Analysing Your Data

This tutorial will introduce a few options for what to do next once you have your data in the workspace. Most researchers will have a preference for what analytical tools they use: here are a few options available to you in the workspace.

Filtering and Combining Database Tables

While your workspace supports the use of SQL, there is a wizard-style tool that allows for the filtering and combining of database tables into what are known as custom views.

Data Table Analytics

Your workspace comes pre-loaded with over 20 analytical modules that cover the core of medical statistics: from frequency bar charts to logistic regression. These modules are written in R, but are operated using a simple point-and-click system.

R and Jupyter Notebook

Your workspace contains R Development Environment and Jupyter Notebook in-built applications by default. The guide below will show you how to interact with the database tables in your workspace through these apps.

Virtual Machines

Many researchers would prefer to use their own specialised tooling in the workspace through a virtual machine. The following will explain how to provision one.

Once you have your virtual machine in the workspace, you can find comprehensive help on the use of them (including set-up, patching, etc.) as well as straightforward instructions on how to install software on them (such as Anaconda, PLINK, GitLab, Docker, etc.)