如何传递 fun1 中 fun2 的值

Question

我尝试像在图像中那样将两列分开显示，但在 rbind(df, subrow) 中出现错误：结果的列数不是向量长度的倍数 (arg 2)

expand_collapse <- function(right_table){
  
  sector_list <- unique(right_table$Sector)
  df = data.frame("Sector1"=c(""),"Sector"=c(""),"Incubatee"=c(""),"Actual"=c(""))
  
  my_fun <- function(s){
    df1 = right_table[right_table$Sector==s,]
    sector1 = df1$Sector[1]
    Sector = df1$Sector[1]
    Incubatee = ""
    actual = as.character(nrow(df1))
    mainrow = c(sector1,Sector,Incubatee,actual) 
    df = rbind(df,mainrow)
    incubatee_list <<- unique(df1$Incubatee)
  }
  df = do.call(rbind,lapply(sector_list, my_fun))
  
  my_fun2 <- function(i){
    df2 = right_table[right_table$Incubatee==i,]
    sector1 = df1$Sector[1]
    Sector = ""
    Incubatee = df2$Incubatee[1]
    actual = nrow(df2)
    subrow = c(sector1,Sector,Incubatee,actual)
    df = rbind(df,subrow)
  }
  df = do.call(cbind,lapply(incubatee_list, my_fun2))
  df = df[2:nrow(df),]
  df$Actual = as.numeric(df$Actual)
  df_total = nrow(right_table)
  df = rbind(df,c("","Total","",df_total))
  return(df)
}

DT::datatable(expand_collapse(x), rownames = F,escape = FALSE,selection=list(mode="single",target="row"),options = list(pageLength = 50,scrollX = TRUE, dom = 'tp',ordering=F,columnDefs = list(list(visible=FALSE, targets=0),list(className = 'dt-left', targets = '_all'))),class='悬停单元格-边框条纹')

Answer 1

我尝试过您的功能，但很难理解您到底想做什么。以下是我在代码中发现的一些不一致和问题：

• 您在开头定义了sector_list，但在my_fun() 中使用<<- 定义了incubatee_list，目前尚不清楚原因。
• my_fun() 和 my_fun2() 似乎实际上没有返回任何内容，我不确定您是否打算在每个函数中的 df 变量上使用 <<-。
• my_fun2() 引用 df1 会导致错误。

您可能不需要采用如此复杂的方法来生成屏幕截图中的表格。以下是您可以做的一个示例。

# First I'm going to add one more row to the data so that I could demonstrate 
# more clearly what will happen if there is more than one Incubatee per sector. 
# I create "Test" under the sector "Energy and CleanTech" with a count of 5.
library(tidyverse)
x = x %>% 
  bind_rows(tibble(
    Incubatee = "Test", 
    Sector = "Energy & CleanTech",
    count = 5,
    type = "Actual",
    qtr = "Q2",
    year = 2021
  ))

# A tibble: 3 x 12
  Incubatee     Sector   date        year month qtr   year_range count type  yrmo 
  <chr>         <chr>    <date>     <dbl> <fct> <chr> <chr>      <dbl> <chr> <fct>
1 "SpotSense"   Health   2021-07-01  2021 Jul   Q2    2021-2022      1 Actu~ Jul-~
2 "Devidayal S~ Energy ~ 2021-08-01  2021 Aug   Q2    2021-2022      1 Actu~ Aug-~
3 "Test"        Energy ~ NA          2021 NA    Q2    NA             5 Actu~ NA   
# ... with 2 more variables: qtr_yr <fct>, m_date <chr>

这是使用 tidyverse 的建议管道。

expand_collapse = function(data) {
  data %>% 
    split(.$Sector) %>% 
    map_df(function(sector_data) {
      main_row = tibble(
        Sector = sector_data$Sector[1],
        Incubatee = "",
        Actual = sum(sector_data$count)
      ) 
    
      incubatee_rows = sector_data %>% 
        group_by(Incubatee) %>% 
        summarise(
          Sector = "",
          Actual = sum(count)
        )
      
      bind_rows(main_row, incubatee_rows)
    }) %>% 
    # Create a total row
    bind_rows(tibble(
      Sector = "Total",
      Incubatee = "",
      Actual = sum(data$count)
    ))
}

演示输出：

> expand_collapse(x)
# A tibble: 6 x 3
  Sector               Incubatee                    Actual
  <chr>                <chr>                         <dbl>
1 "Energy & CleanTech" ""                                6
2 ""                   "Devidayal Solar Solutions "      1
3 ""                   "Test"                            5
4 "Health"             ""                                1
5 ""                   "SpotSense"                       1
6 "Total"              ""                                7

编辑： 基本 R 解决方案，并注意我按行数定义 Actual，而不是使用 sum(count)。

create_sector_section = function(sector_data) {
  main_row = data.frame(
    Sector = sector_data$Sector[1],
    Incubatee = "",
    Actual = nrow(sector_data),
    stringsAsFactors = FALSE
  ) 
  
  incubatee_rows = lapply(
    unique(sector_data$Incubatee),
    function(inc) {
      data.frame(
        Sector = "",
        Incubatee = inc,
        Actual = sum(sector_data$Incubatee == inc),
        stringsAsFactors = FALSE
      )
    }
  )
  Reduce(rbind, x = c(list(main_row), incubatee_rows))
}

expand_collapse = function(data) {
  sector_data = split(data, data$Sector)
  sector_sections = lapply(sector_data, create_sector_section)
  total_row = data.frame(
    Sector = "Total",
    Incubatee = "",
    Actual = nrow(data),
    stringsAsFactors = FALSE
  )
  Reduce(rbind, x = c(sector_sections, list(total_row)))
}

输出

> expand_collapse(x)
              Sector                  Incubatee Actual
1 Energy & CleanTech                                 2
2                    Devidayal Solar Solutions       1
3                                          Test      1
4             Health                                 1
5                                     SpotSense      1
6              Total                                 3

此解决方案对您的原始实现的主要改进：

• 使用split() 获取每个不同扇区的数据帧列表。
• 使用数据框创建所需输出的较小部分。
• 使用 Reduce() 将所有内容重新组合在一起。