A Fine Slice Of SQL Server
Devin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Enlighten Stack Shuffle Custom Visual. The Enlighten Stack Shuffle is helpful when you want to display a Top N set of values. For example if you want to display your top 5 selling employees this visual can make that very easy.
This looks pretty good on a dashboard, especially if you have a top-heavy data set, where the top few items are by far the most important.
Comments closedAnish Sing Walia walks us through creating a dashboard using Shiny:
Shinyis an amazing R package which lets the R developers and users build amazing web apps using R itself. It lets the R users analyze, visualize and deploy their machine learning models directly in the form of the web app. This package lets you host standalone apps on a webpage or embed them in R markdown documents or build dashboards and various forecasting applications. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions.Shinylets us write client-side front-end code in R itself and also lets users write server-side script in R itself. More details on this package can be found here.I recently learned Shiny and started developing a web application using it.And since then I have been in love with it and have been using it in each and every data science and analytics project. The syntax is super easy to understand and there are lots of amazing articles and documentation available for you to learn it and use it. I personally had a background of developing full-stack web applications using HTML, CSS and javascript and other JS based scripting languages so I found the syntax easy.
I keep meaning to learn Shiny and someday I will, just to prove to my intern that she’s not the only one here who can…
1 CommentBharani Akella has an introduction to ggplot2:
Plot10: Scatter-plot
ggplot(data = mtcars,aes(x=mpg,y=hp,col=factor(cyl)))+geom_point()
mpg(miles/galloon) is assigned to the x-axis
hp(Horsepower) is assigned to the y-axis
factor(cyl) {Number of cylinders} determines the color
The geometry used is scatter plot. We can create a scatter plot by using the geom_point() function.
He has a number of similar examples showing several variations on bar, line, and scatterplot charts.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Heat Streams Custom Visual. The Heat Streams visual allows you to visualize and compare a categorical value over time.
This looks pretty interesting when comparing a number of elements over time—more elements than a line chart could easily support.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the HTML Viewer. The HTML Viewer allows you to display the results of HTML code within your Power BI reports.
It does what it says on the label, and that’s good enough for me.
Comments closedRobert Sheldon shows how to use SQL Server R Services to display sparklines for categories:
In this article, we continue our discussion on visualizations, but switch the focus to sparklines and other spark graphs. As with many aspects of the R language, there are multiple options for generating spark graphs. For this article, we’ll focus on using the sparkTable package, which allows us to create spark graphs and build tables that incorporate those graphs directly, a common use case when working with spark images.
In the examples to follow, we’ll import the sparkTable package and generate several graphs, based on data retrieved from the AdventureWorks2014 sample database. We’ll also build a table that incorporates the SQL Server data along with the spark graphs. Note, however, that this article focuses specifically on working with the sparkTable package. If you are not familiar with how to build R scripts that incorporate SQL Server data, refer to the previous articles in this series. You should understand how to use the sp_execute_external_script stored procedure to retrieve SQL Server data and run R scripts before diving into this article.
Sparklines and associated visuals have their place in the world. Read on to see how you can build a report displaying them.
Comments closedTim Bock has a lot of examples of online dashboards:
The classic dashboards are designed to report key performance indicators (KPIs). Think of the dashboard of a car or the cockpit of an airplane. The KPI dashboard is all about dials and numbers. Typically, these dashboards are live and show the latest numbers. In a business context, they typically show trend data as well.
A very simple example of a KPI Dashboard is below. Such dashboards can, of course, be huge. Huge dashboards have lots of pages crammed with numbers and charts, looking at all manner of operational and strategic data.
The single most important question I think you can ask about dashboards is, what does the intended audience need to see (and do, once they’ve seen)? That will drive the kind of dashboard elements you want to use. If you need people to react and perform some maintenance operation, you probably want a KPI chart. If you want to influence readers’ opinions, infographic elements might be the trick.
Comments closedDevin Knight continues his Power BI custom visuals series:
In this module you will learn how to use the Timeline Storyteller. The Timeline Storyteller is a great way to tell a story about your data. It gives you the ability to create multiple representations of your data and then pull them together by creating multiple scenes.
This is a flashy visual and I think Devin’s set is an excellent example of where you might want to use it.
Comments closedReza Rad explains what ribbon charts are and why it’s good that they’re now available in Power BI:
Ribbon Chart shows bigger value in each column at the top, then the next value comes after. Look at the sales amount value for female and male in 2005 and 2006. In 2005, Female (Black) had more sales than Male. However, in 2006, Male (Green) generated more revenue than the female, so it is on the top for the 2006 column.
The main benefit of the ribbon chart is in this re-ordering, so it’s easier to tell which categories are largest in a specific time period.
Comments closedTim Bock shows a dozen methods for visualizing a single number:
There are a number of situations in which it can be advantageous to create a visualization to represent a single number:
To communicate with less numerate viewers/readers;
Infographics and dashboards commonly use one important number;
To attract the attention of distracted or busy viewers/readers;
To add some humanity or “color”, to create an emotional connection;
Or to increase the redundancy of the presentation (see Improve the Quality of Data Visualizations Using Redundancy).
To a great extent, my favorite is the first. There are good cases for many of the others—primarily the shock value of the uncountable pictogram—but typically, the best visualization is simple.
Comments closed