## R

For you to be able to work freely in R, there are som things you need to know regardless of the intended application of the software. The basic skills required are program management, data management as well as the ability to describe data with tables and graphs.

It is also necessary to have a basic understanding of statistics in order to run accurate analyses in R. Therefore, we recommend all novice users to choose one of our course proposals.

Our proposals will help you get started as quickly as possible with both the software and the statistical models required to work independently in R. You will learn how to choose the right statistical analysis method, and how to interpret and validate the results.

## Our three course proposals

## Basic

**Includes****R 1R 2**

**All courses are in English****A total of four daysPrice: SEK 22 500**

**R 1**

**Introduction to r**

Handling data

Data visualisation

Descriptive statistics

**Introduction to modern statistics**

Statistical concepts

Classical statistical methods

Modern statistical methods

**R 2**

**Linear regression & ANOVA**

Linear regression

ANOVA

**Advanced regression models**

Generalised linear models

Survival analysis

Mixed models

## Medium

**Includes****R 1R 2R 3**

**All courses are in English****A total of six daysPrice: SEK 31 800**

**R 1**

**Introduction to r**

Handling data

Data visualisation

Descriptive statistics

**Introduction to modern statistics**

Statistical concepts

Classical statistical methods

Modern statistical methods

**R 2**

**Linear regression & ANOVA**

Linear regression

ANOVA

**Advanced regression models**

Generalised linear models

Survival analysis

Mixed models

**R 3**

**Visualisation & data exploration**

Visualisation

Dimension reduction

**Cluster analysis & structural equation models**

Cluster analysis

Factor analysis

## Complete

**Includes****R 1R 2R 3**

**Propensity Score Matching**

**All courses are in English****A total of seven daysPrice: SEK 36 800**

**R 1**

**Introduction to r**

Handling data

Data visualisation

Descriptive statistics

**Introduction to modern statistics**

Statistical concepts

Classical statistical methods

Modern statistical methods

**R 2**

**Linear regression & ANOVA**

Linear regression

ANOVA

**Advanced regression models**

Generalised linear models

Survival analysis

Mixed models

**R 3**

**Visualisation & data exploration**

Visualisation

Dimension reduction

**Cluster analysis & structural equation models**

Cluster analysis

Factor analysis

**Propensity Score Matching**

Introduction

Identify imbalances in your data set

Matching techniques

Evaluation of the matching quality

Estimate the treatment effects

How to describe methods and results

**To express your interest, please fill out the registration of interest form below.**

**Individual course days – Customize your reservation**

There is a possibility to customize your reservation by adding individual course days. These can be added to our course proposals Basic and Medium as well as to our courses R 1–3. By adding individual course days to your reservation, you are able to tailor the training package completely according to your needs.

## Course days

**Introduction to R****Introduction to modern statistics****Linear regression & ANOVA****Advanced regression models****Visualisation & data exploration****Cluster analysis & SEM****Propensity Score Matching**

**Introduction to r**

Handling data

Data visualisation

Descriptive statistics

**Introduction to modern statistics**

Statistical concepts

Classical statistical methods

Modern statistical methods

**Linear regression & ANOVA**

Linear regression

ANOVA

**Advanced regression models**

Generalised linear models

Survival analysis

Mixed models

**Visualisation & data exploration**

Visualisation

Dimension reduction

**Cluster analysis & structural equation models**

Cluster analysis

Factor analysis

**Propensity Score Matching**

Introduction

Identify imbalances in your data set

Matching techniques

Evaluation of the matching quality

Estimate the treatment effects

How to describe methods and results

## About R

**The program R**

R is a programming language whose main applications include statistical calculations, data visualization and machine learning assisted by AI. Through user-developed add-on packages, the number of methods for statistical calculations and graphical presentations has greatly expanded. The large number of extension packages containing reusable code and documentation has even augmented the core R language.

**Applications**

R is one of the most well-known and most commonly used programs for statistical analysis. It is today fully comparable to commercial statistical programs such as SPSS, SAS and Stata. Software development is constantly driven forward by all the user-created packages that are easy to install and have made R incredibly popular in computer science.

**Interfaces**

Early users preferred to use R purely text-based via the command prompt. Today, users usually prefer the plethora of applications that can be used to visually edit and run R code, such as Rcmdr and RStudio. With these and other extensions such as ggplot2, you can create graphics with as least as good quality as in any other statistics program.

**Communities**

A major difference for the users compared with other programs is that R is available free of charge as a free software license. This has led to local communities all over the world where users can network, share ideas, and learn new things. For you as a user, it is very positive as you can get help and answers to questions to a much greater extent. Because R is open source, you can also use program code that others have developed in your own projects without having to pay for license fees.