21 Function Reference

Quick-lookup table for every exported AutoDeskR function. Click the Chapter link to jump to full documentation and worked examples.

21.1 Authentication

Function	Required Scopes	Key Parameters	Returns
`getToken(id, secret, scope)`	—	`id`, `secret`, `scope` (space-separated string)	`$content$access_token`, `$content$expires_in`

21.2 Data Management

Function	Required Scopes	Key Parameters	Returns
`makeBucket(token, bucket, policy)`	`bucket:create`	`bucket` (globally unique key), `policy` (`"transient"` / `"temporary"` / `"persistent"`)	`$content$bucketKey`, `$content$policyKey`
`checkBucket(token, bucket)`	`bucket:read`	`bucket`	`$content$bucketKey`, `$content$policyKey`
`deleteBucket(token, bucket)`	`bucket:delete`	`bucket` (must be empty)	`$status_code` 200
`uploadFile(file, token, bucket)`	`data:write`	`file` (local path, ≤ 100 MB), `bucket`	`$content$objectId` (URN), `$content$size`
`uploadFileSigned(file, token, bucket)`	`data:write`	`file` (local path, > 100 MB), `bucket`	`$content$objectId` (URN)
`listBuckets(token, limit, startAt, region)`	`bucket:read`	`limit` (default 10), `startAt` (pagination key), `region` (`"US"` / `"EMEA"`)	`$content$items` data frame
`listObjects(token, bucket, limit)`	`data:read`	`bucket`, `limit`	`$content$items` data frame
`deleteObject(token, bucket, object)`	`data:write`	`bucket`, `object` (filename)	`$status_code` 200
`downloadFile(urn, output_urn, token, destfile)`	`data:read`	`urn` (encoded source), `output_urn` (encoded output), `destfile`	`$status_code` 200

21.3 Model Derivative

Function	Required Scopes	Key Parameters	Returns
`translateObj(urn, token)`	`data:read data:write`	`urn` (Base-64 encoded)	`$content$result` (`"created"`), `$content$urn`
`translateStl(urn, token)`	`data:read data:write`	`urn` (Base-64 encoded)	`$content$result`, `$content$urn`
`translateSvf(urn, token)`	`data:read data:write`	`urn` (Base-64 encoded)	`$content$result`, `$content$urn`
`checkFile(urn, token)`	`data:read`	`urn` (Base-64 encoded)	`$content$status` (`"pending"` / `"inprogress"` / `"success"`), `$content$progress`
`getOutputUrn(urn, token)`	`data:read`	`urn` (raw, not encoded)	`$content$derivatives[[1]]$children[[1]]$urn`
`getMetadata(urn, token)`	`data:read`	`urn` (Base-64 encoded)	`$content$data$metadata[[1]]$guid`, `$content$data$metadata[[1]]$name`
`getObjectTree(guid, urn, token)`	`data:read`	`guid`, `urn` (Base-64 encoded)	`$content$data$objects` (nested list)
`getData(guid, urn, token)`	`data:read`	`guid`, `urn` (Base-64 encoded)	`$content$data$collection` (list of objects with properties)

21.4 Design Automation

Function	Required Scopes	Key Parameters	Returns
`makePdf(source, destination, token)`	`code:all`	`source` (public URL to DWG), `destination` (public URL for output)	`$content$id` (WorkItem ID), `$content$status`
`checkPdf(id, token)`	`code:all`	`id` (WorkItem ID from `makePdf()`)	`$content$status` (`"pending"` / `"inprogress"` / `"success"`), `$content$stats`

21.5 Reality Capture

Function	Required Scopes	Key Parameters	Returns
`createPhotoscene(name, format, token)`	`data:read data:write`	`name` (scene label), `format` (`"rcm"` / `"rcs"` / `"obj"` / `"ortho"` / `"report"`)	`$content$photoscene$photosceneid`
`uploadImages(photoscene_id, files, token)`	`data:read data:write`	`photoscene_id`, `files` (character vector of local JPEG/PNG paths)	`$content$Files$file` (list of uploaded file records)
`processPhotoscene(photoscene_id, token)`	`data:read data:write`	`photoscene_id`	`$content$photoscene$progress` (`"0"` on submission)
`checkPhotoscene(photoscene_id, token)`	`data:read data:write`	`photoscene_id`	`$content$photoscene$progress` (string `"0"`–`"100"`), `$content$photoscene$progressmsg`
`waitForPhotoscene(photoscene_id, token, interval, timeout, verbose)`	`data:read data:write`	`interval` (seconds between polls, default 30), `timeout` (max wait in seconds, default 1800)	Final `checkPhotoscene` response when `progress == "100"`

21.6 Viewer

Function	Required Scopes	Key Parameters	Returns
`viewer3D(urn, token, viewerType)`	`data:read`	`urn` (Base-64 encoded), `viewerType` (`"header"` / `"headless"` / `"vr"`)	Opens viewer in browser / Shiny
`viewerUI(id, urn, token, viewerType)`	`data:read`	`id` (Shiny module ID), `urn`, `token`, `viewerType`	Shiny UI element for use in `fluidPage()`

21.7 Scope Quick Reference

Scope	Used by
`data:read`	Model Derivative, Viewer, Reality Capture (read)
`data:write`	Data Management (upload/delete), Model Derivative (translate), Reality Capture
`bucket:create`	Data Management (`makeBucket`)
`bucket:read`	Data Management (`checkBucket`, `listBuckets`)
`bucket:delete`	Data Management (`deleteBucket`)
`code:all`	Design Automation

21.8 Common Patterns

21.8.1 Base-64 encode a URN

The Model Derivative and Viewer APIs require the URN to be Base-64 encoded. Use jsonlite::base64_enc():

myEncodedUrn <- jsonlite::base64_enc(myUrn)

21.8.2 Poll until a job finishes

All async jobs (translateObj, translateSvf, makePdf, processPhotoscene) follow the same pattern:

repeat {
  status <- checkFile(urn = myEncodedUrn, token = myToken)
  if (status$content$status == "success") break
  Sys.sleep(10)
}

Use waitForPhotoscene() as a ready-made equivalent for Reality Capture jobs.

22 Supporting Packages

The chapters outside the Core APIs part rely on the following R packages. This section lists the key functions used in the book and the chapter where each appears.

22.1 httr2

Used for direct Tandem REST API calls in the Digital Twins chapters, and as the underlying HTTP engine for AutoDeskR.

Function	Description	Chapter
`request(url)`	Create a new HTTP request	Tandem Overview, Sensor Streams
`req_auth_bearer_token(req, token)`	Add a Bearer token header	Tandem Overview
`req_url_query(req, ...)`	Append query parameters	Sensor Streams
`req_perform(req)`	Execute the request	Tandem Overview
`req_verbose()`	Enable request/response tracing	Troubleshooting
`resp_body_json(resp)`	Parse response body as JSON	Sensor Streams

22.2 jsonlite

Function	Description	Chapter
`base64_enc(x)`	Base-64 encode a string (required for APS URNs)	Model Derivative, Case Study
`fromJSON(txt)`	Parse a JSON string into an R list	General

22.3 Rvcg

Mesh processing — surface area, volume, smoothing, and surface distance. (Schlager 2024)

Function	Description	Chapter
`vcgImport(file)`	Import OBJ, STL, or PLY mesh as `tmesh3d`	Reading Meshes
`vcgArea(mesh)`	Compute total surface area	Mesh Metrics
`vcgVolume(mesh)`	Compute volume (watertight meshes only)	Mesh Metrics
`vcgSmooth(mesh, iteration, lambda)`	Laplacian smoothing	Mesh Comparison
`vcgDist(mesh1, mesh2)`	Per-vertex surface distance	Mesh Comparison

22.4 rgl

Interactive 3D visualisation. (Adler, Murdoch, et al. 2024)

Function	Description	Chapter
`open3d()`	Open a new 3D graphics device	Mesh Visualisation
`shade3d(mesh, col)`	Render a mesh with per-vertex colours	Mesh Visualisation, Mesh Comparison
`rglwidget()`	Embed interactive 3D widget in HTML	Mesh Visualisation
`readOBJ(file)`	Alternative OBJ reader	Troubleshooting
`options(rgl.useNULL = TRUE)`	Suppress OpenGL window (headless/server use)	Troubleshooting

22.5 geometry

Computational geometry for open (non-watertight) meshes. (Roussel, Sterratt, et al. 2024)

Function	Description	Chapter
`convhulln(pts, options)`	Convex hull with area and volume (`options = "FA"`)	Mesh Metrics

22.6 rayshader

High-quality 3D rendered images. (Morgan-Wall 2024)

Function	Description	Chapter
`render_snapshot(filename)`	Save a ray-traced render to file	Mesh Visualisation

22.7 lidR

Point cloud analysis from LAS/LAZ files. (Roussel et al. 2020)

Function	Description	Chapter
`readLAS(file)`	Read a LAS or LAZ point cloud	Point Cloud Analysis
`normalize_height(pc, algorithm)`	Normalise Z to ground level	Point Cloud Analysis
`rasterize_canopy(pc, res, algorithm)`	Maximum-Z raster (canopy/roof height model)	Point Cloud Analysis
`grid_density(pc, res)`	Point density raster	Point Cloud Analysis
`voxelize_points(pc, res)`	Voxelise point cloud for volume estimation	Point Cloud Analysis
`npoints(pc)`	Total point count	Point Cloud Analysis
`area(pc)`	Survey footprint area	Point Cloud Analysis

22.8 dplyr

Data manipulation — filtering, grouping, joining. Used throughout the DWG Analytics and Digital Twins chapters.

Function	Description	Chapter
`group_by(...)`	Group rows for summarisation	Layer Analysis
`summarise(...)`	Aggregate within groups	Layer Analysis
`filter(...)`	Keep rows matching a condition	Attribute Extraction
`select(...)`	Choose columns	Sensor Streams
`mutate(...)`	Add or transform columns	Attribute Extraction
`inner_join(x, y, by)`	Join keeping rows in both tables	Sensor Streams
`left_join(x, y, by)`	Join keeping all rows from left table	Sensor Streams

22.9 tidyr

Function	Description	Chapter
`pivot_wider(data, names_from, values_from)`	Reshape long data to wide format	Attribute Extraction

22.10 ggplot2

Data visualisation. Used in DWG Analytics, Digital Twins, and the Case Study.

Function	Description	Chapter
`ggplot(data, aes(...))`	Initialise a plot	Multiple
`geom_col()`	Bar chart (pre-counted values)	Layer Analysis
`geom_line()`	Line chart	Sensor Streams
`geom_histogram()`	Histogram	Point Cloud Analysis, Mesh Comparison
`geom_ribbon()`	Shaded confidence band	Sensor Streams
`geom_tile()`	Heatmap tiles	Cross-Drawing Comparison
`facet_wrap(~ var)`	Small multiples	Sensor Streams
`coord_flip()`	Swap x and y axes	Layer Analysis
`theme_minimal()`	Clean theme	Multiple

22.11 gt

Publication-quality tables for automated reports.(Iannone et al. 2024)

Function	Description	Chapter
`gt(data)`	Create a gt table from a data frame	Drawing Reports
`tab_header(title, subtitle)`	Add a title and subtitle	Drawing Reports
`fmt_number(columns, decimals)`	Format numeric columns	Drawing Reports
`cols_label(...)`	Rename column headers	Drawing Reports
`tab_source_note(note)`	Add a footnote	Drawing Reports

22.12 shiny

Interactive web applications and dashboards.

Function	Description	Chapter
`fluidPage(...)`	Standard responsive page layout	Viewer, Live Dashboards
`tabsetPanel(...)` / `tabPanel(...)`	Tabbed layout	Case Study
`renderPlot({})`	Reactive ggplot2 output	Case Study
`plotOutput(id)`	Placeholder for a plot	Case Study
`reactiveTimer(ms)`	Trigger reactive updates on a schedule	Live Dashboards
`shinyApp(ui, server)`	Launch the app	Case Study
`deployApp()`	Deploy to shinyapps.io	Live Dashboards

22.13 dygraphs

Interactive time-series plots for Shiny dashboards. (Vanderkam et al. 2018)

Function	Description	Chapter
`dygraph(data, main)`	Create an interactive time-series chart	Live Dashboards
`dyOptions(...)`	Customise colours, fill, axes	Live Dashboards
`dygraphOutput(id)`	Shiny UI placeholder	Live Dashboards
`renderDygraph({})`	Shiny server renderer	Live Dashboards

22.14 xts

Time-series objects required by dygraphs.

Function	Description	Chapter
`xts(data, order.by)`	Create an extensible time-series object	Live Dashboards

22.15 quarto

Programmatic report rendering.

Function	Description	Chapter
`quarto_render(input, execute_params)`	Render a `.qmd` file with injected parameters	Drawing Reports