split apply recombine，plyr，data.table in R

问题描述：

我在R中做了经典的拆分 - 应用 - 重组事件。我的数据集是一段时间内的一群公司。我正在做的是为每个公司运行回归，并返回残差，因此，我不是由公司聚合。 plyr 是伟大的，但它需要一个非常非常长的时间运行，当企业数量庞大。有没有办法用 data.table ？

I am doing the classic split-apply-recombine thing in R. My data set is a bunch of firms over time. The applying I am doing is running a regression for each firm and returning the residuals, therefore, I am not aggregating by firm. plyr is great for this but it takes a very very long time to run when the number of firms is large. Is there a way to do this with data.table?

示例数据：

dte, id, val1, val2
2001-10-02, 1, 10, 25
2001-10-03, 1, 11, 24
2001-10-04, 1, 12, 23
2001-10-02, 2, 13, 22
2001-10-03, 2, 14, 21

我需要根据每个id进行拆分（即1和2）。运行回归，返回残差并将其作为列附加到我的数据。有没有办法使用 data.table ？

I need to split by each id (namely 1 and 2). Run a regression, return the residuals and append it as a column to my data. Is there a way to do this using data.table?

答

我猜这需要按id排序正确排队。幸运的是，当您设置键时，会自动发生这种情况：

I'm guessing this needs to be sorted by "id" to line up properly. Luckily that happens automatically when you set the key:

dat <-read.table(text="dte, id, val1, val2
 2001-10-02, 1, 10, 25
 2001-10-03, 1, 11, 24
 2001-10-04, 1, 12, 23
 2001-10-02, 2, 13, 22
 2001-10-03, 2, 14, 21
 ", header=TRUE, sep=",")
 dtb <- data.table(dat)
 setkey(dtb, "id")
 dtb[, residuals(lm(val1 ~ val2)), by="id"]
#---------------
cbind(dtb, dtb[, residuals(lm(val1 ~ val2)), by="id"])
#---------------
            dte id val1 val2 id.1            V1
[1,] 2001-10-02  1   10   25    1  1.631688e-15
[2,] 2001-10-03  1   11   24    1 -3.263376e-15
[3,] 2001-10-04  1   12   23    1  1.631688e-15
[4,] 2001-10-02  2   13   22    2  0.000000e+00
[5,] 2001-10-03  2   14   21    2  0.000000e+00



> dat <- data.frame(dte=Sys.Date()+1:1000000, 
                    id=sample(1:2, 1000000, repl=TRUE),  
                    val1=runif(1000000),  val2=runif(1000000) )
> dtb <- data.table(dat)
> setkey(dtb, "id")
> system.time(  cbind(dtb, dtb[, residuals(lm(val1 ~ val2)), by="id"]) )
   user  system elapsed 
  1.696   0.798   2.466 
> system.time( dtb[,transform(.SD,r = residuals(lm(val1~val2))),by = "id"] )
   user  system elapsed 
  1.757   0.908   2.690

来自Matthew的编辑：
这对于v1.8.0在CRAN 。除了 transform 在 j 中的小主题 data.table wiki 点2：对于速度不要 transform / code>按组， cbind（）之后。但是，：= 现在工作组在v1.8.1和是既简单和快速。看到我的答案为例证（但不需要投票）。

EDIT from Matthew : This is all correct for v1.8.0 on CRAN. With the small addition that transform in j is the subject of data.table wiki point 2: "For speed don't transform() by group, cbind() afterwards". But, := now works by group in v1.8.1 and is both simple and fast. See my answer for illustration (but no need to vote for it).

好吧，我投了票。这里是控制台命令安装v 1.8.1在Mac（如果你有正确的XCode工具avaialble，因为它只有在源）：

Well, I voted for it. Here is the console command to install v 1.8.1on a Mac (if you have the proper XCode tools avaialble, since it only there in source):

install.packages("data.table", repos= "http://R-Forge.R-project.org", type="source", 
               lib="/Library/Frameworks/R.framework/Versions/2.14/Resources/lib")

（由于某种原因，我不能获取Mac GUI软件包安装程序以读取r-forge作为存储库。）

(For some reason I could not get the Mac GUI Package Installer to read r-forge as a repository.)

split apply recombine，plyr，data.table in R

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