创建dplyr语句，稍后在R中进行计算

我想创建一个名为eval_data的函数，用户可以在其中输入

数据帧列表 要应用于数据帧的dplyr函数列表 要从每个数据帧中选择的列列表： 这将类似于：

eval_data <- function(data, dplyr_logic, select_vector) {
  data %>%
    # this doesn't work
    eval(dplyr_logic) %>%
    select(
      { select_vector }
    )
}

输入3选择向量： 期望输出

---

我需要在eval_数据和逻辑列表中更改什么才能使其正常工作？谢谢你的帮助

两个变化。首先，您需要将数据%>%包括到dplyr逻辑计算中：

eval_data <- function(data, dplyr_logic, select_vector) {
    rlang::expr( data %>% !!dplyr_logic ) %>%
        eval() %>%
        select( one_of(select_vector) )
}

而不是想要的

mutate(mutate(data, expr1), expr2)

所以，我们需要使用代词。要在复杂表达式中指定管道输入的位置，请执行以下操作：

logic <- rlang::exprs(                # We can use exprs instead of list(expr())
  I(),
  mutate(New_Column1 = case_when(
    Sepal.Length > 7 ~'Big',
    Sepal.Length > 6 ~ 'Medium',
    TRUE ~ 'Small'
    )),
  {mutate(., New_Column2 = case_when(       # <--- NOTE the { and the .
    Sepal.Width > 3.5 ~'Big2',
    Sepal.Width > 3 ~ 'Medium2',
    TRUE ~ 'Small2')) %>%
    mutate(
      New_Column3 = case_when(
        Petal.Width > 2 ~'Big3',
        Petal.Width > 1 ~ 'Medium3',
        TRUE ~ 'Small3'
      ))},                                  # <--- NOTE the matching }
  filter(Sepal.Width > 3)
)

现在一切正常：

res <- pmap(list(dd$data, logic, select_vec), eval_data)

## Compare to desired output
map2_lgl( res, pmap_output, identical )
#  mutate0 mutate1 mutate2 filter1
#     TRUE    TRUE    TRUE    TRUE

pmap_output <- list(
  iris1 = iris %>% I() %>% select("Species", "Sepal.Length"),

  iris2 = iris %>% 
    mutate(New_Column1 = 
             case_when(
               Sepal.Length > 7 ~'Big',
               Sepal.Length > 6 ~ 'Medium',
               TRUE ~ 'Small')) %>% 
    select("Species", "New_Column1"),

  iris4 = iris %>% 
    mutate(New_Column2 = case_when(
      Sepal.Width > 3.5 ~'Big2',
      Sepal.Width > 3 ~ 'Medium2',
      TRUE ~ 'Small2'
    )) %>%
    mutate(
      New_Column3 = case_when(
        Petal.Width > 2 ~'Big3',
        Petal.Width > 1 ~ 'Medium3',
        TRUE ~ 'Small3'
      )
    ) %>%
    select("Species", "New_Column2", "New_Column3"),

  iris3 = iris %>% filter(Sepal.Width > 3) %>% select("Species", "Sepal.Width")
)

eval_data <- function(data, dplyr_logic, select_vector) {
    rlang::expr( data %>% !!dplyr_logic ) %>%
        eval() %>%
        select( one_of(select_vector) )
}

mutate(data, mutate(expr1), expr2)

mutate(mutate(data, expr1), expr2)

logic <- rlang::exprs(                # We can use exprs instead of list(expr())
  I(),
  mutate(New_Column1 = case_when(
    Sepal.Length > 7 ~'Big',
    Sepal.Length > 6 ~ 'Medium',
    TRUE ~ 'Small'
    )),
  {mutate(., New_Column2 = case_when(       # <--- NOTE the { and the .
    Sepal.Width > 3.5 ~'Big2',
    Sepal.Width > 3 ~ 'Medium2',
    TRUE ~ 'Small2')) %>%
    mutate(
      New_Column3 = case_when(
        Petal.Width > 2 ~'Big3',
        Petal.Width > 1 ~ 'Medium3',
        TRUE ~ 'Small3'
      ))},                                  # <--- NOTE the matching }
  filter(Sepal.Width > 3)
)

res <- pmap(list(dd$data, logic, select_vec), eval_data)

## Compare to desired output
map2_lgl( res, pmap_output, identical )
#  mutate0 mutate1 mutate2 filter1
#     TRUE    TRUE    TRUE    TRUE