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## see_sql_tables.sql
use [database];
select * from INFORMATION_SCHEMA.tables;

## dbplyr_copy_error.txt
When you get this error or similar:

`error in `db_copy_to()`: can't copy data to table ... `

Check the installed version of DBI in R. You may need to install an earlier version (1.1.3 has worked in the past) instead of anything later than that.

You can do this using:
devtools::install_version("DBI", version = "1.1.3")

## bold_gt_column_labels
table |>
  cols_label_with(fn = ~md(glue::glue("**{.}**")))

## table_labels_from_redcap.R
  labelled::set_variable_labels(
    .labels = set_names(
       as.list(dic$field_label),  # label
       dic$field_name  # column name
     ),
    .strict = FALSE
  )

## rubins_rules.R
#' Rubin's Rules
#'
#' @param x A data.frame or similar containing one column of imputed point estimates and one column of imputed standard errors
#' @param estims character; name of the column in \code{x} containing imputed point estimates
#' @param stds character; name of column in \code{x} containing imputed standard errors
#'
#' @return A data.frame with two columns containing one row each: theta, the aggregate point estimate; and std.error, the aggregate standard error
#'
#' @examples
rubins_rules <- function(x, estims, stds) {

## remove_header_tbl_summary.R
tbl |>
  modify_header(label = "")

## programmatic_rmarkdown_tabs.Rmd
```{r}

# Function to create tabs - we'll iterate over this
  # Uses Pandoc's div structure (where ::: is analogous to RMarkdown's #) and panel structure (using {.panel})

make_tab <- function(tab_name) {

  cat("::: {.panel}\n")                                     # Open tab

  cat("##", as.character(tab_name), "{.panel-name}\n")      # Label tab

## fisher_sim_p.R
# Sometimes, due to convergence issues, Fisher's Exact Test will exclude a p-value from a tbl_summary() table.

# To get around this, create a custom stat that will be used in your tbl_summary() call:

fisher_sim_p <- function(data, variable, by, ...) {
  result <- list()
  result$p <- stats::fisher.test(x = data %>% pull({{variable}}), y = data %>% pull({{by}}), simulate.p.value = T)$p.value
  result$test <- "Fisher's test with simulated p-value"
  result
}

## annotate_facet_plots.R
# If you want to annotate a faceted ggplot (e.g., one that uses facet_wrap() to facet across a factor variable), use the following form:

# first, create a dataframe to contain your annotation for each facet, making sure to include a column for the facet it'll be included in
ann_text <- data.frame(year = rep(2006, 3),
                       n = rep(2000000, 3),
                       x = c(2005, 2005, 2010),
                       y = rep(1750000, 3),
                       xend = c(2001, 1998, 2020),
                       yend = c(1157, 75621, 101503),
                       lab = c("Office visits go back to\napproximately 2001.",

## crimCV_plot.R
library(crimCV)
library(dplyr)
library(ggplot2)

# load toy data included with crimCV package
data(TO1adj)

# fitting model - don't need to run this, but helpful if need toy data to plot
out1 <- crimCV(TO1adj, 2, dpolyp = 2, rcv = TRUE)
	When you get this error or similar:

	`error in `db_copy_to()`: can't copy data to table ... `

	Check the installed version of DBI in R. You may need to install an earlier version (1.1.3 has worked in the past) instead of anything later than that.

	You can do this using:
	devtools::install_version("DBI", version = "1.1.3")
	labelled::set_variable_labels(
	.labels = set_names(
	as.list(dic$field_label), # label
	dic$field_name # column name
	),
	.strict = FALSE
	)
	#' Rubin's Rules
	#'
	#' @param x A data.frame or similar containing one column of imputed point estimates and one column of imputed standard errors
	#' @param estims character; name of the column in \code{x} containing imputed point estimates
	#' @param stds character; name of column in \code{x} containing imputed standard errors
	#'
	#' @return A data.frame with two columns containing one row each: theta, the aggregate point estimate; and std.error, the aggregate standard error
	#'
	#' @examples
	rubins_rules <- function(x, estims, stds) {
	```{r}

	# Function to create tabs - we'll iterate over this
	# Uses Pandoc's div structure (where ::: is analogous to RMarkdown's #) and panel structure (using {.panel})

	make_tab <- function(tab_name) {

	cat("::: {.panel}\n") # Open tab

	cat("##", as.character(tab_name), "{.panel-name}\n") # Label tab
	# Sometimes, due to convergence issues, Fisher's Exact Test will exclude a p-value from a tbl_summary() table.

	# To get around this, create a custom stat that will be used in your tbl_summary() call:

	fisher_sim_p <- function(data, variable, by, ...) {
	result <- list()
	result$p <- stats::fisher.test(x = data %>% pull({{variable}}), y = data %>% pull({{by}}), simulate.p.value = T)$p.value
	result$test <- "Fisher's test with simulated p-value"
	result
	}
	# If you want to annotate a faceted ggplot (e.g., one that uses facet_wrap() to facet across a factor variable), use the following form:

	# first, create a dataframe to contain your annotation for each facet, making sure to include a column for the facet it'll be included in
	ann_text <- data.frame(year = rep(2006, 3),
	n = rep(2000000, 3),
	x = c(2005, 2005, 2010),
	y = rep(1750000, 3),
	xend = c(2001, 1998, 2020),
	yend = c(1157, 75621, 101503),
	lab = c("Office visits go back to\napproximately 2001.",
	library(crimCV)
	library(dplyr)
	library(ggplot2)

	# load toy data included with crimCV package
	data(TO1adj)

	# fitting model - don't need to run this, but helpful if need toy data to plot
	out1 <- crimCV(TO1adj, 2, dpolyp = 2, rcv = TRUE)