With the DiagrammeR package you can create, modify, analyze, and visualize network graph diagrams. A collection of functions are available for working specifically with graph objects. The output can be viewed in the RStudio Viewer, incorporated in RMarkdown, integrated into Shiny web apps, converted into other graph formats, or exported as image, PDF, or SVG files.

It's possible to make the above graph diagram using a combination of DiagrammeR functions strung together with the magrittr %>% pipe:

library(DiagrammeR)

create_random_graph(140, 100, set_seed = 23) %>%
  join_node_attrs(get_w_connected_cmpts(.)) %>%
  select_nodes_by_id(get_articulation_points(.)) %>%
  set_node_attrs_ws("peripheries", 2) %>%
  set_node_attrs_ws("width", 0.65) %>%
  set_node_attrs_ws("height", 0.65) %>%
  set_node_attrs_ws("penwidth", 3) %>%
  clear_selection() %>%
  add_global_graph_attrs(
    attr =
      c("color",  "penwidth", "width", "height"),
    value =
      c("gray80", "3",        "0.5",   "0.5"),
    attr_type =
      c("edge",   "edge",     "node",  "node")) %>%
  colorize_node_attrs(
    node_attr_from = "wc_component",
    node_attr_to = "fillcolor",
    alpha = 80) %>%
  set_node_attr_to_display() %>%
  select_nodes_by_degree("deg >= 3") %>%
  trav_both_edge() %>%
  set_edge_attrs_ws("penwidth", 4) %>%
  set_edge_attrs_ws("color", "gray60") %>%
  clear_selection() %>%
  render_graph()

DiagrammeR's graph functions allow you to create graph objects, modify those graphs, get information from the graphs, create a series of graphs, perform scaling of attribute values with data values, and many other useful things.

This functionality 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 and specify styling attributes to differentiate nodes and edges by size, color, shape, opacity, length, and more. Here are some of the available graph functions:

Network Graph Example

Let's create a property graph by combining CSV data that pertains to contributors to three software projects. The CSV files (contributors.csv, projects.csv, and projects_and_contributors.csv) are available in the DiagrammeR package. Together they provide the properties name, age, join_date, email, follower_count, following_count, and starred_count to the person nodes; project, start_date, stars, and language to the project nodes; and the contributor_role and commits properties to the edges.

library(DiagrammeR)

# Create the main graph
graph <-
  create_graph() %>%
  set_graph_name("software_projects") %>%
  add_nodes_from_table(
    system.file(
      "extdata", "contributors.csv",
      package = "DiagrammeR"),
    set_type = "person",
    label_col = "name") %>%
  add_nodes_from_table(
    system.file(
      "extdata", "projects.csv",
      package = "DiagrammeR"),
    set_type = "project",
    label_col = "project") %>%
  add_edges_from_table(
    system.file(
      "extdata", "projects_and_contributors.csv",
      package = "DiagrammeR"),
    from_col = "contributor_name",
    to_col = "project_name",
    ndf_mapping = "label",
    rel_col = "contributor_role")

We can always view the property graph with the render_graph() function.

render_graph(graph, output = "visNetwork")

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, cache that attribute with cache_node_attrs_ws() (this function caches a vector of node attribute values in the graph). Get the cache straight away and get its mean (with get_cache() and then mean()).

graph %>% 
  select_nodes("type == 'person'") %>%
  cache_node_attrs_ws("age", "numeric") %>%
  get_cache() %>% 
  mean()
#> [1] 33.6

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) and cache the commits values as a numeric vector with cache_edge_attrs_ws() (this is stored in the graph object itself). Immediately extract the cached vector with get_cache() and get its sum() (all commits to all projects).

graph %>% 
  select_edges() %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  get_cache() %>%
  sum()
#> [1] 5182

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("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 a vector of commits values as a stored, numeric vector with cache_edge_attrs_ws(). Immediately, extract it from the graph with get_cache() and get the sum(). This is the total number of commits.

graph %>% 
  select_nodes("name == 'Josh'") %>%
  trav_out_edge() %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  get_cache() %>% 
  sum()
#> [1] 227

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, nullifying the selection to those edges where the condition is not met. Although there is only a single value in the cache, we'll still use sum() after get_cache() (a good practice because we may not know the vector length, especially in big graphs).

graph %>% 
  select_nodes("name == 'Louisa'") %>%
  trav_out_edge("rel == 'maintainer'") %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  get_cache() %>% 
  sum()
#> [1] 236

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("type == 'person'"). Then, follow up with another select_nodes() call specifying age > 32, and, importantly using set_op = "intersect" (giving us the intersection of both selections). Now we have the selection of nodes we want; get all values of these nodes' name attribute as a cached character vector with the cache_node_attrs_ws() function. Get that cache and sort the names alphabetically with the R function sort().

graph %>% 
  select_nodes("type == 'person'") %>%
  select_nodes("age > 32", set_op = "intersect") %>%
  cache_node_attrs_ws("name", "character") %>%
  get_cache() %>%
  sort()
#> [1] "Jack"   "Jon"    "Kim"    "Roger"  "Sheryl"

Another way to express the same selection of nodes is to use the mk_cond() (i.e., 'make condition') helper function to compose the selection conditions. It uses sets of 3 elements for each condition: (1) the node or edge attribute name (character value), (2) the conditional operator (character value), and (3) the non-attribute operand. A linking & or | between groups is used to specify ANDs or ORs. The mk_cond() helper is also useful for supplying variables to a condition for a number of select_...() and all trav_...() functions.

graph %>% 
  select_nodes(
    mk_cond(
      "type", "==", "person",
      "&",
      "age",  ">",  32)) %>%
  cache_node_attrs_ws("name", "character") %>%
  get_cache() %>%
  sort()
#> [1] "Jack"   "Jon"    "Kim"    "Roger"  "Sheryl"

That supercalc 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. Cache those values, get the cache straight away, take the sum -> 1676 commits.

graph %>% 
  select_nodes("project == 'supercalc'") %>%
  trav_in_edge() %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  get_cache() %>% 
  sum()
#> [1] 1676

How would we find out who committed the most to the supercalc project? This is an extension of the previous problem and there are actually a few ways to do this. We start the same way (at the project node, using select_nodes()), then:

• traverse to the inward edges [trav_in_edge()]
• cache the commits values found in these selected edges [cache_edge_attrs_ws()]
• this is the complicated part but it's good: (1) use select_edges(); (2) compose the edge selection condition with the mk_cond() helper, where the edge has a commits value equal to the largest value in the cache; (3) use the intersect set operation to restrict the selection to those edges already selected by the trav_in_edge() traversal function
• get a new cache of commits values (should only be a single value in this case)
• we want the person responsible for these commits; traverse to that node from the edge selection [trav_out_node()]
• cache the name values found in these selected nodes [cache_node_attrs_ws()]
• get the cache [get_cache()]

graph %>% 
  select_nodes("project == 'supercalc'") %>%
  trav_in_edge() %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  select_edges(mk_cond("commits", "==", get_cache(.) %>% max()), "intersect") %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  trav_out_node() %>%
  cache_node_attrs_ws("name") %>%
  get_cache()
#> [1] "Sheryl"

What is the email address of the individual that contributed the least to the randomizer project? (We shall try to urge that person to do more.)

graph %>% 
  select_nodes("project == 'randomizer'") %>%
  trav_in_edge() %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  trav_in_node() %>%
  trav_in_edge(mk_cond("commits", "==", get_cache(.) %>% min())) %>%
  trav_out_node() %>%
  cache_node_attrs_ws("email") %>%
  get_cache()
#> [1] "the_will@graphymail.com"

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 (ensure they are unique!) to compose the edge, setting use_labels = TRUE. The rel value in add_edge() was set to contributor -- in a property graph we should try to 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: use select_last_edge()), then, use set_edge_attrs_ws() and provide the attribute/value pair. Finally, deselect all selections with clear_selection(). The graph is now changed, have a look.

graph <- 
  graph %>%
  add_edge(
    from = "Kim",
    to = "stringbuildeR",
    rel = "contributor",
    use_labels = TRUE) %>%
  select_last_edge() %>%
  set_edge_attrs_ws("commits", 15) %>%
  clear_selection()

render_graph(graph, output = "visNetwork")

Get all email addresses for contributors (but not maintainers) of the randomizer and supercalc88 projects. Multiple select_nodes() calls in succession is an OR selection of nodes (project nodes selected can be randomizer or supercalc). 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.

graph %>% 
  select_nodes("project == 'randomizer'") %>%
  select_nodes("project == 'supercalc'") %>%
  trav_in_edge("rel == 'contributor'") %>%
  trav_out_node() %>%
  cache_node_attrs_ws("email", "character") %>%
  get_cache() %>% 
  sort()
#> [1] "j_2000@ultramail.io"      "josh_ch@megamail.kn"     
#> [3] "kim_3251323@ohhh.ai"      "lhe99@mailing-fun.com"   
#> [5] "roger_that@whalemail.net" "the_simone@a-q-w-o.net"  
#> [7] "the_will@graphymail.com" 

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.

graph %>%
  select_nodes_by_degree("outdeg > 1") %>%
  cache_node_attrs_ws("name") %>%
  get_cache() %>% 
  sort()
#> [1] "Josh"   "Kim"    "Louisa"

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). It is recommended that RStudio be used as the R IDE to take advantage of its rendering capabilities and the code-coloring support for Graphviz and mermaid diagrams.

You can install the development (v0.9.0) version of DiagrammeR from GitHub using the devtools package.

devtools::install_github('rich-iannone/DiagrammeR')

Or, get the v0.8.4 release from CRAN.

install.packages('DiagrammeR')