Скачать или смотреть R's for loops are horrible. Really? Getting better performance than dplyr with for loops! (CC054)

After disparaging R's for loops in recent episodes, a viewer challenged me to see if they were really all that horrible. Turns out that while nested for loops are horrible, simple for loops can be written to perform better than similar tidyverse code using dplyr functions. In today's episode I show how you can systematically test and improve your R code. This process will help us better understand what makes R work and how to leverage that to write efficient code. This episode is part of a larger arc of episodes investigating the sensitivity and specificity of amplicon sequence variants (ASVs), also known as exact sequence variants (ESVs). ASVs are growing in popularity for analyzing microbial communities using 16S rRNA gene sequences. Pat demonstrates these concepts by live coding at the command line interface using RStudio, GitHub Flow, and make.

0:00 Introduction
2:02 Reaching back into repository
4:26 expand_grid
7:19 nested for loops
9:49 pulling out code that doesn't change
12:02 removing one loop w/ slice
18:05 removing one loop w/o slice
21:39 creating a tibble directly
23:26 directly accessing columns
24:52 base R
29:02 Comparing for loops to map
32:46 Conclusion

The accompanying blog post contains the exercises and solutions can be found at http://www.riffomonas.org/code_club/2...