In this module you will learn how to use the Rotating Tile Custom Visual. The Rotating Tile gives you the ability to display multiple metrics on a single visual that rotates through each value you wish to display. This allows you to save valuable space on your reports!
This feels like the type of thing that works on a dashboard but would get frustrating if you used it for time-sensitive data or data which required thoughtful analysis.
This post explains how to draw connection lines between several localizations on a map, using R. The method proposed here relies on the use of the gcIntermediate function from the geosphere package. Instead of making straight lines, it offers to draw the shortest routes, using great circles. A special care is given for situations where cities are very far from each other and where the shortest connection thus passes behind the map.
Now we know how to make pretty-looking global route charts.
Cumulative distribution graph is a commonly used chart type to express the performance metrics in percentile; it plots the percent of users who had performance metric greater or lesser than the threshold for the website.
The graph below shows the CDF graph for web page response time
From the CDF graph above, we see that at the 90th percentile, the web page response time of a website is 10.3 seconds. This means that 10% of the users in the time frame that the data was collected in had an overall web page load time of more than 10.3 seconds.
These are metrics as they relate to systems operations, but the general rules apply elsewhere as well. Also, 10.3 seconds to load a webpage seems…slow.
To get me started I invested in the expert guidance of data-visualiser-extraordinaire Nathan Yau, aka Flowing Data. Nathan has a whole host of tutorials on how to make really great visualisations in R (including a brand new course focused on mapping) and thankfully one of them deals with how to plot dot density using base R.
Now with a better understanding of the task at hand, I needed to find the required ethnicity data and shapefiles. I recently saw a video of Amelia McNamara’s great talk at the OpenVis Conference titled ‘How spatial polygons shape our world’. The .shp file really is a glorious thing and it seems that the spatial polygon makers are the unsung heros of the datavis world, so a big round of applause for all those guys is in order.
Anyway, I digress. Luckily for me, the good folks over at the London DataStore have a vast array of Shapefiles that go from Borough level all the way down to Super Output Area level. I’m going to use the Output Areas as the boundaries for the dots and the much broader Borough boundaries for ploting area labels and borders.
The ethnic group data itself was sourced from the Nomis website which has a handy 2011 Census table finder tool where you can easily download an Ethnic Group csv file for London output areas. Vamonos.
I’m going to give this a second reading; it’s a great example of how to go from functional to beautiful. H/T David Smith
This is less of a single applied step as it is multiple formatting practices applied throughout the report. I’ve already hit on this subject a little bit in the two previous Power BI visual design practices in regards to using complimentary colors. The two key takeaways in this section are object formatting and color coordination.
Of all my best practices I’m showcasing here I’d say this one is the most subjective. However I think that maintaining complimentary colors goes a long ways to creating a professional looking report. I also have a strong dislike for the default title design for visualizations in Power BI. By default it is left aligned and a grey color (AGAIN…hard to read!). I center that sucker and color the background. An added benefit to coloring the title background is it actually forces me to make sure my objects are aligned, otherwise it is VERY noticeable now if they aren’t.
Definitely read the comments on this one, as some of these tips are subjective.
In this module you will learn how to use the Route Map Power BI Custom Visual. The Route Map uses latitude, longitude, and time to show the trajectory of an object on a map.
For a certain class of dashboard, this is quite powerful.
I would expect to see the CPU spike with the same value no matter what time range I selected. But, to see the spike that fired the alert, I had to to “Edit” the chart and select different time ranges to see the differences. It wasn’t until I selected a narrow custom time range that the CPU graph would display the CPU spike that corresponded to the alert firing. The alert fires if the CPU percentage exceeds 80% over 15 minutes. So, if you “know” something happened, try different time ranges but especially the custom range to find what you are looking for.
For your highbrow reference of the quarter, FA Hayek on John Maynard Keynes’s Treatise of Money: “Mr. Keynes’s aggregates conceal the most fundamental mechanisms of change.”
This isn’t an Azure-specific problem; it’s something we have to think about whenever we aggregate data.
In this module you will learn how to use the Play Axis Power BI Custom Visual. The Play Axis visual works like a dynamic slicer that animates your other report visuals without needing to click every time you want to change your filter value.
This is a valuable custom visual when dealing with time series data, but as Devin shows, you can iterate through other sets, like a set of employee names.
So, with this map I want to show the location of more and less urbanized NUTS-2 regions of Europe. But I also want to show – with subplots – how I defined the three subregions of Europe (Eastern, Southern, and Western) and what is the relative frequency of the three categories of regions (Predominantly Rural, Intermediate, and Predominantly Rural) within each of the subregions. The logic of actions is simple: first prepare all the components, then assemble them in a composite plot. Let’s go!
This is very useful information, well worth the read.
SPARKR & PYSPARK
Most data scientists use R & Python and with SparkR & PySpark respectively they can continue to leverage their familiarity with the R & Python languages. However, they need to use the Spark API to leverage Machine learning with Spark and to take advantage of distributed computations. Both SparkR & PySpark are evolving rapidly and SparkR now supports a number of machine learning algorithms such as LDA, ALS, RF, GMM GBT etc. Another key improvement in SparkR is the ability to deploy a package interactively. This will help Data Scientists deploy their favorite R package in their own environment without stepping on other users.
PySpark now also supports deploying VirtualEnv and this will allow PySpark users to deploy their libraries in their own individual deployments.
There are several large changes, so check it out.