With the DiagrammeR package you can create, modify, analyze, and visualize network graph diagrams. You can either use Markdown-like text to describe and render a diagram, or, use a collection of functions to create graph objects. The output can be viewed in the RStudio Viewer, incorporated in R Markdown, or integrated in shiny web apps.

It's possible to make the above graph diagram using using Graphviz DOT code (as text within the DiagrammeR grViz() function) or through a combination of DiagrammeR functions strung together with the magrittr %>% pipe.

So, with Graphviz:

library(DiagrammeR)

grViz("
digraph DAG {
      
  # Intialization of graph attributes
  graph [overlap = true]
      
  # Initialization of node attributes
  node [shape = box,
        fontname = Helvetica,
        color = blue,
        type = box,
        fixedsize = true]
      
  # Initialization of edge attributes
  edge [color = green,
        rel = yields]
      
  # Node statements
  1; 2; 3; 4; 8; 9; 10; 11
      
  # Revision to node attributes
  node [shape = circle]
      
  # Node statements
  5; 6; 7
      
  # Edge statements
  1->5; 2->6; 3->9; 4->7; 5->8; 5->10; 7->11

  # Revision to edge attributes
  edge [color = red]

  # Edge statements
  1->8; 3->6; 3->11; 3->7; 5->9; 6->10
}
")

With magrittr and DiagrammeR's graph functions:

library(DiagrammeR)
library(magrittr)

graph <-
  create_graph() %>%
  set_graph_name("DAG") %>%
  set_global_graph_attrs("graph", "overlap", "true") %>%
  set_global_graph_attrs("graph", "fixedsize", "true") %>%
  set_global_graph_attrs("node", "color", "blue") %>%
  set_global_graph_attrs("node", "fontname", "Helvetica") %>%
  add_n_nodes(11) %>%
  select_nodes_by_id(1:4) %>% 
  set_node_attrs_ws("shape", "box") %>%
  set_node_attrs_ws("type", "box") %>%
  clear_selection %>%
  select_nodes_by_id(5:7) %>% 
  set_node_attrs_ws("shape", "circle") %>%
  set_node_attrs_ws("type", "circle") %>%
  clear_selection %>%
  select_nodes_by_id(8:11) %>% 
  set_node_attrs_ws("shape", "box") %>%
  set_node_attrs_ws("type", "box") %>%
  clear_selection %>%
  add_edge(1, 5) %>% 
  add_edge(2, 6) %>%
  add_edge(3, 9) %>% 
  add_edge(4, 7) %>%
  add_edge(5, 8) %>% 
  add_edge(5, 10) %>%
  add_edge(7, 11) %>% 
  select_edges %>%
  set_edge_attrs_ws("color", "green") %>%
  add_edge(1, 8) %>% 
  add_edge(3, 6) %>%
  add_edge(3, 11) %>% 
  add_edge(3, 7) %>%
  add_edge(5, 9) %>% 
  add_edge(6, 10) %>%
  select_edges("color", "^$") %>%
  set_edge_attrs_ws("color", "red") %>%
  clear_selection

render_graph(graph)

The graph functions allow you create graph objects, render those graphs, modify those graphs, get information from the graphs, create a series of graphs, perform scaling of attribute values with data values, and do other useful things.

This functionality makes it possible to generate a network graph with data available in tabular datasets. The general idea is to build specialized data frames that contain either node data and attributes (node data frames) and those data frames that contain edge data and edge attributes (edge data frames). These data frames are permitted to have node and edge attributes and also columns of other data. Because the attributes are always kept alongside the node and edge definitions (within the graph object itself), we can easily work with them and modify the values of the styling attributes and differentiate nodes and edges by size, color, shape, opacity, length, etc. Here is a listing of the available graph functions:

Network Graph Example

Let's create a network 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)
library(magrittr)

graph <-
  create_graph() %>%
  set_graph_name("software_projects") %>%
  set_global_graph_attrs(
    "graph", "output", "visNetwork") %>%
  add_nodes_from_table(
    system.file(
      "examples/contributors.csv",
      package = "DiagrammeR"),
    set_type = "person",
    label_col = "name") %>%
  add_nodes_from_table(
    system.file(
      "examples/projects.csv",
      package = "DiagrammeR"),
    set_type = "project",
    label_col = "project") %>%
  add_edges_from_table(
    system.file(
      "examples/projects_and_contributors.csv",
      package = "DiagrammeR"),
    from_col = "contributor_name",
    from_mapping = "name",
    to_col = "project_name",
    to_mapping = "project",
    rel_col = "contributor_role")

View the property graph.

graph %>% render_graph

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

Get the average age of all the contributors:

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

Get the total number of commits to all software projects:

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

Get the total number of commits from Josh as a maintainer and as a contributor:

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:

graph %>% 
  select_nodes("name", "Louisa") %>%
  trav_out_edge %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  get_cache %>% 
  sum
#> [1] 615

Get the names of people in graph above age 32:

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

Get the total number of commits from all people to the supercalc project:

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

Who committed the most to the supercalc project?

graph %>% 
  select_nodes("project", "supercalc") %>%
  trav_in_edge %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  trav_in_node %>%
  trav_in_edge("commits", max(get_cache(.))) %>%
  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?

graph %>% 
  select_nodes("project", "randomizer") %>%
  trav_in_edge %>%
  cache_edge_attrs_ws("commits", "numeric") %>%
  trav_in_node %>%
  trav_in_edge("commits", min(get_cache(.))) %>%
  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. Modify the graph to reflect this and view the updated graph:

graph %<>%
  add_edge(
    get_nodes(.,
      "name", "Kim"),
    get_nodes(.,
      "project", "stringbuildeR"),
    "contributor") %>%
  select_last_edge %>%
  set_edge_attrs_ws("commits", 15) %>%
  clear_selection

graph %>% render_graph

Get all email addresses to contributors (but not maintainers) of the randomizer and supercalc88 projects:

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 committer to the randomizer project has the highest number of followers?

graph %>% 
  select_nodes("project", "randomizer") %>%
  trav_in %>%
  cache_node_attrs_ws("follower_count", "numeric") %>%
  select_nodes("project", "randomizer") %>%
  trav_in("follower_count", max(get_cache(.))) %>%
  cache_node_attrs_ws("name") %>%
  get_cache
#> [1] "Kim"

Which people have committed to more than one project?

graph %>%
  select_nodes_by_degree("out", ">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 version of DiagrammeR from GitHub using the devtools package.

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

Or, get the v0.8.2 release from CRAN.

install.packages('DiagrammeR')