条件加入 r

Question

我想有条件地将两个数据表连接在一起：

library(data.table)
set.seed(1)

key.table <- 
  data.table(
    out = (0:10)/10,
    keyz = sort(runif(11))
  )

large.tbl <- 
  data.table(
    ab = rnorm(1e6),
    cd = runif(1e6)
  )

根据以下规则：匹配key.table中out中keyz值大于cd的最小值。我有以下内容：

library(dplyr)
large.tbl %>%
  rowwise %>%
  mutate(out = min(key.table$out[key.table$keyz > cd]))

提供正确的输出。我遇到的问题是 rowwise 操作对于我实际使用的 large.tbl 来说似乎很昂贵，除非它在特定计算机上，否则它会崩溃。是否有更少的内存消耗操作？以下似乎稍微快一些，但不足以解决我遇到的问题。

large.tbl %>%
    group_by(cd) %>%
    mutate(out = min(key.table$out[key.table$keyz > cd]))

这听起来像是 data.table 答案的问题，但答案不必使用该包。

Answer 1

如果 key.table$out 也像您的玩具示例中那样排序，则以下内容会起作用

ind <- findInterval(large.tbl$cd, key.table$keyz) + 1
large.tbl$out <- key.table$out[ind]
head(large.tbl)
#             ab         cd out
#1: -0.928567035 0.99473795  NA
#2: -0.294720447 0.41107393 0.5
#3: -0.005767173 0.91086585 1.0
#4:  2.404653389 0.66491244 0.8
#5:  0.763593461 0.09590456 0.1
#6: -0.799009249 0.50963409 0.5

如果key.table$out没有排序，

ind <- findInterval(large.tbl$cd, key.table$keyz) + 1
vec <- rev(cummin(rev(key.table$out)))
large.tbl$out <- vec[ind]

Answer 2

你想要的是：

setkey(large.tbl, cd)
setkey(key.table, keyz)
key.table[large.tbl, roll = -Inf]

见?data.table>roll:

Applies to the last join column, generally a date but can be any ordered variable, irregular and including gaps. If roll=TRUE and i's row matches to all but the last x join column, and its value in the last i join column falls in a gap (including after the last observation in x for that group), then the prevailing value in x is rolled forward. This operation is particularly fast using a modified binary search. The operation is also known as last observation carried forward (LOCF). Usually, there should be no duplicates in x's key, the last key column is a date (or time, or datetime) and all the columns of x's key are joined to. A common idiom is to select a contemporaneous regular time series (dts) across a set of identifiers (ids): DT[CJ(ids,dts),roll=TRUE] where DT has a 2-column key (id,date) and CJ stands for cross join. When roll is a positive number, this limits how far values are carried forward. roll=TRUE is equivalent to roll=+Inf. When roll is a negative number, values are rolled backwards; i.e., next observation carried backwards (NOCB). Use -Inf for unlimited roll back. When roll is "nearest", the nearest value is joined to.

（公平地说，我认为这可以进行一些说明，它非常密集）

条件加入 r

Conditional join in r

join

r

dplyr