在R函数中使用summary()进行分组汇总时，要保留原来的变量名

I am trying to write a function in R to summarize a table. The following is an example function and I am using the Iris data as a test.

我试图用R写一个函数来总结一个表。下面是一个示例函数，我将使用Iris数据作为测试。

test_func <- function(data, by_var_nm) {
  by_var_nm <- deparse(substitute(by_var_nm))

  tbl_test_sum <- data %>% 
    group_by(data[[by_var_nm]]) %>% 
    summarise(
      count = n()
    )
  tbl_test_sum
}

test_func(iris, Species)

As you could see, the output in the following section has a problem, in which the first variable in the table is called "data[[by_var_nm]]" instead of "Species". Is there any way that I could maintain the original variable name during the summarizing process?

如您所见，下一节中的输出有一个问题，其中表中的第一个变量被称为“data[[by_var_nm]]”，而不是“Species”。在汇总过程中，是否有办法维护原来的变量名?

# A tibble: 3 x 2
  `data[[by_var_nm]]` count
  <fct>               <int>
1 setosa                 50
2 versicolor             50
3 virginica              50

Thank you.

谢谢你！

Thank you all for very helpful answer. I tried the solutions and it seems snoram's answer solved my initial problem quite well. However, after I combined everything together, I couldn't get the last bit of the plot working properly. The idea is that I want to plot the percentage distribution on the "var_nm" and group them by "by_var_nm". The problem I got is that the bar graph and also the percentage for the data label are not lined up properly.

谢谢大家的帮助。我尝试了解决方案，似乎snoram的回答很好地解决了我最初的问题。然而，在我把所有的东西都结合在一起之后，我无法让最后一点情节正常运行。我的想法是绘制“var_nm”上的百分比分布并将它们分组为“by_var_nm”。我遇到的问题是条形图和数据标签的百分比排列不正确。

test_func <- function(data, var_nm, by_var_nm) {
  var_nm <- deparse(substitute(var_nm))
  by_var_nm <- deparse(substitute(by_var_nm))

  tbl_test_sum <- as.data.frame(table(data[[by_var_nm]], data[[var_nm]]))
  names(tbl_test_sum) <- c(by_var_nm, var_nm, "count")

  # tbl_test_sum

  tbl_test_total <- as.data.frame(table(data[[by_var_nm]]))
  names(tbl_test_total) <- c(by_var_nm, "total")

  # tbl_test_total

  tbl_test_pctg <- full_join(tbl_test_sum, tbl_test_total, by = by_var_nm) %>%
    mutate(
      percentage = count / total
    )

  # tbl_test_pctg

  ggplot(data=tbl_test_pctg, aes(x = tbl_test_pctg[[var_nm]], y = percentage, fill = tbl_test_pctg[[var_nm]])) +
    geom_bar(stat="identity") +
    geom_text(aes(label = scales::percent(percentage))) +
    facet_grid(tbl_test_pctg[[by_var_nm]]~.) +
    coord_flip()
}

test_func(mtcars, cyl, am)

3 个解决方案

#1

Suggesting similar solution as Alexandre but breaking dplyr dependency at the same time. If you are planning on keeping this function I think unnecessary dependencies is not a good idea.

建议类似Alexandre的解决方案，但同时打破dplyr依赖关系。如果您打算保留这个函数，我认为不必要的依赖关系不是一个好主意。

test_func <- function(data, by_var_nm) {
  by_var_nm <- deparse(substitute(by_var_nm))
  tbl_test_sum <- as.data.frame(table(data[[by_var_nm]]))
  names(tbl_test_sum) <- c(by_var_nm, "count")
  tbl_test_sum
}

Speed:

速度:

> microbenchmark::microbenchmark(test_func_Alex(iris, Species), test_func_snoram(iris, Species), unit = "relative")
Unit: relative
                            expr      min       lq     mean   median       uq      max neval cld
   test_func_Alex(iris, Species) 6.910679 6.834064 5.827796 5.622154 5.480321 4.009469   100   b
 test_func_snoram(iris, Species) 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000   100  a

#2

You can use rlang's Quotation syntax, which is designed for this use case; Also read the examples here:

您可以使用rlang的引号语法，它是为这个用例设计的;也可以阅读下面的例子:

library(rlang); library(dplyr)

test_func <- function(data, by_var_nm) {
    by_var_nm <- enquo(by_var_nm)

    tbl_test_sum <- data %>% 
        group_by(!!by_var_nm) %>% 
        summarise(
            count = n()
        )
    tbl_test_sum
}

test_func(iris, Species)

# A tibble: 3 x 2
#  Species    count
#  <fct>      <int>
#1 setosa        50
#2 versicolor    50
#3 virginica     50

#3

I don't know why this is happening but you can use this trick to get back the name :

我不知道为什么会这样但你可以用这个技巧来取回名字

test_func <- function(data, by_var_nm) {
  by_var_nm <- deparse(substitute(by_var_nm))

  tbl_test_sum <- data %>% 
    group_by(data[[by_var_nm]]) %>% 
    summarise(
      count = n()
    )
  names(tbl_test_sum)[grep("by_var_nm",names(tbl_test_sum))] <- by_var_nm
  tbl_test_sum
}

test_func(iris, Species)

You can also use the index names(tbl_test_sum)[1] assuming the group_by() is creating the first column on this variable.

您还可以使用索引名称(tbl_test_sum)[1]，假设group_by()正在创建这个变量的第一列。

Hope this will help you

希望这能对你有所帮助

#1