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我正在尝试使用 data.table 来加快处理由几个较小的合并 data.frames 组成的大型 data.frame (300k x 60)。我是 data.table 的新手。到目前为止的代码如下

library(data.table)
a = data.table(index=1:5,a=rnorm(5,10),b=rnorm(5,10),z=rnorm(5,10))
b = data.table(index=6:10,a=rnorm(5,10),b=rnorm(5,10),c=rnorm(5,10),d=rnorm(5,10))
dt = merge(a,b,by=intersect(names(a),names(b)),all=T)
dt$category = sample(letters[1:3],10,replace=T)

我想知道是否有比以下更有效的方法来总结数据。

summ = dt[i=T,j=list(a=sum(a,na.rm=T),b=sum(b,na.rm=T),c=sum(c,na.rm=T),
                     d=sum(d,na.rm=T),z=sum(z,na.rm=T)),by=category]

我真的不想手动输入所有 50 列计算，并且eval(paste(...))不知何故看起来很笨重。

我查看了下面的示例，但对于我的需求来说似乎有点复杂。谢谢

如何跨多列汇总 data.table

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1 回答 1

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You can use a simple lapply statement with .SD

dt[, lapply(.SD, sum, na.rm=TRUE), by=category ]

   category index        a        b        z         c        d
1:        c    19 51.13289 48.49994 42.50884  9.535588 11.53253
2:        b     9 17.34860 20.35022 10.32514 11.764105 10.53127
3:        a    27 25.91616 31.12624  0.00000 29.197343 31.71285

If you only want to summarize over certain columns, you can add the .SDcols argument

#  note that .SDcols also allows reordering of the columns
dt[, lapply(.SD, sum, na.rm=TRUE), by=category, .SDcols=c("a", "c", "z") ] 

   category        a         c        z
1:        c 51.13289  9.535588 42.50884
2:        b 17.34860 11.764105 10.32514
3:        a 25.91616 29.197343  0.00000

This of course, is not limited to sum and you can use any function with lapply, including anonymous functions. (ie, it's a regular lapply statement).

Lastly, there is no need to use i=T and j= <..>. Personally, I think that makes the code less readable, but it is just a style preference.

Documentation

See ?.SD, ?data.table and its .SDcols argument, and the vignette Using .SD for Data Analysis.

Also have a look at data.table FAQ 2.1.

于 2013-05-13T01:59:49.507 回答