Blog Archives

Running R scripts within in-database SQL Server Machine Learning

October 14, 2018
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Running R scripts within in-database SQL Server Machine Learning

Having all the R functions, all libraries and any kind of definitions (URL, links, working directories, environments, memory, etc) in one file is nothing new, but sometimes a lifesaver. Using R function source is the function to achieve this. Storing…Read more ›

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First release and update dates of R Packages statistics

October 7, 2018
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First release and update dates of R Packages statistics

R has been around long time and the packages have evolved through the years as well. From the initial releases, updates, to new packages. Like many open-source and community driven languages, R is not an exception. And getting the first…Read more ›

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SQL Server 2017 Machine Learning services with R book

August 6, 2018
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SQL Server 2017 Machine Learning services with R book

Yes, I am finally blogging this. ? This blog post is slighty different, since it brings you the tittle of the book, that my dear friend Julie Koesmarno (blog | twitter) and I have written in and it was published…Read more ›

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Real-time data visualization using R and data extracting from SQL Server

July 23, 2018
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Real-time data visualization using R and data extracting from SQL Server

In the previous post, I have showed how to visualize near real-time data using Python and Dash module.  And it is time to see one of the many ways, how to do it in R. This time, I will not…Read more ›

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R null values: NULL, NA, NaN, Inf

July 4, 2018
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R null values: NULL, NA, NaN, Inf

R language supports several null-able values and it is relatively important to understand how these values behave, when making data pre-processing and data munging. In general, R supports: NULL NA NaN Inf / -Inf NULL is an object and is returned when an expression or function results in an undefined value. In R language, NULL … Continue reading R...

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Native scoring in SQL Server 2017 using R

May 28, 2018
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Native scoring in SQL Server 2017 using R

Native scoring is a much overlooked feature in SQL Server 2017 (available only under Windows and only on-prem), that provides scoring and predicting in pre-build and stored machine learning models in near real-time.                                                                                              Icons made by Smashicons from www.flaticon.com is licensed by CC 3.0 BY   Depending on the definition of real-time, and what … Continue reading Native...

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Using R in SQL Server Reporting Services (SSRS)

March 4, 2018
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Using R in SQL Server Reporting Services (SSRS)

SQL Server Reporting services (SSRS) is an outstanding tool for creating, deploying and managing paginated, mobile, KPI reports as well as Power BI reports. Tool provides simple way to share and get data insights in your corporate environment. (Photo source: Microsoft docs) Using the privileges of R language to enrich your data, your statistical analysis … Continue reading Using...

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R or Python? Python or R? The ongoing debate.

January 28, 2018
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R or Python? Python or R? The ongoing debate.

On every SQL community event, where there could be a cluster of sessions dedicated to BI or analytics, I would have people asking me, “which one would you recommend?” or “which one I  prefer?” So, questions about recommendation and preferences are in my opinion the hardest one. And not that I would know my preferences … Continue reading R...

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SQL Saturday statistics – Web Scraping with R and SQL Server

November 13, 2017
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SQL Saturday statistics – Web Scraping with R and SQL Server

I wanted to check a simple query: How many times has a particular topic been presented and from how many different presenters. Sounds interesting, tackling the problem should not be a problem, just that the end numbers may vary, since there will be some text analysis included. First of all, some web scraping and getting … Continue reading SQL...

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Robust IRIS Dataset?

October 25, 2017
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This blog post was born out of pure curiosity about the robustness of the IRIS Dataset. Biological datasets do not need to be that big in comparison to datasets of customers, consumption, stock and anything that might be volatile. When still at the university, on one occasion I can remember, we were measuring the length … Continue reading Robust...

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