问题
I thought that generally speaking using %>% wouldn't have a noticeable effect on speed. But in this case it runs 4x slower.
library(dplyr)
library(microbenchmark)
set.seed(0)
dummy_data <- dplyr::data_frame(
id=floor(runif(10000, 1, 10000))
, label=floor(runif(10000, 1, 4))
)
microbenchmark(dummy_data %>% group_by(id) %>% summarise(list(unique(label))))
microbenchmark(dummy_data %>% group_by(id) %>% summarise(label %>% unique %>% list))
Without pipe:
min lq mean median uq max neval
1.691441 1.739436 1.841157 1.812778 1.880713 2.495853 100
With pipe:
min lq mean median uq max neval
6.753999 6.969573 7.167802 7.052744 7.195204 8.833322 100
Why is %>% so much slower in this situation? Is there a better way to write this?
EDIT:
I made the data frame smaller and incorporated Moody_Mudskipper's suggestions into the benchmarking.
microbenchmark(
nopipe=dummy_data %>% group_by(id) %>% summarise(list(unique(label))),
magrittr=dummy_data %>% group_by(id) %>% summarise(label %>% unique %>% list),
magrittr2=dummy_data %>% group_by(id) %>% summarise_at('label', . %>% unique %>% list),
fastpipe=dummy_data %.% group_by(., id) %.% summarise(., label %.% unique(.) %.% list(.))
)
Unit: milliseconds
expr min lq mean median uq max neval
nopipe 59.91252 70.26554 78.10511 72.79398 79.29025 214.9245 100
magrittr 469.09573 525.80084 568.28918 558.05634 590.48409 767.4647 100
magrittr2 84.06716 95.20952 106.28494 100.32370 110.92373 241.1296 100
fastpipe 93.57549 103.36926 109.94614 107.55218 111.90049 162.7763 100
回答1:
What might be a negligible effect in a real-world full application becomes non-negligible when writing one-liners that are time-dependent on the formerly "negligible". I suspect if you profile your tests then most of the time will be in the summarize clause, so lets microbenchmark something similar to that:
> set.seed(99);z=sample(10000,4,TRUE)
> microbenchmark(z %>% unique %>% list, list(unique(z)))
Unit: microseconds
expr min lq mean median uq max neval
z %>% unique %>% list 142.617 144.433 148.06515 145.0265 145.969 297.735 100
list(unique(z)) 9.289 9.988 10.85705 10.5820 11.804 12.642 100
This is doing something a bit different to your code but illustrates the point. Pipes are slower.
Because pipes need to restructure R's calling into the same one that function evaluations are using, and then evaluate them. So it has to be slower. By how much depends on how speedy the functions are. Calls to unique and list are pretty fast in R, so the whole difference here is the pipe overhead.
Profiling expressions like this showed me most of the time is spent in the pipe functions:
total.time total.pct self.time self.pct
"microbenchmark" 16.84 98.71 1.22 7.15
"%>%" 15.50 90.86 1.22 7.15
"eval" 5.72 33.53 1.18 6.92
"split_chain" 5.60 32.83 1.92 11.25
"lapply" 5.00 29.31 0.62 3.63
"FUN" 4.30 25.21 0.24 1.41
..... stuff .....
then somewhere down in about 15th place the real work gets done:
"as.list" 1.40 8.13 0.66 3.83
"unique" 1.38 8.01 0.88 5.11
"rev" 1.26 7.32 0.90 5.23
Whereas if you just call the functions as Chambers intended, R gets straight down to it:
total.time total.pct self.time self.pct
"microbenchmark" 2.30 96.64 1.04 43.70
"unique" 1.12 47.06 0.38 15.97
"unique.default" 0.74 31.09 0.64 26.89
"is.factor" 0.10 4.20 0.10 4.20
Hence the oft-quoted recommendation that pipes are okay on the command line where your brain thinks in chains, but not in functions that might be time-critical. In practice this overhead will probably get wiped out in one call to glm with a few hundred data points, but that's another story....
回答2:
But here is something I have learnt today. I am using R 3.5.0.
Code with x = 100 (1e2)
library(microbenchmark)
library(dplyr)
set.seed(99)
x <- 1e2
z <- sample(x, x / 2, TRUE)
timings <- microbenchmark(
dp = z %>% unique %>% list,
bs = list(unique(z)))
print(timings)
Unit: microseconds
expr min lq mean median uq max neval
dp 99.055 101.025 112.84144 102.7890 109.2165 312.359 100
bs 6.590 7.653 9.94989 8.1625 8.9850 63.790 100
Although, if x = 1e6
Unit: milliseconds
expr min lq mean median uq max neval
dp 27.77045 31.78353 35.09774 33.89216 38.26898 52.8760 100
bs 27.85490 31.70471 36.55641 34.75976 39.12192 138.7977 100
回答3:
So, I finally got around to running the expressions in OP's question:
set.seed(0)
dummy_data <- dplyr::data_frame(
id=floor(runif(100000, 1, 100000))
, label=floor(runif(100000, 1, 4))
)
microbenchmark(dummy_data %>% group_by(id) %>% summarise(list(unique(label))))
microbenchmark(dummy_data %>% group_by(id) %>% summarise(label %>% unique %>% list))
This took so long that I thought I'd run into a bug, and force-interrupted R.
Trying again, with the number of repetitions cut down, I got the following times:
microbenchmark(
b=dummy_data %>% group_by(id) %>% summarise(list(unique(label))),
d=dummy_data %>% group_by(id) %>% summarise(label %>% unique %>% list),
times=2)
#Unit: seconds
# expr min lq mean median uq max neval
# b 2.091957 2.091957 2.162222 2.162222 2.232486 2.232486 2
# d 7.380610 7.380610 7.459041 7.459041 7.537471 7.537471 2
The times are in seconds! So much for milliseconds or microseconds. No wonder it seemed like R had hung at first, with the default value of times=100.
But why is it taking so long? First, the way the dataset is constructed, the id column contains about 63000 values:
length(unique(dummy_data$id))
#[1] 63052
Second, the expression that is being summarised over in turn contains several pipes, and each set of grouped data is going to be relatively small.
This is essentially the worst-case scenario for a piped expression: it's being called very many times, and each time, it's operating over a very small set of inputs. This results in plenty of overhead, and not much computation for that overhead to be amortised over.
By contrast, if we just switch the variables that are being grouped and summarized over:
microbenchmark(
b=dummy_data %>% group_by(label) %>% summarise(list(unique(id))),
d=dummy_data %>% group_by(label) %>% summarise(id %>% unique %>% list),
times=2)
#Unit: milliseconds
# expr min lq mean median uq max neval
# b 12.00079 12.00079 12.04227 12.04227 12.08375 12.08375 2
# d 10.16612 10.16612 12.68642 12.68642 15.20672 15.20672 2
Now everything looks much more equal.
回答4:
magrittr's pipe is coded around the concept of functional chain.
You can create one by starting with a dot : . %>% head() %>% dim(), it's a compact way of writing a function.
When using a standard pipe call such as iris %>% head() %>% dim(), the functional chain . %>% head() %>% dim() will still be computed first, causing an overhead.
The functional chain is a bit of a strange animal :
(. %>% head()) %>% dim
#> NULL
When you look at the call . %>% head() %>% dim() , it actually parses as `%>%`( `%>%`(., head()), dim()). Basically, sorting things out requires some manipulation that takes a bit of time.
Another thing that takes a bit of time is to handle the different cases of rhs such as in iris %>% head, iris %>% head(.), iris %>% {head(.)} etc, to insert a dot at the right place when relevant.
You can build a very fast pipe the following way :
`%.%` <- function (lhs, rhs) {
rhs_call <- substitute(rhs)
eval(rhs_call, envir = list(. = lhs), enclos = parent.frame())
}
It will be much faster than magrittr's pipe and will actually behave better with edge cases, but will require explicit dots and obviously won't support functional chains.
library(magrittr)
`%.%` <- function (lhs, rhs) {
rhs_call <- substitute(rhs)
eval(rhs_call, envir = list(. = lhs), enclos = parent.frame())
}
bench::mark(relative = T,
"%>%" =
1 %>% identity %>% identity() %>% (identity) %>% {identity(.)},
"%.%" =
1 %.% identity(.) %.% identity(.) %.% identity(.) %.% identity(.)
)
#> # A tibble: 2 x 6
#> expression min median `itr/sec` mem_alloc `gc/sec`
#> <bch:expr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 %>% 15.9 13.3 1 4.75 1
#> 2 %.% 1 1 17.0 1 1.60
Created on 2019-10-05 by the reprex package (v0.3.0)
Here it was clocked at being 13. times faster.
I included it in my experimental fastpipe package, named as %>>%.
Now, we can also leverage the power of functional chains directly with a simple change to your call :
dummy_data %>% group_by(id) %>% summarise_at('label', . %>% unique %>% list)
It will be much faster because the functional chain is only parsed once and then internally it just applies functions one after another in a loop, very close to your base solution. My fast pipe on the other hand still adds a small overhead due to the eval / substitute done for every loop instance and every pipe.
Here's a benchmark including those 2 new solutions :
microbenchmark::microbenchmark(
nopipe=dummy_data %>% group_by(id) %>% summarise(label = list(unique(label))),
magrittr=dummy_data %>% group_by(id) %>% summarise(label = label %>% unique %>% list),
functional_chain=dummy_data %>% group_by(id) %>% summarise_at('label', . %>% unique %>% list),
fastpipe=dummy_data %.% group_by(., id) %.% summarise(., label =label %.% unique(.) %.% list(.)),
times = 10
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval cld
#> nopipe 42.2388 42.9189 58.0272 56.34325 66.1304 80.5491 10 a
#> magrittr 512.5352 571.9309 625.5392 616.60310 670.3800 811.1078 10 b
#> functional_chain 64.3320 78.1957 101.0012 99.73850 126.6302 148.7871 10 a
#> fastpipe 66.0634 87.0410 101.9038 98.16985 112.7027 172.1843 10 a
来源：https://stackoverflow.com/questions/35933272/why-is-using-dplyr-pipe-slower-than-an-equivalent-non-pipe-expression-for