我使用XLConnect包将一堆电子表格导入R作为数据帧列表，并且：
sheet_names <- getSheets(wb)
names(sheet_names) <- sheet_names
sheet_list <- lapply(sheet_names, function(.sheet){
readWorksheet(object=wb, .sheet)})
我现在正试图将这些数据帧（每个134个观察8个变量）组合成一个数据帧，以便我可以进行一些进一步的分析。我发现这行代码让我有了一些方法：
sh_combined <- do.call("cbind", sheet_list)
然而，这产生了一个数据帧，该数据帧由203个变量组成，其中8个变量中的每一个都已被复制。理想情况下，我的组合数据框将有一个变量'name'，它是每个原始数据帧的名称 - n.b.在这种情况下，29个数据框中的每一个都代表对20个不同组织回答的问卷的答复。
我并不习惯使用列表，因此无法找到实现此目的的便捷方法。另一个问题是数据在首次捕获（格式化为excel）时结构严重，因此并不完全“整洁”。但是，各个电子表格都具有一致的行名和列名。
整个清单很大，但结构如下：
List of 29
$ Alliance Youth Group :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "192" "8" "20" "5" ...
..$ Revenue.Streams: num [1:134] 9600 3600 4800 250 NA NA 900 1000 1200 300 ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Bidii Kweli :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "300" "0" "12" "5" ...
..$ Revenue.Streams: num [1:134] 60000 NA 960 600 NA NA 160 NA 240 NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Bidiika :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "82" "N/A" "12" "1" ...
..$ Revenue.Streams: num [1:134] 4592 NA 1800 400 NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ BigShip :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "100" "104" "30" "0" ...
..$ Revenue.Streams: num [1:134] 30000 31200 9000 NA 3500 NA 2100 17500 17500 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Bokole :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "50" "N/A" "N/A" "N/A" ...
..$ Revenue.Streams: num [1:134] 10000 NA NA NA NA NA NA NA 200 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Brilliant Minds :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "20" "N/A" "5" "N/A" ...
..$ Revenue.Streams: num [1:134] 6000 NA 250 NA NA NA NA NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Changing Ambassador :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "300" "4" "0" "0" ...
..$ Revenue.Streams: num [1:134] 75000 600 0 0 NA NA NA NA NA NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Chenda Investments :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "No" "15" "20" "No" ...
..$ Revenue.Streams: num [1:134] NA 27000 60000 NA NA NA NA NA NA NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Customer Segments :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "12" "22" "0" "0" ...
..$ Revenue.Strems: num [1:134] 2400 39600 NA NA NA NA 150 NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA "In almost all their apartments they are devided into 6 section/wings where each wing pays KES 300 per month" NA NA ...
$ Driver Conductor :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "138" "1" "4" "5" ...
..$ Revenue.Streams: num [1:134] 5520 200 200 250 100 NA NA 400 NA NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ District Scouts :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "150" "No" "15" "20" ...
..$ Revenue.Streams: num [1:134] 79950 NA 2400 4800 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Ganjoni Youth :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "150" "No" "8" "11" ...
..$ Revenue.Streams: num [1:134] 4500 NA 240 440 NA NA NA 300 100 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Grandi Youth Bunge :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "630" "No" "50" "10" ...
..$ Revenue.Stream.: num [1:134] 151200 NA 12000 2400 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ King Orani Youth :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "200" "20" "4" "2" ...
..$ Revenue.Streams: num [1:134] 40000 6000 1600 800 NA NA NA NA 400 NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Magongo Santana :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "10" "8" "6" "N/A" ...
..$ Revenue.Streams: num [1:134] 8800 1280 1200 NA NA NA NA NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Mbuyuni Youth :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "150" "0" "50" "0" ...
..$ Revenue.Streams: num [1:134] 15000 NA 3000 NA NA 800 NA NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ More Flow Enterprises:'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "No" "409" "6" "No" ...
..$ Revenue.Streams: chr [1:134] NA "349,000" NA NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] "Company signs contracts only with landlords/resident agents. Data concerning the value of these contracts is not currently avai"| __truncated__ NA NA NA ...
$ Mukono Self Help :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "40" "No" "6" "4" ...
..$ Revenue.Streams: num [1:134] 6000 NA 1200 600 NA NA NA 300 400 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Mombasa Youth Network:'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "No" "76" "No" "No" ...
..$ Revenue.Steams: num [1:134] NA 3800 NA NA NA NA NA NA NA NA ...
..$ Cost.Structure: num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ OneWorld Youth :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "50" "No" "15" "10" ...
..$ Revenue.Streams: num [1:134] 1000 NA 300 500 NA NA NA 20 NA NA ...
..$ Cost.Structures: num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Quatet :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "12" "22" "0" "0" ...
..$ Revenue.Streams: num [1:134] 2400 39600 NA NA NA NA 150 NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Safi Youth Group :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "250" "700" "0" "0" ...
..$ Revenue.Streams: num [1:134] 25000 140000 NA NA NA NA NA 3500 NA NA ...
..$ Cost.Stucture : chr [1:134] NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Sent Kumi Youth :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "186" "4" "8" "4" ...
..$ Revenue.Streams: num [1:134] 18600 400 1280 480 NA NA NA 160 200 NA ...
..$ Cost.structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Smart Guys :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "100" "No" "No" "2" ...
..$ Revenue.Streams: num [1:134] 12000 NA NA 160 NA NA NA NA NA NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Soweto Self Help :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "100" "N/A" "1" "8" ...
..$ Revenue.Streams: num [1:134] 14000 NA 60 640 NA NA NA NA NA NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Stretchers :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "22" "No" "8" "4" ...
..$ Revenue.Streams: num [1:134] 2200 NA 800 200 NA NA NA 200 NA NA ...
..$ Cost.Structure.: num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Taka ni Mali :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "396" "4" "8" "6" ...
..$ Revenue.Streams: num [1:134] 59400 4000 1600 2400 0 0 600 300 900 0 ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
$ Tuliza :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "90" "3" "N/A" "2" ...
..$ Revenue.Streams: num [1:134] 16200 4500 NA 200 NA NA NA 400 400 NA ...
..$ Cost.Stucture : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] "The 18 households are single bedroom houses and they pay KES 100 per month while the rest are double bedroom that pay KES 200 "| __truncated__ NA NA NA ...
$ Zama Uzuke :'data.frame': 134 obs. of 7 variables:
..$ Sector : chr [1:134] "Customer Segements" NA NA NA ...
..$ Subject : chr [1:134] "Waste Generators" NA NA NA ...
..$ Variable : chr [1:134] "Residential (Household)" "Residential (Apartment)" "Commercial (Dukas)" "Commercial (Bandas)" ...
..$ Yes.No.NA : chr [1:134] "17" "12" "10" "No" ...
..$ Revenue.Streams: num [1:134] 4080 2400 2400 NA NA NA NA 1600 1600 NA ...
..$ Cost.Structure : num [1:134] NA NA NA NA NA NA NA NA NA NA ...
..$ Notes : chr [1:134] NA NA NA NA ...
我希望我的最终数据框看起来像这样
部门|主题|变量|是的.No.NA |收入...... |成本...... |备注|名称*
*其中name是一个新变量，表示原始数据帧的名称。
N.B。 “变量”是这里的关键指标（有134个不同的变量）。
真的希望这是有道理的，并且如果在其他地方已经回答道歉 - 我确实尝试在SE的其他地方找到一些答案。
由于
玛蒂
答案 0 :(得分：2)
您可以先使用
添加名称sheet_list <- mapply(function(df, name) {
df$Name <- rep(name, nrow(df))
return(df)
}, df = sheet_list, name = names(sheet_list),
SIMPLIFY = FALSE)
然后，将所有data.frames放到一个
中all_df <- do.call("rbind", sheet_list)
另一个选项， 您还可以在调用do.call后添加姓名：
all_df <- do.call("rbind", sheet_list)
all_df$Name <- gsub("\\.\\d+$", "", row.names(all_df))
修改
如果所有data.frames＆＃39; colnames不一样，你可以做，作为第一步，（例如，为所有data.frames提供与第一个data.frame相同的colnames）：
sheet_list<-lapply(sheet_list, function(x) {colnames(x) <- colnames(sheet_list[[1]]) ; return(x)})
答案 1 :(得分：1)
这是另一种方式。如果您在环境（例如，全局环境）中拥有所有数据框，则可以使用mget()获取所有数据框并创建列表。然后，您可以在unnest()包中使用tidyr;您可以使用此功能创建一个包含数据框名称的列。我创建了一个简单的示例数据并执行了以下操作。希望这会对你有所帮助。
Alliance <- data.frame(Sector = "Customer Segements",
Subject = "Waste Generators",
stringsAsFactors = FALSE)
Bidii <- data.frame(Sector = "Customer Segements",
Subject = "Waste Generators",
stringsAsFactors = FALSE)
# I do not know what kind of patterns you have. You may need to adjust this part.
mylist <- mget(ls(pattern = "^.*"))
# mylist
#$Alliance
# Sector Subject
#1 Customer Segements Waste Generators
#
#$Bidii
# Sector Subject
#1 Customer Segements Waste Generators
library(tidyr)
unnest(mylist, names)
# names Sector Subject
#1 Alliance Customer Segements Waste Generators
#2 Bidii Customer Segements Waste Generators