I. Data visualization course:

• CS171 :This is a great introduction for someone who is interested in learning about data visualization. The course videos are uploaded every year. Student projects are listed under projects and is a great way to learn about some innovative ways in which you can visualize data.
• Flowing data : I love this website. In fact one of the very reason i started using R was because of Nathan Yau’s R tutorials. I visit his website almost every day as he posts some cool visualizations on a daily basis. He has tutorials some of which are free and some are paid. The annual membership is worth every penny.

II. Resources to Learn R:

• R blogger : great way to keep updated about development’s in R. New R packages, conferences, jobs etc. If you are new to R you will learn a lot just by visiting this blog everyday.
• R Studio : This page has really good webinars on R, Shiny and conferences.
• R Programing : The r programing course taught by Roger Peng on coursera website is a great introduction to R.  If you do not want to pay for the course even just auditing the course will help you get started on R.
• Statistical Learning : This course is a great introduction to Machine Learning using R. The two professors have also written a book which they use in the course. The book is free to download.
• Learning XML and JSON : This link has a lot of presentations on using R packages to extract data from XML or JSON files. It also discusses other useful ways to manipulate data using R.

III. Free Online Books: and Journals:

• R for Data Science : Hadley Wickham and Garrett Grolemund have written this amazing resources on using R. Most of the chapters are based on packages developed by Hadley.
• Tidy Text Mining in R : A free book on text mining in R.
• Elements of Statistical Learning : A great way to get acquainted to Machine Learning.
• Interactive Data Visualization (D3) : A great way to get started on learning D3. The book is free to read online.
• Networks : A free online book for learning Network Science.
• Journal of Economic Perspective : All the articles on this website are freely downloadable. Almost all articles have data sets and code for replication.
• 100 free data science books : Has a very good collection of data science books , not just limited to R.

IV. Learning R through Blog entries and Powerpoint slides:

• Shiny official Website : A great place to get started with Shiny and a lot of examples under gallery and references section of the website.
• Tricks in Shiny: Its a very well written blog on tricks one can use in their Shiny Apps.
• Writing HTML widgets : I have never written a html widget but seems like a good resource to me.
• Dealing with dates : A very useful introduction using lubridate package in R to work with dates.
• Text Mining topics:

Trump tweets (text analysis) : A good analysis and well written blog entry on using twitterR packages.

Text mining example of Treaty : A very well written introduction of text mining and visualization.

V. Some good websites for inspirations and More:

• New York Times Visualization : This is a great collection of some of the New York Times visualizations. They are bit old but great to get ideas and inspiration. (Note :Sometimes the site has links that will not work, not sure why.)
• Andy Kirk : The website is full of resources and visualizations
• Visual Complexity : A good collection of visualization.
• shiny apps : Has a good collection of Shiny Apps.
• Jer Thorp : Has source code to some visualization generated by him for New York Times. His Ted Talk is very interesting as well.
• Simply Stat : Writes really interesting blog posts.  Authors also offer data science specialization courses on Coursera.