dplyr中的标准评估：总结以字符串形式给出的变量 [英] standard evaluation in dplyr: summarise a variable given as a character string

问题描述

更新2020年7月：

dplyr 1.0几乎也改变了有关此问题的所有内容作为所有答案。在此处查看 dplyr 编程小插图：

dplyr 1.0 has changed pretty much everything about this question as well as all of the answers. See the dplyr programming vignette here:

https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html

将列的标识符存储为字符向量时引用列的新方法是使用 rlang <的 .data 代词/ code>，然后像在基数R中一样设置子集。

The new way to refer to columns when their identifier is stored as a character vector is to use the .data pronoun from rlang, and then subset as you would in base R.

library(dplyr)

key <- "v3"
val <- "v2"
drp <- "v1"

df <- tibble(v1 = 1:5, v2 = 6:10, v3 = c(rep("A", 3), rep("B", 2)))

df %>% 
    select(-matches(drp)) %>% 
    group_by(.data[[key]]) %>% 
    summarise(total = sum(.data[[val]], na.rm = TRUE))

#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 2 x 2
#>   v3    total
#>   <chr> <int>
#> 1 A        21
#> 2 B        19

如果代码在包函数中，则可以 @ importFrom rlang .data 避免对未定义的全局变量进行R检查。

If your code is in a package function, you can @importFrom rlang .data to avoid R check notes about undefined globals.

原始问题：

我想引用摘要中的未知列名。 dplyr 0.3 中引入的标准评估函数允许使用变量来引用列名，但是当您调用 base <时这似乎不起作用。 / code> R函数，例如摘要。

I want to refer to an unknown column name inside a summarise. The standard evaluation functions introduced in dplyr 0.3 allow column names to be referenced using variables, but this doesn't appear to work when you call a base R function within e.g. a summarise.

library(dplyr)
 
key <- "v3"
val <- "v2"
drp <- "v1"
 
df <- data_frame(v1 = 1:5, v2 = 6:10, v3 = c(rep("A", 3), rep("B", 2)))

df看起来像这样：

> df
Source: local data frame [5 x 3]

  v1 v2 v3
1  1  6  A
2  2  7  A
3  3  8  A
4  4  9  B
5  5 10  B

我要删除v1，按v3分组，以及每个组的v2：

I want to drop v1, group by v3, and sum v2 for each group:

df %>% select(-matches(drp)) %>% group_by_(key) %>% summarise_(sum(val, na.rm = TRUE))

Error in sum(val, na.rm = TRUE) : invalid 'type' (character) of argument

NSE版本的 select（）可以正常工作，因为它可以匹配一个字符串。 group_by（）的SE版本可以正常工作，因为它现在可以接受变量作为参数并对其求值。但是，在 dplyr 函数中使用基本R函数时，我还没有找到一种实现类似结果的方法。

The NSE version of select() works fine, since it can match a character string. The SE version of group_by() works fine, since it can now accept variables as arguments and evaluate them. However, I haven't found a way to achieve similar results when using base R functions inside dplyr functions.

不能t工作：

df %>% group_by_(key) %>% summarise_(sum(get(val), na.rm = TRUE))
Error in get(val) : object 'v2' not found

df %>% group_by_(key) %>% summarise_(sum(eval(as.symbol(val)), na.rm = TRUE))
Error in eval(expr, envir, enclos) : object 'v2' not found

我已经签出几个 相关 问题，但到目前为止，所提出的解决方案均不适用于我。

I've checked out several related questions, but none of the proposed solutions have worked for me so far.

推荐答案

dplyr 1.0具有几乎解答了有关此问题的所有内容以及所有答案。在此处查看 dplyr 编程小插图：

dplyr 1.0 has changed pretty much everything about this question as well as all of the answers. See the dplyr programming vignette here:

https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html

将列的标识符存储为字符向量时引用列的新方法是使用 rlang <的 .data 代词/ code>，然后像在基数R中一样设置子集。

The new way to refer to columns when their identifier is stored as a character vector is to use the .data pronoun from rlang, and then subset as you would in base R.

library(dplyr)

key <- "v3"
val <- "v2"
drp <- "v1"

df <- tibble(v1 = 1:5, v2 = 6:10, v3 = c(rep("A", 3), rep("B", 2)))

df %>% 
    select(-matches(drp)) %>% 
    group_by(.data[[key]]) %>% 
    summarise(total = sum(.data[[val]], na.rm = TRUE))

#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 2 x 2
#>   v3    total
#>   <chr> <int>
#> 1 A        21
#> 2 B        19

如果代码在包函数中，则可以 @ importFrom rlang .data 避免对未定义的全局变量进行R检查。

If your code is in a package function, you can @importFrom rlang .data to avoid R check notes about undefined globals.

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