在dplyr而不是SE中使用get()是否有缺点?

可以使用NSE,使用 rlang .

假设您的用例是:

find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))
# # A tibble: 8 x 6
# # Groups:   cyl, vs, am, gear [5]
#     cyl    vs    am  gear  carb uTargets
#   <dbl> <dbl> <dbl> <dbl> <dbl>    <int>
# 1  4.00  1.00  0     4.00  2.00        2
# 2  4.00  1.00  1.00  4.00  1.00        4
# 3  4.00  1.00  1.00  4.00  2.00        2
# 4  6.00  1.00  0     3.00  1.00        2
# 5  6.00  1.00  0     4.00  4.00        2
# 6  8.00  0     0     3.00  2.00        4
# 7  8.00  0     0     3.00  3.00        3
# 8  8.00  0     0     3.00  4.00        4

您可以:

library(dplyr)

f2 <- function(df, target_column, cols_to_use) {
  group_by_at(df, cols_to_use) %>% 
    summarise(uTargets = n_distinct(!! rlang::sym(target_column))) %>% 
    filter(uTargets > 1)
}

all.equal(
  find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb")),
  f2(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))
)
# [1] TRUE

关于风险问题的实际答案:

现在想象一下 foo <- 3 在您的全球环境中。比较:

find_dataset_inconsistencies(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))
# A tibble: 0 x 6
# Groups:   cyl, vs, am, gear [0]
# ... with 6 variables: cyl <dbl>, vs <dbl>, am <dbl>, gear <dbl>,
#   carb <dbl>, uTargets <int>

将以静默方式返回空数据帧,并且:

f2(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))
# Error in summarise_impl(.data, dots) : variable 'foo' not found

这将引发一个直接指向错误的错误。

编辑

既然你似乎在追求“tidyverse方式”,我建议你做以下几点。基本原理似乎是尽可能不鼓励使用变量名称作为字符串,而不是简单的名称:

f3 <- function(df, target_column, ...) {
  target_column <- enquo(target_column)
  cols_to_use <- quos(...)
  group_by(df, !!! cols_to_use) %>% 
    summarise(uTargets = n_distinct(!! target_column)) %>% 
    filter(uTargets > 1)
}
all.equal(
  find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb")),
  f3(mtcars, target_column = mpg, cyl, vs, am, gear, carb)
)
# [1] TRUE

f3() 的接口也被设计为类似于其他tidyverse函数的接口,并且可能更好地集成到tidyverse转换管道中。

@Aurele已经展示了如何使用rlang来实现它,但我想看看我们是否可以使用 get 也正如我所指出的 收到 不起作用,但经过一些实验后,这似乎如预期的那样起作用。这并不是说我在建议这一点,而是出于兴趣的考虑。

1、获取/执行

如果我们用 do 然后我们可以使用 get(..., .) 像这样,它将按需要工作。这可能是最简单、最直接的使用方法 收到 在内部 group by . 关键的观察结果是 做 点指的是当前组内的行,而不是 做 当在嵌套函数调用的实际参数中使用时,它引用输入的所有行。

find_dataset_inconsistencies <- function(df, target_column, cols_to_use) {
  df %>% 
    group_by_at(cols_to_use) %>% 
    do(summarise(., uTargets = length(unique(get(target_column, .))))) %>% 
    filter(uTargets > 1)
}

# gives desired result
find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))
# ... snip correct output ...

# correctly gives an error indicating it can't find `foo`
foo <- 3
find_dataset_inconsistencies(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))

2、与祖父母接触继承=假

find_dataset_inconsistencies <- function(df, target_column, cols_to_use) {
  df %>% 
    group_by_at(cols_to_use) %>% 
    summarise(uTargets = length(unique(get(target_column,
       parent.env(parent.env(environment())), inherits = FALSE)))) %>% 
    filter(uTargets > 1)
}

# gives desired result
find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))
# ... snip correct output ...

# correctly gives an error indicating it can't find `foo`
foo <- 3
find_dataset_inconsistencies(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))
## Error in summarise_impl(.data, dots) : 
##   Evaluation error: object 'foo' not found.

为了使此解决方案更加精简,我们可以定义 GET 这样地:

GET <- function(x) {
  p <- parent.frame()
  p3 <- parent.env(parent.env(p))
  get(x, p3, inherits = FALSE)
}

find_dataset_inconsistencies <- function(df, target_column, cols_to_use) {
  df %>% 
    group_by_at(cols_to_use) %>% 
    summarise(uTargets = length(unique(GET(target_column)))) %>% 
    filter(uTargets > 1)
}

# gives expected answer    
find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))

# gives expected error
foo <- 3
find_dataset_inconsistencies(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))

3、按键列子集

另一种可能性是通过键列进行子集。 mtcars 没有这样的列,但如果我们将行名称设置为这样的列,则会有一个:

library(tidyr)
find_dataset_inconsistencies <- function(df, target_column, cols_to_use) {
  df %>% 
    rownames_to_column %>%
    group_by_at(cols_to_use) %>% 
    summarise(uTargets = length(unique(
        get(target_column, .[.$rowname %in% rowname, ])))) %>% 
    filter(uTargets > 1)
}

# gives expected answer
find_dataset_inconsistencies(mtcars, "mpg", c("cyl", "vs", "am", "gear", "carb"))

# gives expected error
find_dataset_inconsistencies(mtcars, "foo", c("cyl", "vs", "am", "gear", "carb"))