Argthtjtr

I'm getting in trouble transforming a dataframe object into a transaction object.
I create a dataframe grouped by InvoiceNumber and the list of products separated by ',' (the dataframe then contains two columns), everything is ok,

df = read.csv('Orders.csv', sep = ';', stringsAsFactors = T)

    df$Document.Date = as.Date(df$Document.Date, format = '%d/%m/%Y')



    library(tidyverse)

    library(plyr)



    grouping_for_AA =

        data.frame(

            df %>%

            group_by(Sales.Document,  Material) %>%

            dplyr::select(Sales.Document, Material, Document.Date)

        )





#Create transaction data building a list of material for each sales doc

#separated by a ,

transactionData = ddply(grouping_for_AA, c('Sales.Document'),

                        function(df) paste(df$Material,

                        collapse = ',')

                        )

but when I use the as(data, 'transactions') function R say me to discretize input, so I use as.factor for the Product list column, but doing this each transaction becomes a factor level and no rules can be mined (clearly).

#set column InvoiceNo of dataframe transactionData  

transactionData$Sales.Document <- NULL

#Change name of lists of Materials

colnames(transactionData) = 'Material'



#transform to factor

transactionData = data.frame(lapply(transactionData, factor))





#Create a transaction object: errors can be due to the package containing 'as'

trObj <- as(transactionData, "transactions")

I already tried dataframes in single and basket format, but I could not solve it.

Any Idea on how to transform a dataframe into transaction format without exporting and reloading data?

You can try this, to convert your data.frame in a transaction dataset. I've added a fake date, but I think it's useless, due you are not using it in your elaboration:

data$Document.Date <- Sys.Date()

data

  Sales.Document Material Document.Date

1              1        A    2018-11-21

2              1        B    2018-11-21

3              1        C    2018-11-21

4              2        A    2018-11-21

5              2        C    2018-11-21

6              3        A    2018-11-21

Now exactly your dataset: you can add data.frame() in the dplyr chain:

library(tidyverse)

library(plyr)

grouping_for_AA <- data %>%

                   group_by(Sales.Document,  Material) %>%

                   dplyr::select(Sales.Document, Material, Document.Date) %>%

                   data.frame()

Now you can transform in a transactions data:

library(arules)

library(reshape2)

trans <- as(split(grouping_for_AA[,"Material"], grouping_for_AA[,"Sales.Document"]), "transactions")



inspect(trans)

    items   transactionID

[1] {A,B,C} 1            

[2] {A,C}   2            

[3] {A}     3    

Lastly, you can apply the apriori() function:

rules <- apriori(trans, parameter = list(supp = 0.3, conf = 0.3, target="rules", minlen=2)) 

inspect(rules)

    lhs      rhs support   confidence lift count

[1] {B}   => {C} 0.3333333 1.0000000  1.5  1    

[2] {C}   => {B} 0.3333333 0.5000000  1.5  1    

[3] {B}   => {A} 0.3333333 1.0000000  1.0  1    

[4] {A}   => {B} 0.3333333 0.3333333  1.0  1    

[5] {C}   => {A} 0.6666667 1.0000000  1.0  2    

[6] {A}   => {C} 0.6666667 0.6666667  1.0  2    

[7] {B,C} => {A} 0.3333333 1.0000000  1.0  1    

[8] {A,B} => {C} 0.3333333 1.0000000  1.5  1    

[9] {A,C} => {B} 0.3333333 0.5000000  1.5  1