---
title: "your_title_goes_here"
author: "your_name_goes_here"
date: "`r Sys.Date()`"
output:
  bookdown::html_document2:
    number_sections: false
bibliography: [references.bib, packages.bib]
csl: the-new-england-journal-of-medicine.csl
---

```{r setup, echo=FALSE}
knitr::opts_chunk$set(
                      ########## set global options ############
    echo = FALSE,     # don't show code
    collapse = TRUE,  # keep code from blocks together (if shown)
    message = TRUE,   # show messages
    warning = TRUE,   # show warnings
    error = TRUE,     # show error messages
    comment = "",     # don't show ## with printed output
    dpi = 100,        # image resolution (typically 300 for publication)
    fig.width = 6.5,  # figure width
    fig.height = 4.0  # figure height
)

# R's default rounding is to show 7 digits. This rounds results to 3 digits.
options(digits = 3)
```

```{r tidyverse-tidymodels, echo=FALSE}
library(conflicted)
suppressPackageStartupMessages(library(tidymodels))
tidymodels_prefer()
suppressPackageStartupMessages(library(tidyverse))

# suppress "`summarise()` has grouped output by " messages
options(dplyr.summarise.inform = FALSE)
```

```{r other-packages}
library(glue)  # for glue()
library(rUM)  # needed for the bibliography to include the package
library(rio)  # for import() 

# gtsummary for tbl_summary(), add_n(), add_p(), modify_header(), bold_labels()
suppressPackageStartupMessages(library(gtsummary)) 
```

```{r load-data}
# If you want to import a dataset which is not an R file, put its name inside 
# the "" below.  For example:
# raw_data <- import("the_excel.xlsx")

# If your datasets is an Excel file saved in the data folder, add a path like:
# raw_data <- import("data/the_excel.xlsx")
```

```{r make-analysis}
# preprocess your data and ultimately make a dataset called analysis
analysis <- mtcars |> 
  mutate(auto_man = if_else(am == 0, "Automatic", "Manual")) |> 
  select(mpg, auto_man)
```

# Abstract

# Introduction

# Methods
Analyses were conducted with `r stringr::word(R.Version()$version.string, 1, 3)` with the `tidyverse` (`r packageVersion("tidyverse")`), `rUM` (`r packageVersion("rUM")`), `gtsummary` (`r packageVersion("gtsummary")`) packages used to preprocess and summarize data. [@R-base; @R-tidyverse; @tidyverse2019; @R-rUM; @R-gtsummary]

# Results

```{r table-1}
# To learn how to use tbl_summary look https://www.danieldsjoberg.com/gtsummary/
analysis |> 
  tbl_summary(
    include = c(everything()), # choose your variables here
    # change auto_man to the name of your column variable or delete by = auto_man
    by = auto_man, # split table by group
    missing = "no" # don't list missing data separately
  ) |>
  # add_n() |> # add column with total number of non-missing observations
  # add_p() |> # test for a difference between groups
  modify_header(label = "") |> # update the column header to be blank
  bold_labels() |> 
  modify_caption(
    "Cross references to tables start with tab: then the code chunk label."
  ) 
```

As can be seen in Table \@ref(tab:table-1) or [Table 1](#tab:table-1)

```{r figure1}
#| fig.cap: "Your real caption belongs here.  Remember figure references begin with #fig:"

# To learn how to use ggplot start here: https://ggplot2.tidyverse.org/#learning-ggplot2
analysis |> 
  ggplot() +
		labs(
			title = "Your short title goes here.",
			caption = "Your data sources/citation goes here."
		) +
		geom_blank()
		# remove geom_blank() and add details here
```

See Figure \@ref(fig:figure1) or [Figure 1](#fig:figure1)


# Discussion

# References {-}

```{r, make-references, include=FALSE}
# automatically create a bib database for loaded R packages & rUM
knitr::write_bib(
  c(
    .packages(),
    "rUM"
  ),
  "packages.bib"
)
```