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Category: Visualization

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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Analyzing The Ramones

Published 2017-03-10 by Kevin Feasel

Salvino Salvaggio uses R to analyze The Ramones:

Musical purists always reproached the Ramones for knowing a couple of chords only and making an excessive use of them. Data show that the band knew at least… 11 different chords (out of too-many-to-bother-counting possibilities) although 80% of their songs were built on no more than 6. And there is no evidence of a sophistication of the Ramones’ compositions over time.

It’s a fun analysis with all the R code attached.  This fun analysis, however, includes n-gram analysis, sentiment analysis, and token distribution analysis.

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Getting Familiar With Shape Maps

Published 2017-03-10 by Kevin Feasel

Reza Rad shows how to generate a shape map in Power BI:

I have previously written some blog posts about Map visuals in Power BI. One of them was specifically about Filled Map, titled as Filled Map; the Good, the Bad, the Ugly! Why? you need to read that post to find the reason. In this post I want to explain the power of Shape Map which is one of the visuals Power BI team published recently. This visual is still at preview mode at the time of writing this post. This visual is much more powerful than what it looks. The actual power behind it is that you can have your own map added to it. Let’s take a closer look at this visual with an example. If you want to learn more about Power BI; read Power BI from Rookie to Rock Star.

It’s an interesting look at a new visual.

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Air Travel Route Maps With ggplot2

Published 2017-03-09 by Kevin Feasel

Peter Prevos wants to create a pretty map of flights he’s taken:

The first step was to create a list of all the places I have flown between at least once. Paging through my travel photos and diaries, I managed to create a pretty complete list. The structure of this document is simply a list of all routes (From, To) and every flight only gets counted once. The next step finds the spatial coordinates for each airport by searching Google Maps using the geocode function from the ggmap package. In some instances, I had to add the country name to avoid confusion between places.

The end result is imperfect (as Peter mentions, ggmap isn’t wrapping around), but does fit the bill for being eye-catching.

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