The use of R in photosynthesis research
Yasi Liu
A , Xiangping Wang A , Dayong Fan B * and Jiangshan Lai C D *
+ Author Affiliations
- Author Affiliations
A School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China.
B College of Forestry, Beijing Forestry University, Beijing 100083, China.
C State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093, China.
D University of Chinese Academy of Sciences, Beijing 100049, China.
Functional Plant Biology 1-8 https://doi.org/10.1071/FP21102
Submitted: 6 April 2021 Accepted: 13 September 2021 Published online: 12 October 2021
© 2021 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
R is one of the most commonly used analytical tools in the plant sciences. To identify key trends in general reported R use and patterns in photosynthesis research, we explored the frequency of R use in 2966 articles published in the 377 journals with ‘photosynthesis’ in the title from 2010 to 2019 using the Web of Science search. Solutions provided by each R package cited in the articles or online sources was recorded and classified. The percentage of research articles reporting R use increased linearly from 3.6% in 2010 to 12.5% in 2019. The three main categories of R package solutions were ‘general statistical calculations and graph packages’ (G); ‘photosynthesis special-purpose packages’ (S); and ‘genetic and evolutionary packages’ (E). The top five R packages cited were nlme (G), lme4 (G), multcomp (G), plantecophys (S), and ape (E). The increasing popularity of R use in photosynthesis research is due to its user-friendly and abundant open-source codes online for handling specific issues, particularly in fitting photosynthesis models. These findings are limited by the number of articles and online sources, but they reveal a significant increase in usage in photosynthesis research over the past decade and have a bright prospect in the future.
Keywords: ape, code, data analyses, lme4, multcomp, nlme, open-source, plantecophys, popularity, R language, R packages.
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