我认为 data.table 的理念需要的任务的特殊命名函数比你在 tidyverse 中找到的要少，所以需要一些额外的编码，喜欢:

I reckon that the philosophy of data.table entails fewer specially-named functions for tasks than you'll find in the tidyverse, so some extra coding is required, like:

res = setDT(df)[
  CJ(person = person, observation_id = observation_id, unique=TRUE), 
  on=.(person, observation_id)
]

在此之后，您仍然需要手动处理缺失级别的值的填充.我们可以使用 setnafill 来有效地处理这个 &data.table 最新版本中的引用:

After this, you still have to manually handle the filling of values for missing levels. We can use setnafill to handle this efficiently & by-reference in recent versions of data.table:

setnafill(res, fill = 0, cols = 'value')

请参阅 @Jealie 的回答，了解可以避开此问题的功能.

See @Jealie's answer regarding a feature that will sidestep this.

当然，这里的列名必须输入三次，这很疯狂.但另一方面，可以编写一个包装器:

Certainly, it's crazy that the column names have to be entered three times here. But on the other hand, one can write a wrapper:

completeDT <- function(DT, cols, defs = NULL){
  mDT = do.call(CJ, c(DT[, ..cols], list(unique=TRUE)))
  res = DT[mDT, on=names(mDT)]
  if (length(defs)) 
    res[, names(defs) := Map(replace, .SD, lapply(.SD, is.na), defs), .SDcols=names(defs)]
  res[]
} 

completeDT(setDT(df), cols = c("person", "observation_id"), defs = c(value = 0))

   person observation_id value
1:      1              1     1
2:      1              2     0
3:      2              1     1
4:      2              2     1

作为避免在第一步输入三次名称的快速方法，这是@thelatemail 的想法:

As a quick way of avoiding typing the names three times for the first step, here's @thelatemail's idea:

vars <- c("person","observation_id")
df[do.call(CJ, c(mget(vars), unique=TRUE)), on=vars]

# or with magrittr...
c("person","observation_id") %>% df[do.call(CJ, c(mget(.), unique=TRUE)), on=.]

更新:现在您无需在 CJ 中输入两次姓名，这要感谢@MichaelChirico &@MattDowle 用于改进.

Update: now you don't need to enter names twice in CJ thanks to @MichaelChirico & @MattDowle for the improvement.