AwesomeRmarkdownWordReport.Rmd

---
title: "Awesome Rmarkdown Word report with programmatically inserted headings, outputs and cross-references"
author: "Thomas de Marchin"
date: "17MAR2021"
output:
  word_document:
    reference_docx: "template.docx"
---

```{r setup, include=FALSE}
# Let's setup a few things.

rm(list=ls())
library(knitr)
library(officedown)
library(officer)
library(ggplot2)
library(tidyverse)
library(broom)
library(flextable)

# set chunks defaults
knitr::opts_chunk$set(
  echo       = FALSE,
  message    = FALSE,
  warning    = FALSE
)

# set flextable defaults
knitr::opts_chunk$set(echo = TRUE, fig.cap = TRUE)
  set_flextable_defaults(
  font.family = "Arial", font.size = 9, 
  theme_fun = "theme_vanilla",
  big.mark="", table.layout="autofit")
  
# formatting properties for specific paragraphs
centeredP <- fp_par(text.align = "center")

```

# Introduction

The aim of this document is to introduce a way to generate word reports from R using Rmarkdown with programmatically inserted headings, outputs and Word cross-references. 
See https://towardsdatascience.com/awesome-r-markdown-word-report-with-programmatically-inserted-headings-outputs-and-19ad0de29a22 to understand the context of this example.

# Data 

We will use the built-in iris dataset as an example.
This dataset consists in Petal and Sepal width and length measurements for three iris species.
Table `r run_reference("summaryTable")` shows summary statistics.

```{r data, echo = FALSE}
# this chunk is a normal chunk compared to the next one

# create the bookmark for the summary table
tab_num <- run_autonum(seq_id = "Table", pre_label = "Table ", bkm = "summaryTable")
# add the caption
block_caption(label= "Summary table for the iris dataset",
              style = "caption", autonum = tab_num)

# create the summary table and output it with flextable()
summaryData <- iris %>% gather(value="value", key="Attribute", -Species) %>% group_by(Attribute, Species) %>% 
summarise(n=n(), mean=mean(value), sd=sd(value), min=min(value), max=max(value)) 
summaryData %>% flextable() %>% merge_v(j = "Attribute") %>% colformat_double(j=c("mean", "sd"), digits = 2)
```

# Analysis

```{r analysis, echo = FALSE, results = 'asis'}
# this chunk will programmatically generates Markdown code (results='asis')

# split the data by specie to simulate different attributes to analyze, it is here 3 here but it could be any number.
data <- split(iris, iris$Species)
uniqueSpecies <- levels(iris$Species)

# for loop
for(i in 1:length(uniqueSpecies)){
  
  dataSubset <- data[[uniqueSpecies[i]]]
    
  # print the heading
  cat("\n##", uniqueSpecies[i], "\n")
  
  # print an empty line
  cat("\n  <br>  \n")
  
  # print some text
  cat("Figure ")
  # reference the figure below (note the use of knit_print_run function in 'asis' chunks)
  knit_print_run(run_reference(paste0("pData", uniqueSpecies[i])))
  cat(" shows the relation between Sepal width and length.")
  
  # plot the data 
  pData <- ggplot(aes(x=Sepal.Length, y=Sepal.Width), data=dataSubset) + 
    geom_point() + geom_smooth(method='lm', se=F) + labs(title=uniqueSpecies[i])
  
  # output the plot (note the use of the print function in 'asis' chunks)
  print(pData)
  
  cat("\n") # sometimes you need to add this to make things work
  
  # Add the figure numbering and caption (note the use of the knit_print_block function in 'asis' chunks)
  fig_num <- run_autonum(seq_id = "Figure", pre_label = "Figure ", bkm = paste0("pData", uniqueSpecies[i]))
  knit_print_block(block_caption(paste0("Scatter plot of Sepal Width vs Sepal Length for ", uniqueSpecies[i], ". Blue line is a linear regression."),
              style = "caption", autonum = fig_num))
  
  
  # print some text
  cat("A linear regression was performed using Sepal.Length as the response and Sepal.Width as the explanatory variable. Table ")
  knit_print_run(run_reference(paste0("tabRegression", uniqueSpecies[i])))
  cat(" shows the parameters estimates and 95% Confidence intervals.")
  
  cat("\n") # sometimes you need to add this to make things work
  
  # do a regression generate the fit summary table
  regression <- lm(Sepal.Length ~ Sepal.Width, data=dataSubset)
  tabRegression <- tidy(regression)
  tabRegression <- cbind(tabRegression, confint(regression, level=0.95))
  
  # Add the table numbering and caption (note the use of the knit_print_block function in 'asis' chunks)
  tab_num <- run_autonum(seq_id = "Table", pre_label = "Table ", bkm = paste0("tabRegression", uniqueSpecies[i]))
  knit_print_block(block_caption(paste0("Parameters estimates of the fit for ", uniqueSpecies[i]),
              style = "caption", autonum = tab_num))
  
  
  
  # output the summary table (note the use of knit_print and cat functions in 'asis' chunks)
  tabRegression %>% flextable() %>% knit_print() %>% cat()
  
  cat('\n')
}

# Add a page break
run_pagebreak()
```

# Conclusion

`r fpar(ftext("Congratulations!", fp_text(font.size = 12, bold = TRUE, color = "#C32900")), fp_p = fp_par(text.align = "center"))`

![](cheers.jpg)