A Fine Slice Of SQL Server
Riddhiman shows how to use R and plotly to build charts of Federal Reserve data sets:
In this post we’ll try to replicate some of the charts created by the Federal Reserve which visualize some well known macroeconomic indicators. We’ll also showcase the new Plotly 4.0 syntax.
This is a very code-heavy blog post and is a good way to learn about plotly.
Comments closedDevin Knight’s Power BI Custom Visuals class continues:
Change the Banding type property to one of the following:
Increasing is better – Increasing is best when you’re measuring things like sales or profit. If you go over your profit target that’s a good thing!
Decreasing is better – Decreasing is probably best when you’re looking at something like budgeting. Staying under budget is usually a good thing. Unless you being too far under budget means you won’t get that money again next year which leads to the last option
Closer is better – This is for when you need your data to land in the middle of a bell curve. Meaning if you go too high or too low that’s a bad thing. This is often useful when looking at medical data. For example, if your blood pressure is too high then that’s a bad thing, but if you’re blood pressure is too low that’s also a bad thing too. You need to land in the middle somewhere, which is what this option allows.
There’s plenty of good advice here, so check out the video.
Comments closedDevin Knight continues his custom visuals course:
In this module you will learn how to use the Radar Chart, a Power BI Custom Visual. The Radar Chart is sometimes is also know to some as a web chart, spider chart, or star chart. Using the Radar Chart allows you to display multiple categories of data on each spoke (like spokes on a bicycle wheel) of the chart. The Radar Chart does support the display of multiple metrics, which allows you to compare and contrast the “pull” that each category has on your metrics.
I still say you should stick with the fish chart for all of your visualization needs.
Comments closedDevin Knight continues his Power BI visuals series:
In this module you will learn how to use the Hexbin Scatterplot Power BI Custom Visual. The Hexbin Scatterplot is a variation of the traditional Scatter Chart but instead of using bubble size it relies on color saturation and hexbins to show value distribution. You should consider using this chart when you’re more interested in visualizing density instead of individuals points themselves.
This is worth checking out.
Comments closedDevin Knight shows us how to use the Enlighten Aquarium Power BI custom visual:
In this module you will learn how to use the Enlighten Aquarium Power BI Custom Visual. While it might not be the most practical visualization it does provide a fun way to show categorical data and can have multiple series shown as well.
From now on, all dashboards must look like screensavers from the 1990s.
Comments closedAlex Woodie reports that Qlik Technologies has been acquired by a private equity firm:
After loading data into a server-based associative, in-memory database, Qlik customers could explore the data in a variety of ways from an AJAX Web GUI, enabling them to create and publish all sorts of reports and dashboards. The approach is not entirely dissimilar to the one taken by its rival, Tableau Software, which has also benefited from the big data boom and the democratization of BI.
The combination of market forces and a keen eye for product development were propellant for growth at Qlik. In 2009, the Radnor, Pennsylvania-based company had 11,400 customers and $157 million in revenues. By 2010, it had grown to 13,000 customers and had an IPO. By 2015, the company boasted 37,000 customers, $612 million in revenue, and a market cap north of $2.8 billion.
Qlik is definitely one of the big players in the visualization market, which includes Tableau, and Power BI/SSRS in Gartner’s Leaders quadrant and a bunch of competitors nipping at their heels.
Comments closedMelissa Yu provides advice on improving your data visualization skills:
Common chart clutter items include:
3-dimensional effects
Dark gridlines (use soft gray gridlines or eliminate gridlines when possible)
Overuse of bright, bold colors
Unnecessary use of all uppercase text (uppercase text is only necessary when calling attention to an element)
Basically, remove every visualization “feature” that Excel 97 gave you…
Comments closedVimal Natarajan shows off some R charts using crime incident data:
By analyzing the plot above, we can arrive at the following insights:
The number of crimes steadily decline from midnight and are at the lowest during the early morning hours and then they start increasing and peak around 6 PM in the evening. This is the same insight we arrived in my previous analysis but here we have categorized by the Police district and still see the same pattern.
As seen in the previous plot, Park and Richmond districts have the lowest number of crimes throughout the day.
As highlighted in red in the plot above, the maximum number of crimes happens in Southern district around 6 PM in the evening.
I would prefer to see code here, but it does serve to give you an idea of what R can do.
Comments closedDavid Smith points out an animated flood chart using R:
As more settlements in Texas and France are impacted by severe flooding, this is a good time to thank the hydrologists at the NOAA who forecast river level rises in advance and give residents in affected areas time to move to higher ground. Along with topgraphic, rainfall, and weather data, monitoring stations maintained by NOAA and the USGS along rivers provide critical real-time information about river levels. NOAA scientists access this data using the dataRetrieval package for R, which they then incorporate into flood prediction models and use to generate animations like this one of the flood of the Delaware in February this year
Looks like I’ve got a new blog to follow…
Comments closedJonathan Carroll shows how to use images as labels in R:
There are probably very few cases for which this is technically a good idea (trying to be a featured author on JunkCharts might very well be one of those reasons). Nonetheless, there are at least a couple of requests for this floating around on stackoverflow; here and here for example. I struggled to find any satisfactory solutions that were in current working order (though perhaps my Google-fu has failed me).
Jonathan is rather against this idea, and it does seem like the answer is a hack. I suppose the real answer is “sometimes an image isn’t worth a thousand words.”
Comments closed