What are the best practices for optimizing performance and efficiency in R programming, particularly when dealing with large datasets or computationally intensive tasks? Are there any specific techniques or packages that researchers should be aware of?
Mostly, avoid for cycles, use vectorial operations (built in functions of R are vectorial operations, that means operations on the whole vector, not on elements one by one), use lapply to apply functions to vectors or dataframes, which uses parallel processing.
You may read the articles there on the internet. Here are some links:
1) https://www.dartistics.com/fast-r-code.html
2) https://r.iq.harvard.edu/docs/zelig/3.4-8/How_can_I.html
3) https://bookdown.org/content/d1e53ac9-28ce-472f-bc2c-f499f18264a3/speedtips.html
Dear Dinesh,
A smal addition to the above: you can check how R spends time using a profiling tool called profvis http://rstudio.github.io/profvis/
Also, if you have big datasets, it might be helpful to read them in smaller chunks to reduce memory use. Especially if you have repeated calculations, you could also try caching intermediate results to avoid unnecessary recomputations.
- Badges
- Science topic
- Similar topics
- Mathematics