# R

R has become one of the most widely used programs for statistical calculations, data visualization and AI. At our seven different R courses, you will learn to use the program and the integrated development environment (IDE) RStudio.

## Introduction to R - 1 day

This course helps you get started with R. We’ll cover the basics of R, ranging from importing and handling data to visualisation.

The popular tidyverse package is used for filtering, cleaning, and preparing data for analysis. The powerful plotting capabilities of the ggplot2 package are also covered. Both basic statistical concepts and fundamental topics in R programming are discussed. This course is a great fit if you’re curious about R, or already know that you want to use its many tools for advanced data analysis.

Course goals: To be able to use R to import and wrangle data, describe data using graphs and tables.
Prerequisites: Basic computer skills.

## Introduction to modern statistics - R - 1 day

In this course, you’ll learn about two fundamental tools in statistical analysis: hypothesis tests and confidence intervals. We’ll also discuss important concepts like p-values, power, and sample size calculcations.

Classical statistical tests like the t-test, nonparametric tests and the chi-squared test are covered, along with modern computer-intensive methods like the bootstrap. The latter allows us to obtain p-values and confidence intervals without many of the constraints of traditional methods (such as requiring that the data follow a normal distribution), bringing your statistical toolbox up to the 21st century.

Course goals: To understand the basics of hypothesis testing and confidence intervals and be able to use R for running and computing common tests and intervals.
Prerequisites: Introduction to R or similar.

## Linear regression & ANOVA - R - 1 day

This course provides you with a solid understanding of modern linear regression and ANOVA models.

We will have a closer look at how these models work and how R can be used to build, visualise, and interpret such models. We will use modern techniques like the bootstrap and permutation tests, to obtain confidence intervals and p-values without having to assume a normal distribution for your data.

Course goals: To be able to use R to fit, visualize and interpret linear regression and ANOVA models.
Prerequisites: R 1 (Introduction to R + Introduction to modern statistics) or similar.

## Advanced regression models - R - 1 day

The course covers some common but advanced regression models as well as survival analysis.

We will cover non-linear regression models like logistic regression and Poisson regression, where the response variable can be either binary (yes/no), counts, or prevalence. In survival analysis, we’ll have a look at Kaplan-Meier survival curves and regression models, including Cox proportional hazards regression. Mixed models are used to analyse data with repeated measurements on the same subjects.

Course goals: To be able to use R to fit, visualise and interpret models for logistic regression, count regression, mixed models, and survival analysis.
Prerequisites: R 1 (Introduction to R + Introduction to modern statistics) + Linear regression & ANOVA or similar.

## Visualisation & data exploration - R - 1 day

This course will teach you how visually explore data in R, and how to create great-looking graphics using the powerful ggplot2 package.

Topics covered include outlier detection, visualisation of trends, and multivariate data. It also covers dimension-reduction of complex data using principal component analysis (PCA).

Course goals: To be able to use the R package ggplot2 to visualise and explore data.
Prerequisites: Introduction to R or similar.

## Cluster analysis & structural equation models - R - 1 day

In this course, we discuss cluster analysis, including hierarchichal and centroid-based methods, and factor analysis and structural equation models (SEM), used to measure and analyse the relationship between observed and hidden variables.

Cluster analysis is used to find subgroups in exploratory analyses of your data. SEM allows us to study causal relationships between variables in our data and latent (unobservable) variables, such as difficult-to-measure attitudes.

Course goals: Learn how to do cluster analysis when analysing your data and to perform SEM to study causal relationships between variables.
Prerequisites: Introduction to R or similar.

## Basic course in Propensity score matching - R - 1 day

This course helps you getting started with Propensity score matching applied in R / Rstudio.

We start with a review of the basics theory behind to account for unbalance in observational research using Propensity score matching. During the course we alternate between theory and interactive examples, exercises and discussions where we useutilizing the program R / Rstudio. The course provides a good introduction, both for beginners and for those who want to refresh old knowledge about how to think regardingin comparisons in observational data.

Course goals: To understand the basics of Propensity score matching and to be able to do the most common calculations for matching in R / Rstudio.
Prerequisites: Basic computer skills.