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So please bring your laptops (ideally with Genstat 19ed already installed). The sessions will involve a mixture of examples and practicals.

manipulating and validating complicated data sets.In this workshop we will help you to master recent new features that make it easier for you to identify and validate the most appropriate model, produce graphs in the styles that you prefer, and archive results for convenient future use. Presenters: Roger Payne, David Baird and Vanessa Cave, VSNi Genstat 19ed Masterclass: how to get the analyses you need, the output you want, and the graphs you prefer Cloud computing access will be provided on the day. Attendees should have had some experience working with the R language before and should bring along a machine (Windows, Mac, Linux all great) running a recent build of R and the editor of their choice. We will cover the use of parallel computing for k-fold cross-validation and hyper-parameter optimization. The use of doAzureParallel with the two key machine learning meta-frameworks caret and mlr.This will include approaches to scaling plyr and data.table based manipulations. Parallelism of data intensive tasks such as ETL and feature engineering.Using monte carlo methods, attendees will learn general purpose approaches to executing and reproducing simulation workloads at scale. With doAzureParallel, each iteration of the loop runs in parallel on a pool of Virtual Machines (VM) in the cloud, allowing users to scale up their R jobs to tens to thousands of CPU cores. The doAzureParallel package is a parallel backend that integrates with the native parallel support provided by the R runtime from v2.14. Cloud computing offers the promise of near infinite computing power on tap. This workshop will equip attendees with the skills to scale their R workloads into the cloud. Presenters: Chris Auld and Nigel Parker, Microsoft Scaling R in the Cloud with doAzureParallel