R 将逗号分隔的列转换为带布尔值的列

在我的data.frame的一个名为

services

的列中，有以下逗号分隔的数据

> dput(structure(df$services[1:5]))
list("Global Expense Management, Company Privacy Policy", "Removal Services, Global Expense Management", 
    "Removal Services, Exception & Cost Admin, Global Cost Estimate, Company Privacy Policy", 
    "Removal Services, Exception & Cost Admin, Ancillary Services, Global Cost Estimate, Global Expense Management, Perm Storage, Company Privacy Policy", 
    "Global Expense Management, Company Privacy Policy")

我想在我的数据框中将这些数据转换成单独的列，如果该行包含该服务，则在该服务的列下设置TRUE。否则，将该值设置为FALSE

例如，如果我希望我的数据帧如下所示：

GlobalExpenseManagement    |    CompanyPrivacyPolicy   |   etc...
TRUE                            TRUE
TRUE                            FALSE
FALSE                           TRUE

我假设我必须拆分逗号sep值，将它们分组以删除重复项，然后将它们作为

names（df）

添加到我的数据帧中。但是，我不知道如果行包含该服务，如何迭代数据集并设置true/false

有没有人有什么好主意让我们必须这样做

编辑：合并数据返回 我现在正试图将新的矩阵与现有的数据框架结合起来，用新的列对应项替换服务。我根据@plafort下面的伟大答案尝试了这一点：

names(df) <- headnames
rbind(mat, df)

名称（df）尝试：

我将从我的“SPLITSTACKFILE”包中考虑<代码> CPLITITYE < /代码>。结果是二进制“1”和“0”，而不是
TRUE
和
FALSE
，但这应该很容易转换

样本数据：

df <- data.frame(services = I(
  list("Global Expense Management, Company Privacy Policy", "Removal Services, Global Expense Management", 
       "Removal Services, Exception &amp; Cost Admin, Global Cost Estimate, Company Privacy Policy", 
       "Removal Services, Exception &amp; Cost Admin, Ancillary Services, Global Cost Estimate, Global Expense Management, Perm Storage, Company Privacy Policy", 
       "Global Expense Management, Company Privacy Policy")))

现在把它分开：

library(splitstackshape)
cSplit_e(df, "services", ",", type = "character", fill = 0)
##                                                                                                                                                  services
## 1                                                                                                       Global Expense Management, Company Privacy Policy
## 2                                                                                                             Removal Services, Global Expense Management
## 3                                                              Removal Services, Exception &amp; Cost Admin, Global Cost Estimate, Company Privacy Policy
## 4 Removal Services, Exception &amp; Cost Admin, Ancillary Services, Global Cost Estimate, Global Expense Management, Perm Storage, Company Privacy Policy
## 5                                                                                                       Global Expense Management, Company Privacy Policy
##   services_Ancillary Services services_Company Privacy Policy services_Exception &amp; Cost Admin
## 1                           0                               1                                   0
## 2                           0                               0                                   0
## 3                           0                               1                                   1
## 4                           1                               1                                   1
## 5                           0                               1                                   0
##   services_Global Cost Estimate services_Global Expense Management services_Perm Storage
## 1                             0                                  1                     0
## 2                             0                                  1                     0
## 3                             1                                  0                     0
## 4                             1                                  1                     1
## 5                             0                                  1                     0
##   services_Removal Services
## 1                         0
## 2                         1
## 3                         1
## 4                         1
## 5                         0

看起来棒极了。是否要将这些列及其行附加到现有data.frame的末尾？将
df[]您可以。列长度必须相同。此外，如果对象的类是混合的，那么数据将被强制为字符。如果使用函数调用
df[]Edit:
df[]try
rbind（mat，df）
，但是首先必须将列名与
名称（df）匹配这看起来很好，我试图使用
cSplit
，但没有意识到
cSplit\u e
。问题是，我的CSV数据中有特殊字符，所以它给了我
Error:中的意外符号“df我已经删除了特殊字符，但它仍然给我错误。我就是这样做的：
cleanServiceNames@user1477388，你能用
dput
在一个可重复的小例子中找出问题吗？如果可以的话，我也许能帮上忙，但除此之外，故障排除有点困难。这就是问题所在。我已经在上面
dput（结构（db$services[1:5]）
，但我不知道导致错误的“特殊字符”在哪里。我认为从我的正则表达式中，除了逗号和字母之外的所有内容都应该删除。所以，除了整个数据集非常大之外，我不知道给你什么可以重新编译。@user1477388，我不确定该推荐什么。您能否检查数据集并查看是否存在任何可能导致问题的可疑行？如果是这样，请尝试使用
dput
进行测试。
colnames(mat) <- headnames

Global Expense Management Company Privacy Policy
[1,]                      TRUE                   TRUE
[2,]                      TRUE                  FALSE
[3,]                     FALSE                   TRUE
[4,]                      TRUE                   TRUE
[5,]                      TRUE                   TRUE...

df <- data.frame(services = I(
  list("Global Expense Management, Company Privacy Policy", "Removal Services, Global Expense Management", 
       "Removal Services, Exception &amp; Cost Admin, Global Cost Estimate, Company Privacy Policy", 
       "Removal Services, Exception &amp; Cost Admin, Ancillary Services, Global Cost Estimate, Global Expense Management, Perm Storage, Company Privacy Policy", 
       "Global Expense Management, Company Privacy Policy")))

df$services <- unlist(df$services)

library(splitstackshape)
cSplit_e(df, "services", ",", type = "character", fill = 0)
##                                                                                                                                                  services
## 1                                                                                                       Global Expense Management, Company Privacy Policy
## 2                                                                                                             Removal Services, Global Expense Management
## 3                                                              Removal Services, Exception &amp; Cost Admin, Global Cost Estimate, Company Privacy Policy
## 4 Removal Services, Exception &amp; Cost Admin, Ancillary Services, Global Cost Estimate, Global Expense Management, Perm Storage, Company Privacy Policy
## 5                                                                                                       Global Expense Management, Company Privacy Policy
##   services_Ancillary Services services_Company Privacy Policy services_Exception &amp; Cost Admin
## 1                           0                               1                                   0
## 2                           0                               0                                   0
## 3                           0                               1                                   1
## 4                           1                               1                                   1
## 5                           0                               1                                   0
##   services_Global Cost Estimate services_Global Expense Management services_Perm Storage
## 1                             0                                  1                     0
## 2                             0                                  1                     0
## 3                             1                                  0                     0
## 4                             1                                  1                     1
## 5                             0                                  1                     0
##   services_Removal Services
## 1                         0
## 2                         1
## 3                         1
## 4                         1
## 5                         0