合并更大数据的有效替代方案.框架R

Question

我正在寻找一种有效的(计算机资源方面和学习/实现方式)方法来合并两个更大的(大小> 100万/ 300 KB RData文件)数据帧.

基础R中的"merge"和plyr中的"join"似乎耗尽了我的所有内存,有效地崩溃了我的系统.

示例
负载测试数据框

并尝试

test.merged<-merge(test, test)

要么

test.merged<-join(test, test, type="all")  

-

---

以下帖子提供了合并和备选方案的列表:
如何连接(合并)数据框(内部,外部,左侧,右侧)？

以下允许对象大小检查:https:
//heuristically.wordpress.com/2010/01/04/r-memory-usage-statistics-variable/

匿名制作的数据

Answer 1

以下是data.table与data.frame方法的一些时序.
使用data.table非常快.关于内存,我可以非正式地报告这两种方法在RAM使用方面非常相似(在20%以内).

library(data.table)

set.seed(1234)
n = 1e6

data_frame_1 = data.frame(id=paste("id_", 1:n, sep=""),
                          factor1=sample(c("A", "B", "C"), n, replace=TRUE))
data_frame_2 = data.frame(id=sample(data_frame_1$id),
                          value1=rnorm(n))

data_table_1 = data.table(data_frame_1, key="id")
data_table_2 = data.table(data_frame_2, key="id")

system.time(df.merged <- merge(data_frame_1, data_frame_2))
#   user  system elapsed 
# 17.983   0.189  18.063 


system.time(dt.merged <- merge(data_table_1, data_table_2))
#   user  system elapsed 
#  0.729   0.099   0.821 

Answer 2

这是必须的data.table例子:

library(data.table)

## Fix up your example data.frame so that the columns aren't all factors
## (not necessary, but shows that data.table can now use numeric columns as keys)
cols <- c(1:5, 7:10)
test[cols] <- lapply(cols, FUN=function(X) as.numeric(as.character(test[[X]])))
test[11] <- as.logical(test[[11]])

## Create two data.tables with which to demonstrate a data.table merge
dt <- data.table(test, key=names(test))
dt2 <- copy(dt)
## Add to each one a unique non-keyed column
dt$X <- seq_len(nrow(dt))
dt2$Y <- rev(seq_len(nrow(dt)))

## Merge them based on the keyed columns (in both cases, all but the last) to ...
## (1) create a new data.table
dt3 <- dt[dt2]
## (2) or (poss. minimizing memory usage), just add column Y from dt2 to dt
dt[dt2,Y:=Y]