rinji.net

who is this?

About This Notebook

Welcome to my research diary -- a collection of half-finished ideas, annotated datasets, and the occasional coffee-stained epiphany. This is where curiosity lives without the pressure of publication.

Think of this as the back pages of a thesis binder, where the real thinking happened between the margin doodles and the hastily sketched scatter plots.

Datasets explored with more enthusiasm than rigor
Charts that tell stories numbers alone cannot
Annotations from late nights and early mornings

the fun part

Data Exploration

Every dataset has a story hiding beneath rows and columns. The trick is finding the right chart to coax it out. Some patterns emerge only when you squint at a scatter plot from the right angle.

These visualizations aren't meant to be precise -- they're meant to be provocative. A hand-drawn chart invites questions that a perfect dashboard never would.

how it works

Methodology Notes

The approach here is deliberately informal. Formal methods have their place -- peer-reviewed journals, mostly -- but this notebook operates on a different principle: follow the interesting thread and see where it leads.

Start with a question, not a hypothesis
Sketch the data before analyzing it
Trust the outliers -- they're trying to tell you something
When in doubt, add another chart

see the curve?

Emerging Trends

Some of the most interesting patterns only become visible over time. This line chart tracks a phenomenon I've been watching for months -- the way certain variables move in unexpected tandem, like two strangers walking in sync without realizing it.

The hand-drawn quality is intentional. A perfectly rendered line invites certainty; a sketched one invites curiosity.

aha moments

Key Findings

After weeks of staring at scatter plots and whispering to my datasets, a few patterns have crystallized. These aren't conclusions -- they're starting points for deeper exploration.

Correlation between variables A and B intensifies after the threshold point, suggesting a nonlinear relationship that standard models consistently miss
The outlier cluster in the northeast quadrant isn't noise -- it represents a distinct subpopulation with its own internal logic
Temporal patterns emerge only at the weekly resolution; daily data is too noisy, monthly too smoothed
The most predictive feature was the one I almost excluded for being "too obvious"

the whole pie

Distribution Analysis

Breaking down the composition reveals unexpected proportions. What we assumed was a dominant category turns out to be barely a quarter of the whole -- while the "miscellaneous" bucket hides the most fascinating subgroups.

The donut chart format works because it draws your eye to the relationships between segments rather than their absolute sizes. The hole in the middle is where the questions live.

to do later

What Comes Next

This notebook is never finished -- it just finds convenient stopping points. The next phase involves deeper dives into the subpopulations identified in the scatter analysis, with particular attention to the temporal dimensions that the current visualizations only hint at.

Expand the time series with more granular weekly data
Build interactive versions of the network graph
Cross-reference with external datasets for validation
Add more margin notes (the margins are never full enough)