Course Summary
Learn how to create reproducible research projects using R Markdown, Git, and GitHub. This course will teach you how to organize, document, and share your research in a way that promotes transparency and makes it easy for others to reproduce your results.Key Learning Points
- Understand the importance of reproducibility in research
- Learn how to use R Markdown to create reproducible reports
- Learn how to use Git and GitHub for version control and collaboration
Related Topics for further study
Learning Outcomes
- Create reproducible research reports using R Markdown
- Use Git and GitHub for version control and collaboration
- Understand the importance of reproducibility in research
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of R programming
- Familiarity with Git and GitHub is helpful but not required
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Hands-on exercises
Similar Courses
- Data Science: Reproducible Workflow
- Data Science: Collaboration and Reproducibility
Related Education Paths
Notable People in This Field
- Hadley Wickham
- Karl Broman
Related Books
Description
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.
Knowledge
- Organize data analysis to help make it more reproducible
- Write up a reproducible data analysis using knitr
- Determine the reproducibility of analysis project
- Publish reproducible web documents using Markdown
Outline
- Week 1: Concepts, Ideas, & Structure
- Introduction
- What is Reproducible Research About?
- Reproducible Research: Concepts and Ideas (part 1)
- Reproducible Research: Concepts and Ideas (part 2)
- Reproducible Research: Concepts and Ideas (part 3)
- Scripting Your Analysis
- Structure of a Data Analysis (part 1)
- Structure of a Data Analysis (part 2)
- Organizing Your Analysis
- A Note of Explanation
- Syllabus
- Pre-course survey
- Course Book: Report Writing for Data Science in R
- Week 1 Quiz
- Week 2: Markdown & knitr
- Coding Standards in R
- Markdown
- R Markdown
- R Markdown Demonstration
- knitr (part 1)
- knitr (part 2)
- knitr (part 3)
- knitr (part 4)
- Introduction to Course Project 1
- Week 2 Quiz
- Week 3: Reproducible Research Checklist & Evidence-based Data Analysis
- Communicating Results
- RPubs
- Reproducible Research Checklist (part 1)
- Reproducible Research Checklist (part 2)
- Reproducible Research Checklist (part 3)
- Evidence-based Data Analysis (part 1)
- Evidence-based Data Analysis (part 2)
- Evidence-based Data Analysis (part 3)
- Evidence-based Data Analysis (part 4)
- Evidence-based Data Analysis (part 5)
- Week 4: Case Studies & Commentaries
- Caching Computations
- Case Study: Air Pollution
- Case Study: High Throughput Biology
- Commentaries on Data Analysis
- Introduction to Peer Assessment 2
- Post-Course Survey
Summary of User Reviews
Discover the importance of reproducibility in research with this course on Reproducible Research. Students have consistently praised this course for its comprehensive coverage and engaging lectures, resulting in a high overall rating.Key Aspect Users Liked About This Course
Many users have appreciated the interactive assignments and practical examples that are provided throughout the course.Pros from User Reviews
- Comprehensive course content
- Engaging lectures and interactive assignments
- Practical examples
- In-depth coverage of reproducibility in research
- Great for beginners and advanced learners
Cons from User Reviews
- Some users found the lectures to be too long
- Lack of interaction with course instructors
- Some users found the assignments to be too time-consuming
- Not enough emphasis on statistics and data analysis
- Some users found the course to be too basic
Roger D. Peng, PhD
4.6 Raiting
Docker Containers for Data Science and Reproducible Research
- 4.3
-
![]()
![]()
![]()
![]()
![]()
Course Tutorial to make your work reproducible using Docker Containers Get excited! Course will help to setup Docker Environment on any machine equipped with Docker Engine (Mac, Windows, Linux)....
Statistics and R
- 0.0
-
![]()
![]()
![]()
![]()
![]()
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. You can take the entire series or individual courses that interest you....
Reproducible Research
- 4.6
-
![]()
![]()
![]()
![]()
![]()
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Week 1: Concepts, Ideas, & Structure...
Statistics for Genomic Data Science
- 4.2
-
![]()
![]()
![]()
![]()
![]()
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University....
