # true, false Include the source code in output
	---

	Other frequency tables https://cran.r-project.org/web/packages/janitor/vignettes/tabyls.html

	<!---
	# Erik's compile commands in R:
	fn_qmd <- "Summary_tables_example.qmd"
	setwd("D:/Dropbox/StatAcumen/consult/Rpackages/erikmisc/data-raw/tables")
	quarto::quarto_render(input = fn_qmd)
	-->

	```{r chunk-01, echo=FALSE}
	options(width = 80)
	#options(warn = -1)
	options(str = strOptions(list.len = 1e3))
	options(knitr.kable.NA = '') # Display NAs as blanks
	my_seed <- 34567
	set.seed(my_seed)
	```

	# Data

	```{r}
	library(erikmisc)
	library(tidyverse)

	# Other packages: huxtable, gt, smd
	```

	```{r}
	dat_mtcars_e <-
	erikmisc::dat_mtcars_e

	## ### Version for Max
	## dat_mtcars_e <-
	## datasets::mtcars %>%
	## tibble::as_tibble(
	## rownames = "model"
	## ) %>%
	## dplyr::mutate(
	## cyl = cyl %>% factor(levels = c(4, 6, 8), labels = c("four", "six", "eight"))
	## , vs = vs %>% factor(levels = c(0, 1), labels = c("V-shaped", "straight"))
	## , am = am %>% factor(levels = c(0, 1), labels = c("automatic", "manual"))
	## )
	##
	## # Label columns
	## dat_labels <-
	## tibble::tribble(
	## ~var , ~label
	## , "model" , "Model"
	## , "mpg" , "Miles/(US) gallon"
	## , "cyl" , "Number of cylinders"
	## , "disp" , "Displacement (cu.in.)"
	## , "hp" , "Gross horsepower"
	## , "drat" , "Rear axle ratio"
	## , "wt" , "Weight (1000 lbs)"
	## , "qsec" , "1/4 mile time"
	## , "vs" , "Engine" # (0 = V-shaped, 1 = straight)"
	## , "am" , "Transmission" # (0 = automatic, 1 = manual)"
	## , "gear" , "Number of forward gears"
	## , "carb" , "Number of carburetors"
	## )
	##
	## for (i_row in 1:nrow(dat_labels)) {
	## labelled::var_label(dat_mtcars_e[[dat_labels[["var"]][i_row] ]]) <- dat_labels[["label"]][i_row]
	## }
	##
	## str(dat_mtcars_e)


	dat_mtcars_e %>%
	str()

	# drastically change scale for two numeric variables
	dat_mtcars_e <-
	dat_mtcars_e %>%
	dplyr::mutate(
	drat = drat * 1e3
	, qsec = qsec / 1e3
	)


	## Variable lists
	Var <- list()

	Var[["list"]][["var_ID"]] <-
	c(
	"model"
	)

	Var[["list"]][["var_demo"]] <-
	c(
	"mpg"
	, "cyl"
	, "disp"
	, "hp"
	, "drat"
	, "wt"
	, "qsec"
	, "vs"
	, "am"
	, "gear"
	, "carb"
	)

	Var[["list"]][["var_groups"]] <-
	c(
	"cyl"
	, "vs"
	, "am"
	)
	```

	# Summary Tables

	## Set theme (basic is best for export to Excel)

	```{r}
	library(gtsummary)

	# core https://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html
	# output https://www.danieldsjoberg.com/gtsummary/articles/rmarkdown.html

	# https://www.danieldsjoberg.com/gtsummary/articles/themes.html
	# set themes for compact display
	#gtsummary::theme_gtsummary_journal(journal = "jama") # Setting theme `JAMA`
	#gtsummary::theme_gtsummary_compact() # Setting theme `Compact`
	gtsummary::reset_gtsummary_theme() # Reset theme
	```

	## Basic table

	```{r}
	table1_out <-
	dat_mtcars_e %>%
	gtsummary::tbl_summary(
	by = Var[["list"]][["var_groups"]][1] # use gtsummary::tbl_strata for more than 1
	# , label = NULL # automatically uses the label attribute
	# , statistic = NULL
	# , digits = NULL
	# , type = NULL
	# , value = NULL
	# , missing = NULL
	# , missing_text = NULL
	# , sort = NULL
	# , percent = NULL
	, include = Var[["list"]][["var_demo"]]
	) %>%
	gtsummary::add_p() %>% # add p-values to the output comparing values across groups
	gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	#gtsummary::add_difference() %>% # add column for difference between two group, confidence interval, and p-value
	#gtsummary::add_ci() %>% # add confidence intervals
	gtsummary::add_overall() %>% # add a column with overall summary statistics
	gtsummary::add_stat_label() %>% # add label for the summary statistics shown in each row
	gtsummary::add_n() %>% # add a column with N (or N missing) for each variable
	gtsummary::bold_labels() %>% # bold variable name labels
	gtsummary::italicize_levels() %>% # italicize levels
	gtsummary::modify_caption("Table title")

	# print to Viewer
	table1_out

	# export to Excel
	table1_out %>%
	gtsummary::as_hux_xlsx(file = "Table1_out.xlsx")
	```


	## Table of differences, exactly two levels

	```{r}
	table1_out_diff <-
	dat_mtcars_e %>%
	gtsummary::tbl_summary(
	by = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
	# , label = NULL # automatically uses the label attribute
	# , statistic = NULL
	# , digits = NULL
	# , type = NULL
	# , value = NULL
	# , missing = NULL
	# , missing_text = NULL
	# , sort = NULL
	# , percent = NULL
	, include = Var[["list"]][["var_demo"]]
	) %>%
	#gtsummary::add_p() %>% # add p-values to the output comparing values across groups
	#gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	#gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	gtsummary::add_difference() %>% # add column for difference between two group, confidence interval, and p-value
	#gtsummary::add_ci() %>% # add confidence intervals
	gtsummary::add_overall() %>% # add a column with overall summary statistics
	gtsummary::add_stat_label() %>% # add label for the summary statistics shown in each row
	gtsummary::add_n() %>% # add a column with N (or N missing) for each variable
	#gtsummary::bold_labels() %>% # bold variable name labels
	#gtsummary::italicize_levels() # italicize levels
	gtsummary::modify_caption("Table title")

	# print to Viewer
	table1_out_diff

	# export to Excel
	table1_out_diff %>%
	gtsummary::as_hux_xlsx(file = "Table1_out_diff.xlsx")
	```

	## Strata: Basic table

	```{r}
	# Use one "strata" and one "by", two variables works fine

	table1_out_strata <-
	dat_mtcars_e %>%
	gtsummary::tbl_strata(
	strata = Var[["list"]][["var_groups"]][3] # use gtsummary::tbl_strata for more than 1 (don't use for more than 1 here)
	, .tbl_fun =
	~ .x %>%
	gtsummary::tbl_summary(
	by = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
	# , label = NULL # automatically uses the label attribute
	# , statistic = NULL
	# , digits = NULL
	# , type = NULL
	# , value = NULL
	# , missing = NULL
	# , missing_text = NULL
	# , sort = NULL
	# , percent = NULL
	#, include = Var[["list"]][["var_demo"]] # not working here or in the tbl_strata() argument
	) %>%
	gtsummary::add_p() %>% # add p-values to the output comparing values across groups
	#gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	#gtsummary::add_difference() %>% # add column for difference between two group, confidence interval, and p-value
	#gtsummary::add_ci() %>% # add confidence intervals
	gtsummary::add_overall() %>% # add a column with overall summary statistics
	gtsummary::add_stat_label() %>% # add label for the summary statistics shown in each row
	gtsummary::add_n() # add a column with N (or N missing) for each variable
	#gtsummary::bold_labels() %>% # bold variable name labels
	#gtsummary::italicize_levels() # italicize levels

	#, .header = "{strata}, N = {n}"
	) %>%
	gtsummary::modify_caption("Table title")

	# print to Viewer
	table1_out_strata

	# export to Excel
	table1_out_strata %>%
	gtsummary::as_hux_xlsx(file = "Table1_out_strata.xlsx")
	```


	## Strata: Table of differences, exactly two levels

	```{r}
	table1_out_diff_strata <-
	dat_mtcars_e %>%
	gtsummary::tbl_strata(
	strata = Var[["list"]][["var_groups"]][3] # use gtsummary::tbl_strata for more than 1 (don't use for more than 1 here)
	, .tbl_fun =
	~ .x %>%
	gtsummary::tbl_summary(
	by = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata for more than 1
	# , label = NULL # automatically uses the label attribute
	# , statistic = NULL
	# , digits = NULL
	# , type = NULL
	# , value = NULL
	# , missing = NULL
	# , missing_text = NULL
	# , sort = NULL
	# , percent = NULL
	#, include = Var[["list"]][["var_demo"]] # not working here or in the tbl_strata() argument
	) %>%
	#gtsummary::add_p() %>% # add p-values to the output comparing values across groups
	#gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	#gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	gtsummary::add_difference() %>% # add column for difference between two group, confidence interval, and p-value
	#gtsummary::add_ci() %>% # add confidence intervals
	gtsummary::add_overall() %>% # add a column with overall summary statistics
	gtsummary::add_stat_label() %>% # add label for the summary statistics shown in each row
	gtsummary::add_n() # add a column with N (or N missing) for each variable
	#gtsummary::bold_labels() %>% # bold variable name labels
	#gtsummary::italicize_levels() # italicize levels

	#, .header = "{strata}, N = {n}"
	) %>%
	gtsummary::modify_caption("Table title")

	# print to Viewer
	table1_out_diff_strata

	# export to Excel
	table1_out_diff_strata %>%
	gtsummary::as_hux_xlsx(file = "Table1_out_diff_strata.xlsx")
	```

	## Other details

	```{r}
	## # gtsummary::add_stat() %>% # generic function to add a column with user-defined values
	##
	##
	## ## Note at bottom of table, converts from tbl_summary to gt, which changes some functionality
	## #as_gt() %>% # Add a note
	## #gt::tab_source_note(gt::md("mtcars is from the datasets* package*")) %>%
	```

	```{r}
	## ## strata2 doesn't seem to work very well, especially for numeric variables
	##
	## table1_out_strata2 <-
	## dat_mtcars_e %>%
	## gtsummary::tbl_strata2(
	## strata = Var[["list"]][["var_groups"]][c(1, 3)] # use gtsummary::tbl_strata2 for more than 1
	## , .tbl_fun =
	## ~ .x %>%
	## gtsummary::tbl_summary(
	## by = Var[["list"]][["var_groups"]][2] # use gtsummary::tbl_strata2 for more than 1
	## # , label = NULL # automatically uses the label attribute
	## # , statistic = NULL
	## # , digits = NULL
	## # , type = NULL
	## # , value = NULL
	## # , missing = NULL
	## # , missing_text = NULL
	## # , sort = NULL
	## # , percent = NULL
	## #, include = Var[["list"]][["var_demo"]] # not working here or in the tbl_strata() argument
	## ) %>%
	## gtsummary::add_p() %>% # add p-values to the output comparing values across groups
	## #gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	## gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	## #gtsummary::add_difference() %>% # add column for difference between two group, confidence interval, and p-value
	## #gtsummary::add_ci() %>% # add confidence intervals
	## gtsummary::add_overall() %>% # add a column with overall summary statistics
	## gtsummary::add_stat_label() %>% # add label for the summary statistics shown in each row
	## gtsummary::add_n() # add a column with N (or N missing) for each variable
	## #gtsummary::bold_labels() %>% # bold variable name labels
	## #gtsummary::italicize_levels() # italicize levels
	##
	## #, .header = "{strata}, N = {n}"
	## ) %>%
	## gtsummary::modify_caption("Table title")
	##
	## # print to Viewer
	## table1_out_strata2
	##
	## # export to Excel
	## table1_out_strata2 %>%
	## gtsummary::as_hux_xlsx(file = "Table1_out_strata2.xlsx")
	```





	# Statistics

	## Regression

	https://www.danieldsjoberg.com/gtsummary/articles/tbl_regression.html

	```{r}
	fit_lm <-
	lm( mpg ~
	cyl
	+ disp
	#+ hp
	#+ drat
	+ wt
	#+ qsec
	+ vs
	+ am
	#+ gear
	#+ carb
	, data = dat_mtcars_e
	)

	fit_lm %>% car::Anova(type = 3)
	fit_lm %>% summary()

	tab_fit_lm <-
	fit_lm %>%
	gtsummary::tbl_regression(
	# label = NULL
	#, exponentiate = FALSE
	#, include = everything()
	#, show_single_row = NULL
	conf.level = 0.95
	, intercept = TRUE
	#, estimate_fun = NULL
	#, pvalue_fun = NULL
	#, tidy_fun = NULL
	#, add_estimate_to_reference_rows = FALSE
	, conf.int = TRUE
	) %>%
	#gtsummary::add_q() %>% # add a column of q values to control for multiple comparisons
	gtsummary::bold_p(t = 0.05, q = FALSE) %>% # Bold significant p-values or q-values
	gtsummary::add_global_p() %>% # adds the global p-value for a categorical variables, instead of difference from intercept
	gtsummary::add_glance_source_note() %>% # adds statistics from `broom::glance()` as source note
	gtsummary::add_vif(statistic = c("VIF", "GVIF", "aGVIF", "df")[c(2, 4)]) %>% # adds column of the variance inflation factors (VIF)
	gtsummary::bold_labels() %>% # bold variable name labels
	gtsummary::italicize_levels() %>% # italicize levels
	gtsummary::modify_caption("Table title")

	tab_fit_lm

	## gtsummary::add_significance_stars() %>% # add significance stars to estimates with small p-values (regression table)


	# export to Excel
	tab_fit_lm %>%
	gtsummary::as_hux_xlsx(file = "Table_fit_lm.xlsx")
	```

	## Chi-square

	https://www.danieldsjoberg.com/gtsummary/reference/tbl_cross.html

	```{r}
	## Original tables
	# Tabulate by two categorical variables:
	tab_xtabs <-
	xtabs(
	~ cyl + am
	, data = dat_mtcars_e
	)
	tab_xtabs

	# column proportions
	prop.table(tab_xtabs, margin = 2)

	# Chi-square test approximation
	tab_xtabs_chisq <-
	tab_xtabs %>%
	chisq.test(correct = FALSE)
	tab_xtabs_chisq

	# Fisher's exact test
	tab_xtabs_fisher <-
	tab_xtabs %>%
	fisher.test()
	tab_xtabs_fisher



	## gtsummary tables

	tab_cross <-
	dat_mtcars_e %>%
	gtsummary::tbl_cross(
	row = "cyl"
	, col = "am"
	, label = NULL
	, statistic = NULL
	, digits = NULL
	, percent = c("none", "column", "row", "cell")[2]
	, margin = c("column", "row")
	, missing = c("ifany", "always", "no")[1]
	, missing_text = "Unknown"
	, margin_text = "Total"
	) %>%
	gtsummary::add_p() %>% # add p-values to the output comparing values across groups (tbl_cross options: test = c("chisq.test", "fisher.test")[2])
	gtsummary::bold_labels() %>% # bold variable name labels
	gtsummary::italicize_levels() %>% # italicize levels
	gtsummary::modify_caption("Table title")

	tab_cross

	# export to Excel
	tab_cross %>%
	gtsummary::as_hux_xlsx(file = "Table_cross.xlsx")

	```