README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

```{r packages, message=FALSE, warning=FALSE, include=FALSE}
library(DiagrammeR)
```

# DiagrammeR <img src="man/figures/logo.svg" align="right" height="250px" />

[![Build Status](https://travis-ci.org/rich-iannone/DiagrammeR.svg?branch=master)](https://travis-ci.org/rich-iannone/DiagrammeR)
[![Coverage Status](https://codecov.io/github/rich-iannone/DiagrammeR/coverage.svg?branch=master)](https://codecov.io/github/rich-iannone/DiagrammeR?branch=master)
[![CRAN status](http://www.r-pkg.org/badges/version/DiagrammeR)](https://cran.r-project.org/package=DiagrammeR)
![](http://cranlogs.r-pkg.org/badges/grand-total/DiagrammeR?color=brightgreen)

With the **DiagrammeR** package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into **R Markdown** documents, integrated with **Shiny** web apps, converted to other graph formats, or exported as image files.

This package is made possible by the [**htmlwidgets**](http://www.htmlwidgets.org) R package, which provides an easy-to-use framework for bringing together R and JavaScript.

<img src="man/figures/simple_graph.png">

The graph above can be created with this combination of **DiagrammeR** functions:

```{r}
example_graph <-
  create_graph() %>%
  add_pa_graph(
    n = 50, m = 1,
    set_seed = 23
  ) %>%
  add_gnp_graph(
    n = 50, p = 1/100,
    set_seed = 23
  ) %>%
  join_node_attrs(df = get_betweenness(.)) %>%
  join_node_attrs(df = get_degree_total(.)) %>%
  colorize_node_attrs(
    node_attr_from = total_degree,
    node_attr_to = fillcolor,
    palette = "Greens",
    alpha = 90
  ) %>%
  rescale_node_attrs(
    node_attr_from = betweenness,
    to_lower_bound = 0.5,
    to_upper_bound = 1.0,
    node_attr_to = height
  ) %>%
  select_nodes_by_id(nodes = get_articulation_points(.)) %>%
  set_node_attrs_ws(node_attr = peripheries, value = 2) %>%
  set_node_attrs_ws(node_attr = penwidth, value = 3) %>%
  clear_selection() %>%
  set_node_attr_to_display(attr = NULL)
```

```{r eval=FALSE}
render_graph(example_graph, layout = "nicely")
```

**DiagrammeR**'s graph functions allow you to create graph objects, modify those graphs, get information from the graphs, create a series of graphs, and do many other useful things. This makes it possible to generate a network graph with data available in tabular datasets. Two specialized data frames contain node data and attributes (node data frames) and edges with associated edge attributes (edge data frames). Because the attributes are always kept alongside the node and edge definitions (within the graph object itself), we can easily work with them.

## Graph Basics

Let's create a graph object with `create_graph()` and add some nodes and edges to it. Each node gets a new integer ID upon creation. Each edge also gets an ID starting from 1. The pipes between functions make the whole process readable and understandable.

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

<img src="man/figures/a_graph.png">

We can take away an edge by using `delete_edge()`.

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

<img src="man/figures/b_graph.png">

We can add a node to the graph while, at the same time, defining edges to or from existing nodes in the graph.

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

<img src="man/figures/c_graph.png">

Viewing the graph object in the console will provide some basic information about the graph and some pointers on where to get additional information.

```{r}
c_graph
```

Any time we add a node or edge to the graph, we can add node or edge aesthetic or data attributes. These can be styling properties (e.g., `color`, `shape`), grouping labels (e.g., `type` and `rel`), or data values that are useful for calculations and for display purposes. Most node or edge creation functions (depending on whether they create either edges, nodes, or both) have the arguments `node_aes`, `edge_aes`, `node_data`, and `edge_data`. Using these, we can call the namesake helper functions (`node_aes()`, `edge_aes()`, `node_data()`, and `edge_data()`) to specifically target the created nodes or edges and bind attribute data. An additional benefit in using the helper functions (for the node/edge aesthetic attributes especially) is that RStudio can provide inline help on attribute names and definitions when typing `node_aes(` or `edge_aes(` and pressing the **TAB** key.

Here is an example of adding a node while setting its `color`, `fillcolor`, and `fontcolor` node aesthetic attributes, and, adding an edge with `color`, `arrowhead`, and `tooltip` edge aesthetic attributes. In both the `add_node()` and the `add_edge()` calls, the new node and edge were set with a `value` node/edge data attribute.

```{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
    )
  )
```

<img src="man/figures/d_graph.png">
    
Creating attributes and setting their values is often useful because we can further work with the attributes (e.g., *mutate* values or even use them during traversals). Furthermore, we can create aesthetic properties based on numerical or categorical data. This is important for when you want to display your graph diagram using the `render_graph()` function.

Don’t worry if attribute values weren’t set right during the creation of the associated nodes or edges. They are ways to set attribute values for existing nodes and edges. Functions are available for targeting the specific nodes/edges (i.e., making a *selection*) and other functions are used to set attribute values for the selected nodes or edges. Often, this can be the more efficient strategy as we can target nodes/edges based on their properties (e.g., degree, relationships to neighbors, etc.). Here is an example where we select a node based on its `value` attribute and modify its `color` node aesthetic attribute:

```{r}
e_graph <-
  d_graph %>%
  select_nodes(conditions = value == 2.5) %>%
  set_node_attrs_ws(node_attr = fillcolor, value = "orange") %>%
  clear_selection()
```

To explain this a bit, we take the graph object `d_graph`, select only the nodes that have a node `value` attribute of exactly `2.5`. (We now have an active node selection.) With the selected nodes, we set their node attribute `fillcolor` with the value `orange`. Then we deactivate the selection with `clear_selection()`. Now, if we view the graph with `render_graph()` we get this:

<img src="man/figures/e_graph.png">

There are quite a few functions that allow you to select nodes (e.g., `select_nodes()`, `select_nodes_by_id()`, `select_last_nodes_created()`) and edges (e.g., `select_edges()`, `select_edges_by_edge_id()`, `select_last_edges_created()`). With these selections, we can apply changes using functions that end with `..._ws()` (with selection). As seen, node attributes could be set/replaced with `set_node_attrs_ws()` but we can also mutate attributes of selected nodes (`mutate_node_attrs_ws()`), delete selected nodes (`delete_nodes_ws()`), and even create a subgraph with that selection (`create_subgraph_ws()`). Selections of nodes or edges can be inverted (where non-selected nodes or edges become the active selection) with `invert_selection()`, certain nodes/edges can be removed from the active selection with the `deselect_nodes()`/`deselect_edges()`, and any selection can and should be eventually cleared with `clear_selection()`.

We can create a graph object and add graph primitives such as paths, cycles, and trees to it. 

```{r}
f_graph <-
  create_graph() %>%
  add_path(n = 3) %>%
  add_cycle(n = 4) %>%
  add_balanced_tree(k = 2, h = 2)
```

<img src="man/figures/f_graph.png">

You can add one or more randomly generated graphs to a graph object. Here, let's add a directed GNM graph with 10 nodes and 15 edges (the `set_seed` option makes the random graph reproducible). 

```{r}
g_graph <-
  create_graph() %>%
  add_gnm_graph(
    n = 15, m = 20,
    set_seed = 23
  )
```

<img src="man/figures/g_graph.png">

The undirected version of this graph is can be made using:

```{r}
h_graph <-
  create_graph(directed = FALSE) %>%
  add_gnm_graph(
    n = 15, m = 20,
    set_seed = 23
  )
```

<img src="man/figures/h_graph.png">

We can view the graph using `render_graph()`. There are several layouts to choose from as well (e.g., `nicely`, `tree`, `kk`, `fr`, etc.).

```{r eval=FALSE}
render_graph(h_graph, layout = "fr")
```

<img src="man/figures/h_graph_2.png">

## Using Data from Tables to Generate a Graph

The **DiagrammeR** package contains a few simple datasets that help illustrate how to create a graph with table data. The `node_list_1` and `edge_list_1` datasets are super simple node and edge data frames that can be assembled into a graph. Let's print them side by side to see what we're working with.

```
node_list_1     edge_list_1
```

```
   id label        from to 
1   1     A     1     1  2 
2   2     B     2     1  3 
3   3     C     3     1  4 
4   4     D     4     1  9 
5   5     E     5     2  8 
6   6     F     6     2  7 
7   7     G     7     2  1 
8   8     H     8     2 10 
9   9     I     9     3  1 
10 10     J     10    3  6 
                11    3  8
                12    4  1
                13    5  7
                14    6  2
                15    6  9
                16    8  1
                17    9  3
                18    9 10
                19   10  1
```

To fashion this into a graph, we need to ensure that both the nodes and their attributes (in this case, just a `label`) are added, and, that the edges are added. Furthermore, we must map the `from` and the `to` definitions to the node `id` (in other cases, we may need to map relationships between text labels to the same text attribute stored in the node data frame). We can use three functions to generate a graph containing this data:

1. `create_graph()`
2. `add_nodes_from_table()`
3. `add_edges_from_table()`

Let's show the process in a stepwise fashion (while occasionally viewing the graph's internal ndf and edf) so that we can understand what is actually happening. First, create the graph object with `create_graph()`:

```{r}
# Create the graph object
i_graph_1 <- create_graph()
  
# It will start off as empty
i_graph_1 %>% is_graph_empty()
```

Add nodes from a table with `add_nodes_from_table()`:

```{r}
# Add the nodes to the graph
i_graph_2 <-
  i_graph_1 %>%
  add_nodes_from_table(
    table = node_list_1,
    label_col = label
  )
```

Inspect the graph's internal node data frame (ndf) with `get_node_df()`:

```{r}
# View the graph's internal node data frame
i_graph_2 %>% get_node_df()
```

The graph now has 10 nodes (no edges yet). Each node was automatically assigned an auto-incrementing `id`. The incoming `id` was also automatically renamed `id_external` so as to avoid duplicate column names and also to retain a column for mapping edge definitions. Now, let's add the edges. We need to specify that the `from_col` in the `edge_list_1` table is indeed `from` and that the `to_col` is `to`. The `from_to_map` argument expects a node attribute column that the `from` and `to` columns will map to. In this case it's `id_external`. Note that while `id` also matches perfectly in this mapping, there may be cases where `id` won't match with and `id_external` column (e.g., when there are existing nodes or when the node `id` values in the incoming table are provided in a different order, etc.).

Now, connect the graph nodes with edges from another dataset using `add_edges_from_table()`:

```{r}
# Add the edges to the graph
i_graph_3 <-
  i_graph_2 %>%
  add_edges_from_table(
    table = edge_list_1,
    from_col = from,
    to_col = to,
    from_to_map = id_external
  )
```

Inspect the graph's internal edge data frame (edf) with `get_edge_df()`:

```{r}
# View the edge data frame
i_graph_3 %>% get_edge_df()
```

By supplying the name of the graph object in the console, we can get a succinct summary of the graph's properties. Here, we see that the graph has 10 nodes and 19 edges:

```{r}
i_graph_3
```

There are two other similar datasets included in the package (`node_list_2` and `edge_list_2`). These contain extended attribute data. Let's have a quick look at their column names:

```{r}
colnames(node_list_2)
```

```{r}
colnames(edge_list_2)
```

Because we have unique labels in the `label` column, and categorical labels in the `type` and `rel` columns, we can create a property graph from this data. Like before, we can incorporate the two tables as a graph with `add_nodes_from_table()` and `add_edges_from_table()`. This time, we'll remove the auto-generated `id_external` node attribute with the `drop_node_attrs()` function.

```{r}
j_graph <- 
  create_graph() %>% 
  add_nodes_from_table(
    table = node_list_2,
    label_col = label,
    type_col = type
  ) %>%
  add_edges_from_table(
    table = edge_list_2,
    from_col = from,
    to_col = to,
    from_to_map = id_external,
    rel_col = rel
  ) %>%
  drop_node_attrs(node_attr = id_external)
```

Let's again view the graph summary in the console. Note that the additional node attributes (`value_1` and `value_2`) are present for both the nodes and the edges:

```{r}
j_graph
```

Now, because we have node/edge metadata (categorical labels and numerical data in `value_1` & `value_2` for both nodes and edges), we can do some interesting things with the graph. First, let's do some mutation with `mutate_node_attrs()` and `mutate_edge_attrs()` and get the sums of `value_1` and `value_2` as `value_3` (for both the nodes and the edges). Then, let's color the nodes and edges `forestgreen` if `value_3` is greater than `10` (`red` otherwise). Finally, let's display the values of `value_3` for the nodes when rendering the graph diagram. Here we go!

```{r}
k_graph <-
  j_graph %>%
  mutate_node_attrs(value_3 = value_1 + value_2) %>%
  mutate_edge_attrs(value_3 = value_1 + value_2) %>%
  select_nodes(conditions = value_3 > 10) %>%
  set_node_attrs_ws(node_attr = fillcolor, value = "forestgreen") %>%
  invert_selection() %>%
  set_node_attrs_ws(node_attr = fillcolor, value = "red") %>%
  select_edges(conditions = value_3 > 10) %>%
  set_edge_attrs_ws(edge_attr = color, value = "forestgreen") %>%
  invert_selection() %>%
  set_edge_attrs_ws(edge_attr = color, value = "red") %>%
  clear_selection() %>%
  set_node_attr_to_display(attr = value_3)
```

```{r eval=FALSE}
render_graph(k_graph)
```

<img src="man/figures/k_graph.png">

## A Network Graph Example

Let's create a property graph that pertains to contributors to three software projects. This graph has nodes representing people and projects. The attributes `name`, `age`, `join_date`,  `email`, `follower_count`, `following_count`, and `starred_count` are specific to the `person` nodes while the `project`, `start_date`, `stars`, and `language` attributes apply to the `project` nodes. The edges represent the relationships between the people and the project.

The example graph file `repository.dgr` is available in the `extdata/example_graphs_dgr/` directory in the **DiagrammeR** package (currently, only for the **Github** version). We can load it into memory by using the `open_graph()` function, where `system.file()` helps to provide the location of the file within the package.

```{r}
# Load in a the small repository graph
graph <-
  open_graph(
    system.file(
      "extdata/example_graphs_dgr/repository.dgr",
      package = "DiagrammeR"
    )
  )
```

We can always view this property graph with the `render_graph()` function:

```{r eval=FALSE}
render_graph(graph, layout = "kk")
```

<img src="man/figures/graph_example_1.png">

Now that the graph is set up, you can create queries with **magrittr** pipelines to get specific answers from the graph.

Get the average age of all the contributors. Select all nodes of type `person` (not `project`). Each node of that type has non-`NA` `age` attribute, so, get that attribute as a vector with `get_node_attrs_ws()` and then calculate the mean with R's `mean()` function.

```{r}
graph %>% 
  select_nodes(conditions = type == "person") %>%
  get_node_attrs_ws(node_attr = age) %>%
  mean()
```

We can get the total number of commits to all projects. We know that all edges contain the numerical `commits` attribute, so, select all edges (`select_edges()` by itself selects all edges in the graph). After that, get a numeric vector of `commits` values and then get its `sum()` (all commits to all projects).

```{r}
graph %>% 
  select_edges() %>%
  get_edge_attrs_ws(edge_attr = commits) %>%
  sum()
```

Single out the one known as Josh and get his total number of commits as a maintainer and as a contributor. Start by selecting the Josh node with `select_nodes(conditions = name == "Josh")`. In this graph, we know that all people have an edge to a project and that edge can be of the relationship (`rel`) type of `contributor` or `maintainer`. We can migrate our selection from nodes to outbound edges with `trav_out_edges()` (and we won't provide a condition, just all the outgoing edges from Josh will be selected). Now we have a selection of 2 edges. Get that vector of `commits` values with `get_edge_attrs_ws()` and then calculate the `sum()`. This is the total number of commits.

```{r}
graph %>% 
  select_nodes(conditions = name == "Josh") %>%
  trav_out_edge() %>%
  get_edge_attrs_ws(edge_attr = commits) %>%
  sum()
```

Get the total number of commits from Louisa, just from the maintainer role though. In this case we'll supply a condition in `trav_out_edge()`. This acts as a filter for the traversal and this means that the selection will be applied to only those edges where the condition is met. Although there is only a single value, we'll still use `sum()` after `get_edge_attrs_ws()` (a good practice because we may not know the vector length, especially in big graphs).

```{r}
graph %>% 
  select_nodes(conditions = name == "Louisa") %>%
  trav_out_edge(conditions = rel == "maintainer") %>%
  get_edge_attrs_ws(edge_attr = commits) %>%
  sum()
```

How do we do something more complex, like, get the names of people in graph above age 32? First, select all `person` nodes with `select_nodes(conditions = type == "person")`. Then, follow up with another `select_nodes()` call specifying `age > 32`. Importantly, have `set_op = "intersect"` (giving us the intersection of both selections).

Now that we have the starting selection of nodes we want, we need to get all values of these nodes' `name` attribute as a character vector. We do this with the `get_node_attrs_ws()` function. After getting that vector, sort the names alphabetically with the R function `sort()`. Because we get a named vector, we can use `unname()` to not show us the names of each vector component.

```{r}
graph %>% 
  select_nodes(conditions = type == "person") %>%
  select_nodes(conditions = age > 32, set_op = "intersect") %>%
  get_node_attrs_ws(node_attr = name) %>%
  sort() %>%
  unname()
```

That **supercalc** project is progressing quite nicely. Let's get the total number of commits from all people to that most interesting project. Start by selecting that project's node and work backwards. Traverse to the edges leading to it with `trav_in_edge()`. Those edges are from committers and they all contain the `commits` attribute with numerical values. Get a vector of `commits` and then get the sum (there are `1676` commits).

```{r}
graph %>% 
  select_nodes(conditions = project == "supercalc") %>%
  trav_in_edge() %>%
  get_edge_attrs_ws(edge_attr = commits) %>%
  sum()
```

Kim is now a contributor to the **stringbuildeR** project and has made 15 new commits to that project. We can modify the graph to reflect this.

First, add an edge with `add_edge()`. Note that `add_edge()` usually relies on node IDs in `from` and `to` when creating the new edge. This is almost always inconvenient so we can instead use node labels (we know they are unique in this graph) to compose the edge, setting `use_labels = TRUE`.

The `rel` value in `add_edge()` was set to `contributor` -- in a property graph we always have values set for all node `type` and edge `rel` attributes. We will set another attribute for this edge (`commits`) by first selecting the edge (it was the last edge made, so we can use `select_last_edges_created()`), then, use `set_edge_attrs_ws()` and provide the attribute/value pair. Finally, clear the active selections with `clear_selection()`. The graph is now changed, have a look.

```{r}
graph <- 
  graph %>%
  add_edge(
    from = "Kim",
    to = "stringbuildeR",
    rel = "contributor"
  ) %>%
  select_last_edges_created() %>%
  set_edge_attrs_ws(edge_attr = commits, value = 15) %>%
  clear_selection()
```

```{r eval=FALSE}
render_graph(graph, layout = "kk")
```

<img src="man/figures/graph_example_2.png">

Get all email addresses for contributors (but not maintainers) of the **randomizer** and **supercalc88** projects. With `trav_in_edge()` we just want the `contributer` edges/commits. Once on those edges, hop back unconditionally to the people from which the edges originate with `trav_out_node()`. Get the `email` values from those selected individuals as a sorted character vector. 

```{r}
graph %>% 
  select_nodes(
    conditions = 
      project == "randomizer" | 
      project == "supercalc"
  ) %>%
  trav_in_edge(conditions = rel == "contributor") %>%
  trav_out_node() %>%
  get_node_attrs_ws(node_attr = email) %>%
  sort() %>%
  unname()
```

Which people have committed to more than one project? This is a matter of node degree. We know that people have edges outward and projects and edges inward. Thus, anybody having an outdegree (number of edges outward) greater than `1` has committed to more than one project. Globally, select nodes with that condition using `select_nodes_by_degree("outdeg > 1")`. Once getting the `name` attribute values from that node selection, we can provide a sorted character vector of names.

```{r}
graph %>%
  select_nodes_by_degree(expressions = "outdeg > 1") %>%
  get_node_attrs_ws(node_attr = name) %>%
  sort() %>%
  unname()
```

## Installation

**DiagrammeR** is used in an R environment. If you don't have an R installation, it can be obtained from the [**Comprehensive R Archive Network (CRAN)**](https://cran.r-project.org/).

You can install the development version of **DiagrammeR** from **GitHub** using the **devtools** package.

```{r eval=FALSE}
devtools::install_github("rich-iannone/DiagrammeR")
```

Or, get it from **CRAN**.

```{r eval=FALSE}
install.packages("DiagrammeR")
```

If you encounter a bug, have usage questions, or want to share ideas to make this package better, feel free to file an [issue](https://github.com/rich-iannone/DiagrammeR/issues).

## Code of Conduct

Please note that the **DiagrammeR** project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/1/0/0/code-of-conduct/). By participating in this project you agree to abide by its terms.

## License

MIT &copy; Richard Iannone