如何在大数据上快速从data.table子集中多行

data.table v1.9.7 +已实现非等额联接，并添加了一个inrange使用此新功能并可以实现您想要的功能的新功能

## Loading data
library(data.table) #v 1.9.7+
DT <- data.table(date = c('01.02.2016 10:00:00','01.02.2016 10:01:00',
                          '01.02.2016 10:02:00','01.02.2016 10:03:00',
                          '01.02.2016 10:04:00'),
                 column1 = c(10, 12, 13, 11, 17))

## Converting to POSIXct class
DT[, date := as.POSIXct(date, format = "%d.%m.%Y %H:%M:%S")]

## Validating that forder/bmerge kicks in 
options(datatable.verbose = TRUE)
DT[date %inrange% as.POSIXct(c("2016-02-01 10:00:30", "2016-02-01 10:02:30"))]
# forderv(query) took ... 0 secs
# Starting bmerge ...done in 0 secs <~~~~~~~~ (Thanks to @Arun for fixing the bug)
# Generating final logical vector ... done in 0 secs
#                   date column1
# 1: 2016-02-01 10:01:00      12
# 2: 2016-02-01 10:02:00      13

不过，您应该知道，由于实现了data.table 1.9.4次要键，这意味着对于矢量扫描的某些变化，在第一次运行后会添加一个键，从现在开始，甚至还会使用==和操作。这似乎在类上不起作用，但是您可以在数字列上观察到此行为%in%bmergePOSIXctcolumn1

## Running for first time
options(datatable.verbose = TRUE)
DT[column1 == 10]
# Creating new index 'column1'
# forder took 0 sec <~~~ forder kicks in, hence first time is a bit slow
# Starting bmerge ...done in 0 secs
#                   date column1
# 1: 2016-02-01 10:00:00      10

## Running for second time and on
DT[column1 == 10]
# Using existing index 'column1'
# Starting bmerge ...done in 0 secs <~~ bmerge kicks in from now on
#                   date column1
# 1: 2016-02-01 10:00:00      10

如前所述通过@Jan，这个计划对于非球菌中实现联接也从V2.0.0开始

编辑（26/8/2016）：

正如@Arun指出的那样，尽管inrange使用二进制连接，但它需要首先对整个向量进行排序，以检查x中的每个值是否在上，下提供的任何间隔之间。在您的情况下，这有点麻烦，因为您仅将两个值进行比较，因此最近用Cbetween函数重写的代码将更适合您

set.seed(123)
DT <- data.table(x = sample(5e8))

system.time(res1 <- DT[x > 1e3L & x < 1e5L])
#  user  system elapsed 
# 10.23    1.22   11.45 

system.time(res2 <- DT[x %inrange% c(1e3L, 1e5L)])
# forderv(query) took ... 29.09 secs
# Starting bmerge ...done in 0 secs
# Generating final logical vector ... done in 0.43 secs
#  user  system elapsed 
# 29.28    0.70   30.06 

system.time(res3 <- DT[x %between% c(1e3L, 1e5L)])
# user  system elapsed 
# 2.01    2.60    0.84

因此，您可以看到，尽管bmerge几乎是即时的，但排序需要花费大量时间。Whilebetween是最快的，因为它不需要x两次转换为逻辑向量。哎呀，between太快了，elapsed小到了user+system

不过，如果您的数据已经排序，那么inrange可以很好地赶上

setorder(DT, x)
system.time(res1 <- DT[x > 1e3L & x < 1e5L])
#  user  system elapsed 
# 10.41    1.02   11.45 

system.time(res2 <- DT[x %inrange% c(1e3L, 1e5L)])
# forderv(query) took ... 2.17 secs
# Starting bmerge ...done in 0 secs
# Generating final logical vector ... done in 0.44 secs
#  user  system elapsed 
# 2.47    0.71    3.20 

system.time(res3 <- DT[x %between% c(1e3L, 1e5L)])
#  user  system elapsed 
# 2.30    2.62    0.88