r - 函数内 enquo() 和 toString() 之间的区别

Question

我对 enquo 和 toString 的使用仍然有点困惑。在下面的示例中，我基本上只是尝试过滤数据框并在最后对行进行求和。我真的不明白为什么 enquo 和 toString 对我想做的第一件事做同样的事情(过滤器 --> 选项 1 和 2 给出相同的结果)，但对我想做的第二件事却没有做同样的事情(总和 -->选项 1 有效，但选项 2 给我一个错误)。难道只是因为我在 dplyr 管道中使用它？

library(dplyr)
library(tidyverse)


### define dataframe
dataframe_test <- data.frame(
  column_test = c(100,99,99,90,89,50),
  month_test = c("2020-09-01", "2020-09-01","2020-09-01", "2020-09-01","2020-10-01","2020-10-01")
)



test_function <- function(df, df_col_indicator, df_col_month, char_month) {
  
  
  ### define variables for enquo, ensym, toString
  df_col_indicator_enquo <- enquo(df_col_indicator)
  df_col_indicator_ensym <- ensym(df_col_indicator)
  df_col_indicator_toString <- toString(df_col_indicator)
  
  df_col_month_ensym <- ensym(df_col_month)

  
  dataframe2 <- df %>%
    filter(!!df_col_month_ensym == char_month) %>% # filter for month
    slice_max(!!df_col_indicator_ensym, n = 3) %>% # slice top 3 observations
  
    ## two options for filter
    # option 1
    filter(!!df_col_indicator_ensym == df[2, df_col_indicator_toString]) # filter for observations with same observation as second row
    # option 2
    #filter(!!df_col_indicator_ensym == df[2, !!df_col_indicator_enquo])
  
  
  ## two options for sum
  # option 1
  bb <- sum(dataframe2[ , df_col_indicator_toString]) # sum up observations
  
  # option 2
  #bb <- sum(dataframe2[ , !!df_col_indicator_enquo])
  
  return(bb)
  
}


test_function(df = dataframe_test, df_col_indicator = "column_test", df_col_month = "month_test" , char_month = "2020-09-01")

编辑:

谢谢大家的回答。呵呵，好吧，我不得不承认这个例子有点愚蠢，但我在这里尽量保持简单。我最初的问题实际上是这个(见下文)。我基本上尝试选择一列中的前 5 个数字。存在三种不同的结果。 1)如果超过5个==100，那么我想随机将5个观察结果存储在列表(指示器)中，将其他观察结果存储在列表(星号)中。 2)如果并非所有观察值都==100，但存在平局(第5、6位具有相同的值)，我想随机选择那些具有平局的观察值，并再次将一些观察值放入列表(指标)中，将其他观察值放入列表(星号)中。 3) 如果没有关系，则仅选择前 5 个观察值。我现在的主要问题是，如果我想在最底部的循环(包含所有列)上运行我的函数。不知何故，我总是只得到第一行作为结果...我想我不知何故不明白如何在循环内正确设置函数的变量名称...？

library(dplyr)
library(tidyverse)

remove(list = ls())


dataframe_test <- data.frame(
  county_name = c("a", "b","c", "d","e", "f", "g", "h"),
  column_test1 = c(100,100,100,100,100,100,50,50),
  column_test2 = c(40,90,50,40,40,100,13,14),
  column_test3 = c(100,90,50,40,30,40,100,50),
  month = c("2020-09-01", "2020-09-01" ,"2020-09-01" ,"2020-09-01" ,"2020-09-01" ,"2020-09-01" ,"2020-08-01","2020-08-01"))


choose_top_5 <- function(df, df_col_indicator, df_col_month, char_month, numb_top, df_col_county) {

  ### enquo / ensym / deparse
  df_col_indicator_enquo <- enquo(df_col_indicator)
  df_col_indicator_ensym <- ensym(df_col_indicator)

  df_col_month_ensym <- ensym(df_col_month)
  df_col_month_enquo = enquo(df_col_month)


  ### filter month and top 5 observations
  df_top <- df %>%
    filter(!!df_col_month_ensym == char_month) %>%
    slice_max(!!df_col_indicator_ensym, n = numb_top) %>%
    select(!!df_col_county, !!df_col_month_ensym, !!df_col_indicator_ensym)


  ### if there are more than "numb_top" values and all equals to 100 --> randomly pick "numb_top"
  if (nrow(df_top) > numb_top &
      sum(df_top[ , df_col_indicator  ]) == 100*nrow(df_top)  ) {

    ## randomly pick "numb_top" out of all
    random_shuffle <- df_top[sample(nrow(df_top)),]
    indicator <- random_shuffle[1:numb_top,]
    asterisk <- random_shuffle[(numb_top+1):nrow(random_shuffle),]

    ## return "numb_top" and put names of others in asterisk
    return_list <- list(indicator, asterisk)


    ### if there are more than "numb_top" values but not all 100 (e.g. 100, 100, 100, 99, 99, 99)
    ## --> pick randomly 99 values
  } else if (nrow(df_top) > numb_top) {

    ### filter for all observations that have the same value as "numb_top"
    df_treshold <- df_top %>%
      filter(!!df_col_indicator_ensym == df_top[numb_top, df_col_indicator])

    ## randomly shuffle the observations
    random_shuffle <- df_treshold[sample(nrow(df_treshold)),]

    ## combine observations again an pick "numb_top"
    combine <- rbind(df_top[1:(nrow(df_top)-nrow(df_treshold)), ], random_shuffle)
    indicator <- combine[1:numb_top,]
    asterisk <- combine[(numb_top+1):nrow(combine),]

    ## return "numb_top" and put names of others in asterisk
    return_list <- list(indicator, asterisk)

    ### if there are not more than "numb_top" values
  } else {


    indicator <- df_top
    asterisk <- NA

    ## return "numb_top", asterisk is NA
    return_list <- list(indicator, asterisk)
  }

  return(return_list)


}



### function for 1 column
a=choose_top_5(df = dataframe_test, df_col_indicator = "column_test3",
             df_col_month = "month", char_month = "2020-09-01", numb_top = 5,
             df_col_county = "county_name")
a



### function over all columns and store in list

all_indicators <- c("column_test1","column_test2","column_test3")

my_list <- list()

for (i in all_indicators) {

  my_list[[i]] <- choose_top_5(df = dataframe_test, df_col_indicator = i,
                                        df_col_month = "month", char_month = "2020-09-01", numb_top = 5,
                                        df_col_county = "county_name")
}

my_list

Answer 1

添加到上面的 Allans 答案中:

通常，您不能在不支持准引用的函数内使用强制运算符!!。准引用。然而，正如莱昂内尔·亨利指出的here和 here即将推出的 {rlang} 版本可能会包含一个名为 blast() 的函数，用于立即求值的准引用。下面我用你的例子。在最后一行中，您可以看到如何在本身不支持准引用的基本 R 函数中使用 !!。

library(tidyverse)
library(rlang)

### define dataframe
dataframe_test <- data.frame(
  column_test = c(100,99,99,90,89,50),
  month_test = c("2020-09-01", "2020-09-01","2020-09-01", "2020-09-01","2020-10-01","2020-10-01")
)

blast <- function(expr, env = caller_env()) {
  eval_bare(enexpr(expr), env)
}

test_function <- function(df, df_col_indicator, df_col_month, char_month) {
  
  df_col_indicator_enquo <- enquo(df_col_indicator)
  df_col_indicator_ensym <- ensym(df_col_indicator)
  df_col_month_enquo     <- enquo(df_col_month)
  
  temp <- df %>%
    filter(!!df_col_month_enquo == char_month) %>% 
    slice_max(!!df_col_indicator_enquo, n = 3) %>% 
    filter(!!df_col_indicator_enquo == df %>% 
             select(!!df_col_indicator_enquo) %>% 
             pluck(1, 2)) 
  
  # with `blast()` we can use the forcing operator !! inside sum(`$`...)
  blast(sum(`$`(temp, !! df_col_indicator_ensym)))
}

test_function(df = dataframe_test, column_test, month_test , "2020-09-01")
#> [1] 198

^{由reprex package于2020年11月5日创建(v0.3.0)}