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 www.r-spatial.org
Using data.table and Rcpp to scale geo-spatial analysis with sf
| | www.r-statistics.com
A speed test comparison of plyr, data.table, and dplyr | R-statistics blog
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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
| | tdhock.github.io
Toby Dylan Hocking | data.table asymptotic timings
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| | Statistical machine learning researcher working on fast optimization algorithms for large data.
| | jaredknowles.com
Tutorial 8: Advanced Topics
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| | [AI summary] This tutorial covers advanced R programming techniques including coding style, for loops, functions, mixed effect models, data mining with caret, and performance optimization.
| | easystats.github.io
Dominance Effect Sizes for Rank Based Differences - rank_biserial effectsize
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| Compute the rank-biserial correlation (\(r_{rb}\)) and Cliff's delta (\(\delta\)) effect sizes for non-parametric (rank sum) differences. These effect sizes of dominance are closely related to the Common Language Effect Sizes. Pair with any reported stats::wilcox.test().