r data.table 临时连接的函数包装器（在链中聚合）

Question

[data.table_1.9.6] 问题的背景是我正在尝试在类似星型模式的数据布局中构建类似 olap 的查询功能，即一个大事实 table 和几个元 tables。我正在围绕 data.table join 构建函数包装器，然后在链中进行聚合，如下所示：

# dummy data
dt1 = data.table(id = 1:5, x=letters[1:5], a=11:15, b=21:25)
dt2 = data.table(k=11:15, z=letters[11:15])

# standard data.table query with ad-hoc key -> works fine
dt1[dt2, c("z") := .(i.z), with = F, 
    on = c(a="k")][, .(m = sum(a, na.rm = T),
                   count = .N), by = c("z")]

# wrapper function with setkey -> works fine
agg_foo <- function(x, meta_tbl, x_key, meta_key, agg_var) { 
  setkeyv(x, x_key)
  setkeyv(meta_tbl, meta_key)
  x[meta_tbl, (agg_var) := get(agg_var)][,.(a_sum = sum(a, na.rm=T),
                                            count = .N), 
                                         by = c(agg_var)]
  x[, (agg_var) := .(NULL)]
  }

# call function (works fine)
agg_foo(x=dt1, meta_tbl=dt2, x_key="a", meta_key="k",agg_var="z")

# wrapper function with ad-hoc key -> does not work
agg_foo_ad_hoc <- function(x, meta_tbl, x_key, meta_key, agg_var) { 
  x[meta_tbl, (agg_var) := get(agg_var), 
    on = c(x_key = meta_key)][,.(a_sum = sum(a, na.rm=T),
                                 count = .N), by = c(agg_var)]
  x[, (agg_var) := .(NULL)]
  }

# call function (causes error)
agg_foo_ad_hoc(x=dt1, meta_tbl=dt2, x_key="a", meta_key="k",agg_var="z")

Error in forderv(x, by = rightcols) : 
  'by' value -2147483648 out of range [1,4]

我的猜测是我必须以不同的方式提供临时 "on" 参数。我尝试了 = c(get(x_key) = meta_key) 但后来他抱怨意外的括号。我可以使用该函数的 setkey 版本，但我想知道这是否有效，因为该函数将在不同的元 tables 上工作，具体取决于使用哪个聚合属性，从而不断地重新设置密钥.还是总是首选 setkey？实际情况 table（此处为 x）有 > 3000 万行。

Answer 1

您需要做的就是构建一个带有正确标签的向量。这是一种方法：

agg_foo_ad_hoc <- function(x, meta_tbl, x_key, meta_key, agg_var) { 
  x[meta_tbl, (agg_var) := get(agg_var), 
    on = setNames(meta_key, x_key)][,.(a_sum = sum(a, na.rm=T),
                                       count = .N), by = c(agg_var)]
  x[, (agg_var) := .(NULL)]
}

r data.table 临时连接的函数包装器（在链中聚合）

r data.table function wrapper around ad-hoc join (with aggregation in a chain)

join

r

function

wrapper

data.table