• What and Why
• Getting started
  • Syntax and basic elements
• Hands-on
  • Using code to produce output
• Show and tell

• Markdown is:
  • a lightweight markup language
  • easy-to-read, easy-to-write plain text that gets converted into HTML
• R Markdown is:
  • The above + R code embedded to create graphs, tables, values… any type of R output
• knitr, which is an R package, transforms *.Rmd into *.md, which is then processed by pandoc

"But remember, Markdown was designed for HTML, and LaTeX was for PDF and related output formats."

Yihui Xie

• See Yihui's discussion on Markdown vs. LaTeX

• It is great for research
  • Keeps everything together (analysis, references, text, data…)
  • Easier to iterate over your work (i.e. easier to incorporate suggestions by Reviewer #2)
• It fosters reproducibility

• Slides
  • ioslides
  • slidy presentations
  • beamer*
• HTML
  • HTML notebook
• PDF
• Word
• Shiny (interactive documents)
  • Document
  • Presentation

• Set of key: and value pairs
• Contains variables/parameters passed to knitr and pandoc to control output
• The default one looks like this:

---
title: "Introduction to R Markdown"
author: "Villaseñor-Derbez, J.C."
date: "November 1, 2017"
output: ioslides_presentation
---

• You can also specify many other things:
• fig_caption: yes adds figure captions from chunks
• toc: yes adds a table of contents
• subtitle:"Fancy subtitle" adds a subtitle
• bibliography: references.bib specifies where to look for BibTeX entries
• csl: plos.csl specifies citation format (in this case, PLoS ONE's)

See options for HTML and options for PDF output

• Bullets can be specified by -, +, or *

This:

* Item 1
* Item 2
    + Item 2a
    + Item 2b

Gives:

• Item 1
• Item 2
  • Item 2a
  • Item 2b

This

1. Item 1
2. Item 2
3. Item 3
    + Item 3a
    + Item 3b

Gives:

1. Item 1
2. Item 2
3. Item 3
   • Item 3a
   • Item 3b

• Specify a link: [go to google](www.google.com)

• and see go to google

• You can insert images saved on your computer, or from the web:
  • image: ![](Img/RStudio-Ball.png)
  • 

• There were "r nrow(mtcars)" cars studied

• There were 32 cars studied

• Used to isolate plain text from code
• Allow you to execute R, Python, Rcpp, SQL, and Stan within your file
• Use Ctrl + Alt + I (Windows) Cmd + Alt + I (Mac) to insert

• Always name your chunks!

You need:

• A BibTeX file (provided in the CourseMaterials folder)
• Specify bibliography: and csl: in the header
• That's it…

---
bibliography: references.bib
csl: Citation_styles/ieee.csl
---

"A tidy dataset has one column per variable" [@wickham_2014]

"A tidy dataset has one column per variable" [1]

References

• Citation style library

• Tyler Clavelle has a great tutorial on this

suppressPackageStartupMessages({
  library(stargazer)
  library(knitr)
  library(kableExtra)
})

• echo hides code from output
• eval evaluate chunk?
• fig.width
• fig.height
• fig.cap To add figure caption

model <- lm(mpg ~ disp, mtcars) #Fit a linear model
summary(model) # Look at the summary of the model

## 
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8922 -2.2022 -0.9631  1.6272  7.2305 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29.599855   1.229720  24.070  < 2e-16 ***
## disp        -0.041215   0.004712  -8.747 9.38e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.251 on 30 degrees of freedom
## Multiple R-squared:  0.7183, Adjusted R-squared:  0.709 
## F-statistic: 76.51 on 1 and 30 DF,  p-value: 9.38e-10

## 
## Call:
## lm(formula = mpg ~ disp, data = mtcars)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8922 -2.2022 -0.9631  1.6272  7.2305 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29.599855   1.229720  24.070  < 2e-16 ***
## disp        -0.041215   0.004712  -8.747 9.38e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.251 on 30 degrees of freedom
## Multiple R-squared:  0.7183, Adjusted R-squared:  0.709 
## F-statistic: 76.51 on 1 and 30 DF,  p-value: 9.38e-10

plot(mtcars$disp, mtcars$mpg, col = mtcars$cyl, pch = 20, xlab = "Disp (cu. in.)", ylab = "mpg") # Plot the model

This is my caption

• The above was not great for tables that summarize data, instead, use knitr::kable()
• Using the taxa_table.csv file in the CourseMaterials

• R Markdown from RStudio tutorial

• <div id="refs"></div> to specify placement of references (i.e. when you are writing a manuscript)
• To add line numbers and double-spacing to your manuscript, add to the YAML:

header-includes:
- \usepackage{setspace}
- \doublespacing
- \usepackage{lineno}
- \linenumbers