Since some time, there’s a wrapper for
ggplot2available, bundled in the package
ggformula. One nice thing is that in that it plays nicely with the popular R package
mosaicprovides some useful functions for modeling along with a tamed and consistent syntax. In this post, we will discuss some “ornaments”, that is, some details of beautification of a plot. I confess that every one will deem it central, but in some cases in comes in handy to know how to “refine” a plot using
Note that this “refinement” is primarily controlled via the function
gf_lab()(for labs), and
gf_lims()(for axis limits). Themes can be adjusted using
Click through for several examples.
VGG16 is a built-in neural network in Keras that is pre-trained for image recognition.
Technically, it is possible to gather training and test data independently to build the classifier. However, this would necessitate at least 1,000 images, with 10,000 or greater being preferable.
In this regard, it is much easier to use a pre-trained neural network that has already been designed for image classification purposes.
This is probably the best generally available technique for image classification.
I noted that one of the columns failed to convert VARCHAR to DECIMAL.
The error message is below, and it’s usually fairly easy to sort:
Error converting data type varchar to numeric
Normally, I’d use ISNUMERIC to identify the rows that fail to have a value in that column that could be converted to a number. Then, I could identify the value, and then I could replace or exclude it, as required.
However, on this occasion, using ISNUMERIC failed to identify any columns as being non-numeric.
Click through to see why Jen got this result.
Today, we’re going to talk about combining Stream Analytics with Azure Machine Learning Studio within Power BI. If you haven’t read the earlier posts in this series, Introduction, Getting Started with R Scripts, Clustering, Time Series Decomposition, Forecasting, Correlations, Custom R Visuals, R Scripts in Query Editor, Python, Azure Machine Learning Studio and Stream Analytics, they may provide some useful context. You can find the files from this post in our GitHub Repository. Let’s move on to the core of this post, Stream Analytics.
This post is going to build directly on what we created in the two previous posts, Azure Machine Learning Studio and Stream Analytics. As such, we recommend that you read them before proceeding.
Read on for the demo.
Today Gartner released the 2019 magic quadrant for Business Intelligence. As expected (by me at least), Microsoft is continuing its trail blazing and now has a clear lead over Tableau in both ability to execute and completeness of vision. I thought it would be interesting to see a trend over time for the last 5 years, as this is the time period that I have been a professional Power BI Consultant. I needed some way to extract the numerical data points from the images I had collected. This article shows you how to do that. Here is the final output – a scatter chart with a play axis in Power BI of course.
I was just commenting the other day about how somebody should do this and Matt went and did it.
If you have SQL Server Integration Services installed on your server, and you left the default configurations a table named sysssislog is created on MSDB database, it contains logging entries for packages executed on that instance.
If you are not careful enough, this table can grow uncontrollably over time and can make subsequent insertions very slow.
This kind of batched delete works for more than just the Integration Services tables; it’s a good plan wherever you have a large table and need to delete a fairly significant number of records from it.
This’ll give us the key lookup plan you see above. If I re-run the query and use the 2010-12-30 date, we’ll re-use the key lookup plan.
That’s an example of how parameters are sniffed.
Sometimes, that’s not a good thing. Like, if I passed in 2008-12-30, we probably wouldn’t like a lookup too much.
One common “solution” to parameter sniffing is to tack a recompile hint somewhere.
Click through for Erik’s demonstration.
If you’re even thinking about experimenting with, let alone actively using, Azure Data Studio, you need to plan on installing a few extensions. Buck Woody has a great list that you should look through in this blog post. If you’re just getting started with Azure Data Studio, I have an introduction here.
Depending on the extension, this could be a simple as a mouse click. However, not all the extensions are that easy. Let’s explore this just a little so when you do start using Azure Data Studio, things are easy.
You can reasonably install Management Studio and never think about adding extensions. Don’t do that with Azure Data Studio, though: a lot of the benefit comes from its extensibility. And Microsoft tends to add things as extensions before bringing them into the base product.