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.
The graph above can be created with this combination of DiagrammeR functions:
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)
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.
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.
a_graph <- create_graph() %>% add_node() %>% add_node() %>% add_edge(from = 1, to = 2)
We can take away an edge by using
b_graph <- a_graph %>% delete_edge(from = 1, to = 2)
We can add a node to the graph while, at the same time, defining edges to or from existing nodes in the graph.
c_graph <- b_graph %>% add_node(from = 1, to = 2)
Viewing the graph object in the console will provide some basic information about the graph and some pointers on where to get additional information.
c_graph #> DiagrammeR Graph // 3 nodes / 2 edges #> -- directed / connected / DAG / simple #> #> NODES / type: <unused> / label: <unused> info: `get_node_df()` #> -- no additional node attributes #> EDGES / rel: <unused> info: `get_edge_df()` #> -- no additional edge attributes #> SELECTION / <none> #> CACHE / <none> #> GLOBAL ATTRS / 17 are set info: `get_global_graph_attr_info()` #> GRAPH ACTIONS / <none> #> GRAPH LOG / <3 actions> -> add_edge() -> delete_edge() -> add_node()
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.,
shape), grouping labels (e.g.,
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
edge_data. Using these, we can call the namesake
helper functions (
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
edge_aes( and pressing the TAB key.
Here is an example of adding a node while setting its
fontcolor node aesthetic attributes, and, adding an
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.
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 ) )
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
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
attribute and modify its
color node aesthetic attribute:
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
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:
There are quite a few functions that allow you to select nodes (e.g.,
and edges (e.g.,
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
but we can also mutate attributes of selected nodes
mutate_node_attrs_ws()), delete selected nodes (
and even create a subgraph with that selection (
Selections of nodes or edges can be inverted (where non-selected nodes
or edges become the active selection) with
nodes/edges can be removed from the active selection with the
deselect_edges(), and any selection can and should
be eventually cleared with
We can create a graph object and add graph primitives such as paths, cycles, and trees to it.
f_graph <- create_graph() %>% add_path(n = 3) %>% add_cycle(n = 4) %>% add_balanced_tree(k = 2, h = 2)
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).
g_graph <- create_graph() %>% add_gnm_graph( n = 15, m = 20, set_seed = 23 )
The undirected version of this graph is can be made using:
h_graph <- create_graph(directed = FALSE) %>% add_gnm_graph( n = 15, m = 20, set_seed = 23 )
We can view the graph using
render_graph(). There are several layouts
to choose from as well (e.g.,
render_graph(h_graph, layout = "fr")
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
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.
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
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:
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 the graph object i_graph_1 <- create_graph() # It will start off as empty i_graph_1 %>% is_graph_empty() #>  TRUE
Add nodes from a table with
# 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
# View the graph's internal node data frame i_graph_2 %>% get_node_df() #> id type label id_external #> 1 1 <NA> A 1 #> 2 2 <NA> B 2 #> 3 3 <NA> C 3 #> 4 4 <NA> D 4 #> 5 5 <NA> E 5 #> 6 6 <NA> F 6 #> 7 7 <NA> G 7 #> 8 8 <NA> H 8 #> 9 9 <NA> I 9 #> 10 10 <NA> J 10
The graph now has 10 nodes (no edges yet). Each node was automatically
assigned an auto-incrementing
id. The incoming
id was also
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
from_to_map argument expects a node attribute column that the
to columns will map to. In this case it’s
id_external. Note that
id also matches perfectly in this mapping, there may be cases
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 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
# View the edge data frame i_graph_3 %>% get_edge_df() #> id from to rel #> 1 1 1 2 <NA> #> 2 2 1 3 <NA> #> 3 3 1 4 <NA> #> 4 4 1 9 <NA> #> 5 5 2 8 <NA> #> 6 6 2 7 <NA> #> 7 7 2 1 <NA> #> 8 8 2 10 <NA> #> 9 9 3 1 <NA> #> 10 10 3 6 <NA> #> 11 11 3 8 <NA> #> 12 12 4 1 <NA> #> 13 13 5 7 <NA> #> 14 14 6 2 <NA> #> 15 15 6 9 <NA> #> 16 16 8 1 <NA> #> 17 17 9 3 <NA> #> 18 18 9 10 <NA> #> 19 19 10 1 <NA>
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:
i_graph_3 #> DiagrammeR Graph // 10 nodes / 19 edges #> -- directed / connected / simple #> #> NODES / type: <unused> / label: 10 vals - complete & unique #> -- 1 additional node attribute (id_external) #> EDGES / rel: <unused> info: `get_edge_df()` #> -- no additional edge attributes #> SELECTION / <none> #> CACHE / <none> #> GLOBAL ATTRS / 17 are set info: `get_global_graph_attr_info()` #> GRAPH ACTIONS / <none> #> GRAPH LOG / <1 action> -> add_nodes_from_table() -> add_edges_from_table() -> ()
There are two other similar datasets included in the package
edge_list_2). These contain extended attribute
data. Let’s have a quick look at their column names:
colnames(node_list_2) #>  "id" "label" "type" "value_1" "value_2"
colnames(edge_list_2) #>  "from" "to" "rel" "value_1" "value_2"
Because we have unique labels in the
label column, and categorical
labels in the
rel columns, we can create a property graph
from this data. Like before, we can incorporate the two tables as a
time, we’ll remove the auto-generated
id_external node attribute with
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_2) are present for
both the nodes and the edges:
j_graph #> DiagrammeR Graph // 10 nodes / 19 edges #> -- directed / connected / property graph / simple #> #> NODES / type: 2 vals - complete / label: 10 vals - complete & unique #> -- 2 additional node attributes (value_1, value_2) #> EDGES / rel: 3 vals - complete info: `get_edge_df()` #> -- 2 additional edge attributes (value_1, value_2) #> SELECTION / <none> #> CACHE / <none> #> GLOBAL ATTRS / 17 are set info: `get_global_graph_attr_info()` #> GRAPH ACTIONS / <none> #> GRAPH LOG / <3 actions> -> add_edges_from_table() -> () -> drop_node_attrs()
Now, because we have node/edge metadata (categorical labels and
numerical data in
value_2 for both nodes and edges), we
can do some interesting things with the graph. First, let’s do some
mutate_edge_attrs() and get
the sums of
value_3 (for both the nodes and
the edges). Then, let’s color the nodes and edges
value_3 is greater than
red otherwise). Finally, let’s display
the values of
value_3 for the nodes when rendering the graph diagram.
Here we go!
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)
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
starred_count are specific to
person nodes while the
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.
# 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(graph, layout = "kk")
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
project). Each node of that type has non-
attribute, so, get that attribute as a vector with
and then calculate the mean with R’s
graph %>% select_nodes(conditions = type == "person") %>% get_node_attrs_ws(node_attr = age) %>% mean() #>  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). After that,
get a numeric vector of
commits values and then get its
commits to all projects).
graph %>% select_edges() %>% get_edge_attrs_ws(edge_attr = commits) %>% sum() #>  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(conditions = name == "Josh"). In this graph, we know that
all people have an edge to a project and that edge can be of the
rel) type of
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
and then calculate the
sum(). This is the total number of commits.
graph %>% select_nodes(conditions = name == "Josh") %>% trav_out_edge() %>% get_edge_attrs_ws(edge_attr = commits) %>% sum() #>  227
Get the total number of commits from Louisa, just from the maintainer
role though. In this case we’ll supply a condition in
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
get_edge_attrs_ws() (a good practice because we may not know the
vector length, especially in big graphs).
graph %>% select_nodes(conditions = name == "Louisa") %>% trav_out_edge(conditions = rel == "maintainer") %>% get_edge_attrs_ws(edge_attr = commits) %>% sum() #>  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(conditions = type == "person"). Then, follow up with
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.
graph %>% select_nodes(conditions = type == "person") %>% select_nodes(conditions = age > 32, set_op = "intersect") %>% get_node_attrs_ws(node_attr = name) %>% sort() %>% unname() #>  "Jack" "Jon" "Kim" "Roger" "Sheryl"
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
graph %>% select_nodes(conditions = project == "supercalc") %>% trav_in_edge() %>% get_edge_attrs_ws(edge_attr = commits) %>% sum() #>  1676
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
relies on node IDs in
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.
rel value in
add_edge() was set to
contributor – in a property
graph we always have values set for all node
type and edge
attributes. We will set another attribute for this edge (
first selecting the edge (it was the last edge made, so we can use
select_last_edges_created()), then, use
provide the attribute/value pair. Finally, clear the active selections
clear_selection(). The graph is now changed, have a look.
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()
render_graph(graph, layout = "kk")
Get all email addresses for contributors (but not maintainers) of the
randomizer and supercalc88 projects. With
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
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() #>  "firstname.lastname@example.org" "email@example.com" #>  "firstname.lastname@example.org" "email@example.com" #>  "firstname.lastname@example.org" "email@example.com" #>  "firstname.lastname@example.org"
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
attribute values from that node selection, we can provide a sorted
character vector of names.
graph %>% select_nodes_by_degree(expressions = "outdeg > 1") %>% get_node_attrs_ws(node_attr = name) %>% sort() %>% unname() #>  "Josh" "Kim" "Louisa"
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).
You can install the development version of DiagrammeR from GitHub using the devtools package.
Or, get it from CRAN.
If you encounter a bug, have usage questions, or want to share ideas to make this package better, feel free to file an issue.
Code of Conduct
Please note that the DiagrammeR project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
MIT © Richard Iannone