r - Tidyeval 评估 case_when

Question

与Tidy evaluation programming with dplyr::case_when有些相关和 Making tidyeval function inside case_when ，我想创建字符串(使用 Shiny 的应用程序)稍后在 case_when 函数中进行解析。这是一个例子:

library(tidyverse)

# simulated shiny inputs
new_column = sym("COL_NAME")

number_of_categories = 3

col1_text = "Big"
col1_min = 7.0
col1_max = 8.0

col2_text = "Medium"
col2_min = 5.0
col2_max = 6.9

col3_text = "Small"
col3_max = 4.9
col3_min = 4.0

columninput = sym("Sepal.Length")

期望的输出

iris %>%
  mutate(new_column =
           case_when(
             !!columninput >= col1_min & !!columninput <= col1_max ~ col1_text,
             !!columninput  >= col2_min & !!columninput <= col2_max ~ col2_text,
             !!columninput  >= col3_min & !!columninput <= col3_max ~ col3_text
           )
  )

因为函数之间唯一改变的是索引，我想我们可以使用通用模式来创建一个字符串

# create single string
my_string <-function(i) {
  paste0("!!", columninput, " >= col", i, "_min & ", "!!", columninput, " <= col", i, "_max ~ col", i, "_text")
}

然后为动态案例数重复字符串

mega_string <- map_chr(1:number_of_categories, ~ my_string(.x))

待办事项:

这是我无法完全拼凑的部分:使用这些字符串作为 case_when 中的参数。

# evaluate somehow?
iris %>%
  mutate(
    new_column = case_when(
      # tidyeval mega_string?
      paste(mega_string, collapse = "," )
      )
    )

这是正确的方法吗？您还会如何解决这个问题 - 非常感谢任何高级别或其他方面的帮助!

Answer 1

我们可以创建一个表达式并求值

library(dplyr)
library(stringr)
iris %>% 
   mutate(new_column = eval(rlang::parse_expr(str_c('case_when(', 
          str_c(mega_string, collapse=","), ')'))))
#    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species new_column
#1            5.1         3.5          1.4         0.2     setosa     Medium
#2            4.9         3.0          1.4         0.2     setosa      Small
#3            4.7         3.2          1.3         0.2     setosa      Small
#4            4.6         3.1          1.5         0.2     setosa      Small
#5            5.0         3.6          1.4         0.2     setosa     Medium
#6            5.4         3.9          1.7         0.4     setosa     Medium
#7            4.6         3.4          1.4         0.3     setosa      Small
#8            5.0         3.4          1.5         0.2     setosa     Medium
#9            4.4         2.9          1.4         0.2     setosa      Small
#10           4.9         3.1          1.5         0.1     setosa      Small
# ...

---

或者使用 parse_expr 和 !!!

library(purrr)
iris %>%
   mutate(new_column = case_when(!!! map(mega_string, rlang::parse_expr)))
#    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species new_column
#1            5.1         3.5          1.4         0.2     setosa     Medium
#2            4.9         3.0          1.4         0.2     setosa      Small
#3            4.7         3.2          1.3         0.2     setosa      Small
#4            4.6         3.1          1.5         0.2     setosa      Small
#5            5.0         3.6          1.4         0.2     setosa     Medium
#6            5.4         3.9          1.7         0.4     setosa     Medium
#7            4.6         3.4          1.4         0.3     setosa      Small
#8            5.0         3.4          1.5         0.2     setosa     Medium
#...