Data Science in R

Attendee prerequisites for this tutorial are listed below.

R is an open-source statistical programming environment. It is widely used by academic statisticians and is become increasing popular in many applied domains. In this half-day tutorial, you’ll learn:

• The strengths and weaknesses of a tool that many data scientists use as their secrete sauce
• What the key tools of the data scientists toolbox look like in R. How do you data munging and manipulation, visualisation and modelling?
• Where to go next to scale your R code to deal with massive data.

The course will begin with an brief introduction to the R language. I’ll compare R to languages that you may be more familiar with, discussing R’s functional and OO heritages. I’ll continue with a discussion of how you can use R with your existing tools, and discuss the pros and cons of a CLI vs a GUI for creating visualisations and analysing data. You’ll also learn the basic data structures and some of the tools (like subsetting) most important for fluent R use.

Next I’ll outline the most important tools for data manipulation, visualisation and modelling. We don’t have time to cover them in depth, but you’ll see the key tools and learn where to learn more. I’ll focus on the tools that allow you to fluidly move between visualisation and modelling, illustrated with a case study exploring around half a million deaths in Mexico. You’ll also learn a new strategy for dealing with large data (the inverse of the information-seeking mantra).

TUTORIAL PREREQUISITES
Attendees should have a recent install of R and Rstudio.

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