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Category: Visualization

Conditional Formatting With Power BI Line Charts

Published 2017-11-28 by Kevin Feasel

Daniil Maslyuk shows how to perform conditional formatting on a line chart in Power BI:

Have you ever wished you could change the line colour depending on the overall trend? For example, if your sales increase over time, the line is green; if there is a decline, then the line is red. While this functionality is not yet natively available in Power BI Desktop, it does not mean this cannot be done! In this article, I am going to show you how to achieve this effect.

Read on to see how he does it.

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Getting Started With Zeppelin

Published 2017-11-16 by Kevin Feasel

Sangeeta Gulia shows us how to get started building notebooks with Apache Zeppelin on top of Spark:

There are 3 interpreter modes available in Zeppelin.

1) Shared Mode

In Shared mode, a SparkContext and a Scala REPL is being shared among all interpreters in the group. So every Note will be sharing single SparkContext and single Scala REPL. In this mode, if NoteA defines variable ‘a’ then NoteB not only able to read variable ‘a’ but also able to override the variable.

2) Scoped Mode

In Scoped mode, each Note has its own Scala REPL. So variable defined in a Note can not be read or overridden in another Note. However, still single SparkContext serves all the Interpreter Groups. And all the jobs are submitted to this SparkContext and fair scheduler schedules the job. This could be useful when user does not want to share Scala session, but want to keep single Spark application and leverage its fair scheduler.

3) Isolated Mode

In Isolated mode, each Note has its own SparkContext and Scala REPL.

The default mode of %spark interpreter is ‘Globally Shared’.

This is mostly a step-by-step on installing Zeppelin, but does go into some detail on how Zeppelin works.

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Stack Shuffle Custom Visual

Published 2017-11-02 by Kevin Feasel

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.

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Making a Shiny Dashboard

Published 2017-10-31 by Kevin Feasel

Anish Sing Walia walks us through creating a dashboard using Shiny:

Shiny is 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. Shiny lets 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…

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ggplot2 Basics

Published 2017-10-30 by Kevin Feasel

Bharani 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.

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Sparklines In R

Published 2017-10-06 by Kevin Feasel

Robert 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.

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Online Dashboard Taxonomy

Published 2017-10-04 by Kevin Feasel

Tim 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.

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