我正在一个包含2列(OrderID和产品)的大型数据集上创建一个市场桶分析。集合中有超过一百万行,通过使用apriori包,我能够使用较小的数据子集创建一个有效的规则列表,但是当尝试使用全集时,我无法使用split函数按OrderID聚合数据。有没有另一个功能与split类似的函数可以处理这么多的数据?下面列出了代码:
MyData <- read.csv("C:/Market Basket Analysis/BOD16-Data.csv") #Abreviated for proprietary reasons
View(MyData)
library(arules)
summary(MyData)
#Using the split function, we are able to aggregate the transactions, so that each
#product on the transaction is grouped into its respective, singular, transID
start.time <- Sys.time() #Timer used to measure run time on the split function
aggregateData <- split(MyData$Product, MyData$OrderID)
end.time<- Sys.time()
time.taken = end.time- start.time
time.taken
#Using the split function, we are able to aggregate the transactions, so that each
#product on the transaction is grouped into its respective, singular, transID
aggregateData <- split(MyData$Product, MyData$OrderID)
head(aggregateData)
#Need to convert the aggregated data into a form that 'Arules' package
#can accept
txns <- as(aggregateData, "transactions")
#txns <- read.transactions("Trans", format = "basket", sep=",", rm.duplicates=TRUE)
summary(txns)
#Apriori Algorithem generates the rules
Rules <- apriori(txns,parameter=list(supp=0.0025,conf=0.4,target="Rules",minlen=2))
inspect(Rules)
编辑:我的数据如下:
OrderId Product
1 1234
1 1357
1 2468
1 1324
2 1234
2 2468
3 4321
4 5432
5 1357
AggregateData should be:
[1]
1234,1357,2468,1324
[2]
1234, 2468
[3]
4321
[4]
5432
[5]
1357
目前我正在使用split函数来实现这些结果,但是当将它应用于更大的集合时,在我停止脚本之前运行时间超过了30分钟。
发布于 2018-06-09 02:41:04
这对你来说是不是更快?
library(dplyr)
df <- tribble(
~OrderId, ~Product,
1, 1234,
1, 1357,
1, 2468,
1, 1324,
2, 1234,
2, 2468,
3, 4321,
4, 5432,
5, 1357
)
df %>%
group_by(OrderId) %>%
summarize(Product = list(Product)) %>%
mutate(Product = purrr::set_names(Product, OrderId)) %>%
pull(Product)
因此,对于您的代码,您应该能够执行以下操作:
library(dplyr)
MyData <- read.csv("C:/Market Basket Analysis/BOD16-Data.csv")
aggregateData <- MyData %>%
group_by(OrderId) %>%
summarize(Product = list(Product)) %>%
mutate(Product = purrr::set_names(Product, OrderId)) %>%
pull(Product)
这应该与做以下事情相同(希望更快):
MyData <- read.csv("C:/Market Basket Analysis/BOD16-Data.csv")
aggregateData <- split(MyData$Product, MyData$OrderID)
https://stackoverflow.com/questions/50765474
复制相似问题