如何使用data.table在所有可能的m中运行comboGeneral以获取所有可能的变量组合？然后，如何使用这些变量组合来计算所有子集中的数据帧的非重复计数？
这是purrr和dplyr版本。我需要使用data.table进行nms和计数。
library(data.table); library(dplyr); library(magrittr); library(RcppAlgos); library(purrr)
num_m <- seq_len(ncol(mtcars))
nam_list <- names(mtcars)
nms <- map(num_m, ~comboGeneral(nam_list, m = .x, FUN = c)) %>% unlist(recursive = FALSE)
counts <- map_dbl(nms, ~(mtcars %>% select(.x) %>% n_distinct()))
答案 0 :(得分：2)
不清楚如何通过专门使用Thenable来完成第一部分。 get: function(target, prop) {
if (prop === 'then') return null; // I'm not a Thenable
// ...the rest of my logic
}
来自data.table，因此我认为它的优化程度很高... comboGeneral中的RccpAlgos是替代方案（combn确实不是这样任何实现...）：
base有了这个，data.table中有几种方法：
nms = unlist(lapply(num_m, combn, x = nam_list, simplify = FALSE), recursive = FALSE)
或
data.table或
mtcars = as.data.table(mtcars)
counts = sapply(nms, uniqueN, x = mtcars)
第一个选项似乎不仅最简洁，而且最有效：
sapply(nms, function(nm) nrow(mtcars[ , TRUE, keyby = nm]))
关于加快第一步，您可以通过降低sapply(nms, function(nm) nrow(unique(mtcars, by = nm)))
的糖并使用原始的library(microbenchmark)
microbenchmark(times = 100L,
map_dbl(nms, ~(mtcars %>% select(.x) %>% n_distinct())),
sapply(nms, uniqueN, x = mtcars),
sapply(nms, function(nm) nrow(mtcars[ , TRUE, keyby = nm])),
sapply(nms, function(nm) nrow(unique(mtcars, by = nm))))
# Unit: milliseconds
# expr min lq
# map_dbl(nms, ~(mtcars %>% select(.x) %>% n_distinct())) 2246.10862 2365.33801
# sapply(nms, uniqueN, x = mtcars) 66.16144 68.95391
# sapply(nms, function(nm) nrow(mtcars[, TRUE, keyby = nm])) 1659.20425 1701.79188
# sapply(nms, function(nm) nrow(unique(mtcars, by = nm))) 102.42203 106.87100
# mean median uq max neval
# 2469.50648 2448.44821 2544.00350 3530.6513 100
# 73.28518 71.54861 75.85161 118.5919 100
# 1796.30372 1766.59618 1825.97374 2881.2376 100
# 113.63032 111.28377 118.22441 174.2691 100
来获得大约10％的加速：
map注意：我们不能使用lapply，因为microbenchmark(times = 1000L,
lapply(num_m, combn, x = nam_list, simplify = FALSE),
map(num_m, ~comboGeneral(nam_list, m = .x, FUN = c)),
lapply(num_m, function(m) comboGeneral(nam_list, m, FUN = c)))
# Unit: microseconds
# expr min lq
# lapply(num_m, combn, x = nam_list, simplify = FALSE) 1718.994 1847.3710
# map(num_m, ~comboGeneral(nam_list, m = .x, FUN = c)) 564.076 629.5120
# lapply(num_m, function(m) comboGeneral(nam_list, m, FUN = c)) 473.135 525.2655
# mean median uq max neval
# 2088.7454 1921.8840 2016.0275 7789.501 1000
# 713.8342 661.0455 709.4650 3800.253 1000
# 593.7732 550.2460 583.7005 5190.982 1000
将被解释为lapply(num_m, comboGeneral, v = nam_list, FUN = c)的参数，而不是FUN的参数。