在dplyr中总结的函数中使用quasiquotation

发布于 2025-01-27 22:35:13 字数 612 浏览 4 评论 0原文

我正在尝试编写一个函数，该功能可以将列名作为字符串分配给变量并为其产生汇总输出的字符串，因此

my_function <- function(my_df, columnA,columnB){
    

    summ_df <- my_df %>% 
        group_by(cyl) %>% 
        summarise(base_mean = mean(columnA),
                  contrast_mean = mean(columnB)) 
    
    return(summ_df)
    }


    base = "drat"
    cont = "wt"
    my_function(mtcars,base,cont)

我想要的是，上述功能会返回相同的内容，因为

mtcars %>% 
    group_by(cyl) %>% 
    summarise(base_mean = mean(drat),
              contrast_mean = mean(wt))

我确定它是否是某种组合enexpr，或ensym和！但是我不断获得NA值

原文

I'm trying to write a function that can take column names as strings assigned to variables and produce a summarized output for them like so

my_function <- function(my_df, columnA,columnB){
    

    summ_df <- my_df %>% 
        group_by(cyl) %>% 
        summarise(base_mean = mean(columnA),
                  contrast_mean = mean(columnB)) 
    
    return(summ_df)
    }


    base = "drat"
    cont = "wt"
    my_function(mtcars,base,cont)

What I would want is that the above function would return the same thing as

mtcars %>% 
    group_by(cyl) %>% 
    summarise(base_mean = mean(drat),
              contrast_mean = mean(wt))

I'm sure it's some combination of enexpr, or ensym, and !! but i keep getting NA values

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囍孤女 2025-02-03 22:35:13

!! 使用

my_function <- function(my_df, columnA,columnB){
    

    my_df %>% 
        group_by(cyl) %>% 
        summarise(base_mean = mean(!! ensym(columnA)),
                  contrast_mean = mean(!! ensym(columnB)), .groups = 'drop' )
    
   
    }

使用ENSYM与

> my_function(mtcars, !!base, !!cont)
# A tibble: 3 × 3
    cyl base_mean contrast_mean
  <dbl>     <dbl>         <dbl>
1     4      4.07          2.29
2     6      3.59          3.12
3     8      3.23          4.00

Use ensym with !! so that it can take both unquoted and quoted actual arguments

my_function <- function(my_df, columnA,columnB){
    

    my_df %>% 
        group_by(cyl) %>% 
        summarise(base_mean = mean(!! ensym(columnA)),
                  contrast_mean = mean(!! ensym(columnB)), .groups = 'drop' )
    
   
    }

-testing

> my_function(mtcars, !!base, !!cont)
# A tibble: 3 × 3
    cyl base_mean contrast_mean
  <dbl>     <dbl>         <dbl>
1     4      4.07          2.29
2     6      3.59          3.12
3     8      3.23          4.00

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