R 中关联规则的数据准备 - 数据框到事务

Question

我的数据来自 SQL 数据库，并且是表格形式，其中我有多个行用于单个事务。我不希望只使用 "product" 字段，而是希望使用数据框中的所有其他列。

我的数据如下：

transID <- c('1','1','2','3')
state <- c('TX','TX','CA','MA')
product <- c('Oranges','Banana','Fish','Cheese')
Month <- c('January','January','Febuary','March')
Place <- c('A','A','B','C')

transactions <- data.frame(transID,state,product,Month,Place)

transactions
  transID state product   Month Place
1       1    TX Oranges January     A
2       1    TX  Banana January     A
3       2    CA    Fish Febuary     B
4       3    MA  Cheese   March     C

理想情况下，我的数据如下所示：

1 (TX,Oranges,Banana,January,A)
2 (CA,Fish,Febuary,B)
3 (MA, Cheese, March,C)

将此类数据转化为交易格式的最佳方式是什么？

我尝试了以下方法，但我只是将第 1 行和第 2 行连接在一起作为一个事务：

transactionData <- ddply(transactions,c("transID"),
                         function(df1) paste(df1$state,
                                             df1$product,
                                             df1$Month,
                                             df1$Place,
                                             collapse = ","))

Answer 1

像这样重塑怎么样？

reshape(transactions,v.names = "product",timevar = "product",idvar = "state", direction = "wide")

transID state   Month Place product.Oranges product.Banana product.Fish product.Cheese
1       1    TX January     A         Oranges         Banana         <NA>           <NA>
3       2    CA Febuary     B            <NA>           <NA>         Fish           <NA>
4       3    MA   March     C            <NA>           <NA>         <NA>         Cheese

Answer 2

这是一个基本解决方案：

stack(tapply(transactions[, -1], 
       transactions[, 1, drop = F],
       FUN = function(DF) {
         paste(unique(unlist(DF), use.names = F), collapse = ',')
       }))[, 2:1]

#  ind                      values
#1   1 TX,Oranges,Banana,January,A
#2   2           CA,Fish,Febuary,B
#3   3           MA,Cheese,March,C

主要部分是 tapply() 部分，它被 transID 拆分，然后取消列出 data.frame 的其余部分，只保留唯一值。这是 tapply() 调用的输出。

                            1                             2                             3 
"TX,Oranges,Banana,January,A"           "CA,Fish,Febuary,B"           "MA,Cheese,March,C"

stack() 和 [, 2:1] 纯粹是装饰性的，目的是为了制作一个很好的 data.frame，订购得很好。

Answer 3

由于 data.frames 商店因素，这样做有点尴尬。

library("arules")

# make all columns into items
df <- data.frame(
  id = transactions$transID, 
  items = factor(c(as.character(transactions$state),
    as.character(transactions$product), 
    as.character(transactions$Month), 
    as.character(transactions$Place))))

# remove duplicated state, month and place enties
df <- df[!duplicated(df),]

# this is from the manual page '? transactions'
trans <- as(split(df[,"items"], df[,"id"]), "transactions")    
inspect(trans)


    items                         transactionID
[1] {A,Banana,January,Oranges,TX} 1            
[2] {B,CA,Febuary,Fish}           2            
[3] {C,Cheese,MA,March}           3

希望对您有所帮助。

R 中关联规则的数据准备 - 数据框到事务

Data prep for association rules in R - data frame to transaction

r

data-mining

apriori