Computing and Modelling with R

Day 1: An Introduction to Statistical Computing with R

Day 2: Applied Statistical Modelling with R

Day 3: Data Visualisation with R.

The course is split into three days; participants can attend one day or more. All days will consist of interactive workshops, together with  time for guided computational practice on the material, supported by the lecturer and additional experts on the R language. Lunch will be provided on each day. Computers are provided, or participants can use their own laptop.

Day 1 is suitable for people with no experience of R, and will be an introduction to programming in R. There is little mathematical statistical knowledge assumed, and will be an introduction to the programming language.

Day 2 will be suitable for those that have attended Day 1, or who have some previous experience in  R. It will give an overview of statistical modelling in R.

Day 3 will focus on more advanced techniques for programming in R. It will focus on methods for visualisation in data science, with applications driven from Biological applications, and assumes some programming knowledge in R, such as that from Day 1 of the course.

Course Code S3RICMR

Course Dates 10th April 2018 – 12th April 2018

Places Available 24

Course Leader Dr Ben Parker, Dr Helen Ogden, Prof Ben Macarthur

Course Description

Day 1 Aims:

To introduce a range of statistical methods implemented on computers; to give practice in applying methods and interpreting results from them; to develop the use of computers in the collection, validation, analysis and presentation of data; to help develop the knowledge and experience necessary to implement statistical computing methods.

• enter and manipulate data within R;
• perform basic statistical analyses using R and interpret the output;

Day 1 Syllabus:

• Data manipulation in R
• Getting Help in R
• Writing functions in R
• Conditional execution and loops in R
• Graphics in R
• Apply the above programming skills in R to problems arising in data analysis
• Interpretation of R output
• Introduction to Linear Modelling in R

Day 2 Aims: To introduce, via a hands-on approach, the basic concepts and principals in statistical modelling in a computational paradigm.

After taking this module, students should understand

• why statistical modelling is important,
• the terminology and statistical principles associated with modelling,
• sufficient theory to deal with simple examples and have gained practical hands-on experience in more complex examples,
• how to use R to fit, explore and exploit a variety of statistical models

Day 2 Syllabus:

• Revision on R: Data input, plotting and summaries
• Principles of statistical inference
• Regression: linear and generalised linear modelling
• Model construction and estimation
• Model selection and information criteria
• Shrinkage regression (Lasso and ridge methods)
• Interpretation of random effects and mixed models
• Discrete data and generalised linear mixed models
• Introduction to estimation of mixed models
• Introduction to time series: Autoregression and moving average models

Day 3 Aims:

To introduce a range of data visualisation, dimensionality reduction and clustering techniques and their implementation in R and to give practice in applying these methods to a range of different datasets, primarily arising from the biological sciences.

Day 3 Syllabus:

Introduction to dimensionality reduction methods, including:

• Principal components analysis
• Multidimensional scaling
• t-Distributed Stochastic Neighbour Embedding (t SNE)
  

Introduction to clustering methods, including:

• k-means clustering
• Hierarchical clustering and heatmaps
• Network clustering