Tag Archives: R
Jana Beck was diagnosed with Type 1 diabetes when she was 19 years old and has been interested in tracking her health ever since. Last year when she received a continuous blood glucose monitor she decided to take a more active role in understand what was effecting her blood glucose levels and insulin dosing. Spurred by reading about carbohydrate restricted diets, she decided to see if she could see changes in her blood glucose readings and as a result of changing her diet. In this talk at the New York QS Meetup she describes exactly what she found and shares some really neat visualizations that help tell her story.
You can read more about the last New York QS Meetup here. If you’re interested in using theses data visualizations with your own blood glucose data be sure to check our Jana’s iPancreas project on GitHub.
Karsten W. is on an amazing journey towards understanding his personal finances. Thankfully for us he’s been writing about his methods and what he’s learned along the way over at his blog: FactBased. Let’s dive in!
Step 1. Track
Karsten decided to use twitter to track his expenses and supplement that data with his normal bank statements. Not simply satisfied with this seemingly simple step, he went a bit further and compared his twitter entries to the data from his bank to see how well he was able to self-track:
Step 2: Classify
Having compiled his full year of monetary tracking, Karsten then looked to how to better understand where his money was going by classifying his spending. He looked into classification schemes and settled on using the United Nations Classification of Individual Consumption According to Purpose. Why?
It is made by people who have thought more about consumption classification than I ever will.
Again, he did some amazing number crunching and visualized his entire year.
Step 3. Compare
So Kartsen has his expenditure data and he has it classified according to a simple schema. What’s next? Why not compare it to what is typical for someone like him! Karsten did some digging and found the German Federal Statistics office completes surveys of consumption and income every five years. He pulled the data that most closely reflected his income level and created a neat comparison:
I found this series of blog posts to be fascinating. For one, Karsten wasn’t just satisfied with tracking his finances for a full year. He went above and beyond and did some personal data analysis using some really neat tools and methods. I highly suggest you read his posts on tracking, classification, and comparison. Not only are the posts interesting, they also include short “how-to” write ups if you want to implement his analytical and visualization methods using R (an free statistical program). Keep up the great work Karsten!
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.