random nw generation function and updates to github documentation

NAMESPACE

@@ -2,6 +2,8 @@
 
 export(aggregate_cld)
 export(count_motifs)
+export(generate_random_ml_adjmat)
+export(generate_random_ml_net)
 export(get_entry_from_mapping)
 export(get_shortest_inv_distance)
 export(get_weighted_sum_of_paths)

R/generate_baseline_ml_graph.R

@@ -1,5 +1,15 @@
 
-generate_random_ml_graph <- function(n_social_nodes,
+#' Generate a random multi-level adjacency matrix
+#'
+#' @param n_social_nodes the number of social nodes in the network
+#' @param n_non_social_nodes the number of "non-social" nodes in the network
+#' @param density density of total network, defaults to random
+#'
+#' @return labeled adjacency matrix
+#' @export
+#'
+#' @examples
+generate_random_ml_adjmat <- function(n_social_nodes,
                                      n_non_social_nodes,
                                      density = NULL){
   load_python_sma()
@@ -11,3 +21,38 @@ generate_random_ml_graph <- function(n_social_nodes,
   adj_mat <- reticulate::py_to_r(adj_mat)
   adj_mat
 }
+
+#' Generate a random multi-level network (statnet network object)
+#'
+#' @param n_social_nodes the number of social nodes in the network
+#' @param n_non_social_nodes the number of "non-social" nodes in the network
+#' @param density density of total network, defaults to random
+#' @param to_py should the object be returned as a sma compatible python object
+#'
+#' @return either a statnet network object with an attribute "sesType" specifying node type or an equivalent python compatible object
+#' @export
+#'
+#' @examples
+generate_random_ml_net <- function(n_social_nodes,
+                                   n_non_social_nodes,
+                                   density = NULL,
+                                   to_py = FALSE){
+  adjmat <- generate_random_ml_adjmat(n_social_nodes = n_social_nodes,
+                                      n_non_social_nodes = n_non_social_nodes,
+                                      density = density)
+  net <- network::network(adjmat)
+  network::set.vertex.attribute(net, "sesType", ifelse(grepl(pattern = "non-social",
+                                                             x = network::get.vertex.attribute(net,
+                                                                                               "vertex.names")),
+                                                       1,0))
+  if(to_py == TRUE){
+    pynet <- toPyGraph(net,typeAttr = "sesType")
+    return(pynet)
+  }
+  else{
+    return(net)
+  }
+}
+
+# first idea for baseline distribution, incredibly slow
+# hist(unlist(lapply(netlist[1:500], integrateR::count_motifs, "sesType", 3, "II.A", "social")))

README.Rmd

@@ -32,16 +32,17 @@ knitr::opts_chunk$set(
 
 This package provides tools to analyze and visualize multi-level networks in general, and so-called integrated networks specifically.
 
-Multi-level networks combine multiple networks in one, e.g. social-ecological networks.
+Multi-level networks combine multiple networks in one representation, e.g. social-ecological networks.
 Integrated networks are specific networks combining causal loop diagrams (CLDs) and social networks.
 
 The package has three main areas: Visualization, Aggregation and Analysis.
 
   - Visualization: The package visualizes CLDs and two-level networks.
 
-  - Aggregation: The package implements ways to aggregate CLDs to use as inputs in multi-level networks by way of finding common causes of parts of a CLD.
+  - Aggregation: The package implements ways to aggregate CLDs by creating a network between a subset of nodes in a CLD based on how these nodes are related via other parts (common causes) of the network.
 
-  - Analysis: The package is in many parts a R wrapper for the excellent [sesmotifanalyser](https://gitlab.com/t.seppelt/sesmotifanalyser) Python framework written by Tim Seppelt to count multi-level network motifs, compare them to a baseline and much more. It further identifies and visualizes functional gaps in integrated networks and virtuous and vicious cycles in CLDs.
+  - Analysis: The package is in many parts a R wrapper for the excellent [sesmotifanalyser](https://gitlab.com/t.seppelt/sesmotifanalyser) Python framework written by Tim Seppelt to count multi-level network motifs, compare them to a baseline and much more. Only small part of sesmotifanalyser are yet wrapped, so consult the python framework for full functionality.
+  IntegrateR further identifies and visualizes functional gaps in integrated networks and virtuous and vicious cycles in CLDs.
 
 
 ## Installation
@@ -76,7 +77,7 @@ cld_viz$plot
 
 <img src="man/figures/cld_example.svg">
 
-### Visualize a multi-level network.
+### Visualize a multi-level network
 
 Visualizations exist for two-level networks at the moment. The following uses DiagrammeR and is based on a network model gathered in a Swiss wetland. Actors are red, activities in wetlands governance are blue. Relations between activities are based on causal interdependence:
 

README.md

@@ -26,7 +26,7 @@ integrateR
 This package provides tools to analyze and visualize multi-level
 networks in general, and so-called integrated networks specifically.
 
-Multi-level networks combine multiple networks in one,
+Multi-level networks combine multiple networks in one representation,
 e.g. social-ecological networks. Integrated networks are specific
 networks combining causal loop diagrams (CLDs) and social networks.
 
@@ -35,16 +35,19 @@ Analysis.
 
   - Visualization: The package visualizes CLDs and two-level networks.
 
-  - Aggregation: The package implements ways to aggregate CLDs to use as
-    inputs in multi-level networks by way of finding common causes of
-    parts of a CLD.
+  - Aggregation: The package implements ways to aggregate CLDs by
+    creating a network between a subset of nodes in a CLD based on how
+    these nodes are related via other parts (common causes) of the
+    network.
 
   - Analysis: The package is in many parts a R wrapper for the excellent
     [sesmotifanalyser](https://gitlab.com/t.seppelt/sesmotifanalyser)
     Python framework written by Tim Seppelt to count multi-level network
-    motifs, compare them to a baseline and much more. It further
-    identifies and visualizes functional gaps in integrated networks and
-    virtuous and vicious cycles in CLDs.
+    motifs, compare them to a baseline and much more. Only small part of
+    sesmotifanalyser are yet wrapped, so consult the python framework
+    for full functionality. IntegrateR further identifies and visualizes
+    functional gaps in integrated networks and virtuous and vicious
+    cycles in CLDs.
 
 ## Installation
 
@@ -80,7 +83,7 @@ cld_viz$plot
 
 <img src="man/figures/cld_example.svg">
 
-### Visualize a multi-level network.
+### Visualize a multi-level network
 
 Visualizations exist for two-level networks at the moment. The following
 uses DiagrammeR and is based on a network model gathered in a Swiss