Evaluating the popularity of R in ecology

Abstract

The programming language R is widely used in many fields. We explored the extent of reported R use in the field of ecology using the Web of Science and text mining. We analyzed the frequencies of R packages reported in more than 60,000 peer-reviewed articles published in 30 ecology journals during a 10-yr period ending in 2017. The number of studies reported using R as their primary tool in data analysis increased linearly from 11.4% in 2008 to 58.0% in 2017. The top 10 packages reported were lme4, vegan, nlme, ape, MuMIn, MASS, mgcv, ade4, multcomp, and car. The increasing popularity of R has most likely furthered open science in ecological research because it can improve reproducibility of analyses and captures workflows when scripts and codes are included and shared. These findings may not be entirely unique to R because there are other programming languages used by ecologists, but they do strongly suggest that given the relatively high frequency of reported use of R, it is a significant component of contemporary analytics in the field of ecology.

Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

“…As an example, a recent survey found that the number of papers published in ecological journals that reported the use of the R statistical language increased fivefold from 2007 to 2018 (Lai et al. ).…”

“…These techniques require both software applications and hardware systems that extend beyond the desktop environment, and thus expertise in computer science. For example, ecologists are increasingly archiving and sharing their data via curated repositories that enable data discovery, reuse, and citation (e.g., via the Dryad Digital Repository; Environmental Data Initiative, EDI; and Knowledge Network for Biocomplexity, KNB); using computer programming in R, Python, C, FOR-TRAN, and other languages to conduct data management and visualization (Valle andBerdanier 2012, Hampton et al 2015); deploying high-frequency wireless sensors for environmental monitoring (e.g., the Global Lake Ecological Observatory Network, GLEON and National Ecological Observatory Network, NEON); and analyzing datasets with computationally intensive software programs and models (Lai et al 2019).…”

“…Given the growing collection of long-term monitoring data, high-frequency sensor data, bioinformatics and molecular data, remote sensing data, Earth system model output data, and many other big datasets, ecologists are increasingly conducting data-driven computational science (Hampton et al 2013(Hampton et al , 2017. As an example, a recent survey found that the number of papers published in ecological journals that reported the use of the R statistical language increased fivefold from 2007 to 2018 (Lai et al 2019).…”

staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R

“…The need for a user-friendly software to correct and analyze EEMs was stated earlier [45]. We additionally identified the need for free software in a programming environment that ecologists and biogeochemists are familiar with [37] to reduce the initial hurdle of applying advanced data analysis methods. In this paper, we present and test a new package for fast and comprehensive spectroscopic analysis of DOM in R, called staRdom ("spectroscopic analysis of DOM in R"), which is suitable for the demands of both beginners and experienced users.…”

“…Due to its open-source nature, the R software environment for statistical computing and graphics [36] is widely used in ecological research nowadays [37]. In R, a reliable PARAFAC toolbox is the multiway package [38].…”