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tdhock.github.io | ||
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www.r-statistics.com
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| | | | | Guest post by Jake Russ For a recent project I needed to make a simple sum calculation on a rather large data frame (0.8 GB, 4+ million rows, and ~80,000 groups). As an avid user of Hadley Wickham's packages, my first thought was to use plyr. However, the job took plyr roughly 13 hours to complete. plyr is extremely efficient | |
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www.cedricscherer.com
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| | | | | Discover how to effortlessly generate custom and even complex graphics for subsets of your data by seamlessly integrating {ggplot2}'s versatile plotting functionalities with {purrr}'s powerful functional programing capabilities. This is especially helpful for data featuring many categories or step-by-step graphical storytelling | |
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www.nicholas-ollberding.com
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| | | | | Inherent limitations with one-at-a-time (OaaT) feature testing (i.e., single feature differential abundance analysis) have contributed to the increasing popularity of mixture models for correlating microbial features with factors of interest (i. | |
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tdhock.github.io
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| | | Statistical machine learning researcher working on fast optimization algorithms for large data. | ||
