R has a reputation as a statistics tool — the thing you reach for when you need a regression or a publication-quality plot. That framing undersells it. The part of R that earns its place in a practical engineering workflow is its I/O story: the breadth of data sources it can connect to, and the consistency of the analysis layer once data arrives in a data frame regardless of where it came from.
Read more →There is a measurable cost to serving static images. Not a performance cost — that’s a different conversation, one I covered in an earlier post on Cloudinary as a DAM. This is an engagement cost: the gap between what a user feels when they interact with a product image and what they feel when they look at one.
A 2017 paper in Computers in Human Behavior quantified this gap directly. Blazquez Cano et al. ran a controlled experiment with 218 participants browsing fashion clothing on an iPad — split across three conditions: static images, 360° visual rotation, and tactile simulation (a scrunch gesture that deformed the fabric texture on screen). The engagement scores across dimensions like novelty, felt involvement, and endurability were significantly higher in both interactive conditions than in the control group. The static image condition scored 1.34 out of 7 for novelty — participants essentially disagreed that they felt any curiosity or interest. The interactive conditions scored 4.63 and 4.95 on the same measure. The paper is readable in full here.
Read more →Colour blindness affects roughly 8% of men and 0.5% of women with Northern European ancestry. For a data visualisation that uses colour as the primary encoding — a choropleth, a multi-series line chart, a heatmap — that’s a meaningful share of the audience for whom the visual may be conveying the wrong information, or none at all.
The right time to test this is during design, not after a complaint. ImageMagick’s -color-matrix flag lets you simulate the major forms of colour vision deficiency directly on any image — screenshots, design exports, chart renders — from the command line. This makes it scriptable, batchable, and easy to drop into a CI pipeline alongside screenshot tests.
SVG paths are written as a single d attribute — a mini-language of commands and coordinates that describes shapes. Each command is a letter; uppercase means absolute coordinates, lowercase means relative to the current position. This is a reference for all path commands with examples, followed by a worked pie chart that puts the arc command through its paces.
The full MDN reference lives at developer.mozilla.org/en-US/docs/Web/SVG/Tutorial/Paths.
M x y
Lifts the pen and moves to (x, y) without drawing. Every path starts with an M. On its own it’s invisible — it’s a positioning command.