Motivation
The Problem
The built environment is full of data. Layer structure, geometry, material properties, object attributes, energy performance figures, sensor readings linked to physical elements, all of it sits inside proprietary file formats that the rest of the data science world can’t easily reach. The tools that can read these files (AutoCAD, Revit, Rhino) are desktop applications built for design, not analysis. They’re not going to produce a ggplot2 chart or feed a Shiny dashboard.
The result is an unnecessary gap. Engineers and architects generate rich, spatially-grounded data every day. Data scientists who could turn that data into insight have no good way to access it. Both teams end up frustrated, exporting things to CSV by hand and losing half the structure in the process.
The Opportunity
R has quietly become the lingua franca for quantitative work in architecture, engineering, and construction (AEC). Structural engineers use it to crunch sensor data. Sustainability consultants model energy performance in it. BIM managers audit model quality across large portfolios with it. And Shiny brings all of that to non-technical stakeholders through interactive dashboards.
What’s been missing is a clean bridge between R’s data science ecosystem and the 3D model data locked inside CAD files. A DWG file isn’t just a drawing, it’s geometry, layer structure, material properties, and embedded attributes that are genuinely interesting to analyse. An as-built point cloud from a drone survey can be compared against a design mesh to quantify construction deviations. A BIM model wired to live sensor data becomes a digital twin that a Shiny app can query in real time.
The Solution
This book builds that bridge using the AutoDesk Platform Services (APS) cloud API as its primary engine. AutoDesk is the dominant platform in the AEC industry, and APS can translate design files between dozens of formats, extract rich geometry and metadata from BIM models, automate DWG processing at scale, and render 3D models interactively in a browser — all without a local AutoCAD or Revit installation.
The catch is that APS speaks REST. Every operation means authenticated HTTP requests, base64-encoded URNs, polling asynchronous job queues, and parsing deeply nested JSON. AutoDeskR (Govan 2024) wraps all of that in idiomatic R functions. Authentication, encoding, polling — handled. A workflow that would otherwise mean dozens of raw HTTP calls comes down to a handful of lines:
library(AutoDeskR)
token <- getToken(id, secret, scope = "data:read data:write bucket:create")
bucket <- makeBucket(token, "my-project-bucket")
upload <- uploadFile(token, "model.dwg", bucket$bucketKey)
job <- translateFile(token, upload$objectId, "svf")The goal isn’t to paper over APS entirely. Understanding what’s happening under the hood makes you more effective. AutoDeskR just removes the boilerplate so you can spend your time on the analysis, not the plumbing.
Who This Book Is For
This book is for R users who work with or alongside CAD and BIM data and want to automate, analyse, or visualise it without leaving the R environment. The techniques here, mesh analysis, layer analytics, digital twin patterns, point cloud processing, apply to BIM and CAD data broadly, not just data that came from AutoDesk software. AutoDesk is where we start because that’s where most of the industry’s data currently lives, but the analytical mindset carries over wherever the files come from.
No prior experience with APS, AutoDesk, or the AEC industry is assumed. If you can install an R package and run a script, you have everything you need.