Make Business Decisions: Market Basket Analysis Part 2


In the Part one I have explained the main concepts of Market basket analysis (associative Rules) and how to write the code in R studio. In this post I will explained the process of doing market basket analysis in Power BI.

for doing this post I have used the data set from [1].

Power BI Desktop, is a self service BI tool. you can download it from below link;

to do the market basket analysis, I first create a new Power BI file


Power BI is a great tools for visualization and cleaning data, most of data wrangling can be happen there. most of data cleaning like remove missing variables, replace values, remove columns and so forth.

the middle area is for creating reports  (Number 1). At the top, the main tools for creating reports, data wrangling, and so forth is located in number 2.

Moreover, the report elements like bar chart pie chart and so on has been shown in right side (number 3). finally, we able to see the relationship between tables and data in left side (number 4)


We get data from excel (local PC), so click on Get data in top menu and choose Excel from sources. as can be see in below picture.


after loading the data set, now we can see the transaction shopping  data for each customers (see below pictures).


so, we click on load option to get data from local pc into power bi.

then we want to do Market Basket analysis on data to get more insight out of it. In power bi, it possible to write R code!

first you should install a version of R in your pc. I already installed Microsoft R Open 3.3.2. from

after installing R in your machine, in power BI you should specify the R version. to do that, in Power BI, click on “File”, then “Options” (below picture)


Then in “Global”, find he “R scripting”. In R scripting, Power Bi automatically detect the available R version in your PC. It is important that you select the R version that you already tested your R code there. as when we install a package in R, in Power BI that package is also become available, so there should be some connectivity between R version that you run your code and then one you select in power BI.


Now we want to write Market Basket analysis code in Power BI. To do this we have to click on “Edit Query”  and then choose ” Edit Queries” from there.


After selecting “edit query” , you will see the query editor environment.  in top right, there is a “R transformation” icon.


By clicking on the “R transformation” a new windows will show up. This windows is a R editor that you can past your code here. however there are couple of things that you should consider.

1. there is a error message handling but always recommended to run and be sure your code work in R studio first (in our example we already tested it in Part 1).

2. the all data is holding in variable “dataset”.

3. you do not need to write “install.packages” to get packages here, but you should first install required packages into your R editor and here just call “library(package name)”

so we have below editor


for doing market basket analysis I need two main libraries: “Matrix” and “arules”, hence I wrote two line code to have these libraries here:


then as the data is not in format of transaction I have to reload the data from my PC again to make them as transaction type by writing below code

groceries <- read.transactions("C://Users//leila//Dropbox//Leila Speak//Difnity//groceries.csv", sep = ",")

then,  call the “apriori” function to find the rules in customers shopping behaviour. apriori get the dataset “groceries” as input, also it accepts the parameters like support ,confidence , and minlen. the output of the function will be in “Temp” variable

Temp<-apriori(groceries, parameter = list(support = 0.006, confidence = 0.25, minlen = 2))

now we inspect the first 100 rules by calling the “inspect” function and put the output of function in “Output” variable.


output variable is the result of the  query.


after clicking on the “Output” (above picture number 1), we will see the below results. the result of finding rules in customer behavior has been shown in below image. the first column (lhs) is the main item that people purchase the third column (rhs) is the related items to (lhs). the support, confidence, and lift measures has been show in column forth to sixth.


totally 100 rules has been shown (number 6). finally  click on close and apply in top left side (see below picture)


now we can create a visualization for showing item and related items in Power BI visualization part.

I have used the custom visualization from power BI website name as “Forced-Directed Graph” to show the relationships.


just click on the visualization and download it.


after downloading it, then import it to visualization part. first click on the 3 dots and select the “Import a customer visual” and import the downloaded one.


then in visualization first click on the right side and visualization part on the “Forced-Direct” chart (number 1) then in the right side in “Source” (number 3,4,5) bring the lhs, rhs and lift. this char shows the lhs (main items) as main node, the rhs ( the related items in shopping basket) as the related nodes. the thickness of the line between the nodes, show the lift value. the bigger value for lift the thicker line. and the product is much more importance. I had also a drop down list to items.


so by selecting for example “beef” from drop down the graphs show the related items to beefs. such as “root vege, whole mil, rolls, and Veg. the importance and possibility of purchasing these items has been shown by the thickness of the line so in this example “Root Veg” is main rules and has much more importance than the others.


there are some other useful visualization that can be used for showing the customer shopping behavior .

[1] Machine Learning with R,Brett Lantz, Packt Publishing,2015



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Leila Etaati
Trainer, Consultant, Mentor
Leila is the first Microsoft AI MVP in New Zealand and Australia, She has Ph.D. in Information System from the University Of Auckland. She is the Co-director and data scientist in RADACAD Company with more than 100 clients in around the world. She is the co-organizer of Microsoft Business Intelligence and Power BI Use group (meetup) in Auckland with more than 1200 members, She is the co-organizer of three main conferences in Auckland: SQL Saturday Auckland (2015 till now) with more than 400 registrations, Difinity (2017 till now) with more than 200 registrations and Global AI Bootcamp 2018. She is a Data Scientist, BI Consultant, Trainer, and Speaker. She is a well-known International Speakers to many conferences such as Microsoft ignite, SQL pass, Data Platform Summit, SQL Saturday, Power BI world Tour and so forth in Europe, USA, Asia, Australia, and New Zealand. She has over ten years’ experience working with databases and software systems. She was involved in many large-scale projects for big-sized companies. She also AI and Data Platform Microsoft MVP. Leila is an active Technical Microsoft AI blogger for RADACAD.

4 thoughts on “Make Business Decisions: Market Basket Analysis Part 2

  • library(Matrix)
    groceries <- read.transactions("C://Users//joe//Documents//arules//groceries.csv", sep=",")
    Temp<-apriori(groceries, parameter = list(support = 0.006, confidence = 0.25, minlen = 2))

    I have error:

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