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Category: Visualization

Network Navaigator Custom Visual

Published 2017-04-07 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Network Navigator Power BI Custom Visual.  You may find the need to use the Network Navigator when you’re trying to find links between different attributes in a dataset. It does this by visualizing each attribute as a node and the strength of activity between those nodes can be represented in multiple ways.

Click through to get to Devin’s video.  This visual looks interesting for graphical analysis, like trying to tease out common connections or discovering dependencies.

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ggedit 0.2.0

Published 2017-04-04 by Kevin Feasel

Jonathan Sidi announces ggedit 0.2.0:

ggedit is an R package that is used to facilitate ggplot formatting. With ggedit, R users of all experience levels can easily move from creating ggplots to refining aesthetic details, all while maintaining portability for further reproducible research and collaboration.
ggedit is run from an R console or as a reactive object in any Shiny application. The user inputs a ggplot object or a list of objects. The application populates Bootstrap modals with all of the elements found in each layer, scale, and theme of the ggplot objects. The user can then edit these elements and interact with the plot as changes occur. During editing, a comparison of the script is logged, which can be directly copied and shared. The application output is a nested list containing the edited layers, scales, and themes in both object and script form, so you can apply the edited objects independent of the original plot using regular ggplot2 grammar.

This makes modifying ggplot2 visuals a lot easier for people who aren’t familiar with the concept of aesthetics and layers—like, say, the marketing team or management.

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OLAP Limitations In Tableau

Published 2017-04-04 by Kevin Feasel

Tim Cost points out areas of friction when trying to use Tableau to connect to a multi-dimensional Analysis Services cube:

I love Tableau, I do NOT however, love working with Tableau when it is connected to an OLAP cube (like Microsoft SQL Server Analysis Services).  I don’t enjoy working with cube data in Tableau because basically all the coolest parts of Tableau won’t work or won’t work in the ways you might expect.  I don’t see this as a failing of Tableau, I lay the blame on the OLAP cube.  The main issue with working against a cube in Tableau is that you talk to a cube with MDX, where we talk to almost every other data source with SQL.  MDX (or Mind Destroying Expressions as I think of them), are just a huge pain to work with.  As hard as it is for ME to write MDX, for Tableau it’s even harder. Here are some things that you should consider before committing to a Tableau project with Microsoft SQL Server Analysis Services as a data source

Click through for ten such considerations.

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Attribute Slicer

Published 2017-03-30 by Kevin Feasel

Devin Knight continues his Power BI custom visuals series:

In this module you will learn how to use the Attribute Slicer Power BI Custom Visual.  Using the Attribute Slicer you have the ability to filter your entire report while also being able to visibly see a measure value associated with each attribute.

Click through for the video as well as more details.  This looks like a very interesting way of integrating a slicer with some important metric, like maybe including dollar amounts per sales region and then filtering by specific regions to show more detailed analyses.

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Grafana On Elasticsearch

Published 2017-03-28 by Kevin Feasel

Daniel Berman shows how to replace Kibana with Grafana:

While very similar in terms of what can be done with the data itself within the two tools. The main differences between Kibana and Grafana lie in configuring how the data is displayed. Grafana has richer display features and more options for playing around with how the data is represented in the graphs.

While it takes some time getting accustomed to building graphs in Grafana — especially if you’re coming from Kibana — the data displayed in Grafana dashboards can be read and analyzed more easily.

I prefer Grafana over Kibana for a few reasons, so I’m happy to see Grafana articles popping up.

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Datashader

Published 2017-03-24 by Kevin Feasel

John Mount is a bit jazzed when it comes to a new package:

I recently got back from Strata West 2017 (where I ran a very well received workshop on R and Spark). One thing that really stood out for me at the exhibition hall was Bokeh plus datashader from Continuum Analytics.

I had the privilege of having Peter Wang himself demonstrate datashaderfor me and answer a few of my questions.

I am so excited about datashader capabilities I literally will not wait for the functionality to be exposed in R through rbokeh. I am going to leave my usual knitr/rmarkdown world and dust off Jupyter Notebook just to use datashader plotting. This is worth trying, even for diehard R users.

For the moment, it looks like datashader is only available for Python, but it’s coming to R.

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Visualizing Market Basket Analyses With Power BI

Published 2017-03-23 by Kevin Feasel

Leila Etaati explains how to use Power BI and a Force-Directed Graph custom visual to display results of a market basket analysis:

By clicking on the “R transformation” a new windows will show up. This windows is a R editor that you can past your code here. however there are couple of things that you should consider.

1. there is a error message handling but always recommended to run and be sure your code work in R studio first (in our example we already tested it in Part 1).

2. the all data is holding in variable “dataset”.

3. you do not need to write “install.packages” to get packages here, but you should first install required packages into your R editor and here just call “library(package name)”

Leila takes this step-by-step, leading to a Power BI visual with drill-down.

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Power BI Matrix Preview

Published 2017-03-20 by Kevin Feasel

Matt Allington reports that Power BI Desktop supports a new type of matrix (in preview):

Expand, Collapse, Drill and Filter

Expand and collapse behaves just like a pivot table however with a slightly different UI. The new matrix experience is however entirely consistent with the chart drill experience so it is very intuitive.

The new cross filter behaviour is of course not possible in a regular pivot table in Excel (without VBA). You can select any column, row or cell in the matrix and it will cross drill the other visuals on the canvas as can be seen above.

This looks like an interesting change, and Matt shows how to enable the preview.

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Storyboarding: Uncovering The Problem

Published 2017-03-17 by Kevin Feasel

Jonathan Stewart continues his series on storyboarding:

If you have heard my “Data Visualization: How to truly tell a great story!” presentation, you will have heard me mention about using a storyboard to get a better understanding of the problem. Cole Nussbaumer Knaflic does a great job of introducing this concept in her book “Storytelling with Data” which is a great read and an excellent reference tool for anyone in the data viz world.

I have adapted to using her basic storyboard as my basis for my development and we will use it today as the foundation of our series.

Jonathan ends with a set of sample questions to ask.  These are just starter questions, but they’ll help uncover important but hidden business requirements.

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