Tag Archives: data viz

2015 QS Visualization Gallery: Part 3

We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1 and Part 2 as well!

eddie-flights Name: Edward Dench
Description: All recorded flights I’ve taken.
Tools: Manual entry into openflights.org (there is an interface using TripIt though).

 

QS Visualization Name: Siva Raj
Description: After 6 months of regular exercise failed to improve my fitness and blood pressure levels, I switched to training above my endurance limit (anaerobic threshold). This was higher intensity but half the cycling time, yet my fitness and blood pressure improved within weeks.
Tools:Revvo – tracking fitness and intensity of workout; Withings – weight; iHealth BP Monitor – BP. Visualization created by overlaying Revvo screenshot with other information in photoshop.

 

Screenshot 2015-06-05 08.07.14 Name: Kurt Spindler
Description: Grafana is a common tool in the Software community to create beautiful dashboards to visualize server health (network, requests, workers, cpu, etc.) and therefore more easily diagnose problems. I created a custom iOS app that allows me to publish metrics to the same backend as Grafana, giving me Grafana dashboards for my personal health.
Tools:Custom iOS app, Grafana, Graphite
RyanODonnell_PagesReadPerMonthName: Ryan O’Donnell
Description: This semi-logarithmic graph is called the Standard Celeration Chart (SCC). It’s beauty is that anything a human does can be placed on this chart (i.e., standardized display). This also allows for cool metrics to be developed that lend well to predictability. I charted the number of pages that I read for my field of study, Behavior Analysis. I wrote a blog post on the display to speak some to the reading requirements suggested by professionals in the field. There were many variables that led to variations in reading rate, but the point of this work was to try and establish a steady reading repertoire. A recent probe in May of 2015 was at 2800 pages read. Essentially, I learned how to incorporate reading behavior analytic material almost daily in my life, which indirectly aids in the effectiveness I have as a practitioner and supervisor.
Tools: Standard Celeration Chart and paper-based data collection system (pages read each day on a sheet of paper).

 

Graph4_red_black Name: Francois-Joseph Lapointe
Description: This *Microbial Selfie* depicts the gene similarity network among various families of bacteria sampled from my gut microbiome (red) and oral microbiome (black). Two bacteria are connected in the network when their gene sequences are more similar than a fixed threshold (80%). The different clusters thus identify bacterial families restricted to a single body site (red or black) versus those inhabiting multiple body sites (red and black).
Tools: In order to generate this data visualization, samples of my oral and gut microbiome have been sequenced on a MiSeq platform by means of 16S rRNA targeted amplicon sequencing, and the resulting data have been analyzed using QIIME, an open-source bioinformatics pipeline for performing microbiome analysis. The gene similarity network was produced with the open graph viz platform Gephi, using the Fruchterman–Reingold algorithm.

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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Anna Nicanorova: My Year in Numbers

AnnaNican_2014

Anna Nicanorova is a data scientist. Starting in 2013 she started making an annual report, but what stuck by how difficult it was to access her own data she was collecting through different apps and services. Early this year she put together her 2014 annual report based on a few different tools and using If This Then That as a data backup service. In this short talk, presented at the New York QS meetup group, Anna describes her process, her data, and what she learned from examining a year in numbers.

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QS 101: Make It Visual

So here we are with post #4 in the QS 101 series. We’ve already talked about keeping it simple, using the SMART system, and using social support to help you in your self-tracking process. Today we’re going to talk about what to do once you’ve collected your data – make it visual.

The Visual Cortex

You see, we humans are primarily visual animals. A large portion of our brains are dedicated to processing and deciphering the world we see. It makes intuitive sense that when it comes to self-tracking that we spend time creating images, charts, graphs, and visualizations that represent our collected data. One of the great things about our brains, especially our visual cortex, is that it is very, very good at recognizing patterns. Pattern recognition is a key aspect of the self-tracking practice. Being able to identify and recognize patterns related to behavior, thoughts, location, etc. helps us to start to tease out the intricate patterns that make up the complex cause and effect game we call life.

 

But Aren’t Numbers Enough?

Glad you asked! While we all love the numbers we generate from our different self-tracking methods, being able to see a visual representation of the data allows us to look deeper into the intricacies of the numerical values. Consider for a moment the wonderful example provided by English statistician, Francis Anscombe. Let’s consider the following four graphs:

You can immediately see the difference among the four graphs, they obviously represent very different characteristics of a measured phenomena. What’s interesting here, and what Anscombe’s Quartet demonstrates, is that simple statistics can be woefully inadequate for understanding datasets. Funny thing about each of those graphs – the data displayed has the same summary characteristics across each graph. That is, each graph has the same mean, variance, correlation and regression line. Only when the data is plotted can differences among the data be observed.

What Now?

There are a variety of tools and services out there to help you visualize your data, generate patterns and bring insight to your data – and we’ve highlighted them many times before here on the QS Blog. Hopefully looking back through these previous posts will help you and inspire you to spend some time making your data visual!

Do you have a favorite visualization or data viz tool? Share it in the comments below!

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