library(tidyverse)
library(knitr)

mtcars <- mtcars

In this tutorial, we will provide an introduction to R Markdown. This is an amazing framework for creating fully reproducible reports while leveraging the power of the R programming language. The beauty of R Markdown is that you can write code, perform analysis, and view output in it. It can then be rendered into a document that can be shared with your audience of choice.

An excellent resource for R Markdown, for which this tutorial takes partial influence from, is Chapter 27 of the book “R for Data Science” by Hadley Wickham and Garrett Grolemund.

Additionally, we will be referencing the cheat sheet for R Markdown, which can be located here: https://rstudio.com/wp-content/uploads/2015/02/rmarkdown-cheatsheet.pdf

We can divide the content into three categories: 1) The YAML header at the top with the title, author, date, and output information (separated by —); 2) the code chunks separated by ``` which can be run by clicking the green play button on the side; and 3) text, such as this.

Text in an R Markdown document can utilize markdown syntax. At first, you might have to rely fairly heavily on the cheat sheet along with some trial and error, but you will get used to it fairly often. Here are some examples:

bold text and bold text
italics text and italics text
superscript²
subscript₀
~~strikethrough~~
embedded links such as link

You can create white space with this character:

You can create a block quote like this:

block quote

and you can create a faint horizontal rule this way:

Next, you can create headers - this is common when you want to separate your document into multiple sections.

Biggest header

Second biggest header

Third biggest header

Fourth biggest header

Fifth biggest header

Smallest header

We can embed equations inline in the following way: \(E = m * c^{2}\)

But perhaps most importantly, R code can be embedded in the following way. Suppose I want to return the mean of the “hp” variable from the “mtcars” dataset. When code is surrounded by back ticks and the letter r, this can be done. For example, the mean is 146.6875. As mentioned earlier, a new R chunk can be created by enclosing code by a pair of ```.

A list can be created like this:

unordered list
item 2
- sub-item 1
- sub-item 2

ordered list
item 2

sub-item 1
sub-item 2

A table can be created like this:

First Header	Second Header
Content Cell	Content Cell
Content Cell	Content Cell

However, creating tables from R data gets particularly interesting. Consider these two for instance:

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

kable(head(mtcars))

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.460	20.22	1	0	3	1

A great degree of customizability is available through the “knitr” package, along with packages like

kableExtra
pander
formattable

This will be the subject for another tutorial.

Let’s conclude by looking at the cheat sheet.

R Tutorial 012: R Markdown

RichardOnData

12/18/2020