R Lab and Code

Welcome! Here you will find material for R lab and code for this course.

Set up

You need R for these exercises. It is recommended that you have R and RStudio on your laptop, but if there are problems with installation, you can choose Posit Cloud.

If you don’t have R yet, please read this page to get started.

Data and R scripts

Datasets can be found in this folder. R scripts used for each section are placed in this folder.

In the first R lab session, we will go through how to download them from GitHub; you can also try it yourself following instructions on this page.

If you have trouble downloading datasets from GitHub, please let us know as soon as possible.

Lab notes and exercises

In the lab sessions, you will have time to go through the examples and exercises with the help from the instructors. In the lab notes you can find:

  • Brief summary of the topics discussed in class;
  • Worked data analysis examples with code and visualization;
  • Exercises and solution
How much programming do I need to learn?

If this is the first time you write R code, you might find it a bit overwhelming. It is ok! Programming is NOT the focus of this course.

Not all code shown in class are mandatory; it’s more for you to understand the topics we discussed.

To help you with the exercises and homework, we have created this list of commands. They should cover the most important commands for this course.

Week 1

Topics Lab notes
Lab 1 Create new project, workspace navigation Get started in Rstudio
Variable, data types and structure, basic data manipulation, import data Introduction to R
Lab 2 Exploring a dataset, descriptive statistics Descriptive statistics
Lab 3 Evaluation of diagnostic tests, sensitivity, specificity Probability, diagnostic tests
Lab 4 Binomial and normal distribution Statistical distributions
Lab 5 Inference and t-test t-test
Lab 6 Proportions, contingency table, chi-square test Categorical data analysis

Week 2

Topics Lab notes
Lab 7 Wilcoxon signed test, rank sum test Non-parametric tests
Sample size, power calculation Exercises, code: lecture, code: exercises
Lab 8 Correlation, univariate analysiss Linear regression I
Lab 9 Multiple regression Linear regression II
Lab 10 Model diagnostics, selection Linear regression III
Lab 11 Logistic regression Logistic regression
Lab 12 Kaplan-Meier curve, log rank tests Survival analysis

(EDA: exploratory data analysis)

Useful resources

List of commands that are useful for this course: list of commands

Book (Wickham et al) R for Data Science (2e)

Demo of webR