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The parallel code structure looks very different from the standard for loops in python. So there is a lot of rewriting to get things to work on parallel in pyt
by j7ake 3y ago
The parallel code structure looks very different from the standard for loops in python.
So there is a lot of rewriting to get things to work on parallel in python compared to R.
In R you just replace lapply with mclapply.
- qfwfq_ 3y agoBut, many R tools are already vectorised, so your shift from lapply() to mclapply() is about as fair a comparison as claiming it's "just" a shift from python's builtin map() to pool.map(). Anybody can play this game, and it's not helpful. I've been using+teaching R now for nearly seven years and the number of times I've used lapply can be counted on one hand. > just https://justsimply.dev/ https://justsimply.dev/
- Bootvis 3y agoI use sapply all the time to transform data all the time. It tends to be less code (no counter, no output initialisation ) and easier to follow if that style is familiar.
- j7ake 3y agoI am curious now, what do you use instead of lapply (or other *apply variants)?
- nequo 3y agoThere is also this in the futureverse if you like for loop style code more: library(doFuture) plan(multisession) y <- foreach(x = 1:4, y = 1:10) %dofuture% { z <- x + y slow_sqrt(z) } https://dofuture.futureverse.org/ https://dofuture.futureverse.org/
- Dylan16807 3y ago> just This isn't documentation or a guide or helping someone. It's a friendly competition between languages so it gets to use the perspective of someone that's familiar with things.