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Data Science AnalyticsTop 10 Best R Graphing Software of 2026
Compare r graphing software options with tradeoffs for data analysis workflows, including Posit Cloud and open alternatives, for shortlisting.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Posit Cloud is the best pick for teams that want R graphing to run and publish smoothly in the browser because it ties Quarto and Shiny deployment to the same managed project workspace, whereas Plotly R Open Source Graphing Library suits R users needing reusable interactive HTML charts thanks to trace-and-layout composition that renders consistently.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Posit Cloud
Quarto and Shiny deployment from the same managed R project workspace reduces duplication of chart logic.
Best for: Fits when teams need browser execution for R graphing plus Quarto and Shiny publishing from shared projects.
Plotly R Open Source Graphing Library
Editor pickTrace and layout composition creates reusable interactive charts that render consistently in HTML contexts.
Best for: Fits when R teams need interactive charts in reports or Shiny dashboards.
Lattice
Editor pickTrellis conditioning and panel layout come from the formula interface, enabling coordinated multi-panel graphics with fewer custom steps.
Best for: Fits when reports need consistent multi-panel R graphics without widget interactivity.
Comparison Table
Posit Cloud
SMBBrowser-based R environment for coding, plotting, and sharing interactive analyses.
Quarto and Shiny deployment from the same managed R project workspace reduces duplication of chart logic.
Posit Cloud runs R projects in a managed, browser-first workspace for writing, executing, and publishing R outputs. It supports Quarto documents and Shiny apps with a deployment path that keeps the same R codebase across notebooks, reports, and interactive dashboards.
Built-in project artifacts include package libraries and content routing, which reduces friction when teams need repeatable environments. For R graphing workflows, it produces consistent interactive previews in the session and reliable published artifacts for sharing.
- +Quarto publishing keeps charts and reports aligned across outputs
- +Integrated Shiny app hosting ties interactivity to the same R project
- +Project-based workflow helps reproduce package and file dependencies
- +Browser-based execution reduces local environment mismatch
- –Production governance and access control depend on Posit Cloud administrative features
- –Large datasets can bottleneck session throughput without explicit optimization
Data science teams
Publish Quarto reports with interactive plots
Consistent shared reports
Analytics engineering groups
Deploy Shiny apps from shared projects
Fewer environment mismatches
Show 1 more scenario
Team analysts
Standardize package libraries for plots
Reproducible chart builds
Built-in project artifacts manage package libraries so ggplot2 rendering matches across team sessions and publications.
Best for: Fits when teams need browser execution for R graphing plus Quarto and Shiny publishing from shared projects.
Plotly R Open Source Graphing Library
specialistR package providing an interactive, browser-based graphing library built on the open-source JavaScript graphing library Plotly.js.
Trace and layout composition creates reusable interactive charts that render consistently in HTML contexts.
Plotly R Open Source Graphing Library provides a trace-and-layout model that maps well to data transformation workflows in R and to interactive features like hover tooltips, zooming, and legend-driven trace toggling. The library integrates with R’s htmlwidgets system, so outputs can render inside interactive documents and Shiny sessions. Static rendering is also supported for cases where an HTML deliverable is not desired.
A tradeoff appears when existing ggplot2-heavy pipelines need plotly conversion, since not all ggplot2 behaviors map cleanly into plotly’s trace semantics. Use Plotly R Open Source Graphing Library when interactive exploration matters, like dashboard-style analysis in RMarkdown or Shiny, and when team workflows can adopt plotly’s object structure for repeatable chart generation.
- +Interactive hover, zoom, and legend controls without extra front-end work
- +htmlwidgets integration supports RMarkdown and Shiny rendering paths
- +Consistent trace-based API helps standardize chart generation
- +Exportable chart outputs support report and embed workflows
- –Some ggplot2 conversion edge cases require manual rework
- –Large datasets can slow client-side rendering and hover behavior
- –Styling parity with ggplot2 themes can take more manual adjustments
Data analysts in RMarkdown
Publish interactive exploratory report charts
Faster reader-driven inspection
Shiny app developers
Embed interactive visual components
More usable analytics UI
Show 2 more scenarios
R developers building dashboards
Generate chart templates from traces
Consistent chart outputs
Reusable trace-and-layout patterns help standardize multi-chart dashboards programmatically.
ETL analysts preparing drilldowns
Create scatter and heatmap explorations
Quicker anomaly triage
Hover tooltips and zoom support rapid identification of outliers and dense regions.
Best for: Fits when R teams need interactive charts in reports or Shiny dashboards.
Lattice
specialistR package for Trellis graphics, enabling the visualization of multivariate data through conditioned panels.
Trellis conditioning and panel layout come from the formula interface, enabling coordinated multi-panel graphics with fewer custom steps.
Lattice is designed for multi-panel plotting through formula-driven specifications that create small multiples and coordinated scales. It supports faceting-style layouts using trellis logic and provides theme and label control through grid-focused elements. The ggproto API and ggplot2 extension ecosystem do not apply here, so interoperability with ggplot-based tooling depends on the shared R graphics output rather than a unified object model.
A key tradeoff is that Lattice’s mental model and function set differ from ggplot2, so teams must invest in learning its conditioning and paneling approach. Lattice fits best when the data structure naturally maps to conditioning variables and when consistent panel layout matters more than widget interactivity. It also works well for static rendering workflows that prioritize reproducible figures in RMarkdown or Quarto exports.
- +Formula-driven trellis layouts reduce custom code for paneling
- +Grid-based rendering enables high-control typography and spacing
- +Static figure output stays consistent across repeated report builds
- +Conditioning and grouping logic is native to the plotting call
- –API differs from ggplot2, making cross-tool migration slower
- –Interactivity and widget-based workflows are not its primary surface
- –Some advanced plot customizations require deeper grid knowledge
- –Ecosystem integrations focus on R graphics pipelines, not new runtimes
R data analysts
Build consistent small-multiple study figures
Repeatable figures for reports
Statistical modeling teams
Diagnose model behavior by subgroup
Faster subgroup diagnostics
Show 1 more scenario
R report authors
Export multi-panel plots to documents
Consistent publication-ready graphics
Generate static outputs that integrate into RMarkdown and Quarto figure pipelines.
Best for: Fits when reports need consistent multi-panel R graphics without widget interactivity.
ggplot2
specialistR package implementing the Grammar of Graphics for declarative data visualization.
ggplot2’s layering model ties geoms and stat transformations to a single plot object.
ggplot2 renders plots as static images by default and drives most customization through a theme system, legend guides, axis scales, and coordinate systems. Data-to-visual logic uses layers such as geoms and stats, which makes it easier to reuse a base plot and swap components for variants. Faceting features like facet_grid and facet_wrap let a single specification produce multiple panels from the same data. RMarkdown integration and Quarto export support common reporting workflows that need deterministic figures in documents.
- +Layered grammar keeps plot modifications localized and reviewable
- +Vector output via SVG and PDF preserves text sharpness for documents
- +Facet_grid and facet_wrap produce consistent multi-panel comparisons
- +Theme and guides centralize styling across many charts
- –Interactivity typically needs plotly conversion or a separate widget layer
- –Some complex statistical summaries require custom stat work
- –Debugging aesthetics can be time-consuming when mappings are indirect
- –Large plot objects can slow rendering in bigger datasets
biostatistics analysts
standardize figures for clinical reports
faster figure production and review
data science developers
parameterized plot templates in pipelines
consistent chart sets
Show 2 more scenarios
RMarkdown and Quarto authors
deterministic figures in documents
less formatting drift
Render static outputs into RMarkdown or Quarto builds with stable vector exports.
research data analysts
publication-ready multi-panel comparisons
clearer cross-group interpretation
Use facet_grid and guide control to compare groups while keeping axes aligned.
Best for: Fits when R-based teams need reproducible static plots with consistent styling across reports.
Shiny
specialistR framework for building interactive web applications and dashboards directly from R code.
First-class reactive programming model that automatically re-runs dependent outputs from Shiny server binding inputs.
Shiny turns R code into reactive web apps where server-side inputs update outputs without a full page refresh. It provides built-in Shiny server binding for rendering UI from R objects and running app logic in the same R process.
- +Reactive graph updates use R-side state so interactivity stays tied to computations
- +Works directly with ggplot2 rendering and incremental UI updates
- +RMarkdown and Quarto workflows can publish content alongside app development
- +Modular UI and server code patterns support reuse across multiple apps
- –Long-running reactive computations can block sessions without async-aware patterns
- –Access control and deployment governance require additional configuration and operational discipline
R dashboard owners
Drive UI updates from reactive computations
Interactive dashboards
Data product teams
Render plots from ggplot2 objects
Responsive plot exploration
Show 1 more scenario
Documentation teams
Embed apps into Quarto reports
Publishable interactive content
Shiny apps integrate into RMarkdown and Quarto publishing workflows for browser-ready visualization in documents.
Best for: Fits when teams need R-native interactivity in browser apps with reactive control over plot updates.
DataCamp Workspace
SMBCloud notebook environment with support for R coding, charts, and shareable analysis.
Workspace execution model keeps R notebook outputs and project artifacts in one managed environment for consistent graph rendering.
DataCamp Workspace pairs a managed cloud IDE with R-focused workflows, so R code, notebooks, and project work can run in a shared environment without self-hosting. It supports interactive R development via notebooks and editor sessions, with tight integration around DataCamp’s learning and project flow.
- +Notebook-driven R sessions make ggplot2 iteration and sharing straightforward
- +Managed execution avoids local setup for R dependencies during graph development
- +Project-style organization keeps related plots, outputs, and documents together
- +Consistent runtime behavior reduces surprises from machine-to-machine differences
- –Limited control over the underlying RStudio-like server settings and process layout
- –Interactive widget rendering can lag behind a full local browser-plus-R setup
- –No native access to low-level plotting device tuning like vector backends
- –Workflow automation depends on the DataCamp execution model rather than custom hooks
Learning analysts
Iterate ggplot2 charts inside notebooks
Faster plot iteration
Team training groups
Create repeatable report artifacts
Repeatable chart reports
Show 1 more scenario
Curriculum builders
Standardize environments without hosting
Lower setup overhead
Workspace avoids self-hosting by running R in a shared cloud environment for course projects.
Best for: Fits when teams need managed notebook execution for ggplot2 graphics without operating their own R server.
Positron
SMBDesktop data science IDE from Posit with support for R analysis and visualization workflows.
Document-first chart workflows for Quarto and RMarkdown embed rendered plots directly in the same build context.
Positron is an R-first graphing and analysis editor built on the same language ecosystem as RStudio, with plot rendering centered on ggplot2 workflows and interactive HTML output. It provides a notebook-style development flow that keeps plot iteration tied to code execution, including support for RMarkdown and Quarto documents that embed figures.
- +Tight ggplot2 plot-to-code loop with responsive rendering during editing
- +Quarto and RMarkdown figure workflow keeps embedded plots consistent
- +Extensible editor capabilities for adding language and workflow tooling
- +Project-scoped behavior supports repeatable chart building across files
- –Interactive widget outputs can require extra effort for nonstandard embedding
- –Great results depend on maintaining stable Quarto and RMarkdown document structure
Reporting developers
Embed figures in Quarto documents
Aligned visuals and text
R plot authors
Refine ggplot2 visuals through execution
Tighter iteration loops
Show 1 more scenario
Extension developers
Add workflow features via extensions
Customized editing workflow
The extension system supports adding editor behaviors that fit project-based plot building.
Best for: Fits when teams need an R-native notebook workflow that turns ggplot2 figures into Quarto and RMarkdown outputs.
igraph
open-source libraryNetwork analysis and graph visualization library with native R package bindings.
Layout and graph-analytic routines are tightly coupled so coordinates and styling can be derived from computed graph structure.
igraph renders network graphs from R data structures and provides layout algorithms, edge styling, and graph analytics utilities in the same workflow. The package supports static SVG and PDF output for documents and can generate high-control visualizations from adjacency and edge lists.
- +Built-in graph data structures and layout algorithms reduce glue code.
- +Export to vector formats like SVG and PDF suits publication workflows.
- +Fine control of vertices and edges through R-level styling parameters.
- +Conveys graph structure via analytics integrated with plotting inputs.
- –Interactive exploration is limited compared with HTML widget graph tools.
- –Complex styling can take time to express for large graphs.
- –Plot customization lives in igraph plotting APIs rather than ggplot-style layers.
- –Rendering very large graphs can hit practical performance ceilings.
Graph researchers
Export reproducible network figures
Reproducible network graphics
Systems analysts
Style edges from adjacency lists
Consistent relationship views
Show 1 more scenario
Visualization engineers
Generate high-control layouts for reports
Document-ready diagrams
Layout algorithms and programmable plot rules produce fixed-position diagrams suitable for static document inclusion.
Best for: Fits when R teams need network-graph layouts, analytics, and vector figure export in one workflow.
Graphviz
open-source libraryOpen-source graph visualization software callable from R via the DiagrammeR and other interface packages.
Graphviz layout engines produce consistent node placement across runs, which helps keep rendered graphs stable in versioned reports.
Graphviz uses the DOT language to specify graph structure, attributes, and styling, so the core workflow is authoring text that a renderer converts into images.
- +DOT language enables exact control over nodes, edges, and layout
- +SVG and PDF exports keep diagrams sharp for documentation workflows
- +Deterministic layouts support reproducible diagram builds in CI
- +Works well with text pipelines that generate diagrams from code
- –Interactive widgets are not a native part of the rendering workflow
- –DOT-to-R data binding requires custom glue code and conventions
- –Complex styling can become verbose compared with R plotting grammars
- –Layout tuning is iterative when diagrams exceed modest size
Software architects
Render system architecture diagrams
Repeatable visuals in docs
Engineering operations teams
Regenerate process flow diagrams
Consistent flow diagrams
Show 2 more scenarios
Developers on build pipelines
Visualize directed task dependencies
Clear directed relationship views
DOT graphs with directed relationships can be laid out automatically for dependency or task ordering diagrams.
Analysts and researchers
Produce entity relationship maps
Compact visual maps
A compact, text-defined graph maps relationships among entities using a renderer that outputs vector figures.
Best for: Fits when build pipelines need reproducible static graph diagrams from code-generated DOT.
Amazon SageMaker Studio
enterpriseManaged notebook and IDE environment that can run R for visualization and analytics.
End-to-end automation via SageMaker jobs and endpoints lets batch-generate R plots from Studio execution.
Amazon SageMaker Studio brings an R-focused authoring environment inside AWS-managed workspaces, with notebook, terminal, and managed endpoints built from the same IAM and networking settings. It supports R workflows through Jupyter-based notebooks and R kernel sessions, plus R language packages installed into SageMaker environments.
- +R notebooks run in managed Studio environments with consistent AWS access controls
- +Studio job and endpoint automation supports repeatable plot generation workflows
- +RMarkdown and Quarto style report execution fits notebook-first publishing flows
- +VPC and IAM settings control data access for plot datasets and export artifacts
- –Interactive HTML widget use can require extra runtime permissions and browser support
- –Plot rendering and export formats depend on the chosen notebook execution context
Machine learning engineers
Run R notebooks to generate plots
Plots generated in Studio
Analytics teams
Export RMarkdown reports from Studio
Report-ready charts
Show 1 more scenario
Platform teams
Automate plot generation pipelines
Automated chart generation
SageMaker job and endpoint operations can wrap data preparation and plot creation into repeatable automation flows.
Best for: Fits when R analysts need AWS-governed notebooks and automated plot generation for pipelines.
Conclusion
After evaluating 10 r graphing software, Posit Cloud stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right R Graphing Software
R graphing software determines how R code becomes static figures, interactive widgets, or browser-rendered dashboards, with output formats like SVG, PDF, and HTML contexts shaping where charts land. This guide covers Lattice, ggplot2, Plotly R Open Source Graphing Library, Shiny, Posit Cloud, Positron, DataCamp Workspace, Amazon SageMaker Studio, igraph, and Graphviz across R-native plotting, notebook execution, and deployment-driven chart generation.
Teams often need different mechanics for paneling, layering, and reactivity, because the plot object model and the rendering pipeline change what can be automated. The comparisons below focus on how each tool handles chart composition and export rather than generic “graphing” features.
R Graphing Software for static reports and interactive dashboards
R graphing software is the toolchain that turns R computations into rendered charts, including multi-panel graphics, layered static plots, and interactive HTML widgets. Lattice targets trellis conditioning and panel layout through a formula interface that coordinates multi-panel graphics with less custom panel code, while ggplot2 centers a layered grammar that binds geoms and stat transformations to one plot object for reproducible styling across documents.
Interactive options shift the rendering path so R output becomes hoverable, zoomable charts in browser contexts, which Plotly R Open Source Graphing Library accomplishes via trace and layout composition and htmlwidgets integration. For application-style interactivity, Shiny couples reactive programming with R-side state so plot updates run as dependent outputs change, and Posit Cloud extends that workflow into a managed workspace tied to Quarto publishing and Shiny hosting.
R Graphing Software decision points: plot object model, interactivity path, and rendering outputs
R graphing tools differ most in how they turn R objects into rendered output, because the plot object model drives what can be automated and what needs conversion steps. Rendering paths also determine where charts land, with static exports like SVG and PDF handled differently than browser-rendered widgets built for HTML contexts.
Paneling and multi-panel composition via formula or plot objects
Lattice focuses on trellis conditioning and panel layout through a formula interface, which reduces custom steps for coordinated multi-panel graphics. ggplot2 and its layered plot object model can handle multi-panel designs, but Lattice targets paneling with a different API surface.
Layering model and static vector export quality
ggplot2 ties geoms and stat transformations to one plot object so changes stay localized and reviewable. ggplot2 also supports vector output via SVG and PDF to keep text sharp in documents.
Interactive chart construction for HTML contexts via trace and layout
Plotly R Open Source Graphing Library builds interactive charts by composing traces and layout in a way that renders consistently in HTML contexts. It uses htmlwidgets integration so RMarkdown and Shiny rendering paths can carry the interactive output.
Reactive plot updates tied to R-side state in browser apps
Shiny couples reactive programming with a plot rendering workflow so dependent outputs re-run when inputs change through a Shiny server binding. It works directly with ggplot2 rendering and incremental UI updates, which keeps interactivity tied to computations.
Managed R workspace that connects Quarto publishing and Shiny hosting
Posit Cloud provides a managed R project workspace that supports Quarto publishing and Shiny app hosting without duplicating chart logic. The workflow keeps chart interactivity aligned with the same project context.
How to choose R Graphing Software by rendering pipeline and automation surface
The fastest choice comes from matching the expected rendering path to the tool's native chart composition model. Lattice and ggplot2 optimize different static composition mechanics, while Plotly and Shiny optimize browser interactivity. Next, match operational constraints to where execution happens, because managed notebooks and job-run automation change throughput and governance behavior compared with local R execution and browser rendering.
Choose the composition mechanic that matches the chart structure
If the work depends on coordinated panel layouts driven by one specification, Lattice is designed around trellis conditioning and formula-driven paneling. If the work depends on localized edits to a single layered plot object, ggplot2’s layering model keeps geoms and stat transformations coupled.
Pick the interactivity path that fits the output target
If interactive hover, zoom, and legend controls must render inside HTML outputs, Plotly R Open Source Graphing Library composes interactive traces and layout and supports htmlwidgets for RMarkdown and Shiny. If the requirement is reactive browser apps that recompute plots from R-side state, Shiny runs dependent outputs via reactive graph updates.
Decide whether execution and publishing should be managed or notebook-scoped
If Quarto and Shiny must publish from the same managed project workspace, Posit Cloud ties browser execution to the same R project context. If the workflow needs notebook execution without operating an R server, DataCamp Workspace provides a managed execution environment for consistent graph rendering.
Match automation needs to where batch generation happens
If chart generation must run as automated jobs inside an AWS-governed environment, Amazon SageMaker Studio supports R notebook automation via jobs and endpoints. If automation is primarily about reproducible static diagrams from code-generated graph structure, Graphviz uses DOT language and stable node placement across runs.
Separate static publication from interactive exploration early
If diagrams and publication-quality vector exports matter more than interactive exploration, Graphviz and igraph lean toward static output workflows with SVG or PDF exports. If interactive exploration and browser rendering matter, Plotly and Shiny keep output in HTML contexts where widget behavior is native.
Who needs which R Graphing Software mechanics
Different teams face different failure modes in R chart workflows, and the right tool depends on whether interactivity is a rendering feature or an application runtime behavior. The audience fit below maps common work patterns to the chart composition and execution model each tool uses.
R analysts standardizing multi-panel static reports
Lattice fits teams that need consistent multi-panel graphics with formula-driven trellis conditioning and reduced custom panel code. This audience typically values stable static rendering over interactive widgets.
R teams building reproducible static figures for documents
ggplot2 fits teams that want a single plot object where geoms and stat transformations stay coupled for reviewable changes. This audience benefits from SVG and PDF vector output for sharp typography in documents.
Data teams embedding interactive charts into RMarkdown and Shiny pages
Plotly R Open Source Graphing Library fits teams that need interactive hover, zoom, and legend controls without building a full reactive app runtime. This audience also benefits from htmlwidgets integration that carries interactive output through RMarkdown and Shiny.
Teams shipping R-driven browser apps with reactive plot recomputation
Shiny fits teams that need reactive graph updates where dependent outputs re-run from R-side state via Shiny server binding inputs. This audience also expects interactive UI updates tied to R computations.
Enterprises managing published R workspaces and app hosting
Posit Cloud fits organizations that want Quarto publishing and Shiny hosting from the same managed R project workspace. This audience relies on the platform’s administrative features for governance and access control behavior.
Common pitfalls in R Graphing Software selection
A frequent mistake is treating static plot composition and interactive widget composition as interchangeable, because conversion steps and rendering behavior can fail for edge cases. ggplot2-to-interactivity workflows often require plotly conversion or a separate widget layer when hover and zoom are required.
Selecting ggplot2 when the required output is browser-native interactivity without an HTML widget pipeline.
Plotly R Open Source Graphing Library provides trace-based interactive charts in HTML contexts through htmlwidgets integration, which reduces reliance on manual conversion for interactivity behavior.
Choosing Lattice for interactivity-first workflows that depend on widgets and hover behavior.
Lattice is optimized for trellis conditioning and panel layout in static multi-panel graphics, so interactive widget workflows are not its primary surface.
Building reactive Shiny workloads with long computations that run on the request thread.
Shiny’s reactive model can block sessions when computations are not async-aware, so long-running plot updates should be structured to avoid blocking.
Assuming interactive output will behave the same in every execution context.
Plotly chart performance can slow on large datasets due to client-side hover rendering, and Shiny access and deployment governance adds operational configuration requirements.
Ignoring governance behavior when charts must be published and hosted for multiple users.
Posit Cloud ties production governance and access control to platform administrative features, so governance requirements must be matched to that deployment model early.
How We Selected and Ranked These Tools
We evaluated Lattice, ggplot2, Plotly R Open Source Graphing Library, Shiny, Posit Cloud, Positron, DataCamp Workspace, Amazon SageMaker Studio, igraph, and Graphviz using features at 40%, ease at 30%, and value at 30%. Features weighted how each tool’s chart composition and rendering pipeline work for static output or browser interactivity, including Lattice’s formula-driven trellis conditioning and ggplot2’s layering model with SVG and PDF vector export.
Ease weighted workflow fit such as Lattice’s reduced custom panel code and Plotly’s reusable trace and layout composition inside HTML contexts. Value weighted practical outcomes for common R graphing workflows and cited why Lattice ranked highest by combining high feature coverage and strong ease for consistent multi-panel R graphics without widget interactivity focus.
Frequently Asked Questions About R Graphing Software
When should R analysts pick ggplot2 over Lattice for report-ready graphics?
How do Shiny and Plotly differ for interactive R graphics in browser outputs?
Which tool is best for generating reproducible SVG and PDF figures from R workflows?
How does Positron support Quarto and RMarkdown figure builds during iteration?
What breaks if teams treat Apache Superset-style BI dashboards as a substitute for Shiny reactive apps built from R objects?
How can SageMaker Studio and Jupyter-based notebooks improve automation for batch plot generation?
Which graphing stack handles network diagrams directly from R graph data structures?
When do teams prefer Graphviz over R-native diagram generation for stable node placement?
How do integration and API surfaces compare between Posit Cloud and SageMaker Studio for connecting R graph builds to other systems?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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