The use of R in forestry research

The field of forestry research has greatly benefited from the integration of computational tools and statistical methods in recent years. Among these tools, the programming language R has emerged as a powerful and versatile platform for conducting various aspects of forestry research, from data analysis, modeling to visualization. However, the key trends in general reported R use and patterns in forestry research remains unknown. We conducted an analysis of R and R package usage frequencies across a span of ten years, from 2013 to 2022, within the context of more than 14,800 research articles published in eight top forestry journals. Among these articles, a notable amount of 6,790 (accounting for 45.7%) explicitly utilized R as their primary tool for data analysis. The adoption of R exhibited a linear growth trend, rising from 28.3% in 2013 to 60.9% in 2022. The top five used packages reported were vegan, lme4, nlme, MuMIn, and ggplot2. Diverse journals have their unique areas of emphasis, resulting in disparities in the frequency of R package application among journals. The average number of R packages used per article also shows an increasing trend over time. The study underscores the recognition that R, with its powerful statistical and data visualization capabilities, plays a pivotal role in enabling researchers to conduct in-depth analyses and gain comprehensive insights into various aspects of forestry science.