# DiagrammeR for building attributed network graphs in R

> DiagrammeR is an R package for creating, modifying, analyzing, and rendering graphs whose nodes, edges, and attributes remain together. It fits workflows that begin with tabular data and end in R Markdown, Shiny, exported images, or other graph formats.

**rich-iannone/DiagrammeR** — Graph and network visualization using tabular data in R

- Repository: https://github.com/rich-iannone/DiagrammeR
- Website: https://rich-iannone.github.io/DiagrammeR/
- Stars: 1,751 · Forks: 244
- Language: R
- License: NOASSERTION
- Published: 2026-10-16 · Updated: 2026-10-16 · Language: en
- Canonical page: https://hysenlabs.com/projects/rich-iannone-diagrammer

## DiagrammeR's tabular graph object and intended users

DiagrammeR addresses the gap between ordinary R data and graph objects that retain analytical attributes. It is aimed at R users who receive nodes and edges as tabular records, need to calculate graph properties, and then want to style or publish the result without separating structure from metadata. The package can create graphs, modify them, analyze them, and produce visualizations for R Markdown documents, Shiny applications, image exports, or conversion to other graph formats.

Its central design choice is a graph object containing two specialized data frames. Node data frames hold node information and attributes, while edge data frames hold edges and their associated attributes. Because these records remain inside the graph object, a numeric value assigned to a node or edge is available for later calculations, selection, and display logic. That matters for graphs whose visual encoding should derive from analysis rather than manual appearance settings.

This package is therefore more directly suited to reproducible R data workflows than to projects that only need a hand-authored diagram. It can also serve users who want a programmatic interface for graph creation and editing. The README does not describe a separate graphical editor, so adoption should be judged as an R package and programming workflow rather than as an interactive drawing application.

## How DiagrammeR builds and records graph state

A DiagrammeR graph begins with `create_graph()`. Nodes receive integer IDs when they are created, and edges receive IDs starting from 1. Functions such as `add_node()`, `add_edge()`, and `delete_edge()` return modified graph objects that can be connected through R pipes. This makes each transformation visible in the construction sequence.

The graph also tracks more than topology. Its console summary reports directedness, connectivity, whether the graph is a DAG, whether it is simple, the selection, cached values, global attributes, and a log of graph actions. In the README example, creating three nodes, deleting one edge, and adding a new connected node leaves an action log containing `add_edge()`, `delete_edge()`, and `add_node()`. That internal history provides project-specific traceability for graph construction.

Nodes and edges can carry aesthetic properties, data values, labels, types, and relationships. Functions including `node_aes()`, `edge_aes()`, `node_data()`, and `edge_data()` attach these values when graph elements are created. Aesthetic values can control properties such as colors, shapes, arrowheads, and tooltips. Data attributes can represent values such as `betweenness` or `total_degree`, which can then drive rescaling or color selection. The README specifically notes that the aesthetic helpers expose attribute names through RStudio's inline help, reducing guesswork when constructing these records.

## Creating and inspecting a first DiagrammeR graph

The README links DiagrammeR through CRAN, but it does not provide an installation command or dependency setup steps. Package installation therefore cannot be reproduced from the documented examples alone. The documented source for obtaining the package is its CRAN listing rather than an unspecified package manager command.

A first real use starts with two explicitly created nodes and an edge between IDs 1 and 2. The following block returns a graph containing those elements:

```r
a_graph <-
  create_graph() |>
  add_node() |>
  add_node() |>
  add_edge(from = 1, to = 2)
```

Printing `a_graph` in an R session displays basic graph information and pointers to functions such as `get_node_df()` and `get_edge_df()`. The README then demonstrates removing the edge while preserving the graph-oriented workflow:

```r
b_graph <- a_graph |> delete_edge(from = 1, to = 2)
```

Nodes can subsequently be added with edges to existing IDs. The following example adds the third node and reconnects it to node 1, producing the state summarized as three nodes and two edges:

```r
c_graph <- b_graph |> add_node(from = 1, to = 2)
```

These examples are small, but they expose the package's main interaction model: create, mutate, inspect, and continue transforming the returned graph. For a tabular workflow, the next step would be to populate node and edge data through the documented attribute helpers, although the README excerpt does not show a complete import example.

## Attribute-driven analysis and visualization in DiagrammeR

The most useful documented example goes beyond topology. It creates a graph with `add_pa_graph()` and `add_gnp_graph()`, then joins calculated betweenness and total-degree values onto nodes. Those values remain attached to node records and can drive appearance. The example colorizes `total_degree` with the `Greens` palette and maps `betweenness` into a node-height range through `rescale_node_attrs()`.

Analysis can also affect selection. The README selects nodes by their IDs using `get_articulation_points()`, applies selected-node attributes with `set_node_attrs_ws()`, and then clears the selection. This avoids hard-coding a list of articulation-point IDs into the styling pipeline. The sequence is a useful pattern for interactive or repeatable graph reports: compute a property, select the relevant nodes, assign a visual distinction, and clear temporary selection state.

A smaller example shows how aesthetics and data coexist on newly created elements:

```r
d_graph <-
  c_graph |>
  add_node(
    type = "type_a",
    node_aes = node_aes(
      color = "steelblue",
      fillcolor = "lightblue",
      fontcolor = "gray35"
    ),
    node_data = node_data(
      value = 2.5
    )
  ) |>
  add_edge(
    from = 1, to = 3,
    rel = "interacted_with",
    edge_aes = edge_aes(
      color = "red",
      arrowhead = "vee",
      tooltip = "Red Arrow"
    ),
    edge_data = edge_data(
      value = 5.2
    )
  )
```

This separates presentation fields from analytical values. The reader should see node and edge attributes available through `get_node_df()` and `get_edge_df()`, then usable for later mutation, traversal, or rendering.

## Rendering DiagrammeR output in reports, apps, and exports

Rendering begins with `render_graph()`, which can also receive a layout choice. The main example uses `layout = "nicely"` to render its calculated graph. That is the documented interface, but the README does not identify the underlying rendering engine, enumerate supported layouts, or document behavior for malformed, disconnected, or unusually large graphs. Those details should not be inferred from the package name or from output images.

The output is not limited to an interactive graph console view. DiagrammeR supports incorporation into R Markdown documents and Shiny applications. It can also export image files and convert graphs to other graph formats. This makes it possible to keep graph construction in R while choosing the eventual delivery context. A static report, an application view, and an interchange export are distinct use cases, and the README treats them as supported output paths without documenting configuration for each one.

A genuine constraint follows from the package's graph-centered architecture. It is a strong candidate when graph calculations and attributes are the core problem. It is the wrong choice when the real requirement is merely to present static vector artwork with no analytical graph object. The documentation is oriented around nodes, edges, selections, attributes, and graph actions, so a diagram that only needs manually positioned shapes would not benefit from this model.

## DiagrammeR maintenance, licensing, and a data-first alternative

The repository is not archived, and its last push was on 2026-04-27. Release `v1.0.12` was also published on 2026-04-27. The preceding listed releases were `v1.0.11` on 2024-02-03 and `v1.0.10` on 2023-05-19. These dates establish recent release activity, but they do not by themselves define compatibility risk, upgrade effort, or the breadth of future support.

The repository identifies its license as a custom license that GitHub cannot classify and directs readers to `LICENSE` and `LICENSE.md`. Because the README excerpt does not state the custom terms, it would be incorrect to summarize them as permissive, copyleft, or compatible with a particular commercial use. Anyone incorporating DiagrammeR into a distributed product should inspect those files directly before choosing an installation or upgrade policy.

A real alternative is a two-stage workflow that prepares graph tables in R and sends them to another graph-processing or rendering format. That approach separates data transformation from visualization and can fit projects whose graph output is controlled by another system. DiagrammeR instead keeps graph construction, attributes, selections, analysis, graph actions, and rendering-facing data in one R object. The choice depends on whether the R graph object is useful as the working model or merely an intermediate table.

## Conclusion

DiagrammeR is appropriate for R users who want graph construction, mutation, analysis, and presentation to remain in one object-oriented workflow. It is a poor fit when a project requires a documented non-R runtime or needs evidence of rendering behavior on a particular graph engine. Before adoption, verify the custom LICENSE terms, confirm compatibility with the intended graph format and output target, and inspect the specific DiagrammeR functions needed for the required node, edge, analysis, and rendering operations.

## FAQ

### How do I install DiagrammeR for use in R?

The README links to DiagrammeR's CRAN package page, but it does not include an installation command or list dependency setup instructions. No installation command should be inferred from the CRAN badge alone.

### How do I create a basic graph with DiagrammeR?

Start with `create_graph()`, add nodes with `add_node()`, and connect IDs with `add_edge()`. The README demonstrates two nodes and an edge between IDs 1 and 2.

### Can DiagrammeR create visualizations from node and edge data frames?

Yes. Its graph object keeps node information and edge attributes in specialized data frames within the graph. The documented workflow supports tabular data, attribute-driven styling, graph analysis, and rendering through `render_graph()`.

## Sources

- [Issues](https://github.com/rich-iannone/DiagrammeR/issues)
- [Project website](https://rich-iannone.github.io/DiagrammeR/)
- [README](https://github.com/rich-iannone/DiagrammeR/blob/main/README.md)
- [Releases](https://github.com/rich-iannone/DiagrammeR/releases)
- [rich-iannone/DiagrammeR on GitHub](https://github.com/rich-iannone/DiagrammeR)

---

Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/rich-iannone-diagrammer