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.
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.
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.
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.
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.
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.
I just add a layer to the above furmula “coord_polar()” this function also has been used for creating pie charts. it gets the “theta” variable, in below example I put theta=y axis, so we have below charts
Normally I don’t much like this type of polar chart, though I’m a big fan of radar charts, which follow a similar concept.
GTFS (General Transit Feed Specification) is a specification that defines a data format for public transportation routes, stop, schedules, and associated geographic information.
In this post, we’ll use R with ggplot2 and ggmap to visualize GTFS route and schedule information on a map.
This post uses a GTFS feed from CARRIS, which is a bus public transport operator from the city of Lisbon.
Click through for code and a few interesting maps of Lisbon, Portugal.
In this module you will learn how to use the Impact Bubble Chart Custom Visual. The Impact Bubble Chart is an animated bubble chart that allows you to compare two entities against each other.
It looks like there’s an issue with the video, but Devin does have links to the custom visual and a sample data set, so you can follow along without the video.
Using Power BI to connect to multiple baselines in SentryOne allows me to make fast comparisons. I previously translated baseline values into charts manually or through some Excel/SQL Server connections. See this post on data compression as an example. I wanted to compare performance between different compression levels (None, Row, and Page), and include the Average, Minimum, Maximum, and Standard Deviation values. Now, I have a standard template that looks better in Power BI. Once I create a baseline in my database, I have access to it in my charts. If you don’t have Power BI, you can use the query from this post in Excel or another reporting method for your comparisons.
Even if you don’t use SentryOne, the principles are generally applicable.