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 tdhock.github.io
Toby Dylan Hocking | data.table asymptotic timings
| | timogrossenbacher.ch
Bivariate maps with ggplot2 and sf
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| | This post guides you through creating a beautiful, bivariate thematic map using solely two R packages, ggplot2 and sf.
| | www.nicholas-ollberding.com
Applying Topic Models to Microbiome Data in R | Academic
2.7 parsecs away

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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.
| | www.r-statistics.com
A speed test comparison of plyr, data.table, and dplyr | R-statistics blog
2.1 parsecs away

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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
| | indrajeetpatil.github.io
ggplot2 Based Plots with Statistical Details ggstatsplot
32.3 parsecs away

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| Extension of ggplot2, ggstatsplot creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) .