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Day: August 14, 2026

Dual Y Axes in R with ggplot()

Published 2026-08-14 by Kevin Feasel

Andrea Onofri builds a complex chart:

I have often found myself needing to plot a single graph with two y-axes having different scales. For example, this might be useful for representing temperature and rainfall data at a given location. Unfortunately, doing this with ggplot() is not straightforward.

As Andrea mentions, there are specific circumstances in which having a dual-axis chart is reasonable, and click through to learn how. ggplot2 tends to be rather opinionated regarding visual choice and design. I happen to like those opinions and think they are generally correct, but I can also recognize that there may be exceptions to the rules. For those instances, I think “Somewhat difficult but not impossible” is a solid answer, as it keeps people from doing things inappropriately with a couple of mouse clicks, like setting up those stupid 3D bar charts in Excel. H/T R-Bloggers.

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Applying Color Thoughtfully

Published 2026-08-14 by Kevin Feasel

Amy Esselman provides some guidance:

One of our top tips for explanatory communications is to use color sparingly and purposefully to help your audience understand your data and message. Color should be an explicit choice, not something your software applies by default, whether that’s a graphing tool or an AI assistant generating your first draft. These tools can build a chart in seconds. They might even add highlighting on their own. But they don’t know which data matters most to your audience. That call is still yours. Used thoughtfully, color is often one of the quickest ways to improve a graph.

Color is an extremely powerful pre-attentive attribute, meaning that it’s something we intuitively see and respond to without explicit thought. That’s why choosing what to color can be so powerful. If everything has bright, distracting colors, you lose an avenue to guide the viewer’s eye. But Amy’s example is a good one to show the particular element(s) in the visual that you want people to focus on, and the viewer’s eyes will automatically go there.

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The Value of TRY_PARSE()

Published 2026-08-14 by Kevin Feasel

Steve Jones answers a question:


Someone asked why I would use TRY_PARSE after I posted a question at SQL Server Central: Getting the Average. Isn’t is slower?

A fair question. This quick post looks at why.

I’d use TRY_CAST() or TRY_CONVERT() in this particular scenario. The main reason I’d use TRY_PARSE() would be if you need .NET-specific parsing functionality, such as parsing dates by a specific locale. The reason is that PARSE() and TRY_PARSE() are an order of magnitude slower than their CAST() and CONVERT() cousins.

That said, Steve’s example reminds me of a PolyBase demo I used to do, in which I took a CSV of North Carolina populations by county and read in the information. In that particular dataset, they would use the letter “A” to describe either “Not enough people to show an answer without potentially violating PII” or “We don’t know what the answer is.” So even though it was clearly a numeric attribute in “Population,” the output process overloaded the definition of that attribute and the only way you would know is to happen to see the three rows in ~1500 that happened to have an A in the column value.

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Diagnosing High-Cardinality Workloads in Postgres

Published 2026-08-14 by Kevin Feasel

Ryan Booz has a video:

In Part 6 of this special Postgres in Production deep dive series, Ryan Booz asks a question that determines how useful pg_stat_statements can be for you at all: do you have a high cardinality workload? This episode covers what that actually means, why ORMs, dynamic SQL, and AI-assisted development tools generate more unique queries than you might expect, a side by side demo of the same workload on Postgres 17 and Postgres 18, and the concrete checks that tell you whether pg_stat_statements is losing the data you need for query tuning.

Click through for the video and transcript.

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Configuring OneLake Security for a Microsoft Fabric Lakehouse

Published 2026-08-14 by Kevin Feasel

Gilbert Quevauvilliers locks things down:

In this blog post, I will show you how to configure OneLake security for a Microsoft Fabric Lakehouse. We will remove the default broad-access role, grant the required Lakehouse access, configure object-, row-, and column-level security, and test the results. The goal is to create a secure-by-default setup in which users can access only the data they need.

Click through for the guide.

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The Good, the Bad, and the Ugly of Temp Tables

Published 2026-08-14 by Kevin Feasel

Andy Levy answers a question:

As with almost everything in SQL Server, there is no one-size-fits-all answer when it comes to temp tables. There are times where temp tables have rescued a query, and others where they’ve just caused trouble. And then there’s the times where you’re just scratching your head.

Can you use a CTE or a derived table in place of a temp table? In many cases, yes! But it doesn’t always work out well. When things get particularly complex, the SQL Server optimizer can struggle to find a good plan that doesn’t involve lots of table scans.

Read on for Andy’s thoughts.

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