Bookdown R


The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of. I have written a draft book in R Markdown. Two questions: Is it possible to include a 'cover page' with graphic and text in the index.rmd file so that the rendering in bookdown- shows a cover page. Data Scientist / R & Shiny Developer.


Typically when creating a website there are various common elements you want to include in all pages (e.g. output options, CSS styles, header and footer elements, etc.). Here’s some additions to the example above that makes use of common elements:



Sky go without app. styles.css

Note that we’ve included an output element within our _site.yml file. This defines shared output options for all R Markdown documents within a site. Note that individual documents can also include their own output options, which will be merged with the common options at render time.

As part of our common output options we’ve specified an HTML footer (via the include: after-body: option) and CSS stylesheet. Note that you can also include HTML before the body or in the document <head>, see the documentation on includes for more details.

In addition to whatever common options you define, the are two output options which are automatically set when rendering a site:

  1. The self_contained option is set FALSE; and

  2. The lib_dir option is set to site_libs.

These options are set so that dependent files (e.g jQuery, Bootstrap, HTML widget libraries, etc.) are shared across all documents within the site rather than redundantly embedded within each document.

EPSY 887: Intro to R for Academic Researchers

Instructor: Jason Bryer, Ph.D. ([email protected])
Class Time: Thursdays 4:15 p.m. - 7:05 p.m.
Class Location: AS 013
Grading: 3 credits, S/U grading
Course Website:

R is a statistical program language that has grown in popularity over the last two decades arguably becoming the de facto standard in statistics, data science, and learning analytics (see e.g. Muenchen, 2018; Suda, 2017). R was designed to be scriptable and extensible which makes it well suited for conducting reproducible research and creating publication quality figures. Moreover, the over 10,000 packages available on the Comprehensive R Archive Network (CRAN) provide researchers with access to a rich community and library of tools for conducting research.


This course will explore the skills and tools necessary for conducting data preparation and analysis with R. The first third of the course will focus on learning R. The middle third will explore some of the more common statistical procedures in R including: classification and regression trees; logistic regression; propensity score analysis; missing data imputation; and other topics as time permits. The final third of the class will be left for topics of special interest to students and their research agendas. Class examples will utilize the Programme of International Student Assessment (PISA), a large scale international study conducted every three years. Other open and freely available datasets will also be discussed as appropriate.

Learning Objectives

At the completion of this course, students will be able to…

Bookdown R Reviews

  • Conduct a complete analysis in R including data entry, tidying, analysis, and reporting.
  • Structure analyses to facilitate reproducible research.
  • Create advanced visualizations using the grammar of graphics.
  • Conduct advanced statistical analyses in R.


It is recommended that students have at least two graduate level statistics courses (EPSY 530 and EPSY 630 or equivalent). Those with fewer than two graduate statistics courses but with other relevant experience should get instructor permission. No prior experiences with R is expected, but some experience with using statistical software would be helpful.


This course is graded as pass/fail. Successful students will attend and participate in the weekly classes as well as contribute to the course wiki. Weekly assignments will be assigned as appropriate.

  • Participation (10%) - Participate in class and the Slack discussions.
  • Labs (20%) - R Labs will be assigned as appropriate.
  • Website (30%) - Students are to create a website using either the blogdown or bookdown R package.
  • Presentation (40%) - The culmination of the course will be a short (20 to 30 minutes) presentation and document outlining the analysis you conducted with your dataset. Students are encouraged to bring their own dataset (e.g. data to be used for a dissertation), but that is not necessary. Many free and public datasets are available for use and will be discussed in the first couple classes.


Muenchen, R. A. (2018). The popularity of data science software. Retrieved from

Bookdown Reference

Suda, B. (2017). 2017 data science salary survey. Sebastopol, CA: O’Reilly Median, Inc.

Thieme, N. (2018). R generation: The story of a statistical programming language that became a subcultural phenomenon. Significance, 4(14), 14-19.

Academic Integrity

Whatever you produce for this course should be your own work and created specifically for this course. You cannot present work produced by others, nor offer any work that you presented or will present to another course. If you borrow text or media from another source or paraphrase substantial ideas from someone else, you must provide a reference to your source.

The University policy on academic dishonesty is clearly outlined in the Student Bulletin, and includes, but is not limited to plagiarism, cheating on examinations, multiple submissions, forgery, unauthorized collaboration, and falsification. These are serious infractions of University regulations and could result in a failing grade for the work in question, a failing grade in the course, or dismissal from the University.

Reasonable Accommodation

Bookdown Render_book Pdf

Reasonable accommodations will be provided for students with documented physical, sensory, systemic, cognitive, learning and psychiatric disabilities. If you believe you have a disability requiring accommodation in this class, please notify the Director of Disabled Student Services (Campus Center 137, 442-5490). That office will provide the course instructor with verification of your dis- ability, and will recommend appropriate accommodations. For more information, visit the website of the UAlbany Office for Disabled Student Services.