Video: knitr, R Markdown, and R Studio: Introduction to Reproducible Analysis

This post presents the video of a talk that I presented in July 2012 at Melbourne R Users on using knitr, R Markdown, and R Studio to perform reproducible analysis. I also provide links to a github repository where the R markdown examples can be examined and the slides can be downloaded.

Converting Sweave LaTeX to knitr LaTeX: A case study

The following post documents the steps I needed to take in order to convert a project using Sweave LaTeX into one using knitr LaTeX.

How to Convert Sweave LaTeX to knitr R Markdown: Winter Olympic Medals Example

The following post shows how to manually convert a Sweave LaTeX document into a knitr R Markdown document. The post (1) reviews many of the required changes; (2) provides an example of a document converted to R Markdown format based on an analysis of Winter Olympic Medal data up to and including 2006; and (3) discusses the pros and cons of LaTeX and Markdown for performing analyses.

Getting Started with R Markdown, knitr, and Rstudio 0.96

This post examines the features of R Markdown using knitr in Rstudio 0.96. This combination of tools provides an exciting improvement in usability for reproducible analysis. Specifically, this post (1) discusses getting started with R Markdown and knitr in Rstudio 0.96; (2) provides a basic example of producing console output and plots using R Markdown; (3) highlights several code chunk options such as caching and controlling how input and output is displayed; (4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and (5) discusses the implications of R Markdown. This post was produced with R Markdown. The source code is available here as a gist. The post may be most useful if the source code and displayed post are viewed side by side. In some instances, I include a copy of the R Markdown in the displayed HTML, but most of the time I assume you are reading the source and post side by side.

R versus Matlab in Mathematical Psychology

I recently attended the 2011 Australasian Mathematical Psychology Conference. This post summarises a few thoughts I had on the use of R, Matlab and other tools in mathematical psychology flowing from discussions with researchers at the conference.

Video of Reproducible Research with R: Melbourne R Users 1st Dec 2010

As previously mentioned I gave a talk at Melbourne R Users Group titled "Reproducible Research and R Workflow". It covered technologies including LaTeX, Sweave, R, make, Eclipse, and git. This post shares the video.

R Workflow: Slides from a Talk at Melbourne R Users (1st Dec 2010)

I gave a presentation at Melbourne R Users on the topic of R Workflow. The presentation covered R code organisation, and useful R related tools including Eclipse, StatET, Git, make, Sweave and LaTeX. Also, the slides from the presentation provide links to four complete examples of using R, Sweave, LaTeX, and make.

Sweave Tutorial 3: Console Input and Output - Multiple Choice Test Analysis

This post provides an example of using Sweave to perform an item analysis of a multiple choice test. It is designed as a tutorial for learning more about using Sweave in a mode where console input and output is displayed. Copies of all source code and the final PDF report is provided.

Sweave Tutorial 2: Batch Individual Personality Reports using R, Sweave, and LaTeX

This post documents an example of using Sweave to generate individualised personality reports based on responses to a personality test. Each report provides information on both the responses of the general sample and responses of the specific respondent. All source code is provided, and selected aspects are discussed, including makefiles use of \Sexpr, figures, and LaTeX tables using Sweave.

Sweave Tutorial 1: Using Sweave, R, and Make to Generate a PDF of Multiple Choice Questions

In this post I present an example of using Sweave to prepare a PDF of formatted multiple choice questions. More broadly the example shows how to use Sweave to incorporate elements of a database into a formatted LaTeX document. It aims to be useful to anyone wanting to learn more about the almost magical powers of make, Sweave, and R.

makefiles for Sweave, R and LaTeX using Eclipse on Windows

This post provides a brief introduction to make and makefiles. In particular it describes how to set up make on Windows with an emphasis on using make in Eclipse on projects involving R, Sweave, and LaTeX.

Getting Started with Sweave: R, LaTeX, Eclipse, StatET, & TeXlipse

Being able to press a single button that runs all your statistical analyses and integrates the output into your final report is a beautiful thing. If you have not already heard, this is what Sweave can do for you. However, getting your computer to run Sweave can be a little bit fiddly. Thus, this post: (1) sets out the benefits of Sweave; (2) sets out how to install and configure R, Sweave, and Eclipse on Windows; (3) lists resources for people wanting to learn more about how to use LaTeX and Sweave; and (4) lists some specific resources relevant to researchers in psychology wanting to use these tools.

Linking text, results, and analyses: Increasing transparency and efficiency

I have recently been thinking about the relationship between text in a final report and data analysis. The broader concern is with making the conduct and reporting of statistical analyses more transparent. I am inspired by the ideas of literate programming, Sweave, and open access to data.

Something to aspire to:

• Raw data is shared  (ethics, copyright, and other considerations permitting). 
• Code is shared that shows how the data was imported, transformed, and analysed. This code is well written, commented, and documented.
• The report is shared as opposed to requiring a paid subscription.
• Report output including tables, figures, and some text is linked directly to the analyses in code.

While the aspirations transcend R, I like the prospect of having analyses in R integrated with a final report. The inclusion of tables and figures , at least conceptually is a straightforward idea. However, the inclusion of text in a results section is a little fuzzier. Surely, text in a results section (I'll call it "results text" for short) varies in how it relates to actual analyses. Thus, I had the following questions: 1) What is the unit of results text? 2) How does results text vary and what should be automatically supplied by R?; 3) For results text that should not be supplied by R, how should it be integrated into an analysis process?

Initial thoughts: After a little reflection I had the following thoughts: