按索引列和行的子集数据表 [英] subset data.table by indexed column and rows

问题描述

我希望通过更改列 z 的索引并同时基于某些筛选行来递归地对数据表进行子集化基于％in％的向量。

I am looking to subset a data table recursively, by changing the index of the column z AND at the same time filter rows based on some %in% based vector.

dt <- setDT(copy(diamonds))
dt <- setDT(data.frame(lapply(dt, as.character), stringsAsFactors=FALSE))
z=4
subset_by <- unique(dt[,z])[1:2]
### obviously does not work
###dt1<-dt[ z %in% subset_by]

我正在寻找最节省内存的操作来执行此操作，并且我敢肯定有一种方法不使用colnames，但是我只是找不到它。我看了很多帖子，其中此最相关的

I am looking for the most memory-efficient operation to do this and I am sure there is a way without using colnames, but I just cannot find it. I looked at a lot of posts, with this beign the most relevant

推荐答案

如果我们根据索引或名称进行子集设置，则可以在中进行指定。 SDcols

If we are subsetting based on the index or names, we can specify it in .SDcols

i1 <- dt[, .I[.SD[[1]] %chin% subset_by], .SDcols = z]
dt[i1]

data.table / tbl_df / data_frame 中的列将是 [[或 $

subset_by <- unique(dt[[z]])[1:2]

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