Question

fastmatch包为重复匹配（例如在循环中）实现了更快的match版本：

set.seed(1)
library(fastmatch)
table <- 1L:100000L
x <- sample(table, 10000, replace=TRUE)
system.time(for(i in 1:100) a <-  match(x, table))
system.time(for(i in 1:100) b <- fmatch(x, table))
identical(a, b)

%in%是否有类似的实现可以用来加速重复查找？

Answer 1

查看%in%：

的定义

R> `%in%`
function (x, table) 
match(x, table, nomatch = 0L) > 0L
<bytecode: 0x1fab7a8>
<environment: namespace:base>

您可以轻松编写自己的%fin%函数：

`%fin%` <- function(x, table) {
  stopifnot(require(fastmatch))
  fmatch(x, table, nomatch = 0L) > 0L
}
system.time(for(i in 1:100) a <- x %in% table)
#    user  system elapsed 
#   1.780   0.000   1.782 
system.time(for(i in 1:100) b <- x %fin% table)
#    user  system elapsed 
#   0.052   0.000   0.054
identical(a, b)
# [1] TRUE

Answer 2

匹配几乎总是通过将两个向量放在数据帧和合并中来完成（参见dplyr的各种连接）

例如，类似这样的内容可以为您提供所需的所有信息：

library(dplyr)

data = data_frame(data.ID = 1L:100000L,
                  data.extra = 1:2)

sample = 
  data %>% 
  sample_n(10000, replace=TRUE) %>%
  mutate(sample.ID = 1:n(),
         sample.extra = 3:4 )

# join table not strictly necessary in this case
# but necessary in many-to-many matches
data__sample = inner_join(data, sample)

#check whether a data.ID made it into sample
data__sample %>% filter(data.ID == 1)

或left_join，right_join，full_join，semi_join，anti_join，具体取决于对您最有用的信息

％运算符的％更快

2 个答案: