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	<title>Survey | Capstone Analytics</title>
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		<title>Visualising ratings data in Power BI</title>
		<link>https://capstoneanalytics.com.au/visualising-ratings-data-in-power-bi/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=visualising-ratings-data-in-power-bi</link>
					<comments>https://capstoneanalytics.com.au/visualising-ratings-data-in-power-bi/#respond</comments>
		
		<dc:creator><![CDATA[Abhijith DSouza]]></dc:creator>
		<pubDate>Tue, 21 Jan 2020 10:32:21 +0000</pubDate>
				<category><![CDATA[Power BI]]></category>
		<category><![CDATA[DAX]]></category>
		<category><![CDATA[Ratings]]></category>
		<category><![CDATA[Survey]]></category>
		<category><![CDATA[Wall Street analysts]]></category>
		<guid isPermaLink="false">http://capstone.comxdesign.com.au/?p=621</guid>

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										<content:encoded><![CDATA[<div class="et_pb_section et_pb_section_0 et_section_regular" >
				
				
				
				
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				<div class="et_pb_text_inner"><p>The inspiration for this article comes from the app Webull which is a stock market app for investors which includes real time quotes, financial news, investing advice etc. It is one of the best apps in the market if you are a frequent investor in the stock market. It is an extremely well-designed app with an awesome user interface which makes it a pleasure to use.</p>
<p>&nbsp;</p>
<p>The visual below is from the app and is the recommended rating for a stock by Wall Street analysts. This is a great visual and you immediately know if the stock is worth buying or not. In this case you are better off holding on to it if you have purchased it or hold off on purchasing till the ratings improve on the stock.</p></div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_d3b207f5f2a644a88007770f18e712a4mv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">When I first saw this visual I immediately thought of recreating it in Power BI. I created some dummy data on survey ratings for a set of questions for three different cities Brisbane, Melbourne, and Sydney. My data model and relationship looked like this</div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_a04e63c538f643b68e657540a4248b2fmv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">My first choice of visual was the Bullet Chart. For this I needed to divide my data into bins. Since the ratings are out of 5, I created a new table like the below</div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_45e769e1d67d40f386f672c257d342demv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">My bullet chart looked like this</div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_a280b759e9a545098fbd75df4fef1c68mv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">Which was a good start but not quite like the chart, I had in mind. So I went back to the drawing board and created a new table like the one below</div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_3b42e9a4920c4026a9dcf7b6b5b0e158mv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">Min and Max remained at 0 and 5 respectively. The other columns were derived as follows</p>
<p>Fair = Max([Value],1)</p>
<p>Satisfactory = Max([Value],2)</p>
<p>Good = Max([Value],3)</p>
<p>Very good = Max([Value],4)</p>
<p>With a little bit of formatting and text I got the chart I was looking for</div>
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				<span class="et_pb_image_wrap "><img decoding="async" src="https://capstoneanalytics.com.au/wp-content/uploads/2020/01/00bd8a_462dc58e37c34de284f4c34fc9ecd472mv2.jpg" alt="" title="" /></span>
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				<div class="et_pb_text_inner">Link to dashboard<a href="https://app.powerbi.com/view?r=eyJrIjoiYTY3Njc3MmYtZGIzYy00NDNiLWEwNDItYjE3Zjg3MjhjMWEyIiwidCI6IjZjZDI2M2IwLTQxNmItNGY5MC04ZDRmLTJmNmE1YjU2OGM1ZSJ9"> here</a></div>
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