Using R for teaching and research

TL;DR

This paper discusses the advantages of R as a versatile tool for statistical education and research, providing guidance, resources, and infrastructure to enhance teaching and scientific analysis.

Contribution

It offers a comprehensive overview of R's benefits, practical recommendations, and infrastructure development for improving statistical teaching and research applications.

Findings

R is the preferred software in statistical education.

Development of curricular and assessment tools for R in classrooms.

Guidelines for leveraging R in scientific research.

Abstract

R is a language and environment for statistical computing and graphics, which provides a wide variety of statistical tools (modeling, statistical testing, time series analysis, classification problems, machine learning, ...), together with amazing graphical techniques and the great advantage that it is highly extensible. Nowadays, there is no doubt that it is the software par excellence in statistical courses for any level, for theoretical and applied subjects alike. Besides, it has become an almost essential tool for every research work that involves any kind of analysis or data visualization. Furthermore, it is one of the most employed programming languages for general purposes. The goal of this work is helping to share ideas and resources to improve teaching and/or research using the statistical software R. We will cover its benefits, show how to get started and where to locate specific resources, and will make interesting recommendations for using R, according to our experience. For the classroom we will develop a curricular and assessment infrastructure to support both dissemination and evaluation, while for research we will offer a broader approach to quantitative studies that provides an excellent support for work in science and technology.