IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004140.html
   My bibliography  Save this article

Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

Author

Listed:
  • Marco D Visser
  • Sean M McMahon
  • Cory Merow
  • Philip M Dixon
  • Sydne Record
  • Eelke Jongejans

Abstract

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.

Suggested Citation

  • Marco D Visser & Sean M McMahon & Cory Merow & Philip M Dixon & Sydne Record & Eelke Jongejans, 2015. "Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-11, March.
    • Handle: RePEc:plo:pcbi00:1004140
    DOI: 10.1371/journal.pcbi.1004140
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004140
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004140&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1004140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zeeya Merali, 2010. "Computational science: ...Error," Nature, Nature, vol. 467(7317), pages 775-777, October.
    2. Barry W. Brook & Julian J. O'Grady & Andrew P. Chapman & Mark A. Burgman & H. Resit Akçakaya & Richard Frankham, 2000. "Predictive accuracy of population viability analysis in conservation biology," Nature, Nature, vol. 404(6776), pages 385-387, March.
    3. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. Bachoc, François & Genton, Mark G. & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2019. "Spatial Blind Source Separation," TSE Working Papers 19-998, Toulouse School of Economics (TSE).
    3. Napoleón Vargas Jurado & Kent M. Eskridge & Stephen D. Kachman & Ronald M. Lewis, 2018. "Using a Bayesian Hierarchical Linear Mixing Model to Estimate Botanical Mixtures," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 190-207, June.
    4. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
    5. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.
    6. Andrii ROSKLADKA & Roman BAIEV, 2021. "Digitalization of data analysis tools as the key for success in the online trading markets," Access Journal, Access Press Publishing House, vol. 2(3), pages 222-233, September.
    7. Etienne Côme & Nicolas Jouvin & Pierre Latouche & Charles Bouveyron, 2021. "Hierarchical clustering with discrete latent variable models and the integrated classification likelihood," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(4), pages 957-986, December.
    8. Mihai C. Giurcanu, 2017. "Oracle M-Estimation for Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 479-504, May.
    9. Aaron T L Lun & Hervé Pagès & Mike L Smith, 2018. "beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-15, May.
    10. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
    11. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    12. LaRue, Michelle A. & Nielsen, Clayton K., 2016. "Population viability of recolonizing cougars in midwestern North America," Ecological Modelling, Elsevier, vol. 321(C), pages 121-129.
    13. Enrique Martínez García & Efthymios Pavlidis & Kostas Vasilopoulos, 2020. "exuber: Recursive Right-Tailed Unit Root Testing with R," Globalization Institute Working Papers 383, Federal Reserve Bank of Dallas, revised 19 Oct 2021.
    14. Berrisch, Jonathan & Ziel, Florian, 2023. "CRPS learning," Journal of Econometrics, Elsevier, vol. 237(2).
    15. Savitsky, Terrance & Paddock, Susan, 2014. "Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i03).
    16. John M Drake, 2005. "Density-Dependent Demographic Variation Determines Extinction Rate of Experimental Populations," PLOS Biology, Public Library of Science, vol. 3(7), pages 1-1, June.
    17. D.C & Nwankwoala & H. O & Okujagu, 2021. "A Review Of Wetlands And Coastal Resources Of The Niger Delta: Potentials, Challenges And Prospects," Environment & Ecosystem Science (EES), Zibeline International Publishing, vol. 5(1), pages 37-46, March.
    18. Battauz, Michela & Vidoni, Paolo, 2022. "A likelihood-based boosting algorithm for factor analysis models with binary data," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    19. Cardona Jiménez, Johnatan & de B. Pereira, Carlos A., 2021. "Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    20. Andrew Chesher & Adam Rosen & Zahra Siddique, 2019. "Estimating Endogenous Effects on Ordinal Outcomes," CeMMAP working papers CWP66/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1004140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.