Jonás Fouz-González is an Associate Professor in the Department of Didactics of Language and Literature at the University of Murcia (Spain), where he teaches courses in English Phonetics and Pronunciation Teaching as well as several EFL modules. His research interests include computer-assisted pronunciation training, L2 speech learning and pronunciation teaching, and English phonetics and phonology. Most of his research to date has focused on the ways in which technology can facilitate pronunciation training, with studies exploring the potential of social networking sites such as Twitter (Language Learning & Technology, 2016; Computer Assisted Language Learning, 2017), podcasts (ReCALL, 2019), mobile apps (Language Learning & Technology, 2020), High Variability Phonetic Training (Studies in Second Language Acquisition, 2021) or enhanced captioned video (in preparation). He is also co-editor of Investigating English Pronunciation: Trends and Directions (Palgrave Macmillan, 2015) and English Pronunciation Instruction: Research-based Insights (John Benjamins, 2021), and he is currently finishing a monograph titled Technology-Assisted Pronunciation Training: Bridging Research and Pedagogy (Equinox Publishing, scheduled to appear in the Applied Phonology and Pronunciation Teaching series in 2025).

R is free and open-source statistical analysis software, and R Studio is a free and open-source user interface that allows researchers to easily write, publish, and annotate their analyses in markdown. When used together, these tools can facilitate open, transparent, and reproducible analyses. Yet, getting started in any new interface is challenging, and R can be daunting for researchers who are accustomed to a point-and-click interface such as SPSS.

In this workshop, I will introduce you to the core syntax of R and the basic principles of authoring code using R with R Studio. You will learn to import, wrangle, and transform data using tidyverse. You will also learn to generate descriptive statistics and informative plots using ggplot2. Once you are comfortable with the basic workflow of reading, cleaning, and summarizing data, I will introduce you to two widely used inferential analyses in R: analysis of variance (ANOVA) and mixed-effects modeling. I will provide sample data, code, and exercises for you to practice during the workshop and for you to use on your own afterwards. By the end of this workshop, you will understand how you can approach data analysis in R using R Studio, and you will be able to recycle and update the code I provide for your future work. 

Participants should download and install the latest version of R and R Studio before the workshop. All are welcome to join the workshop. No knowledge of R is required.