THE CHALLENGES AND NUANCES OF TEACHING THE R PROGRAMMING LANGUAGE TO ECOLOGISTS

Challenges of teaching the R programming language to ecologists

Abstract

The R programming language, esteemed for its statistical prowess, data management, data visualization, and machine learning capabilities, has become a cornerstone of data science applied to Ecology. Its open-source nature and broad array of analytical tools have garnered a user base, mainly among ecologists. Nevertheless, instructing R to biology and ecology students presents some key challenges. Here, we delve into the nuances of teaching R in graduate and undergraduate ecology courses, focusing on data wrangling, data visualization, and statistics. We provide insights from educators at various career stages, with a strong focus on the Brazilian context. No single way of teaching R fits all situations; however, some general guidelines can be provided and followed. Before starting this educational journey, the foundations must be addressed, including infrastructure, hardware, and software. Once these prerequisites are secured, students confront the intricacies of R’s programming landscape. Abstract concepts, coding idiosyncrasies, package compatibility, and the interplay between code and data need to be mastered. The narrative progresses to the challenge of interpreting R’s outputs and integrating them seamlessly into statistical and ecological analyses. Additionally, we consider the impact of structural challenges and global pressures on R education, such as the COVID-19 pandemic and the evolving influence of artificial intelligence tools. In conclusion, exploring R within ecology envisions a future where professionals possess the analytical skills to unlock innovative solutions and applications. As this educational journey concludes, we present our perspective on the future of R teaching and how it can help develop our science.