The tutorial you need to maximize your use data frames in R (CC277)

What is your preferred method for building a data frame in R? Do you know its performance characteristics relative to other methods and the size of the data frame? In this tutorial, Pat compares 18 methods of building data frames including similar structures from the tibble, data.table, and Matrix packages. He uses the microbenchmark package to evaluate their speed for different sized vectors. You'll likely be surprised by the results! This episode is part of an ongoing effort to develop an R package that implements the naive Bayesian classifier.

If you want to get a physical copy of R Packages: https://amzn.to/43pMR8L
If you want a free, online version of R packages: https://r-pkgs.org/

You can find my blog post for this episode at https://www.riffomonas.org/code_club/....

Check out the GitHub repository at the:
* Beginning of the episode: https://github.com/riffomonas/phyloty...
* End of the episode: https://github.com/riffomonas/phyloty...


#rstats #microbenchmark #vectors #rdp #16S #classification #classifier #microbialecology #microbiome

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0:00 Introduction
2:46 Data frame basics
7:06 Strategies for building data frames
18:28 Strategies for building tibbles
24:00 Strategies for building data tables
31:08 Representing data frames as atomic vectors or lists
32:46 Strategies for building data frames with Rcpp
36:43 Strategies for building full and sparse matrices