Course

Reproducible Templates for Analysis and Dissemination

Emory University

Reproducible Templates for Analysis and Dissemination is a valuable course that addresses the challenges of recreating and documenting work. With a focus on reproducibility and dynamic documentation, learners gain insights into creating consistent formats and workflows, leveraging RStudio and R Markdown tools.

The course comprises five modules, each delving into different aspects of reproducibility and dynamic documentation. From understanding the importance of reproducibility and literate programming to customizing document templates and disseminating files, participants gain practical skills to build a professional online portfolio.

  • Module 1: Introduction to Reproducible Research and Dynamic Documentation
  • Module 2: R Markdown: Syntax, Document, and Presentation Formats
  • Module 3: R Markdown Templates: Processing and Customizing
  • Module 4: Leveraging Custom Templates from Leading Scientific Journals
  • Module 5: Working in Teams and Disseminating Templates and Reports

This course is ideal for individuals seeking to enhance their reproducibility skills, whether recreating previous work or disseminating templates effectively to their teams.

Certificate Available ✔

Get Started / More Info
Reproducible Templates for Analysis and Dissemination
Course Modules

This course comprises five modules, covering topics such as reproducible research, R Markdown syntax, customizing document templates, leveraging custom templates, and disseminating templates and reports effectively.

Introduction to Reproducible Research and Dynamic Documentation

Module 1 delves into the history and importance of reproducibility, literate programming, and an introduction to R and RStudio. Participants also gain insights into Git and Github, creating their first document, and organizing files effectively. Additionally, the module offers resources for further learning and practice.

R Markdown: Syntax, Document, and Presentation Formats

Module 2 focuses on R Markdown syntax, document structure, and various presentation formats. Participants learn to work with figures, tables, equations, images, and videos, along with resources for embedding media and creating book formats. The module concludes with a graded quiz prep and practice exercises.

R Markdown Templates: Processing and Customizing

Module 3 explores customizing HTML, Word, and other document formats, along with working with R packages and building document templates. Participants gain insights into adding parameters in templates and resources for further learning and practice.

Leveraging Custom Templates from Leading Scientific Journals

Module 4 provides examples and demos of existing templates, exploring R packages and creating R Markdown templates and packages. Participants also gain access to resources for custom template packages and forking repositories to further their learning.

Working in Teams and Disseminating Templates and Reports

Module 5 focuses on organizing files, disseminating work via RPubs and GitHub, and effectively communicating with teams. Participants also gain access to extensive resources for dissemination and practice opportunities.

More Data Analysis Courses

Accounting Data Analytics

University of Illinois at Urbana-Champaign

Accounting Data Analytics specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques relevant to accounting.

Python Data Products for Predictive Analytics

University of California San Diego

Python Data Products for Predictive Analytics empowers learners to master Python for data-driven predictive tasks, statistical models, and machine learning deployments...

Data Analysis in R: Predictive Analysis with Regression

Coursera Project Network

Data Analysis in R: Predictive Analysis with Regression is a hands-on project that equips you with the skills to build and interpret regression models to make predictions...

Importer des Données dans R

Coursera Project Network

Learn how to import various types of data into R, including CSV files, Excel files, web data, and relational databases in this hour-long guided project.