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Day: April 5, 2024

Multidimensional Scaling in R

Published 2024-04-05 by Kevin Feasel

Steven Sanderson is from the 5th dimension:

Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where multidimensional scaling (MDS) comes in handy. It allows us to explore these relationships in a lower-dimensional space, typically 2D or 3D for easier interpretation. In R, the cmdscale() function from base R and is a great tool for performing classical MDS.

Click through to see how this works. In case you’re curious, cmdscale() is an example of principal coordinates analysis. If you’re familiar with principal components analysis, that’s a different form of multidimensional scaling.

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Tips for Dealing with Large Spatial Datasets

Published 2024-04-05 by Kevin Feasel

Rhian Davies consults the map:

I love playing with spatial data. Perhaps because I enjoy exploring the outdoors, or because I spend hours playing Geoguessr, or maybe it’s just because maps are pretty but there’s nothing more fun than tinkering with location data.

However, reading in spatial data, especially large data sets can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and breaking large data sets into more manageable chunks.

Click through for three tips when dealing with spatial data. The code is in R but the tips make sense in any language.

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The Power of the (Scatter) Plot

Published 2024-04-05 by Kevin Feasel

Kurt Buhler digs into scatter plots:

In many reports, it is common to find large tables or matrixes with a lot of information. Business users come to these reports to get the details they need to answer questions, make decisions, and take actions. However, these detailed tables can be inefficient, requiring users sort, drilldown, and filter them to find actionable data.

Consider the following example of a matrix to analyze gaps in year-over-year growth by product, and dissect those by region. In this scenario, the intended purpose of the report is for a sales team to analyze why we did not hit our revenue growth target.

Read on to learn what you can do with a scatter plot, either natively in Power BI or via custom visual.

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Inferring Data Types in SQL Server Tables

Published 2024-04-05 by Kevin Feasel

Emanuele Meazzo tidies up the database:

Do you know the feeling when someone gives you a flat file to import, without any schema information? You have to dump it in your database as-is, then spend precious time figuring out the data type for each column
Let me help you with that, as I’m helping myself too.

Click through for the scenario, as well as a link to the script Emanuele has created. It looks like the script doesn’t try to estimate string lengths, instead focusing on types alone.

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A Review of Powerops

Published 2024-04-05 by Kevin Feasel

Nikola Ilic reviews a new tool:

In the last 2-3 months, I had an opportunity to try and test the new external tool, called Powerops. This is an external tool developed by Truviz, the company you might already know as the creators of various Power BI “goodies”.

Now, you are probably expecting “another Power BI external tool that solves a specific challenge”…I’ll tell you straight away – you can’t be more wrong! Powerops is a mix of various existing external tools – think of DAX Studio, Tabular Editor, ALM Toolkit, Measure Killer, etc. – but it’s also more than a simple sum of the features of the aforementioned tools.

Read on for the full review. Pricing is free for the first two months, and then either buy a license or you can upload up to two files per month and remain in the free tier.

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