R Markdown Courses and Certifications

Reporting with R Markdown

331 ratings at DataCamp

R Markdown is an easy to use formatting language you can use to reveal insights from data and author your findings as a PDF, HTML file, or Shiny app.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Reproducible Research

27 ratings

Publish analyses others can rerun: structure a data analysis, write literate documents with knitr and Markdown, and apply a reproducibility checklist to your work.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Developing Data Products

17 ratings

Build data products in R: create Shiny applications, interactive graphics with GoogleVis, Plotly and Leaflet maps, R Markdown presentations, and your own R packages.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Reproducible Templates for Analysis and Dissemination

20 ratings at Coursera

Build reproducible documents in R Markdown and RStudio: customize syntax and layouts, automate templates, apply journal templates from Elsevier and IEEE, and publish online.

• Coursera
• 5 weeks, 4 hours a week
• Self-Paced
• Paid Course

R Programming 101

Quarto: Creating Professional Data Analysis Documents in R Studio

Discover how Quarto enhances R programming by enabling professional document creation with integrated text, graphics, references, and data visualization capabilities.

• YouTube
• 14 minutes
• Self-Paced
• Free Video

Samuel Chan

Making a CV with R and RStudio: Professional and Semantic

Learn to create professional CVs using R and RStudio with the vitae package. Explore 6 templates and leverage markdown for customization in this efficient, programmer-friendly approach to resume building.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Building Dashboards with flexdashboard

51 ratings at DataCamp

Communication is a key part of the data science process.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Codio Career Certificate

Data Science and Analysis Tools - from Jupyter to R Markdown

26 ratings at Coursera

Learn to wrangle, analyze, and visualize data sets in R with RStudio and Tidyverse and in Python, communicating results from Jupyter notebooks to R Markdown.

• Coursera
• 12 weeks, 2 hours a week
• Self-Paced
• Paid Course
• 4 courses

Getting Started with Data Visualization in R

313 ratings at Coursera

Import data into R, manipulate it with tidyverse packages, and produce reproducible reports and figures using R Markdown.

• Coursera
• 3 weeks, 4 hours a week
• Self-Paced
• Paid Course

Introduction to R Programming and Tidyverse

49 ratings at Coursera

Learn R from scratch: write functions, visualize data with ggplot2, wrangle it with dplyr verbs, and build reproducible reports in RMarkdown.

• Coursera
• 4 weeks, 6 hours a week
• Self-Paced
• Paid Course

Data Science: Productivity Tools

40 ratings at edX

Keep your projects organized and produce reproducible reports using GitHub, git, Unix/Linux, and RStudio.

• edX
• 8 weeks, 1-2 hours a week
• Self-Paced
• Free Online Course (Audit)

Ball State University

Data Visualization

Create data visualizations in R: apply the grammar of graphics, build histograms, scatter plots and boxplots with ggplot2, and embed images and tables in R Markdown.

• Coursera
• 5 weeks, 3 hours a week
• Self-Paced
• Paid Course

Codio

Visualizing Data & Communicating Results in R with RStudio

Create comparison, composition, distribution, and specialized charts in R — bar charts, histograms, heat maps — then export them as documents with R Markdown.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Exploratory Data Analysis and Visualization

Explore datasets in R: clean data with tidyverse, visualize with ggplot2 and Plotly, apply PCA and correlation heatmaps, and report findings in R Markdown.

• Coursera
• 5 weeks, 5 hours a week
• Self-Paced
• Paid Course

Coursera

Transform, Analyze, and Report Data with R

Build end-to-end R workflows: wrangle data with dplyr, automate parameterized reports in R Markdown and knitr, and evaluate models with ROC curves and k-fold cross-validation.

• Coursera
• 3 weeks, 1 hour a week
• Self-Paced
• Paid Course