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Reproducible reports & research with knitr in R Studio

Arguably, knitr (CRAN link) is the most outstanding R package of this year and its creator, Yihui Xie is the star of the useR! conference 2012. This is because the ease of use comparing to Sweave for making reproducible report. Integration of knitR and R Studio has made reproducible research much more convenience, intuitive and easier to use.

R Studio: A user friendly and cross platform IDE for R

Screenshot of R Studio (Windows PC)

This post is an example, based on the demo by Yihui Xie himself, I will show how to create a reproducible report consisting the R code in a LaTeX style in the Cardiff R User Group session at the Cardiff Business School (CARBS) Research Fair tomorrow (19 June 2012).

knitr option for sweave document in R Studio

In the code

• Lines 001-043 are just normal preamble syntax of the LaTeX code I took from the Template of useR! conference abstract.
• Lines 044-96 are the R codes and descriptions.

A chuck of R code is wrapped in the following code:

<<chunk1, echo=TRUE, results='hide'>>=
Put your R code here
@

chunk1 is the name of the chuck

echo = TRUE to show your R code in the chuck, = FALSE if you do not want to show the code.

result = ‘markup’ to show the result unless = ‘hide’

The code of the whole document is as follow:

\documentclass[11pt, a4paper]{article}
\usepackage{amsfonts, amsmath, hanging, hyperref, natbib, parskip, times}
\hypersetup{
 colorlinks,
 linkcolor=blue,
 urlcolor=blue
}
\setlength{\topmargin}{-15mm}
\setlength{\oddsidemargin}{-2mm}
\setlength{\textwidth}{165mm}
\setlength{\textheight}{250mm}

\let\section=\subsubsection
\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
\let\proglang=\textit
\let\code=\texttt
\renewcommand{\title}[1]{\begin{center}{\bf \LARGE #1}\end{center}}
\newcommand{\affiliations}{\footnotesize}
\newcommand{\keywords}{\paragraph{Keywords:}}

\begin{document}
\pagestyle{empty}

\title{Using knitR (R + \LaTeX) in R Studio: A Demo}

\begin{center}
 {\bf Pairach Piboonrungroj$^{1,2,^\star}$}
\end{center}

\begin{affiliations}
1. Logistics Systems Dynamics Group, Cardiff Business School, Cardiff University, United Kingdom \\[-2pt]
2. Chiang Mai School of Economics, Chiang Mai University, Thailand \\[-2pt]
%3. Second affiliation of author B \\[-2pt]
$^\star$Email: \href{mailto:me@pairach.com}{me@pairach.com}
\end{affiliations}
\vskip -0.5cm
%%%%%%%%%%%%%%%%%%
%Add Breaking Line
\begin{center}
\linethickness{1mm}
\line(1,0){480}
\end{center}
%%%%%%%%%%%%%%%%%%
1. Show only R source code
<<chunk1, echo=TRUE, results='hide'>>=
1 + 1
@

2. Show only output
<<chunk2, ref.label='chunk1', echo=FALSE, results='markup'>>=
@

3. Show both source code and output
<<chunk3, echo=TRUE, results='markup'>>=
1 + 1
@

4. Show source code in grey shade but the output
<<chunk4, echo=TRUE, results='asis'>>=
1 + 1
@

5. Now, testing a linear model
<<chunk5, echo=TRUE, results='markup'>>=
# generating value for x variable from 1 to 100
x <- c(1:100)
# creat error term
e <- rnorm(100, mean = 5, sd = 10000)
# computing y equal to 3 plus five times x plus random number
y = 10 + 100*x + e
@

Set the format of all object called pdf()
<<custom-dev2>>=
my_pdf = function(file, width, height) {pdf(file, width = 5, height = 5, pointsize = 10)}
@

6. See the scatter plot
<<chunk6, echo=TRUE, results='markup', dev='my_pdf', fig.ext='pdf'>>=
plot(x, y)
@

7. Let's build a linear model by regressing y on x
<<chunk7, echo=TRUE, results='markup'>>=
# creating a linear model by regressing y on x as 'lm1' object
lm1 <- lm(y ~ x)
# calling a summary of linear model result
summary(lm1)
@

8. Now we can create a post-hoc plots to check assumptions of regression
<<chunk8, echo=TRUE, results='markup', dev='my_pdf', fig.ext='pdf'>>=
# Creating post-hoc plot for lm1
par(mfrow=c(2,2))
plot(lm1)
@
\end{document}

And this is the Output

 

		
View this document on Scribd

Related posts

• List of FREE R tutorials from universities and academics websites
• List of FREE R tutorials from dedicated webpages

List of Free Online R Tutorials

According to the post on FREE online R tutorials from universities, I have received many email suggesting more and more tutorials. However some tutorials are not hosted in an academic institutes, so I decided to create this post to list such tutorials. If you know other tutorials, please kindly suggest me by email to me@pairach.com or post the link in the comment section.

A list of R tutorials, which are hosted in the webpages of academic institutes can be found here.

The tutorials are listed  in no particular order but categorised by subjects and/or topics.

General guides

1. R Wiki Documentation about R contributed by the R community
2. Quick-R by Rob Kabacoff
3. R Programming by Wikibooks 
4. How to use R by Wikiuniversity
5. R4stats – R for SAS and SPSS Users – R for Stata Users by Bob Muenchen
6. Programming in R by  Vincent Zoonekynd
7. R programming for those coming from other languages by John D. Cook
8. Cookbook for R by Winston Chang (Not related to Paul Teetor’s R Cookbook!)
9. A short introduction to the R programming language by Leibniz-Rechenzentrum
10. The Guerilla Guide to R by Nikhil Gopal

Online Tutorial

1. Introduction to R by Data Camp
2. Introduction to statistical programming in R by Leada
3. R Coder by José Carlos

VDO/Audio tutorials

1. Twotorials More than 90 Two minute tutorials on several topics  by Anthony Damico
2. The R-Podcast by Eric Nantz
3. R language for Statistical Computing by  Sentiment Mining Research Center
4. A list of Videos on Data Analysis with R: Introductory, Intermediate, and Advanced Resources by Jeromy Anglim

Subject specific

Economics & Econometrics

Ecology

1. R in Ecology and Evolution
2. Ecology and Epidemiology in R by Various authors

Psychology

1. Using R for Personality Research – Introduction to R [slide] – R: Statistics for all of us [slide] by  William Revelle
2. Learning R for Researchers in Psychology by Jeromy Anglim

Related posts

• A list of R tutorials hosted in the webpages of academic institutes