使用 grep 对 data.table 中的行进行子集化，比较行内容

问题描述

DT <- data.table(num=c("20031111","1112003","23423","2222004"),y=c("2003","2003","2003","2004"))

> DT
    num    y
1: 20031111 2003
2:  1112003 2003
3:    23423 2003
4:  2222004 2004

我想比较两个单元格的内容，并根据布尔值执行一个动作.例如，如果num"与年份匹配，则创建一个包含该值的列 x.我考虑过基于 grep 的子集，这很有效，但自然每次都检查 whole 列，这似乎很浪费

I want to compare the two cell content, and perform an action based on the boolean value. for instance, if "num" matches the year, create a column x holding that value. I thought about subsetting based on grep, and that works, but naturally checks the whole column every time which seems wasteful

DT[grep(y,num)] # works with a pattern>1 warning

我可以 apply() 我的方式，但也许有 data.table 方式?

I could apply() my way but perhaps there's a data.table way?

谢谢

推荐答案

如果您对使用 stringi 包感到满意，这是一种利用 stringi 函数向量化模式和字符串:

If you're happy using the stringi package, this is a way that takes advantage of the fact that the stringi functions vectorise both pattern and string:

DT[stri_detect_fixed(num, y), x := num])

根据数据，它可能比 Veerenda Gadekar 发布的方法更快.

Depending on the data, it may be faster than the method posted by Veerenda Gadekar.

DT <- data.table(num=paste0(sample(1000), sample(2001:2010, 1000, TRUE)),
                 y=as.character(sample(2001:2010, 1000, TRUE)))
microbenchmark(
    vg = DT[, x := grep(y, num, value=TRUE, fixed=TRUE), by = .(num, y)],
    nk = DT[stri_detect_fixed(num, y), x := num]
)

#Unit: microseconds
# expr      min       lq     mean   median       uq      max neval
#   vg 6027.674 6176.397 6513.860 6278.689 6370.789 9590.398   100
#   nk  975.260 1007.591 1116.594 1047.334 1110.734 3833.051   100

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