Table with Embedded Chart in Power BI using R



Visualization is any kind of graphical representation that enable us to:

Exploration, it help us to get more deep insight through huge amount of data

Discovery, It also gives extra vision

Analysis, it can be used to find pattern

Communication, visualization help us to communicate better

A few years ago Dr. Andrew Abela published a good diagram helping to decide about which chart are a better fit for a given data and problem at hand.


This chart manily focused on the main four purposes that we do visualization.

1-Comparison– is about comparision regarding the items or regarding the time.

2-Distribution-is about the finding distribution of single varibale, two-variable, and three variables.

3-Relationship-is about the finding relationship among items

4-Composition-finding data composition over time or static

Most of these charts are avilabe in Power BI, however some of them are not,

Comparison, can be comparing items overtime, or among items.

For comparision over time, sometimes we prefer to compare  items for many periods. Power BI there is a chart name “Radar Chart”. Below picture shows the radar chart, we can see the Radar chart that shows the sales amounth by month name. and this sales amounth will repeated.


for comparing the non-cyclical data for many periods. we able to use “Line Chart”.


for comparison over time, and for few periods we able use “Clustered Column Chart” or “Line Chart”.

another type of comparison is comparison among items. for Two-Variable per items we can use columnwith chart that I have explained in  Column Width Chart post.



For comparision,  one-variable per items, we can use many categories or few categories. for few categories if there is lots of items we can use “Stacked Bar Chart”. or if there is few items we can use “Stacked Column Chart”.

However for comparing one variable per items and for different categories we need a chart that is not exist in Power BI yet the name is “Table with Embeded Chart “.

I am going to create a custom visual for this chart.

I followed the steps from Post,  to create a custom visual, so in the “script.r” I have wrote the below codes

as you see in above code I need to have two different  libraries : “ggplot2” and “reshape2”.

however, there is a possibility to drwa it in R visual section in Power BI

by writting the below codes

in this chart I am going to use the “Adventure works dataset” for showing the sales amonth for different products color and also for different year.



In the above chart. I have shown the product sales for different years and for different product colors.

I can change the type of the chart type to different one like “Box Plot” to show the data distributions.

then I have below picture



in above picture, as you can see, we have sales amounth distribution for different year (color) and different product colors.


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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.

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