数据透视表类似于重塑熔化函数 [英] Pivot on data.table similar to rehape melt function

问题描述

我已经读了一些引用类似的问题在这里SO，但是还没有能够找到一个解决方案，并想知道是否有任何方法做以下使用只是data.table。

I have read some references to similar problems here on SO, but haven't been able to find a solution yet and wondering if there is any way to do the following using just data.table.

我将使用一个简化的例子，但在实践中，我的数据表有> 1000列类似于var1，var2，... var1000等。

I'll use a simplified example, but in practice, my data table has > 1000 columns similar to var1, var2, ... var1000, etc.

dt <- data.table(uid=c("a","b"), var1=c(1,2), var2=c(100,200))

我正在寻找一个解决方案，这将允许我得到类似于reshape的熔化函数的输出 - -

I am looking for a solution that will allow me to get an output similar to reshape's melt function --

> melt(dt, id=c("uid"))
uid variable value
1   a     var1     1
2   b     var1     2
3   a     var2   100
4   b     var2   200

也就是说，除了uid之外的所有列都列在具有相应值在邻接的列中。我已经尝试这个与列表等的组合，但可能会缺少一些显而易见的东西。

That is, all the columns except for uid are listed under a single column with the corresponding values in an adjoining column. I have tried this with a combination of list, etc, but might be missing something that is obvious.

dt中的所有uid是唯一的。

All uids in dt are unique.

提前感谢。

推荐答案

对于data.table重塑，请尝试以下操作： p>

For a data.table reshape, try the following:

dt[, list(variable = names(.SD), value = unlist(.SD, use.names = F)), by = uid]

语法的代价是值得的;该函数运行非常快！

The cost of the syntax is worth it; the function runs very quickly!

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