A condensed introduction to the software package R.

Creating models of our data is the ultimate challenge of statistics and allow us to generalise our testing and adjust for other factors: ~ Crash course in why we build models (prediction and understanding) ~ The linear model and what it means ~ Adjusting for other variables (how and why) ~ Models vs Tests - What's the difference? ~ What can't modelling do (Correlation vs Causation)

Now that we know how to bring data into R, now we need to know what to do with it: ~ What tests can we do? ~ What are p-values? ~ Which test do I need? ~ What are we assuming when we do these tests?...

After the crash course on the basics of R, we'll delve into data by tidying a messy dataset using a set of extensions to R known as the "tidyverse" of packages designed to make: ~ What are packages and what is the "tidyverse"? ~ Getting to our data the tidy way, how to use the read_* functions ~ sticking data together (joins, binds and tribbles) ~ dplyr and piping, the good grammar of R ~ tibbles and mutating, modernisation data structures and editing them ~ lubridate to smooth out date operations

We'll start our R Tutorial course by looking at the essential information needed to use and understand R including getting to grips with its syntax and how the language works: ~ Introduction to R and RStudio and the open source heritage ~ Vectors, vectorisation and data types (numbers, strings, logicals and more) ~ Assignment of variables and the use of functions ~ Essential descriptive statistics ~ Probability density functions (pdfs) and Cumulative density functions (cdfs) ~ Plotting with Base R ~ Advanced programming - Loops & Branches (for, while and if...else) ~ How to find the help you need