7 ms·
From the article: >R is extremely slow at a lot of tasks, for one thing, even more than Python. Base R is quite slow. R + data.table is faster than Python + Pa
by hermitcrab 4y ago
From the article:
>R is extremely slow at a lot of tasks, for one thing, even more than Python.
Base R is quite slow. R + data.table is faster than Python + Pandas in a benchmark that I did recently.
For a 1 million row CSV file, Read + Sort + self-Join + Write took on a Windows box:
Base R: 47.56s
Python + Pandas: 6.44s
R + data.table: 2.99s
More details at:
https://www.easydatatransform.com/data_wrangling_etl_tools.h https://www.easydatatransform.com/data_wrangling_etl_tools.h...
- laichzeit0 4y agoReading the data is hardly the problem. Most R libraries seem to be inherently single threaded. Unlike sklearn where just about everything has a n_jobs parameter. Even stuff like xgboost is insanely parallelizable. Try fitting a really big mixed effects model with LMER, you will cry (I have submitted multiple fixes and performance improvements to LMER and Mertools). Cool I have a model. Now I want to serve predictions in real time to downstream APIs. Hello single threaded R runtime my old friend.