R根据聚合数据生成多个Excel文件

Question

我正在做一个关于 R 的小项目，我的目标是在我的数据框中为每个 Site 创建多个 Excel 文件。数据框由来自调查的 cmets 组成，其中每一行代表对给定站点的响应。共有 10 列，第一列用于 Site，其他 9 列每个主题都有 cmets。

这些 cmets 列可以分为以下几个块 -

第 1 区：总体 = 座位 + 装饰 + 接待处 + 厕所

块 2：舒适与速度 = 舒适 + 速度

第 3 部分：运营 = 效率 + 礼貌 + 响应能力

可重现的数据框如下所示

#Load libraries
 library(dplyr)
 library(xlsx)
 
#Reproducible Data Frame

df=data.frame(Site=c("Tokyo Harbor","Tokyo Harbor","Tokyo Harbor","Arlington","Arlington","Cairo Skyline","Cairo Skyline"),
       Seating=c("comfy never a problem to find","difficult","ease and quick","nobody to help","nice n comfy","old seats","nt bad"),
         Decor=c("very beautiful","i loved it!!!","nice","great","nice thanks","no response","yea nice"),
     Reception=c("always neat","I wasn't happy with the decor on this site","great!","immaculate","happy very helpful","","I wont bother again"),
       Toilets=c("well maintained","nicely managed","long queues could do better","","cleaner toilets needed!","no toilet roll in the mens loo","flush for god's sake!!!"),
       Comfort=c("very comfortable and heated","I felt like I was home","","couldn't be better","very nice and kush","not comment","fresh eyes needed"),
         Speed=c("rapid service","no delays ever got everything I needed on time","","","I have grown accustomed to the speed of service","machines","super duper quick"),
    Efficiency=c("very efficient, the servers were great","spot on","","I was quite disappointed in the efficiency","clockwork","parfait",""),
      Courtesy=c("Staff were very polite","smiling faces everywhere, loved it","very welcoming and kind","the hostess was a bit rude","trés impoli","noo",""),
Responsiveness=c("On the ball all the time","super quick whenever help was needed","","","","want more service like this",""))

#Transform all columns with empty cells to NAs

df[df==""]  <- NA 

我的目标

为每个站点创建一个 Excel 文件，其中 cmets 分组为块（如上定义）。 Excel 文件中的每张 Sheet 代表一个块，所以一共有三张。

更详细的：

第 1 步 - 对于每个站点，将 cmets 组合成三个块，然后过滤掉 cmets。

第 2 步 - 用三张纸编写 Excel 文件，每张用于给定的块

我希望将 Excel 文件保存为以下格式 -

COMMENTS_SITENAME_2017.xlsx

所以对于这个df，所需的输出将是三个 Excel 文件，因为有三个站点...

COMMENTS_Tokyo Harbor_2017.xlsx

COMMENTS_Arlington_2017.xlsx

COMMENTS_Cairo Skyline_2017.xlsx

我的尝试

我首先定义了我的块，后来我用它来过滤掉 cmets

###########################
#STEP 1: Define the blocks

#Block 1: Overall = Seating + Decor + Reception + Toilets
BlockOverall=c(names(df)[2],names(df)[3],names(df)[4],names(df)[5])

#Block 2: Comfort & Speed = Comfort + Speed
BlockComfortSpeed=c(names(df)[6],names(df)[7])

#Block 3: Operations = Efficiency + Courtesy + Responsiveness
BlockOps=c(names(df)[8],names(df)[9],names(df)[10])

然后我根据这些块对cmets进行分组，过滤掉数据

###############################################
#STEP 2: Group comments based on defined blocks

#Group Overall
Data_Overall= df %>%
select(BlockOverall)

Data_Overall = Data_Overall %>%
do(.,data.frame(Comments_Overall=unlist(Data_Overall,use.names = F))) %>%
filter(complete.cases(.))

#Group Comfort & Speed
Data_ComfortSpeed= df %>%
select(BlockComfortSpeed)

Data_ComfortSpeed = Data_ComfortSpeed %>%
do(.,data.frame(Comments_ComfortSpeed=unlist(Data_ComfortSpeed,use.names = F))) %>%
filter(complete.cases(.))

#Group Operations
Data_Operations= df %>%
select(BlockOps)

Data_Operations = Data_Operations %>%
do(.,data.frame(Comments_Operations=unlist(Data_Operations,use.names = F))) 
%>%
filter(complete.cases(.))

最后，我将数据写入 Excel

#Write each group to an individual tab in an Excel file

 library(xlsx)
 write.xlsx(Data_Overall,"Comments_Global_2017.xlsx",sheetName = 
'Overall',row.names = F) #Tab 1
 write.xlsx(Data_ComfortSpeed,"Comments_Global_2017.xlsx",sheetName = 
'Comfort_&_Speed',row.names = F,append = T) #Tab 2
 write.xlsx(Data_Operations,"Comments_Global_2017.xlsx",sheetName = 
'Operations',row.names = F,append = T) #Tab 3

在全球范围内，这很好用。我无法弄清楚如何将其转换为 for 循环，该循环遍历数据框中的所有站点并生成站点级 Excel 文件。

作为新手程序员，任何指点或建议都将受到高度重视！

Answer 1

如果您使用 tidyverse 中的 purrr，则可以避免 for 循环。

如果您使用上面的代码并将其包装成一个基本函数，您可以使用purrr::map 对每个站点名称的函数进行迭代。

您的设置：

#Load libraries
library(dplyr)
library(xlsx)
library(purrr)

#Reproducible Data Frame

df=data.frame(Site=c("Tokyo Harbor","Tokyo Harbor","Tokyo Harbor","Arlington","Arlington","Cairo Skyline","Cairo Skyline"),
              Seating=c("comfy never a problem to find","difficult","ease and quick","nobody to help","nice n comfy","old seats","nt bad"),
              Decor=c("very beautiful","i loved it!!!","nice","great","nice thanks","no response","yea nice"),
              Reception=c("always neat","I wasn't happy with the decor on this site","great!","immaculate","happy very helpful","","I wont bother again"),
              Toilets=c("well maintained","nicely managed","long queues could do better","","cleaner toilets needed!","no toilet roll in the mens loo","flush for god's sake!!!"),
              Comfort=c("very comfortable and heated","I felt like I was home","","couldn't be better","very nice and kush","not comment","fresh eyes needed"),
              Speed=c("rapid service","no delays ever got everything I needed on time","","","I have grown accustomed to the speed of service","machines","super duper quick"),
              Efficiency=c("very efficient, the servers were great","spot on","","I was quite disappointed in the efficiency","clockwork","parfait",""),
              Courtesy=c("Staff were very polite","smiling faces everywhere, loved it","very welcoming and kind","the hostess was a bit rude","trés impoli","noo",""),
              Responsiveness=c("On the ball all the time","super quick whenever help was needed","","","","want more service like this",""))

#Transform all columns with empty cells to NAs

df[df==""]  <- NA 

您在函数中的步骤：

1. 获取您的数据框并按参数站点名称过滤
2. 执行上述所有步骤
3. 将网站 df 写入电子表格

功能：

export_site_data <- function(site.name){
  ###########################
  #STEP 0: filter by block site
  df <- df %>% filter(Site %in% site.name)


  ###########################
  #STEP 1: Define the blocks

  #Block 1: Overall = Seating + Decor + Reception + Toilets
  BlockOverall=c(names(df)[2],names(df)[3],names(df)[4],names(df)[5])

  #Block 2: Comfort & Speed = Comfort + Speed
  BlockComfortSpeed=c(names(df)[6],names(df)[7])

  #Block 3: Operations = Efficiency + Courtesy + Responsiveness
  BlockOps=c(names(df)[8],names(df)[9],names(df)[10])



  ###############################################
  #STEP 2: Group comments based on defined blocks

  #Group Overall
  Data_Overall= df %>%
    select(BlockOverall)

  Data_Overall = Data_Overall %>%
    do(.,data.frame(Comments_Overall=unlist(Data_Overall,use.names = F))) %>%
    filter(complete.cases(.))

  #Group Comfort & Speed
  Data_ComfortSpeed= df %>%
    select(BlockComfortSpeed)

  Data_ComfortSpeed = Data_ComfortSpeed %>%
    do(.,data.frame(Comments_ComfortSpeed=unlist(Data_ComfortSpeed,use.names = F))) %>%
    filter(complete.cases(.))

  #Group Operations
  Data_Operations= df %>%
    select(BlockOps)

  Data_Operations = Data_Operations %>%
    do(.,data.frame(Comments_Operations=unlist(Data_Operations,use.names = F))) %>%  filter(complete.cases(.))

  library(xlsx)
  write.xlsx(Data_Overall, paste0("Comments_",site.name,"_2017.xlsx"), sheetName = 
               'Overall',row.names = F) #Tab 1
  write.xlsx(Data_ComfortSpeed, paste0("Comments_",site.name,"_2017.xlsx"), sheetName = 
               'Comfort_&_Speed',row.names = F,append = T) #Tab 2
  write.xlsx(Data_Operations, paste0("Comments_",site.name,"_2017.xlsx"), sheetName = 
               'Operations',row.names = F,append = T) #Tab 3
}

使用 Map 遍历站点名称

site.name <- unique(df$Site)
site.name %>% map(export_site_data )

结果：

list.files(pattern = "Comments_")
[1] "Comments_Arlington_2017.xlsx"     "Comments_Cairo Skyline_2017.xlsx"
[3] "Comments_Tokyo Harbor_2017.xlsx"