We offer beginner's level, intermediate and advanced workshops and courses in R, and workshops and courses that emphasise important skills (e.g. Multivariable Regression, Logistic Regression, Principal Components Analysis or Repeated Measures Analysis). 

We have mentored researchers, statisticians and managers in R, and have conducted on-line workshops and courses for learners based in places such as London and Singapore. Following the delivery of these workshops and courses, we have been invited to provide further on-line instruction in statistical methods and on R to users internationally. 

Our preferred approach to instructing in R face-to-face is as follows:

1. Identify the audience and determine their statistical capability and prior experience in R

2. Agree on the precise topics to be covered

3. Make copies of the relevant materials for each session (code, explanatory material and data files) available to all learners several days in advance of each session

4. Discuss the material in detail during sessions, and suggest further reading and exercises, as appropriate

5. Provide the required assistance for each learner, as necessary.

To run each session we suggest the use of a laptop computer and data show screen to demonstrate the use of the R code that we will have provided earlier. Each learner will have access to a computer on which R has been installed. We give brief Power Point presentations where we believe that this approach adds real value. We then demonstrate the important analytic techniques using the laptop and data show screen, while learners follow the instruction using their own computers.


1. The main statistical functions in R
2. Reading and writing data files
3. Sub-setting data, sorting, ranking and ordering data
4. Merging arrays
5. Set membership
6. Creating factors and making summary tables of factors
7. The Normal Distribution (including percentiles, quantiles, correlation, tests for normality and confidence intervals)
8. The Poisson distribution; the Binomial distribution; other distributions
9. Ordinary Least Squares Regression, Multivariable Regression, Logistic Regression (for count data, proportions and binary outcomes)
10. T-tests, Analysis of Variance and Multivariable Analysis of Variance, Analysis of Covariance
11. Introductory Experimental Design
12. Factor Analysis and Principal Components Analysis
13. Power tests and sample size estimation
14. Chi-square tests for categorical variables
15. Writing functions in R
16. Writing software (scripts) in R
17. Control structures (e.g. Loops)
18. Dealing with dates
19. Introductory Monte Carlo parameter estimation
20. Time Series 
21. ARIMA methods for forecasting
22. Graphical methods (including scatterplots, bar charts, pie charts, histograms, box plots and dot charts)
23. Graphical User Interfaces for R
24. Our workshops and  courses also include examples of software for practical data analysis of large data sets.

We offer the following workshops and courses, tailored to suit your specific needs.

Introduction to R
Intermediate R
Advanced R
R for Students
R for the Bio-Medical Sciences
R for the Physical Sciences
R for the Social Sciences and Psychology
R for Economics and Econometrics
Regression through R
Graphics using R

These workshops and courses can be held over one, two or three or more days, or else held in shorter sessions at agreed times. For example, an Introduction to R workshop could include the following topics:

1. A first R session
2. Syntax for analysing one- dimensional data arrays
3. Syntax for analysing two-dimensional data arrays
4. Reading and writing data files
5. Sub-setting data; sorting, ranking and ordering data
6. Merging arrays
7. Set membership
8. The main statistical functions in R
9. The Normal Distribution (correlation, probabilities, tests for normality and confidence intervals)
10. Ordinary Least Squares Regression 
11. T-tests, Analysis of Variance and Multivariable Analysis of Variance
12. Chi-square tests for categorical variables
13. Writing functions in R
14. Writing software (scripts) in R
15. Control structures (e.g. Loops)
16. Graphical methods (including scatterplots, bar charts, pie charts, histograms, box plots and dot charts)
17. Graphical User Interfaces for R

Prior to running any workshop or course, we agree on the topics, and Sigma sends you the relevant materials prior to the sessions.