使用data.table按组对应于最大值的子集行 [英] Subset rows corresponding to max value by group using data.table

问题描述

假设我有一个包含一些棒球运动员的 data.table:

Assume I have a data.table containing some baseball players:

library(plyr)
library(data.table)

bdt <- as.data.table(baseball)

对于每个组(由玩家id"给出)，我想选择与最大游戏数g"相对应的行.这在 plyr 中很简单:

For each group (given by player 'id'), I want to select rows corresponding to the maximum number of games 'g'. This is straightforward in plyr:

ddply(baseball, "id", subset, g == max(g))

data.table 的等效代码是什么?

我试过了:

setkey(bdt, "id") 
bdt[g == max(g)]  # only one row
bdt[g == max(g), by = id]  # Error: 'by' or 'keyby' is supplied but not j
bdt[, .SD[g == max(g)]] # only one row

这行得通:

bdt[, .SD[g == max(g)], by = id] 

但它只比 plyr 快 30%，这表明它可能不是惯用的.

But it's is only 30% faster than plyr, suggesting it's probably not idiomatic.

推荐答案

这是快速的 data.table 方式:

bdt[bdt[, .I[g == max(g)], by = id]$V1]

这避免了构造 .SD，这是表达式中的瓶颈.

This avoids constructing .SD, which is the bottleneck in your expressions.

实际上，OP 运行缓慢的主要原因不仅在于它包含 .SD，还在于它以特定方式使用它- 通过调用 [.data.table，目前它的开销很大，因此在循环中运行它(当一个 by 执行时)会累积非常大的惩罚.

edit: Actually, the main reason the OP is slow is not just that it has .SD in it, but the fact that it uses it in a particular way - by calling [.data.table, which at the moment has a huge overhead, so running it in a loop (when one does a by) accumulates a very large penalty.

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