使用do.call与dplyr标准评估版本 [英] Using do.call with dplyr standard evaluation version

问题描述

如何使用参数和函数的变量列表来获取 do.call ，以便使用标准评估版本总结_ in dplyr？

How can I get a do.call with a variable list of arguments and functions to work with the standard evaluation version of summarise_ in dplyr?

## Some sample data, function, and variables to interpolate
set.seed(0)
dat <- data.frame(a=runif(10), b=runif(10))
fn <- function(x, y) IQR(x / y, na.rm = TRUE)
funs <- list(fn="fn")
targs <- list("a", "b")

这是 lazyeval :: interp 我正在努力工作

library(dplyr)
interp(~do.call(fn, xs), .values=list(fn=funs$fn, xs=targs))
# ~do.call("fn", list("a", "b"))

但它不工作，

dat %>%
  summarise_(out = interp(~do.call(fn, xs), .values=list(fn=funs$fn, xs=targs)))

预期结果

dat %>%
  summarise(out = do.call(fn, list(a, b)))
#        out
# 1 1.084402

如果我添加一些打印声明，我知道问题是a和b没有得到正确的解释，但是我无法弄清楚如何引用它们。

If I add in some print statements, I know the problem is that the "a" and "b" aren't being interpreted properly, but I haven't been able to figure out how to quote them properly.

fn <- function(x, y) { print(x); print(y); IQR(x / y, na.rm = TRUE) }
dat %>%
  summarise_(out = interp(~do.call(fn, xs), fn=funs$fn, xs=targs))
# [1] "a"
# [1] "b"
# Error: non-numeric argument to binary operator

推荐答案

targs 参数需要为code>调用类。 调用（ a 和 b ）中的变量需要成为名称类。所有这一切都在下面的第二（和第三）行中完成。 ？调用，？as.name 和？is.language 可能使线更容易理解。

The targs argument needs to be a call class. The variables in the call (a and b) need to be a name class. All this is done in the second (and third) line below. ?call, ?as.name, and ?is.language might make the line more understandable.

dat <- data.frame(a=runif(10), b=runif(10), grp=rep(1:2, each=5))
targs_quoted = do.call(call, c("list", lapply(targs, as.name)), quote=TRUE)
# In hardcoded form, targs_quoted = quote(list(a, b))
dat %>%
  group_by(grp) %>%
  summarise_(out = interp(~do.call(fn, xs), 
                          .values=list(fn=funs$fn, xs=targs_quoted)))

# Source: local data frame [2 x 2]
#     
#       grp       out
#     (int)     (dbl)
#  1     1  1.0754497
#  2     2  0.9892201

dplyr 的nse（非标准评估）小插曲在这里非常有帮助。我发现。总是引用整个表，而不是分组表。这就是为什么评论中的一些建议没有按需要工作。

dplyr's "nse" (non-standard evaluation) vignette was very helpful here. I found that the . always referred to the entire table, not the grouped table. That's why some of the recommendations in the comments didn't "work" as wanted.

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