Merge pull request #64 from jhollway/develop

Migrated data to tidygraph-based structure with options

Author: jhollway

.gitignore

@@ -5,3 +5,4 @@ inst/doc
 .Ruserdata
 *.Rd
 data-raw/*
+docs/

DESCRIPTION

@@ -1,16 +1,17 @@
 Package: roctopus
-Title: Utility functions for multimode and multilevel network analysis
-Version: 0.2.6
+Title: Multimodal and multilevel network analysis
+Version: 0.3.0
 Description: This package assembles a number of utility functions for 
    loading, visualising, and analysing multimode, multiplex, and multilevel networks.
 URL: https://github.com/jhollway/roctopus
 BugReports: https://github.com/jhollway/roctopus/issues
 Depends: R (>= 3.4.0)
-License: MIT + file LICENSE
+License: MIT
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.1.1
 Imports: igraph,
+    tidygraph,
     geosphere,
     threejs,
     roxygen2,

NAMESPACE

@@ -1,13 +1,13 @@
 # Generated by roxygen2: do not edit by hand
 
 export(col_project)
-export(create_lattice)
+export(create_chain)
 export(create_match)
 export(create_nest)
-export(create_poles)
-export(create_random)
+export(create_silos)
 export(df_to_mat)
 export(netlm2)
+export(play_twomode)
 export(plot_2x2)
 export(plot_globalnet)
 export(plot_multilevel)

NEWS.md

@@ -1,3 +1,18 @@
+# roctopus 0.3.0
+
+2020-11-06
+
+## Data
+
+* Data creation updated for `{tidygraph}` defaults
+  * Renamed `create_lattice()` to `create_chain()` to avoid conflicts with `tidygraph::create_lattice()`
+  * Renamed `create_poles()` to `create_silos()`
+  * Renamed `create_random()` to `play_twomode()` to avoid conflicts with `tidygraph::play_bipartite()`
+  * Added export options for all `create_` and `play_` functions as `tbl_graph` (default), `igraph`, and base matrix
+  * Updated tests for new `create_` and `play_` function names
+* Packaged data updated for `{tidygraph}` defaults
+  * Renamed packaged data from book to `mpn_`
+
 # roctopus 0.2.6
 
 2020-08-18

R/create.R

@@ -1,102 +1,142 @@
-#' Two-mode lattice
+#' Two-mode chain
 #'
-#' Creates a two-mode lattice
-#' @param node1 Number of nodes in the first node set
-#' @param node2 Number of nodes in the second node set
+#' Creates a two-mode chain
+#' @param n1 Number of nodes in the first node set
+#' @param n2 Number of nodes in the second node set
+#' @param as What type of object to return.
+#' One of "matrix", "tbl_graph", "igraph".
+#' By default, creates a "tbl_graph" object.
 #' @details Will construct a bilateral lattice,
 #' with two ties for every second-mode node.
 #' @export
 #' @examples
 #' \dontrun{
-#' create_lattice(10, 12)
+#' create_chain(5,10) %>% ggraph() +
+#' geom_edge_fan(aes(alpha = stat(index)), show.legend = FALSE) +
+#' geom_node_point(aes(size = 5))
 #' }
-create_lattice <- function(node1, node2) {
-  mat <- matrix(0, node1, node2)
+create_chain <- function(n1, n2, 
+                         as = c("tbl_graph", "igraph", "matrix")) {
+  
+  mat <- matrix(0, n1, n2)
   out <- suppressWarnings(((row(mat) - col(mat)) == 0 |
-    (row(mat) - col(mat)) == (-seq.int(0, node2 - 1, node1)[-1]) |
+    (row(mat) - col(mat)) == (-seq.int(0, n2 - 1, n1)[-1]) |
     (row(mat) - col(mat)) == -1 |
-    (row(mat) - col(mat)) == -seq.int(1 + node1, node2 - 1, node1) |
+    (row(mat) - col(mat)) == -seq.int(1 + n1, n2 - 1, n1) |
     (row(mat) - col(mat)) == nrow(mat) - 1) * 1)
+  
+  as <- match.arg(as)
+  if(as == "tbl_graph") out <- tidygraph::as_tbl_graph(out)
+  if(as == "igraph") out <- igraph::graph_from_incidence_matrix(out)
   out
 }
 
-#' Two-mode random graph
+#' Matched two-mode graph
 #'
-#' Creates a random two-mode network
-#' @param node1 Number of nodes in the first node set
-#' @param node2 Number of nodes in the second node set
-#' @param density Number of edges in the network over the number of edges possible
+#' Creates a matched two-mode network
+#' @param n1 Number of nodes in the first node set
+#' @param n2 Number of nodes in the second node set
 #' @details Will construct an affiliation matrix,
-#' with a random probability of a tie.
+#' with by default both n1 and n2 matched.
+#' TODO: Incorporate into create_chain (chordal_ring of certain breadth w).
 #' @export
 #' @examples
 #' \dontrun{
-#' create_random(10, 12, 0.25)
+#' create_match(10, 12)
 #' }
-create_random <- function(node1, node2, density) {
-  mat <- matrix(rbinom(node1 * node2, 1, density), node1, node2)
+create_match <- function(n1, n2,
+                         as = c("tbl_graph", "igraph", "matrix")) {
+  mat <- matrix(0, n1, n2)
+  mat <- matrix(
+    ((row(mat) - col(mat)) %in% unique(-seq(0, n2 - 1, n1), 
+                                       seq(0, n1 - 1, n2))) * 1,
+    n1, n2
+  )
+
+  as <- match.arg(as)
+  if(as == "tbl_graph") mat <- tidygraph::as_tbl_graph(mat)
+  if(as == "igraph") mat <- igraph::graph_from_incidence_matrix(mat)
   mat
 }
 
-#' Two-component two-mode graph
+#' Two-mode random graph
 #'
-#' Creates a two-component two-mode network
-#' @param node1 Number of nodes in the first node set
-#' @param node2 Number of nodes in the second node set
+#' Creates a random two-mode network
+#' @param n1 Number of nodes in the first node set
+#' @param n2 Number of nodes in the second node set
+#' @param p Number of edges in the network over the number of edges possible
+#' @param m Number of edges in the network
+#' @param as What type of object to return.
+#' One of "matrix", "tbl_graph", "igraph".
+#' By default, creates a "tbl_graph" object.
 #' @details Will construct an affiliation matrix,
-#' with full component diagonal.
+#' with a random probability of a tie.
 #' @export
 #' @examples
 #' \dontrun{
-#' create_poles(10, 12)
+#' play_twomode(10, 12, 0.25) %>% ggraph() +
+#' geom_edge_fan(aes(alpha = stat(index)), show.legend = FALSE) +
+#' geom_node_point(aes(size = 5))
 #' }
-create_poles <- function(node1, node2) {
-  mat <- matrix(0, node1, node2)
-  mat[1:round(node1 / 2), 1:round(node2 / 2)] <- 1
-  mat[rowSums(mat) < 1, colSums(mat) < 1] <- 1
-  mat
+play_twomode <- function(n1, n2, p, m, directed = TRUE, mode = "out",
+                           as = c("tbl_graph", "igraph", "matrix")) {
+  
+  g <- tidygraph::play_bipartite(n1, n2, p, m, directed, mode)
+
+  as <- match.arg(as)
+  if(as == "igraph" | as == "matrix") g <- as.igraph(g)
+  if(as == "matrix") g <- as_adjacency_matrix(g)
+  g
 }
 
-#' Matched two-mode graph
+#' Two-component two-mode graph
 #'
-#' Creates a matched two-mode network
-#' @param node1 Number of nodes in the first node set
-#' @param node2 Number of nodes in the second node set
+#' Creates a two-component two-mode network
+#' @param n1 Number of nodes in the first node set
+#' @param n2 Number of nodes in the second node set
 #' @details Will construct an affiliation matrix,
-#' with by default both node1 and node2 matched.
-#' TODO: Make node set matching optional.
+#' with full component diagonal.
+#' TODO: Allow specfication of how many silos/components to create
 #' @export
 #' @examples
 #' \dontrun{
-#' create_match(10, 12)
+#' create_silos(10, 12)
 #' }
-create_match <- function(node1, node2) {
-  mat <- matrix(0, node1, node2)
-  mat <- matrix(
-    ((row(mat) - col(mat)) %in% unique(-seq(0, node2 - 1, node1), 
-                                       seq(0, node1 - 1, node2))) * 1,
-    node1, node2
-  )
+create_silos <- function(n1, n2,
+                         as = c("tbl_graph", "igraph", "matrix")) {
+  mat <- matrix(0, n1, n2)
+  mat[1:round(n1 / 2), 1:round(n2 / 2)] <- 1
+  mat[rowSums(mat) < 1, colSums(mat) < 1] <- 1
+
+  as <- match.arg(as)
+  if(as == "tbl_graph") mat <- tidygraph::as_tbl_graph(mat)
+  if(as == "igraph") mat <- igraph::graph_from_incidence_matrix(mat)
   mat
 }
 
 #' Nested two-mode graph
 #'
 #' Creates a nested two-mode network
-#' @param node1 Number of nodes in the first node set
-#' @param node2 Number of nodes in the second node set
+#' @param n1 Number of nodes in the first node set
+#' @param n2 Number of nodes in the second node set
 #' @details Will construct an affiliation matrix,
-#' with decreasing fill across node2.
-#' TODO: Make node set matching optional.
+#' with decreasing fill across n2.
 #' @export
 #' @examples
 #' \dontrun{
 #' create_nest(10, 12)
 #' }
-create_nest <- function(node1, node2) {
-  mat <- matrix(0, node1, node2)
-  mat[(row(mat) - col(mat)) >= 0] <- 1
-  mat
+create_nest <- function(n1, n2, 
+                        as = c("tbl_graph", "igraph", "matrix")) {
+  
+  as <- match.arg(as)
+  
+  out <- matrix(0, n1, n2)
+  out[(row(out) - col(out)) >= 0] <- 1
+
+  if(as == "tbl_graph") out <- tidygraph::as_tbl_graph(out)
+  if(as == "igraph") out <- igraph::graph_from_incidence_matrix(out)
+  out
 }
 
 # mat.dist <- matrix(0,5,3)

R/data_bristol.R

@@ -11,4 +11,4 @@
 #'   \item{3_}{17 Major Protest and Civic Events in Bristol, 1990-2002}
 #' }
 #' @source Knoke, Christopoulos, Diani, and Hollway. 2020. Multimodal Political Networks. Cambridge University Press: Cambridge.
-"bristol"
+"mpn_bristol"

R/data_evs.R

@@ -8,8 +8,8 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name evs
-#' @usage data(IT_1990)
+#' @name mpn_evs
+#' @usage data(mpn_IT_1990)
 #' @format Matrices with 14 columns:
 #' \describe{
 #'   \item{Welfare}{1 if individual associated}
@@ -28,15 +28,15 @@
 #'   \item{Health}{1 if individual associated}
 #' }
 #' @source Knoke, Christopoulos, Diani, and Hollway. 2020. Multimodal Political Networks. Cambridge University Press: Cambridge.
-"IT_1990"
+"mpn_IT_1990"
 
-#' @rdname evs
-#' @usage data(IT_2008)
-"IT_2008"
+#' @rdname mpn_evs
+#' @usage data(mpn_IT_2008)
+"mpn_IT_2008"
 
-#' @rdname evs
-#' @usage data(DE_1990)
-"DE_1990"
+#' @rdname mpn_evs
+#' @usage data(mpn_DE_1990)
+"mpn_DE_1990"
 
 #' @rdname evs
 #' @usage data(DE_2008)

R/data_mexicanpower.R

@@ -11,12 +11,12 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name mexicanpower
-#' @usage data(mexicanpower)
+#' @name mpn_mexicanpower
+#' @usage data(mpn_mexicanpower)
 #' @format Matrix with 11 rows/columns
 #' @source Knoke, David. 1990. \emph{Political Networks}. 
 #' 
 #' Knoke, Christopoulos, Diani, and Hollway. 2020. 
 #' \emph{Multimodal Political Networks: The Structural Perspective}. 
 #' Cambridge University Press: Cambridge.
-"mexicanpower"
+"mpn_mexicanpower"

R/data_opensecrets.R

@@ -9,8 +9,8 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name opensecrets
-#' @usage data(opensecrets)
+#' @name mpn_opensecrets
+#' @usage data(mpn_opensecrets)
 #' @format Matrix with 26 rows and 6+6 columns
 #' @source Domhoff, G William. 2016. 
 #' “Who Rules America? Power Elite Database.” http://www2.ucsc.edu/whorulesamerica/power_elite/.
@@ -20,4 +20,4 @@
 #' Knoke, Christopoulos, Diani, and Hollway. 2020. 
 #' \emph{Multimodal Political Networks: The Structural Perspective}. 
 #' Cambridge University Press: Cambridge.
-"opensecrets"
+"mpn_opensecrets"

R/data_powerelite.R

@@ -9,13 +9,13 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name powerelite
-#' @usage data(powerelite)
+#' @name mpn_powerelite
+#' @usage data(mpn_powerelite)
 #' @format Matrix with 14 rows and 20 columns
 #' @source Domhoff, G William. 2016. 
 #' “Who Rules America? Power Elite Database.” http://www2.ucsc.edu/whorulesamerica/power_elite/.
 #' 
 #' Knoke, Christopoulos, Diani, and Hollway. 2020. 
 #' \emph{Multimodal Political Networks: The Structural Perspective}. 
 #' Cambridge University Press: Cambridge.
-"powerelite"
+"mpn_powerelite"

R/data_ryanair.R

@@ -9,12 +9,12 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name ryanair
-#' @usage data(ryanair)
+#' @name mpn_ryanair
+#' @usage data(mpn_ryanair)
 #' @format Matrix with 20 rows/columns
 #' @source Christopoulos, Dimitrios C. 2006. 
 #' “Relational Attributes of Political Entrepreneurs: a Network Perspective.” 
 #' \emph{Journal of European Public Policy} 13 (5): 757–78.
 #' 
 #' Knoke, Christopoulos, Diani, and Hollway. 2020. \emph{Multimodal Political Networks}. Cambridge University Press: Cambridge.
-"ryanair"
+"mpn_ryanair"

R/data_senate112.R

@@ -19,20 +19,20 @@
 #'
 #' @docType data
 #' @keywords datasets
-#' @name senate112
-#' @usage data(DemSxP)
+#' @name mpn_senate112
+#' @usage data(mpn_DemSxP)
 #' @format Matrix of 51 rows (Senators) and 63 columns (PACS)
 #' @source Knoke, Christopoulos, Diani, and Hollway. 2020. 
 #' \emph{Multimodal Political Networks}. 
 #' Cambridge University Press: Cambridge.
-"DemSxP"
+"mpn_DemSxP"
 
-#' @rdname senate112
-#' @usage data(RepSxP)
+#' @rdname mpn_senate112
+#' @usage data(mpn_RepSxP)
 #' @format Matrix of 62 rows (Senators) and 72 columns (PACS)
-"RepSxP"
+"mpn_RepSxP"
 
-#' @rdname senate112
-#' @usage data(OverSxP)
+#' @rdname mpn_senate112
+#' @usage data(mpn_OverSxP)
 #' @format Matrix of 20 rows (Senators) and 32 columns (PACS)
-"OverSxP"
+"mpn_OverSxP"