拆分一串连接的逗号分隔数据并将输出重新编码为因子

I am trying to clean up some data that has been incorrectly entered. The question for the variable allows for multiple responses out of five choices, numbered as 1 to 5. The data has been entered in the following manner (this is just an example--there are many more variables and many more observations in the actual data frame):

我正在尝试清理一些输入错误的数据。变量的问题允许五个选项中的多个响应，编号为1到5.数据已按以下方式输入（这只是一个示例 - 实际数据中有更多变量和更多观察值帧）：

data
          V1
1    1, 2, 3
2    1, 2, 4
3 2, 3, 4, 5
4    1, 3, 4
5    1, 3, 5
6 2, 3, 4, 5

Here's some code to recreate that example data:

以下是重新创建示例数据的一些代码：

data = data.frame(V1 = c("1, 2, 3", "1, 2, 4", "2, 3, 4, 5", 
                         "1, 3, 4", "1, 3, 5", "2, 3, 4, 5"))

What I actually need is the data to be treated more... binary--like a set of "yes/no" questions--entered in a data frame that looks more like:

我真正需要的是要处理的数据更多...二进制 - 就像一组“是/否”问题 - 输入一个看起来更像的数据框：

data
    V1.1  V1.2  V1.3  V1.4  V1.5
1      1     1     1    NA    NA
2      1     1    NA     1    NA
3     NA     1     1     1     1
4      1    NA     1     1    NA
5      1    NA     1    NA     1
6     NA     1     1     1     1

The actual variable names don't matter at the moment--I can easily fix that. Also, it doesn't matter too much whether the missing elements are "O", "NA", or blank--again, that's something I can fix later.

实际变量名称目前无关紧要 - 我可以轻松解决这个问题。此外，缺少的元素是“O”，“NA”还是空白并不重要 - 再次，这是我以后可以解决的问题。

I've tried using the transform function from the reshape package as well as a fed different things with strsplit, but I can't get either to do what I am looking for. I've also looked at many other related questions on *, but they don't seem to be quite the same problem.

我已经尝试过使用reshape包中的转换函数以及使用strsplit来提供不同的东西，但我无法做到我正在寻找的东西。我还在*上查看了许多其他相关问题，但它们似乎并不是完全相同的问题。

2 个解决方案

#1

You just need to write a function and use apply. First some dummy data:

你只需要编写一个函数并使用apply。首先是一些虚拟数据：

##Make sure you're not using factors
dd = data.frame(V1 = c("1, 2, 3", "1, 2, 4", "2, 3, 4, 5", 
                         "1, 3, 4", "1, 3, 5", "2, 3, 4, 5"), 
                     stringsAsFactors=FALSE)

Next, create a function that takes in a row and transforms as necessary

接下来，创建一个接受一行并根据需要进行转换的函数

make_row = function(i, ncol=5) {
  ##Could make the default NA if needed
  m = numeric(ncol)
  v = as.numeric(strsplit(i, ",")[[1]])
  m[v] = 1
  return(m)
}

Then use apply and transpose the result

然后使用apply并转置结果

t(apply(dd, 1, make_row))

#2

A long time later, I finally got around to creating a package ("splitstackshape") that deals with this kind of data in an efficient manner. So, for the convenience of others (and some self-promotion, of course) here's a compact solution.

很久以后，我终于开始创建一个以有效方式处理这类数据的包（“splitstackshape”）。因此，为了方便他人（当然还有一些自我推销），这是一个紧凑的解决方案。

The relevant function for this problem is cSplit_e.

此问题的相关功能是cSplit_e。

First, the default settings, which retains the original column and uses NA as the fill:

首先是默认设置，它保留原始列并使用NA作为填充：

library(splitstackshape)
cSplit_e(data, "V1")
#           V1 V1_1 V1_2 V1_3 V1_4 V1_5
# 1    1, 2, 3    1    1    1   NA   NA
# 2    1, 2, 4    1    1   NA    1   NA
# 3 2, 3, 4, 5   NA    1    1    1    1
# 4    1, 3, 4    1   NA    1    1   NA
# 5    1, 3, 5    1   NA    1   NA    1
# 6 2, 3, 4, 5   NA    1    1    1    1

Second, with dropping the original column and using 0 as the fill.

其次，删除原始列并使用0作为填充。

cSplit_e(data, "V1", drop = TRUE, fill = 0)
#   V1_1 V1_2 V1_3 V1_4 V1_5
# 1    1    1    1    0    0
# 2    1    1    0    1    0
# 3    0    1    1    1    1
# 4    1    0    1    1    0
# 5    1    0    1    0    1
# 6    0    1    1    1    1

#1