Lazy Evaluation Thoughts

This is just a draft of my reply to Gavin’s thought-provoking post pushing back on pipes and NSE in R.

Gavin, really nice post and discussion here. I am still conflicted about some these issues myself.

For me, the issues of NSE are somewhat separate and above my concern with pipes. I agree completely with your observation that intermediate assignment is easier to read than nested assignment, and not that much harder to read than a pipe chain. However I also agree with other commenters that not having to think about intermediate assignment variables is nice (e.g. I usually do something like replacing the_data with tmp or some such in each occurrence after the first in your example to make this more explicit). But for the most part I do not find the pipe syntax to be problematic in the same way that NSE is – indeed I can use the pipe operator with the SE versions of the same functions, or with any other SE R function.

NSE to me is a whole different can of worms. It does make the syntax much more consise and more semantic, (even while it obfuscates what is a string, a numeric, or a variable). But this whole “don’t use it in programming” thing seems very impractical to me. When I first started R, I got the same advice – only it was for R itself – use it interactively, but for real programming write everything in C. Then maybe add some R wrappers on top. Um.. yeah, I actually did that for a few years… long ago. Most of the time I don’t even know if I’m programming or not. Sure, it’s easy to use the SE versions when working on some function you know will be part of an R package, but NSE has bitten me several times in various research scripts. Stupid things perhaps – where I have done things like changing a filter argument to a variable with the same name as a column (filter(x == .5) into a filter(x == x)) – but very difficult to debug since they do not throw errors.

Nevertheless, both the performance and consise abstractions of common manipulation tasks make it hard to walk away from dplyr and friends. However, I find the syntax required to use the SE versions of the dplyr functions immensely cumbersome and opaque (e.g. Some of these can be written more concisely with a different SE syntax, but having 4 different ways to introduce a variable value, not all of which cover all the same cases, is even worse to me than just sticking with the most complex. No doubt I lack appreciation of the complexity here, but it seems like it should be possible to have a syntax that is nearly as consise as the NSE but where I can replace values with variables (e.g. filter("Y" == "X") to filter(y == x)) without needing something like

dots <- lapply(names(query), function(level){
value <- query[[level]]
interp(~y == x, 
.values = list(y =, x = value))

Anyway, apologies for the rant – just trying to say I share your hesitation regarding NSE but pipes don’t bother me. (I do think lazy eval on pipes is a win btw – e.g. try testing out the same long pipe-string analysis on data coming from a remote database; say, just to check that the first 10 rows of output look right before evaluation the whole database)