markov_model

R

### Install ChannelAttribution Pro (only required on Google Colab)

system("wget https://app.channelattribution.io/repository/install/installChProColab.R")
system("Rscript installChProColab.R")

### Import libraries

library(ChannelAttributionPro)
options(max.print=100)

### Set your password

password="mypassword"

### Load data

Data = read.csv("https://app.channelattribution.io/data/Data.csv",sep=";")
print(Data)

### Perform transaction level attribution reading data from a data.frame

res=markov_model(Data=Data, var_path="path", var_conv="total_conversions", var_value="total_conversion_value",
                 var_null="total_null", cha_sep=">", password=password)
path_attribution=res$attribution
print(path_attribution)

### Return non converting paths in the output data.frame

res=markov_model(Data=Data, var_path="path", var_conv="total_conversions", var_value="total_conversion_value",
                 var_null="total_null", cha_sep=">", flg_write_nulls=1, password=password)
path_attribution=res$attribution
print(path_attribution)

### Return paths in the output data.frame

res=markov_model(Data=Data, var_path="path", var_conv="total_conversions", var_value="total_conversion_value",
                 var_null="total_null", cha_sep=">", flg_write_paths=1, password=password)
path_attribution=res$attribution
print(path_attribution)