As someone very interested in storytelling, ggplot2 is easily my data visualization tool of choice. It is like the Swiss army knife for data visualization. One of my favorite features is the ability to pack a graph chock-full of dimensions. This ability is incredibly handy during the data exploration phases. However, sometimes I find myself wanting to look at trends without all the noise. Specifically, I often want to look at very dense scatterplots for outliers. Ggplot2 is great at this, but when we’ve isolated the points we want to understand, we can’t easily examine all possible dimensions right in the static charts.
Enter plotly. The plotly package and ggploty function do an excellent job at taking our high quality ggplot2 graphs and making them interactive.
Read on for several quality, interactive visuals.
If you want to work in the above way we suggest giving our
cdatapackage a try. We named the functions
unpivot_to_blocks. The idea was: by emphasizing the record structure one might eventually internalize what the transforms are doing. On the way to that we have a lot of documentation and tutorials.
This is your regular reminder that the Tidyverse is very useful, but it is not the entirety of R.
I’m getting close to the end of my series on near-zero downtime deployments. This latest post involves identity column changes:
There are some tables where you create an identity value and expect to cycle through data. An example for this might be a queue table, where the data isn’t expected to live permanently but it is helpful to have a monotonically increasing function to determine order (just watch out for those
wrap-aroundsand you’re fine). An example of reseeding is below:
DBCC CHECKIDENT('dbo.MyTable', RESEED, 1);
This operation needs to take a
LCK_M_SCH_Mlock, otherwise known as a schema modification lock. Any transactions which are writing to the table will block your transaction but so will any readers unless you have Read Committed Snapshot Isolation turned on or the reader is in
If you are using RCSI and don’t have extremely long-running transactions, this is an in-and-out operation, so even though there’s a little bit of blocking, it’s minimal.
Not all changes are this easy, though.
Calling all Database Administrators, Developers, Analysts, Consultants, and Managers: Redgate has a survey open asking how you monitor your SQL Servers.
Your time is valuable. The survey will take 5 – 10 minutes to complete. That’s not a ton of time, but it’s a noticeable part of your day, and there should be something in it for you. Here’s why it’s worthwhile to take the survey.
Read the whole thing and take that survey.
Obviously, you have to have the module installed, and a copy of AdventureWorksDW2017 db restored to a SQL Server. After that, all you have to do is loop through the tables, ‘query’ them with the Read-SqlTableData cmdlet, and pipe the results to the Export-Excel cmdlet.
I did some trial and error with this yesterday. I settled on exporting all of the Dimension tables to separate Worksheets within the same Excel file, and exporting all of the Fact tables to their own individual files (since they tend to be much larger).
Click through for Aaron’s script.
I know I have been writing a lot about ADS recently, but this is even bigger than the Notebook announcement.
A Postgres plugin has been announced in the insider release of ADS, and it just works!
If the term Postgres is unfamiliar – PostgreSQL is one of the preeminent open source database solutions and is showing wide adoption due to its quality and of course, price.
Read on for additional notes.