将csv转换为交易规则 [英] Transform csv into transactions for arules

问题描述

我从csv中的数据库中获得一个子集，该子集具有多个不同的列，我想将数据转换为事务.我已经读过此帖子

I have a subset from a database in csv which has several different columns and I would like to convert the data into transactions. I've already read this post

library(arules)
library(arulesViz)

trans = read.transactions("data.csv", format = "single", sep = ",",
                     cols = c("EMAIL", "BRAND"))

但是无法使用建议的解决方案转换我的数据:

However wasn't able to convert my data with the proposed solution:

CATEGORY   BRAND   SKU   EMAIL         SEGMENT   SALES
shorts     gap     1564  one@mail.x    1         1
tops       gap     8974  one@mail.x    1         2
shoes      nike    3245  two@mail.x    4         3
jeans      levis   8956  two@mail.x    4         1

现在，我想使用规则来了解客户通常一起购买的品牌.为了使用规则，我需要转换数据，使其看起来如下:

Now I want to use arules to understand what brands customers generally buy together. In order to use arules I need to convert my data so it looks as follows:

gap, gap
nike, levis

有人可以帮助我弄清楚如何相应地转换我的数据吗?

Can anybody help me figure out how to convert my data accordingly?

推荐答案

如果我们将EMAIL列视为一种交易ID，则可以通过以下方式将您的data.frame转换为类transactions:

If we consider the column EMAIL as a sort of transaction ID, we can transform your data.frame to class transactions by:

library(arules)
trans <- as(split(df[,"BRAND"], df[,"EMAIL"]), "transactions")

# To explore the rules we could do
rules <- apriori(trans)
inspect(rules)
#  lhs        rhs     support confidence lift
#1 {levis} => {nike}  0.5     1          2   
#2 {nike}  => {levis} 0.5     1          2   

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