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Standard Deviation Analysis Using RStudio with Northcraft Analytics

Last modified: November 23, 2019
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Estimated reading time: 3 min

(Basic & Advanced) How to use R with Northcraft Analytics — Standard Deviation Analysis 

In this first section of the article you will learn how to use the Free RStudio to create a bar chart to show the standard deviation of incidents. For the Advanced part at the bottom, we’ll discuss the pre-requisites for Microsoft R Server, for handling large data volume and live data.

To learn more about Northcraft, take a look at our Resources page.


This process requires the use of a pre-written R script. Notice the limitations of RStudio in presenting the bar chart to the user.

Step 1: Select AMITSM – ServiceNow – PublicDemo

Step 2: Select INC – Incident Management

Step 3: Select “Ok”

Step 4: Create a table

Step 5: Select the parameter “Model ID”

Step 6: Select the second parameter “Incidents Priority Critical”

Step 7: Click on the ellipsis icon in the bottom right corner of the chart

Step 8: Export the data

Step 9: Save the file

Step 10: We’re going to edit the file a bit, because there are large values that throw the calculations out of whack.

Step 11: Find the file where you saved it

Step 12: Open the file

Step 13: Scroll down and delete the “Not Specified” field

Step 14: Shift the cells up

Step 15: Scroll up to the first entry and delete the empty field

Step 16: Again, shift the cells up


Step 17: Next, open RStudio

Step 18: Select the ‘File’ tab in the upper left corner of the screen

Step 19: Select ‘New Project’

Step 20: Select ‘New Directory’

Step 21: Select ‘New Project’

Step 22: Name the project and select ‘Create Project’

Step 23: Select the icon in the top left corner of the screen

Step 24: Select ‘R Script’. This will immediately open a tab that we can use for our R Script later.

Step 25: Go back to the File icon in the top left and select ‘Open File’

Step 26: Find the file we obtained from PowerBI

Step 27: Now the data is loaded into a CSV file in our workspace. Next, open the file directory.

Step 28: Select the R Script to be used for the project.

Step 29: Copy the script

Step 30: Paste the text into the R Script tab we opened earlier

Step 31: Click the ‘Run’ button in the top. Continuously click the ‘Run’ button until the plot does not change anymore, and no new lines of code are used.

Step 32: Select the ‘Export’ tab.

Step 33: Select ‘Save as PDF’

Step 34: Change the parameters to 100 x 100 so that all the Model IDs will be visible

Step 35: You are then brought to a page where you can see the Models related to the graph.


Support Information

Advanced – Contact Support, we’ll step you through the items below at no cost (Unlimited & Above Customers).



1. SQL Server 2016, SQL Server 2016 R Services,

SQL Azure (Machine Learning Studio), Azure ML,(Optional for cloud customers)

Power BI – Brings the Glam – 🙂

2. Time Series Forecasting in Azure Machine Learning

(Experiment) using R



4. Power BI -> Get Data -> R script

(executed on the MS R server, formerly revolution analytics)

1. Northcraft Data Warehouse (NCA_DW), Northcraft Multi-dimensional cubes (SSAS)

2. R Studio

3. Microsoft R Server (Gets you the scalability for R)

4. HortonWorks Data Platform (Optional)

5. MongoDB (Optional)

6. SQL Server 2016 PolyBase

(Use T SQL to combine SQL Server

and Hadoop/HortonWorks/MongoDB)

7. Hive on top of Hadoop (Make Excel csvs look

like relational data)

8. Azure Data Warehouse can replace most of this architecture.

9. Azure Data Lake allows you to use .net rather than Java for the heavy lifting.



1. Microsoft R Open (Free)

R Client

R Server (for Hadoop, Teradata, Linux . . . )

SQL Server 2016 R Services

R Open IN Azure ML

2. 7000+ statistical services for R (check out amap 0.8-14)

3. Do servers running Windows have longer resolution times

than Linux?

4. R Server 3.2.2 (R Studio) (Make sure to point R studio

to Microsoft R server)

5. Microsoft R Server version 8.0 (64-bit)

6. Microsoft R Server adds scale to R

7. R Tools for Visual Studio

8. Correlate Resolution time with customer satisfaction

(logitObj package has logistic regression tools)

9. Select * cannot be parallelized

10. Microsoft R Server: rx prefix = normal function

ex: rxSummary() = Summary()

11. Execute billions of rows of data in ~20 seconds


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