I'm going to give a course about R, but it's take a lot of time to finish. I will give at least one lesson a week. You can track it here
- (next) Data visualization with R
- Everything you need to know about R
- Read and Write Data
- Manipulate Data
- Manipulate String and Datetime
Actually, beside my works, there are a lot of excellent and free courses in the internet for you
tryr is a course for beginners created by codeschool. This course contains R Syntax, Vectors, Matrices, Summary Statistics, Factors, Data Frames and Working With Real-World Data sections.
This course created by datacamp - a "online learning platform that focuses on building the best learning experience for Data Science in specific". Here is the introduction about this course quoted from authors "In this introduction to R, you will master the basics of this beautiful open source language such as factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis." It contains 6 chapters: Intro to basics, Vectors, Matrices, Factors, Data frames and Lists.
Intermediate and Advanced
R Programming of Johns Hopkins University in coursera Learn how to program in R and how to use R for effective data analysis. This is the second course in the Johns Hopkins Data Science Specialization. It's a 4-weeks course, contains: Overview of R, R data types and objects, reading and writing data (week 1), Control structures, functions, scoping rules, dates and times (week 2), Loop functions, debugging tools (week 3) and Simulation, code profiling (week 4)
This course was introduced by Kevin Markham in r-blogger in september 2014. "I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book." In this course you will learn about Statistical Learning, Linear Regression, Classification, Resampling Methods, Linear Model Selection and Regularization, Moving Beyond Linearity, Tree-Based Methods, Support Vector Machines and Unsupervised Learning