The Basics of R: A Quick Refresher
Evgeny Osin, the deputy head of the lab, held a master class on data analysis in R. Inside you will find an interesting links for self-study of R.
Here are a few resources for self-study R from Polina Beloborodova, an employee of our laboratory:
English resources:
1) DataCamp (they are taught to program in R from the basics to the application of specific statistical methods, there are also many tutorials and articles there)
There are many courses, but it seems to me that these tasks will be most useful for us:
- Introduction to R
- Intermediate R
- Intermediate R: Practice
- R Programming Track
- Data Visualization with ggplot2: part 1, part 2, part 3
- Dealing with Missing Data in R
- Exploratory Data Analysis
- Experimental Design in R
- Factor Analysis in R
- Longitudinal Analysis in R
2) Statistics with R specialization in Coursera (Duke University; 4 courses + final project; I am now finishing the first course, I like it very much - clear lectures, technical support works fine in forums)
3) Basic Statistics and Inferential Statistics courses from Methods and Statistics in Social Sciences specialization at Coursera (University of Amsterdam; practical tasks are performed in R; good courses to refresh statistics knowledge a bit, but I like Duke more)
About other resources you can read here.