Accessing values from data frames, data tables, tibbles, matrices, and vectors (CC278)

Watch along as Pat shows a variety of approaches for obtaining rows from data frames, data tables, tibbles, sparse matrices, and vectors and then compares their performance. Which do you think will be the most performant? 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
6:57 Retrieving data for a single kmer
15:38 Retrieving data for three kmers
19:51 How would we retrieve more kmers?
26:08 Filtering with a join