![]() ![]() The code might have referred directly to specific underlying data dimensions (e.g., year, budget, tier, product category, etc.) Rather, it was at least generalized to the display dimensions of this grouped/stacked bar chart. In the construction of legends, filters, tooltips, titles, and the calculation of totals or year-to-year differences, x0, x1, y0, and y1 were referred to throughout the code, and ubiquitously throughout the code was conditional logic to handle the presence or absence of optional dimensions. The bar chart allowed the user to display 2, 3, or 4 arbitrary dimensions internally called x0, x1, y0, and y1, with x1 and y1 being optional. The application already had an attractive grouped/stacked bar chart visualization and required a choropleth map visualization to be built. It was a D3.js application for analyzing marketing data. The Problemĭuring my first Toptal engagement, I was convinced from the very first day that the API and data management routines of the codebase I was adding to had been hopelessly over-specified. ![]() In this article, we will take a look at the JavaScript library Supergroup.js - equipped with some powerful in-memory data collection manipulation, grouping and aggregation functions - and how it can help you solve some common manipulation challenges on limited datasets. ![]() As our skills develop, we get better at making any chunk of data munging code clever and concise, but spaghetti still seems to proliferate. We bring in complex loops, list comprehensions, database analytical functions, map or groupBy functions, or even full-fledged reporting engines. #D3 js gallery stack bar based on keys how toDesigning complex object classes that somehow know how to handle all the contexts they end up in.Īfter building data-centric software for 20 or 30 years like I have, one begins to suspect that they are solving the same set of problems over and over again.Writing new transformation functions that have to join some already processed node to the global data source to bring in new details.Stuffing more details and more levels into the transformed data until it’s huge and ungainly but satisfies the needs of all the nooks and crannies of the application it eventually visits.In visualization or complex reporting applications particularly, after shoehorning data into some structure for a given need, one realizes that tooltips or synchronized highlights or drilldowns put unexpected pressures on the transformed data. A similar transformation of the data may be required in another part of the application, or more details may be needed: metadata, context, parent or child data, etc. The manipulation itself might be simple enough: grouping, aggregating, creating hierarchies, and performing calculations but once the data munging code is written and the results are sent off to the part of the application where they’re needed, related needs continue to arise. In-memory manipulation of data often results in a pile of spaghetti code. ![]()
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