A Primer in Biological Data Analysis and Visualization Using R
(eBook)

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Published
Columbia University Press, 2014.
Status
Available Online

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Format
eBook
Language
English
ISBN
9780231537049

Citations

APA Citation, 7th Edition (style guide)

Gregg Hartvigsen., & Gregg Hartvigsen|AUTHOR. (2014). A Primer in Biological Data Analysis and Visualization Using R . Columbia University Press.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Gregg Hartvigsen and Gregg Hartvigsen|AUTHOR. 2014. A Primer in Biological Data Analysis and Visualization Using R. Columbia University Press.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Gregg Hartvigsen and Gregg Hartvigsen|AUTHOR. A Primer in Biological Data Analysis and Visualization Using R Columbia University Press, 2014.

MLA Citation, 9th Edition (style guide)

Gregg Hartvigsen, and Gregg Hartvigsen|AUTHOR. A Primer in Biological Data Analysis and Visualization Using R Columbia University Press, 2014.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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Grouped Work ID296412e2-f38c-b746-f91a-4fb937e7c40e-eng
Full titleprimer in biological data analysis and visualization using r
Authorhartvigsen gregg
Grouping Categorybook
Last Update2023-04-05 18:45:54PM
Last Indexed2024-04-17 02:47:53AM

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First LoadedJun 13, 2022
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    [synopsis] => R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterplots, before/after line plots, pie charts, and graphs. He covers data normality, outliers, and nonnormal data and examines frequently used statistical tests with one value and one sample; paired samples; more than two samples across a single factor; correlation; and linear regression. The volume also includes a section on advanced procedures and a final chapter on possible extensions into programming, featuring a discussion of algorithms, the art of looping, and combining programming and output.
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