#End R Code<\/strong><\/p>\nThe new dataset after applying the linear regression model to the Adj Close can be seen below.\u00a0 We called it New_DataFrame.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image2\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image2.png\")
<\/p>\n
Two additional columns have been created: RowCount and Fitted column. The RowCount was used to assist with the regression calculation, and the Fittedcolumn was the output that we will want to visualize.<\/p>\n
Connecting to Power BI<\/h3>\n
We are now ready to Get Data into Power BI.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image3\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image3.png\")
<\/p>\n
There are\u00a0 a plethora of data sources available for connectivity with Power BI. We just need to find the R Script connection.<\/p>\n
Of course, we want to make sure that R is installed on the same machine that has Power BI running!<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image4\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image4.png\")
<\/p>\n
Once connected, we are prompted with a blank editor screen asking us for our R Script.<\/p>\n
We can copy and paste our R code into the editor and execute.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image5\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image5.png\")
<\/p>\n
When pasting into the Power BI editor, we do not need to include code for installing R packages. That is only required to be done once and through R itself.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image6\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image6.png\")
<\/p>\n
Once the script is executed, we view all of the available datasets that were produced from the R code we pasted in.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image7\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image7.png\")
<\/p>\n
In our case, we had our original dataset (Microsoft_df) and the derived dataset (New_DataFrame). For our purposes, we are only interested in loading theNew_DataFrame. Once the new dataset is loaded, we can select the appropriate fields for visualization and few the results.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image8\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image8.png\")
<\/p>\n
We can change the default view from a table to a line chart by selecting the visualization picker on the right-hand side.<\/p>\n
![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image9\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image9.png\")
<\/p>\n
We can now see our new data visualized in Power BI with our original Adj Close as well as the Fitted points against the Date.<\/p>\n
![\"ANALYZE](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ANALYZE%20THE%20STOCK%20MARKET%20USING%20POWER%20BI_image16.png\")
<\/p>\n
We can see that while Microsoft stock has had a few peaks and valleys over the 23-day period, overall, they seem to be trending upward according to this simple linear regression model.<\/p>\n","protected":false},"excerpt":{"rendered":"
Microsoft was a late mover into the Data Discovery space when they first launched Power…<\/p>\n","protected":false},"author":21,"featured_media":11858,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[394,260,297],"tags":[],"class_list":["post-11646","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence","category-data-ai","category-data-platform","topics-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/11646","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/users\/21"}],"replies":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/comments?post=11646"}],"version-history":[{"count":0,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/posts\/11646\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media\/11858"}],"wp:attachment":[{"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/media?parent=11646"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/categories?post=11646"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/3cloudsolutions.com\/wp-json\/wp\/v2\/tags?post=11646"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"powerbi_microsoft\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/Images\/Blog%20\/BI%20and%20Reporting%20\/powerbi_microsoft.png\")
<\/p>\n![\"ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image1-1\"](\"https:\/\/cdn2.hubspot.net\/hubfs\/5670923\/ApplyLinearRegressionswithRandIntegrateMicrosoftPowerBi_image1-1.png\")
<\/p>\n