当变量名称存储在字符向量中时，选择/分配给data.table [英] Select / assign to data.table when variable names are stored in a character vector

问题描述

如果变量名称存储在字符向量中，如何在 data.table 中引用变量？例如，这适用于 data.frame ：

How do you refer to variables in a data.table if the variable names are stored in a character vector? For instance, this works for a data.frame:

df <- data.frame(col1 = 1:3)
colname <- "col1"
df[colname] <- 4:6
df
#   col1
# 1    4
# 2    5
# 3    6

如何使用或不使用：= 表示法对data.table执行相同的操作？ dt [，list（colname）] 显然不起作用（我也不希望这样做）。

How can I perform this same operation for a data.table, either with or without := notation? The obvious thing of dt[ , list(colname)] doesn't work (nor did I expect it to).

推荐答案

以编程方式选择变量的两种方法：

Two ways to programmatically select variable(s):

1. with = FALSE ：

DT = data.table(col1 = 1:3)
colname = "col1"
DT[, colname, with = FALSE] 
#    col1
# 1:    1
# 2:    2
# 3:    3

'点点'（ .. ）前缀：

DT[, ..colname]    
#    col1
# 1:    1
# 2:    2
# 3:    3

有关点点（ .. ）表示法，请参见 1.10.2中的新功能（当前未在帮助文本中进行描述）。

For further description of the 'dot dot' (..) notation, see New Features in 1.10.2 (it is currently not described in help text).

要 assign 到变量，将：= 的LHS括在括号中：

To assign to variable(s), wrap the LHS of := in parentheses:

DT[, (colname) := 4:6]    
#    col1
# 1:    4
# 2:    5
# 3:    6

后者称为列 plonk ，因为您通过引用替换了整个列向量。如果存在子集 i ，它将通过引用进行子分配。 （colname）周围的括号是2014年10月CRAN v1.9.4版中引入的简写。这是新闻项：

The latter is known as a column plonk, because you replace the whole column vector by reference. If a subset i was present, it would subassign by reference. The parens around (colname) is a shorthand introduced in version v1.9.4 on CRAN Oct 2014. Here is the news item:

使用和= FALSE 和：= 现在，在所有情况下均不建议使用，因为将
包裹在LHS中的：= 并带有括号已被使用一段时间了。

Using with = FALSE with := is now deprecated in all cases, given that wrapping the LHS of := with parentheses has been preferred for some time.

colVar = "col1"
DT[, colVar := 1, with = FALSE]                 # deprecated, still works silently
DT[, (colVar) := 1]                             # please change to this
DT[, c("col1", "col2") := 1]                    # no change
DT[, 2:4 := 1]                                  # no change
DT[, c("col1","col2") := list(sum(a), mean(b)]  # no change
DT[, `:=`(...), by = ...]                       # no change

另请参见中的详细信息部分？`：=`：

See also Details section in ?`:=`:

DT[i, (colnamevector) := value]
# [...] The parens are enough to stop the LHS being a symbol

---

回答评论中的其他问题，这是一种方法（照常很多方法）：

---

And to answer further question in comment, here's one way (as usual there are many ways) :

DT[, colname := cumsum(get(colname)), with = FALSE]
#    col1
# 1:    4
# 2:    9
# 3:   15 

，或者，您可能会发现更容易阅读，编写和调试，只需 eval a paste ，类似于构造动态SQL语句以发送到服务器：

or, you might find it easier to read, write and debug just to eval a paste, similar to constructing a dynamic SQL statement to send to a server :

expr = paste0("DT[,",colname,":=cumsum(",colname,")]")
expr
# [1] "DT[,col1:=cumsum(col1)]"

eval(parse(text=expr))
#    col1
# 1:    4
# 2:   13
# 3:   28

如果您经常这样做，则可以定义一个辅助函数 EVAL ：

If you do that a lot, you can define a helper function EVAL :

EVAL = function(...)eval(parse(text=paste0(...)),envir=parent.frame(2))

EVAL("DT[,",colname,":=cumsum(",colname,")]")
#    col1
# 1:    4
# 2:   17
# 3:   45

现在 data.table 1.8.2自动优化 j 的效率，它最好使用 eval 方法。例如， j 中的 get（）会阻止某些优化。

Now that data.table 1.8.2 automatically optimizes j for efficiency, it may be preferable to use the eval method. The get() in j prevents some optimizations, for example.

或者，有 set（）。 ：= 的低开销，函数形式，在这里很好。请参阅？set 。

Or, there is set(). A low overhead, functional form of :=, which would be fine here. See ?set.

set(DT, j = colname, value = cumsum(DT[[colname]]))
DT
#    col1
# 1:    4
# 2:   21
# 3:   66

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