data.frame分组依据列 [英] data.frame Group By column

问题描述

我有一个数据框DF。

说DF是：

  A B
1 1 2
2 1 3
3 2 3
4 3 5
5 3 6 

现在，我想将A列的行合并在一起，并获得B列的总和。

Now I want to combine together the rows by the column A and to have the sum of the column B.

例如：

  A B
1 1 5
2 2 3
3 3 11

我目前正在使用带有sqldf函数的SQL查询来执行此操作。但是由于某种原因，它非常缓慢。还有更方便的方法吗？我也可以使用for循环手动完成此操作，但速度又很慢。我的SQL查询是从DF组按A选择A，Count（B）。

I am doing this currently using an SQL query with the sqldf function. But for some reason it is very slow. Is there any more convenient way to do that? I could do it manually too using a for loop but it is again slow. My SQL query is " Select A,Count(B) from DF group by A".

通常，每当我不使用向量化操作并且使用for循环时，即使对于单个过程，其性能也极慢。

In general whenever I don't use vectorized operations and I use for loops the performance is extremely slow even for single procedures.

推荐答案

这是一个常见问题。在基础中，您要查找的选项是汇总。假设您的 data.frame 被称为 mydf，则可以使用以下内容。

This is a common question. In base, the option you're looking for is aggregate. Assuming your data.frame is called "mydf", you can use the following.

> aggregate(B ~ A, mydf, sum)
  A  B
1 1  5
2 2  3
3 3 11

我也建议您查看 data.table包。

I would also recommend looking into the "data.table" package.

> library(data.table)
> DT <- data.table(mydf)
> DT[, sum(B), by = A]
   A V1
1: 1  5
2: 2  3
3: 3 11

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