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Melinda Higgins
This course will assist you with recreating work that a previous coworker completed, revisiting a project you abandoned some time ago, or simply reproducing a document with a consistent format and workflow. Incomplete information about how the work was done, where the files are, and which is the most recent version can give rise to many complications. This course focuses on the proper documentation creation process, allowing you and your colleagues to easily reproduce the components of your workflow. Throughout this course, you'll receive helpful demonstrations of RStudio and the R Markdown language and engage in active learning opportunities to help you build a professional online portfolio.
Register for this course and see more details by visiting:
OpenCourser.com/course/zq1ata/reproducible
What's inside
Syllabus
Introduction to Reproducible Research and Dynamic Documentation
This module provides an introduction to the concepts surrounding reproducibility and the Open Science movement, RStudio and GitHub, and foundational cases and authors in the field.
Read more
Syllabus
Introduction to Reproducible Research and Dynamic Documentation
This module provides an introduction to the concepts surrounding reproducibility and the Open Science movement, RStudio and GitHub, and foundational cases and authors in the field.
Read more
R Markdown: Syntax, Document, and Presentation Formats
This module explores the R Markdown syntax to format and customize the layout of presentations or reports and will also look at inserting and creating objects such as tables, images, or video within documents.
R Markdown Templates: Processing and Customizing
This module goes further with R Markdown to help turn documents, reports, and presentations into templates for easier automation, reproducibility, and customization.
Leveraging Custom Templates from Leading Scientific Journals
This module delves into custom templates available for websites, books, and scientific publishers, such as Elsevier and the IEEE, with the chance to create your first R Package.
Working in Teams and Disseminating Templates and Reports
This module focuses on helpful tips for sharing and using the templates you create, as well as methods for organizing content. We'll also look at a few web-publishing services.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Tailored to learners who want to revisit abandoned projects or recreate work from previous colleagues
Focuses on proper documentation creation, allowing for easy reproduction of workflow components
Leverages RStudio and R Markdown for demonstrations
Incorporates active learning for building an online portfolio
Suitable for learners interested in reproducible research and dynamic documentation
Led by instructors Melinda Higgins
Save this course
Save Reproducible Templates for Analysis and Dissemination to your list so you can find it easily later:
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Reviews summary
Well-received course for learning git and r markdown
Learners say that this course is an effective and engaging introduction to using Git, GitHub, R, RStudio, and R Markdown, especially for beginners. Expert instructors and well-structured materials make the course easy to follow, while efficient assignments help learners master the concepts. However, be aware that final assignment evaluations may be delayed due to the need for peer review.
Instructors are knowledgeable and make the course interesting.
"Professor Melinda has excellent communication skills that make the course much more exciting."
Course is ideal for learners new to Git and R Markdown.
"Especially if you are in the first 20 hours of learning to use Git, github, R|RStudio, R Markdown, I think this course will be well-worth your time; an efficient and friendly introduction to those tools/platforms."
Excellent course with clear structure and helpful readings.
"Very well structured, the classes and readings are perfectly planned to learn how to use R Markdown."
Assignments are well-designed and help learners improve.
"Great skill!"
"Outstanding course!"
Final assignment evaluations may be delayed due to peer reviews.
"You need to wait for the action of other students to have your final assignment evaluated."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Reproducible Templates for Analysis and Dissemination with these
activities:
Read 'R for Data Science'
Show steps
This book provides a comprehensive introduction to R and data science, which will strengthen your foundation for this course.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read through the book, paying attention to the concepts and techniques covered
-
Work through the exercises and examples provided in the book
Review R and RStudio
Show steps
R and RStudio are the base technologies used in this course, so becoming comfortable with them will allow you to maximize course outcomes.
Browse courses on
R
Show steps
-
Review R syntax and data structures
-
Practice writing and executing simple R scripts
-
Familiarize yourself with the RStudio interface
Organize course materials
Show steps
Organizing your materials will make it easier to find and review information, enhancing your learning experience.
Show steps
-
Create a system for organizing your notes, assignments, and other materials
-
Regularly review and update your materials to ensure they are complete and up-to-date
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete R Markdown exercises
Show steps
Writing R Markdown documents is a core skill in this course, so practicing with exercises will enhance your fluency and problem-solving abilities.
Browse courses on
R Markdown
Show steps
-
Follow along with R Markdown tutorials
-
Complete practice exercises from the course materials
-
Create your own R Markdown documents
Participate in a study group
Show steps
Discussing concepts with peers can clarify your understanding and enhance your problem-solving abilities.
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss course materials and work on projects together
Explore R packages for reproducibility
Show steps
R packages offer powerful tools for enhancing reproducibility, so exploring them will expand your knowledge and improve your workflow.
Browse courses on
Reproducibility
Show steps
-
Identify R packages relevant to reproducibility
-
Install and learn how to use these packages
-
Apply these packages to your own projects
Develop a reproducible report
Show steps
Creating a reproducible report is the ultimate goal of this course, so working on a project will help you integrate and apply the skills you learn.
Browse courses on
Reproducibility
Show steps
-
Choose a dataset and research question
-
Analyze the data using R and R Markdown
-
Write a report that documents your workflow and results
-
Publish your report online
Create a personal portfolio
Show steps
Building a portfolio will showcase your skills and knowledge in a tangible way, making it a valuable asset for your future career.
Browse courses on
Portfolio
Show steps
-
Choose a platform for your portfolio
-
Develop projects that demonstrate your abilities
-
Create a website or online presence to showcase your work
-
Share your portfolio with potential employers or clients
Read 'R for Data Science'
Show steps
This book provides a comprehensive introduction to R and data science, which will strengthen your foundation for this course.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read through the book, paying attention to the concepts and techniques covered
- Work through the exercises and examples provided in the book
Review R and RStudio
Show steps
R and RStudio are the base technologies used in this course, so becoming comfortable with them will allow you to maximize course outcomes.
Browse courses on
R
Show steps
Show steps
Show steps
- Review R syntax and data structures
- Practice writing and executing simple R scripts
- Familiarize yourself with the RStudio interface
Organize course materials
Show steps
Organizing your materials will make it easier to find and review information, enhancing your learning experience.
Show steps
Show steps
Show steps
- Create a system for organizing your notes, assignments, and other materials
- Regularly review and update your materials to ensure they are complete and up-to-date
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete R Markdown exercises
Show steps
Writing R Markdown documents is a core skill in this course, so practicing with exercises will enhance your fluency and problem-solving abilities.
Browse courses on
R Markdown
Show steps
Show steps
Show steps
- Follow along with R Markdown tutorials
- Complete practice exercises from the course materials
- Create your own R Markdown documents
Participate in a study group
Show steps
Discussing concepts with peers can clarify your understanding and enhance your problem-solving abilities.
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss course materials and work on projects together
Explore R packages for reproducibility
Show steps
R packages offer powerful tools for enhancing reproducibility, so exploring them will expand your knowledge and improve your workflow.
Browse courses on
Reproducibility
Show steps
Show steps
Show steps
- Identify R packages relevant to reproducibility
- Install and learn how to use these packages
- Apply these packages to your own projects
Develop a reproducible report
Show steps
Creating a reproducible report is the ultimate goal of this course, so working on a project will help you integrate and apply the skills you learn.
Browse courses on
Reproducibility
Show steps
Show steps
Show steps
- Choose a dataset and research question
- Analyze the data using R and R Markdown
- Write a report that documents your workflow and results
- Publish your report online
Create a personal portfolio
Show steps
Building a portfolio will showcase your skills and knowledge in a tangible way, making it a valuable asset for your future career.
Browse courses on
Portfolio
Show steps
Show steps
Show steps
- Choose a platform for your portfolio
- Develop projects that demonstrate your abilities
- Create a website or online presence to showcase your work
- Share your portfolio with potential employers or clients
Career center
Learners who complete Reproducible Templates for Analysis and Dissemination will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop and implement data-driven solutions to complex problems. They use a range of techniques, including machine learning, artificial intelligence, and statistical modeling. The course's focus on reproducibility and documentation would help Data Scientists ensure the accuracy and reliability of their results.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patrones. They use their findings to make recommendations and improve business decisions. The course's focus on data wrangling, visualization, and reproducible workflows would provide a valuable toolkit for Data Analysts.
Researcher
Researcher
Researchers design and conduct studies to answer questions and advance knowledge. They use a variety of methods, including data analysis, surveys, and experiments. The course's focus on reproducible research and open science aligns well with the ethical and methodological standards of research.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use their findings to make informed decisions and solve problems. The course's focus on reproducible research and open science aligns well with the ethical and methodological standards of statistics.
Risk Analyst
Risk Analyst
Risk Analysts identify, assess, and manage risks. They work with organizations to develop and implement strategies to minimize potential losses. The course's focus on reproducible research and documentation would provide a valuable foundation for Risk Analysts, as they need to ensure the accuracy and reliability of their risk assessments.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models. They work with data scientists and other stakeholders to identify business problems that can be solved using machine learning. The course's focus on reproducible research and documentation would provide a valuable foundation for Machine Learning Engineers, as they need to ensure the accuracy and reliability of their models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They play a critical role in the financial industry, helping to manage risk and make informed investment decisions. The course's focus on reproducible research and documentation would provide a valuable foundation for Quantitative Analysts, as they need to ensure the accuracy and reliability of their models.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve scientific and medical problems. They design and conduct studies, analyze data, and develop new statistical methods. The course's emphasis on reproducibility and open science aligns well with the ethical and methodological standards of biostatistics.
Healthcare Data Analyst
Healthcare Data Analyst
Healthcare Data Analysts use data to improve the delivery and quality of healthcare. They analyze patient records, claims data, and other sources to identify trends, patterns, and opportunities for improvement. The course's focus on reproducible research and open science would provide a solid foundation for Healthcare Data Analysts, as they rely on accurate and reliable data analysis to make informed decisions.
Analyst
Analyst
Analysts plan and execute research projects for companies or organizations. They gather, analyze, and interpret data using various techniques, including data analytics, forecasting, and modeling. The course's focus on reproducible research and documentation would provide a solid foundation for an Analyst, as they rely on accurate and consistent data analysis and presentation.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to improve the efficiency and effectiveness of organizations. They develop and implement models to optimize processes, reduce costs, and improve decision-making. The course's focus on reproducible research and documentation would provide a solid foundation for Operations Research Analysts, as they rely on accurate and reliable data analysis to make informed decisions.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software, hardware, and other technical products. They work with engineers and other stakeholders to understand the product and develop clear and concise documentation. The course's focus on reproducible templates and documentation would provide a valuable foundation for Technical Writers, as they need to ensure the accuracy and consistency of their documentation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with stakeholders to understand requirements, design solutions, and implement and test code. The course's focus on reproducible workflows and documentation would provide a valuable foundation for Software Engineers, as they need to ensure the accuracy and reliability of their software.
Web Developer
Web Developer
Web Developers design and develop websites. They work with clients to understand their needs and create websites that are both functional and visually appealing. The course's focus on reproducible templates and documentation would provide a valuable foundation for Web Developers, as they need to ensure the accuracy and consistency of their websites.
For more career information including salaries, visit:
OpenCourser.com/course/zq1ata/reproducible
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Reproducible Templates for Analysis and Dissemination .
Save
Comprehensive guide to R Markdown, and it is essential reading for anyone who wants to use R Markdown to create reproducible documents.
Save
Is the definitive guide to the R Markdown language, which is used to create dynamic documents that combine code, text, and graphics. It is an essential resource for anyone who wants to use R Markdown to create presentations, reports, or websites.
Is an introduction to the R programming language, focusing on good programming practices and how to write efficient and maintainable R code. It would be a valuable resource for anyone who wants to learn how to write better R code.
Guide to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for anyone who wants to learn how to use R for machine learning.
Save
Guide to creating and using R packages. It covers topics such as package design, testing, and documentation. It valuable resource for anyone who wants to learn how to create and use R packages.
Save
Comprehensive introduction to the R programming language, and it valuable resource for anyone who wants to learn more about R. It also contains some information on R Markdown, which will be useful for students in this course.
Guide to deep learning with R. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn how to use R for deep learning.
Guide to Bayesian statistics with examples in R and Stan. It covers topics such as Bayesian inference, model checking, and posterior predictive checks. It valuable resource for anyone who wants to learn how to use Bayesian statistics with R and Stan.
Save
Is less directly relevant to this course than the previous two books listed, it valuable resource for anyone who wants to learn more about advanced R programming techniques. It provides more depth to some of the topics covered in this course, such as data manipulation, visualization, and modeling.
Great resource for anyone who wants to learn more about reproducible research with R and RStudio. It more accessible introduction for beginners.
Save
Quick reference guide to R Markdown, and it useful resource for anyone who wants to learn more about the basics of R Markdown.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zq1ata/reproducible
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Good to know
Tailored to learners who want to revisit abandoned projects or recreate work from previous colleagues
Focuses on proper documentation creation, allowing for easy reproduction of workflow components
Leverages RStudio and R Markdown for demonstrations
Incorporates active learning for building an online portfolio
Suitable for learners interested in reproducible research and dynamic documentation
Led by instructors Melinda Higgins
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