Objectives of Day 1

In this practical session you will learn to use RStudio, Rmarkdown and how to implement some of the ideas taught in the first lesson using the R programming language.

Topics

• reading a dataset
• cleaning a dataset
• making plots
• simple hypothesis testing

Interactive exercises

In the next chapters, you will have several simple exercices to complete. In order to learn how to use Rmarkdown, we recommend that you create a new Rmarkdown document (click on the top left of the RStudio interface on the little black arrow next to the green + sign...)

• open a new Rmarkdown document, and give the title corresponding to the day (Day 1 or Day 2)
• save the document under the name day1.Rmd or day2.Rmd
• organize the document into sections corresponding to the different chapters
• write the R code into chunks (under Macos, you can create a new empty chunk using the command + i combination)

Hence, you will be able to redo the whole flow of the analysis.

Dataset used

Throughout this course we will use a common diabetes dataset to practically implement the concepts of data analysis using R.

Diabetes is a collection of metabolic disorders where the blood glucose levels increases drastically due to defective insulin secretion and/or insulin resistance. There are many risk factors associated with diabetes like obesity, old age etc. Diabetes in turn majorly increases the risk of cardiovascular disease. For more information see here.

Our dataset has various pathophysiological measurements of >400 individuals. Clinical parameters like blood glucose levels, cholesterol levels, age, body size, weight, blood pressure etc have been measured. We will use this dataset to explore the statistical properties of each variable (like its distribution, mean value etc) and question relations between variables (like is blood glucose level correlated with obesity?).

Next Chapter (Getting started with RStudio)