Applied Research with R

This repository contains a collection of tutorials, lectures and hands-on material, developed at the University of Maastricht (Institute of Data Science) for Applied Research course that use R software.

The goal is to organize relevant material into modular components, for more efficient design and maintenance of material, that can be used across courses, and that are accessible to students during and after their studies.

Introduction

IDS aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. The lessons below were designed for those interested in working with social science data in R.

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in a day (~ 8 hours). They start with some basic information about R syntax, the RStudio interface, and move through how to import files, the structure of data frames, how to deal with factors, how to add/remove rows and columns, how to calculate summary statistics from a data frame, and a brief introduction to inferential statistics. All lessons demonstrate how to work with databases directly from R.

Learning outcomes

The curriculum for these students will be:

Introduction to R
Introduction to Exploratory Data Analysis
Create graphs for exploring your data
Research Data Management
Interpret Pearson correlation and scatter plots
Loop functions in R
Inferential Statistics

Materials

Lecture	Day	Website	R Markdown	Description
Lecture 1	13 April 2021	-	-	Introduction to Stats with R
Lecture 2	13 May 2021	-	-	Descriptive Statistics
Lecture 3	08 June 2021	Demo 3	.rmd	Intro to R Markdown, independent t-test and correlations
Lecture 4	10 June 2021	Demo 4	.rmd	Paired t-test and ANOVA
Lecture 5	11 June 2021	Demo 5,Demo 6	.rmd,.rmd	Linear, Multiple and Logistic Regression

Assignments

Tutorial	Learning Outcomes
Intro to R	What are the essential components of R stats?
Exploratory Data Analysis	What are the steps for Exploratory Data Analysis?
Inferential Statistics I	How to conduct and interpret t-test in R?
Inferential Statistics II	How to conduct and interpret paired T-test and ANOVA?
Inferential Statistics III	How to conduct and interpret linear & logistic regression?

Recommended literature:

Read Chapters 1-8 from R for Data Science

Reference cards

General R Basics:

License

Code

The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.

Text

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.