This post will outline how to summarise variables in R in terms of means and standard deviations etc.

In this post, we will cover the basics on how to convert date/time variables in R.

In this post we will introduce you to graphs in R. We will start with how to graph in base R, followed by an introduction to the most popular R graphing package, ggplot2.

Now you have learn’t the basics of plotting in r, we will show you how to edit the plots to be publication ready.

Inferential statistics, including t-test and ANOVA, are commonly used when analysing health research data. This post will walk you through how to conduct both statistical tests as well as discuss their alternatives.

Correlations are a highly useful statistic to explore relationships within your data. R offers a simple syntax to calculate correlations and a number of packages are available to improve the output of these calculations.

Linear models are used to examine the relationship between two or more variables. R has a simple syntax to define such models as well as a range of functions to examine and present your results.

The Bland-Altman is a way of both numerically and visually assessing the agreement between two different or repeated measurements. In this post, we will cover the basics of how to create the necessary values and how to create a plot in R.

This post will outline how to export processed data summaries or graphs from RStudio so that you can either save outputs for later or use them for publications .

Loops and conditionals are essential tools in any programmers arsenal to solve complex problems. This post provides an introduction to these key programming concepts.