Evolution of the R software ecosystem: Metrics, relationships, and their impact on qualities

Plakidas, Konstantinos; Schall, Daniel; Zdun, Uwe

doi:10.1016/j.jss.2017.06.095

Cited by 21 publications

(3 citation statements)

References 15 publications

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“…The use of statistical programming tools—largely R, sometimes Python—has become ubiquitous for researchers of global change biology (Lai et al, 2019). This competence has developed in tandem with the emergence of vast software ecosystems that provide the many interoperable open‐source packages we combine to process data and build models (Hoving et al, 2013; Plakidas et al, 2017). It is rare for researchers to write their own statistical algorithms: instead, statistical modelers combine freely available tools to analyze their specific problem using high‐level model definitions.…”

Section: Limitations and Opportunitiesmentioning

confidence: 99%

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

Briscoe

Morris

Mathewson

et al. 2022

Global Change Biology

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A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models

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Section: Limitations and Opportunitiesmentioning

confidence: 99%

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

Briscoe

Morris

Mathewson

et al. 2022

Global Change Biology

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“…The land use/land cover (LULC) pattern is a spatial arrangement and combination of various LULC types and represents the heterogeneity [4,5]. Reasonable LULC changes can generate positive ecological environmental effects, while excessive development of ecosystems can lead to desertification, vegetation degradation, and reduction of water areas, among other ecological environmental problems [6][7][8][9]. Landscape pattern mainly focuses on the analysis of landscape heterogeneity and the dynamics, and the landscape pattern index can quantitatively describe the changes of landscape pattern [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Changes of Land use/cover and Landscape pattern in the Upper and Middle Reaches of the Fen River Basin in Shanxi Province, China

Zhao,

Wang

2023

Preprint

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The composition and pattern of ecosystems are important factors determining the overall status and spatial differences of ecosystem service functions. However, under the background of differential land policies and ecological protection policies, research on the trend of ecological system pattern changes in the Fenhe River Basin is insufficient. Taking the upper and middle reaches of the Fenhe River Basin as the study area, based on long-term NDVI index and multi-period LUCC remote sensing images, this study used spatial statistics and time trend analysis methods to analyze the spatio-temporal dynamic changes of vegetation, land use, landscape pattern, and explored the impact of major driving factors on ecosystem changes. The results show that: (1) From 2010 to 2020, the vegetation cover in the upper and middle reaches of the Fenhe River Basin increased, with an annual NDVI increment of 0.003 (P<0.001). NDVI showed an increasing trend spatially, with significant statistics (P<0.001) and significant changes (P<0.05) in vegetation in high-altitude mountain areas, and the vegetation cover was mostly forests or grasslands. There was no significant change in vegetation cover in the low-lying urban agglomeration area. (2) From 2010 to 2020, the area of water bodies or wetlands in the study area significantly decreased, with 51.3% converted to arable land and 33.9% transferred to construction land, while only 2.2% remained as water bodies or wetlands. From 2015 to 2020, the trend of water body changes slowed down, with the proportions of conversion to arable land and construction land being 44.0% and 18.4% respectively, while the area of wetlands or water bodies remained at 16.3%. During the period of 2015-2020, the area of water bodies or wetlands converted to other land types increased by more than 14% compared to 2010-2015. This proportion reached more than 30% compared to the 13th Five-year Plan Period. (3) From 2010 to 2020, the spatial changes of landscape diversity (SHDI) and evenness (SHEI) of LUCC showed heterogeneous characteristics. In the high-altitude areas near the river basin boundary, the values of SHDI and SHEI were below 1.0. While in the low-altitude plain areas and urban areas with relatively frequent human activities, the values of SHDI and SHEI were above 1.0, and the values in urban areas could reach above 1.5. The evolution of ecosystem patterns in the upper and middle reaches of the Fenhe River Basin in the past decade has been clarified, providing a scientific basis for the construction and management of ecological environment governance and restoration projects in the Fenhe River Basin, and providing practical references for ecological protection and high-quality development practice in the Yellow River Basin.

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“…Software Engineering Research in R Programming. A recurrent topic of research in R is the analysis of package and dependency networks, e.g., dependency management and the impact of GitHub in non-CRAN packages [17], measuring package activity and lifecycle [41] and the growth and maintainability capabilities of CRAN packages [42]. Another popular area, given R's mix of programming paradigms, is programming language theory, addressing problems such as code profiling [43], wrappers to other languages (e.g., C++) [44], type systems [45], and lazy evaluation [46].…”

Section: Introductionmentioning

confidence: 99%

Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study

Codabux¹,

Vidoni²,

Fard³

2021

Preprint

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Context: Technical Debt (TD) is a metaphor used to describe code that is "not quite right." Although TD studies have gained momentum, TD has yet to be studied as thoroughly in non-Object-Oriented (OO) or scientific software such as R. R is a multi-paradigm programming language, whose popularity in data science and statistical applications has amplified in recent years. Due to R's inherent ability to expand through user-contributed packages, several community-led organizations were created to organize and peer-review packages in a concerted effort to increase their quality. Nonetheless, it is well-known that most R users do not have a technical programming background, being from multiple disciplines. Objective: The goal of this study is to investigate TD in the documentation of the peer-review of R packages led by rOpenSci. Method: We collected over 5,000 comments from 157 packages that had been reviewed and approved to be published at rOpenSci. We manually analyzed a sample dataset of these comments posted by package authors, editors of rOpenSci, and reviewers during the review process to investigate the types of TD present in these reviews. Results:The findings of our study include (i) a taxonomy of TD derived from our analysis of the peer-reviews (ii) documentation debt as being the most prevalent type of debt (iii) different user roles are concerned with different types of TD. For instance, reviewers tend to report some types of TD more than other roles, and the types of TD they report are different from those reported by the authors of a package. Conclusion: TD analysis in scientific software or peer-review is almost non-existent. Our study is a pioneer but within the context of R packages. However, our findings can serve as a starting point for replication studies, given our public datasets, to perform similar analyses in other scientific software or to investigate the rationale behind our findings.

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Evolution of the R software ecosystem: Metrics, relationships, and their impact on qualities

Cited by 21 publications

References 15 publications

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology

Dynamic Changes of Land use/cover and Landscape pattern in the Upper and Middle Reaches of the Fen River Basin in Shanxi Province, China

Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study

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