Can Julia really make your R code faster?! | R Programming

The fastest Julia algorithm was 30,249 times faster than the worst R! Check out how the others approaches compare! Calling other languages in your R programming code is easy. In this tutorial, we made use of Julia programming language directly in R to see if it could accelerate our code. We contrast 5 different algorithms and talk about when you should (or should not!) use them. Subscribe to learn more about RStats and how you can make your code faster. 👀 00:00 Introduction and dataset 01:26 Setting up Julia 02:18 1st approach: Growing a vector with for loops & translating R code to Julia code 06:57 2nd approach: Dataframe pre-allocation 08:25 3rd approach: Lists - vectorization 10:04 4th approach: Linear algebra - matrices 11:35 5th approach: Dataframe manipulation 13:29 Global all-approach benchmarking 🐱 Get the code here: https://github.com/MaximeRivest/dds/blob/master/Can%20Julia%20really%20make%20your%20R%20code%20faster.R 🏎️ R performance playlist https://www.youtube.com/playlist?list=PLyogaPCPr32UaTp-9Fsj4tb_aIcAQ3jKb 🧮 dplyr playlist https://www.youtube.com/playlist?list=PLyogaPCPr32W9wbszOANRJiAvUbbymcCS #R #Rprogramming #Juliaprogramming #Rtutorial #RStats #performance #RStudio #datascience #DDS #DDSR #datatable #dplyr