在R中使用ffdfdply函数进行聚合 [英] aggregation using ffdfdply function in R

问题描述

我尝试在R中使用 ffdfdply 函数使用'ffbase'包对大型数据集进行聚合。

可以说我有三个名为Date，Item的变量和销售。在这里，我想使用sum函数汇总Date和Item上的销售额。

在这里我这样尝试过：

I tried aggregation on large dataset using 'ffbase' package using ffdfdply function in R.
lets say I have three variables called Date,Item and sales. Here I want to aggregate the sales over Date and Item using sum function. Could you please guide me through some proper syntax in R.
Here I tried like this:

grp_qty <- ffdfdply(x=data[c("sales","Date","Item")], split=as.character(data$sales),FUN = function(data)  

summaryBy(Date+Item~sales, data=data, FUN=sum)).

对于您的解决方案，我将不胜感激。

I would appreciate for your solution.

推荐答案

标记ffdfdply是ffbase的一部分，而不是ff。
为了显示ffdfdply用法的示例，让我们生成一个具有50Mio行的 ffdf 。

Mark that ffdfdply is part of ffbase, not ff. To show an example of the usage of ffdfdply, let's generate an ffdf with 50Mio rows.

  require(ffbase)
  data <- expand.ffgrid(Date = ff(seq.Date(Sys.Date(), Sys.Date()+10000, by = "day")), Item = ff(factor(paste("Item", 1:5000))))
  data$sales <- ffrandom(n = nrow(data))
  # split by date -> assuming that all sales of 1 date can fit into RAM
  splitby <- as.character(data$Date, by = 250000)
  grp_qty <- ffdfdply(x=data[c("sales","Date","Item")], 
                      split=splitby, 
                      FUN = function(data){
                        ## This happens in RAM - containing **several** split elements so here we can use data.table which works fine for in RAM computing
                        require(data.table)
                        data <- as.data.table(data)
                        result <- data[, list(sales = sum(sales, na.rm=TRUE)), by = list(Date, Item)]
                        as.data.frame(result)
                      })
  dim(grp_qty)

将grp_qty标记为 ffdf 驻留在磁盘上。

Mark that grp_qty is an ffdf which resides on disk.

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