Walkthrough on achieving this! Optimizing your code for performance can mean waiting a few minutes instead of days for your code to run. For loops, dataframes, lists, linear algebra, dplyr package and datatable package are all compared. In the abridged version of this RStats tutorial, we conceptually compare and give you tricks for the different approaches. Check out the channel for the unabridged version of this tutorial where we walk you through the details of the code and to learn how you can make your R code faster with Julia, Python and SQL. 🏎️ 💾 Get the code here: https://github.com/MaximeRivest/How-to-make-your-R-code-18-878-times-faster-Detailed-version-R-Programming/ 00:00 Introduction and dataset 00:54 1st approach: Growing a vector with for loops 01:50 2nd approach: Dataframe pre-allocation 02:55 3rd approach: Lists - vectorization 05:11 4th approach: Linear algebra - matrices 09:22 5th approach: dplyr package 11:02 6th approach: datatable package 13:55 Benchmarking 15:35 Tips and tricks for optimization 🏎️ R performance playlist    • R Programming - Performance   🧮 dplyr playlist    • dplyr   #R #Rtutorial #Rprogramming #RStats #performance #tidyverse #dplyr #RStudio #datascience #DDS #DDSR #datatable