我传递一些data.tables一个函数，并希望收集了多个函数调用传递data.tables日益增长的结果。的行被添加（附加）的功能之内。
有没有办法“参照/就地”追加行到data.table？
任何解决办法，如果这是不可能的？
编辑：我的目标是在函数里一次添加多个行和列的数量可以是非常大的（这就是为什么我使用的是“data.table”）。
library(data.table)
validate <- function(data, rule, valid.result, checked.rules) {
# ... find errors
# How to append "rule" to "checked.rules"?
findings <- data.table(err.code = rule$rule.id, msg = "some blah blah") # just an stupid example
# How to append all "finding"s to "valid.results"?
}
data <- data.table(a=1:10, b=21:30)
valid.result <- data.table(err.code = integer(0), msg = character(0)) # empty validation results table
checked.rules <- data.table(rule.id = integer(0), rule.name = character(0)) # empty table
rules <- data.table(rule.id = 1:4, rule.name = c("too big", "too small", "too late", "empty"))
validate(data, rules[3, ], valid.result, checked.rules)
validate(data, rules[1, ], valid.result, checked.rules)
validate(data, rules[4, ], valid.result, checked.rules)
预期成绩：
checked.rules
# rule.id rule.name
# 1: 3 too late
# 2: 1 too big
# 3: 4 empty
valid.results
# err.code msg
# 1: 3 some blah blah
# 2: 1 some blah blah
# 3: 4 some blah blah
作为由@Henrik提供的链接已经提到目前data.tables不能被引用添加行。因此我想用rbindlist去（这也工作得很好，以添加多行）：
library(data.table)
validate <- function(data, rule, valid.result, checked.rules) {
# ... find errors
# How to append "rule" to "checked.rules"?
checked.rules <<- rbindlist(list(checked.rules, rule))
findings <- data.table(err.code = rule$rule.id, msg = "some blah blah") # just an stupid example
# How to append all "finding"s to "valid.results"?
valid.result <<- rbindlist(list(valid.result, findings))
}
data <- data.table(a=1:10, b=21:30)
valid.result <- data.table(err.code = integer(0), msg = character(0)) # empty validation results table
checked.rules <- data.table(rule.id = integer(0), rule.name = character(0)) # empty table
rules <- data.table(rule.id = 1:4, rule.name = c("too big", "too small", "too late", "empty"))
validate(data, rules[3, ], valid.result, checked.rules)
validate(data, rules[1, ], valid.result, checked.rules)
validate(data, rules[4, ], valid.result, checked.rules)
print(checked.rules)
print(valid.result)
阅读评论的链接和@ismirsehregal的建议使用list我结束了使用environment这样我就可以“参照”收集多个结果之后。
我做了一个标杆两个变种：
rbind在每个函数调用进入“累积”的结果结束时的中间结果（“函数内追加”）。rbindlist仅一次在端部中间结果（“追加功能的外部”）。该代码被简化导致生根粉。经过20函数调用9个MIO行：
library(data.table)
library(microbenchmark)
validate.rbind <- function(data, results) {
findings <- data.table(err.code = 100, msg = rep("some blah blah", sample(1E6, 1) + 1)) # just an stupid example
results$valid.result <- rbind(results$valid.result, findings) # same as: rbindlist(list(results$valid.result, findings))
}
validate.rbindlist <- function(data, results) {
findings <- data.table(err.code = 100, msg = rep("some blah blah", sample(1E6, 1) + 1)) # just an stupid example
assign(paste0("res", sprintf("%02d", results$counter)), findings, envir = results)
results$counter = results$counter + 1
}
microbenchmark(
rbind.per.call = {
set.seed(0815) # make random numbers reproducible
data <- data.table(a=1:100, b=21:30)
results <- new.env() # use an environment to pass arguments by reference
results$valid.result <- data.table(err.code = integer(0), msg = character(0)) # empty validation results table
for (i in 1:20) {
validate.rbind(data, results)
}
},
rbindlist.once = {
set.seed(0815) # make random numbers reproducible
data <- data.table(a=1:100, b=21:30)
results <- new.env() # use an environment to pass arguments by reference
results$counter <- 1
for (i in 1:20) {
validate.rbindlist(data, results)
}
result.vars <- ls(envir = results, pattern = "^res.*") # identify the result tables via the used naming pattern
results$valid.result <- rbindlist(mget(result.vars, envir = results))
rm(list = result.vars, envir = results) # remove the intermediate result tables (keep only the total result)
},
times = 10)
方案二是快四倍
Unit: milliseconds
expr min lq mean median uq max neval
rbind.per.call 1021.2956 1114.8187 1198.7033 1153.7775 1324.6672 1477.5669 10
rbindlist.once 231.0477 249.7195 305.0974 260.2499 275.3446 713.1155 10
和存储器足迹（与gc()观察到的），甚至更好：
# Memory consumption for rbind.per.call:
# used (Mb) gc trigger (Mb) max used (Mb)
# Ncells 510152 27.3 940480 50.3 847768 45.3
# Vcells 19636460 149.9 55027624 419.9 52254173 398.7
# Memory consumption for rbindlist.once:
# used (Mb) gc trigger (Mb) max used (Mb)
# Ncells 604335 32.3 1168576 62.5 940480 50.3
# Vcells 19859703 151.6 55503896 423.5 39082073 298.2
PS：我没有测试链接set变化，因为我不希望一个更好的性能，因为它是更复杂的使用