什么时候plyr比data.table好？ [英] when is plyr better than data.table?

问题描述

更好的意思是更快或更容易阅读/更短的语法，或者也可能意味着该命令在 data.table 中甚至不可行。

Better here can mean faster or easier to read/shorter syntax or it could also mean that the command is not even doable in data.table.

我不使用 plyr 很多，并想知道是否有情况下我应该。因为我不使用它很多，我可以想出的唯一例子是 rbind.fill ，据我所知没有一个 data.table 模拟和所有其他例子我看到smth在 plyr 和 data.table ，后者更快，更容易阅读/更紧凑。

I don't use plyr a lot and would like to know if there are cases when I should. Because I don't use it a lot, the only example I can come up with is rbind.fill that to my knowledge doesn't have a data.table analog and every other example I've seen of smth being done in both plyr and data.table, the latter was faster and easier to read/more compact.

推荐答案

目的。

这里是每个包的简要摘要，从包本身：

Here is the brief summary of each package, from the packages themselves:

plyr包是一组干净且一致的工具，实现R中的拆分 - 应用 - 组合模式。数据分析中的常见模式：通过将其分解成小块，对每个块执行某些操作，然后将结果重新组合在一起，可以解决复杂的问题。

The plyr package is a set of clean and consistent tools that implement the split-apply-combine pattern in R. This is an extremely common pattern in data analysis: you solve a complex problem by breaking it down into small pieces, doing something to each piece and then combining the results back together again.

和

data.table ...以短且灵活的语法提供快速子集，快速分组，快速更新，快速排序连接和列表列，以加快开发速度。它受到A中的A [B]语法的启发，其中A是矩阵，B是2列矩阵。

data.table ... offers fast subset, fast grouping, fast update, fast ordered joins and list columns in a short and flexible syntax, for faster development. It is inspired by A[B] syntax in R where A is a matrix and B is a 2-column matrix.

它们重叠的位置在快速分组中，plyr也通过分割data.frames，转换为单个数据帧。 data.table 有许多其他功能，使对data.frame类结构的操作更快; plyr 具有将拆分 - 应用 - 组合范例应用于其他数据结构（例如列表和数组（作为输入和输出））的功能。

Where they overlap is in the "fast grouping" which plyr also does by splitting data.frames, operating on pieces, and recombining them into a single data.frame. data.table has many other features which make operations on data.frame like structures fast; plyr has features which apply the split-apply-combine paradigm to other data structures such as lists and arrays (both as inputs and outputs).

所以，真的，他们是两个不同的工具，碰巧有一个小区域的重叠，解决同一个问题域，但每一个做得更多，如果你想/需要额外的功能，然后应使用该包。

So, really, they are two different tools that happen to have a small area of overlap which address the same problem domain, but each does much more than that and if you want/need that additional functionality, then that package should be used.

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