v1.1.2

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: migraph
 Title: Multimodal Network Analysis and More
-Version: 1.1.1
-Date: 2023-10-11
+Version: 1.1.2
+Date: 2023-10-18
 Description: A set of tools for analysing multimodal networks.
    It includes functions for measuring 
    centrality, centralization, cohesion, closure, constraint and diversity,
@@ -12,7 +12,7 @@ Description: A set of tools for analysing multimodal networks.
    Built on the 'manynet' package, all functions operate with matrices, edge lists, 
    and 'igraph', 'network', and 'tidygraph' objects,
    and on one-mode, two-mode (bipartite), and sometimes three-mode networks.
-URL: https://github.com/snlab-ch/migraph
+URL: https://snlab-ch.github.io/migraph/
 BugReports: https://github.com/snlab-ch/migraph/issues
 License: MIT + file LICENSE
 Language: en-GB

NEWS.md

@@ -1,3 +1,17 @@
+# migraph 1.1.2
+
+2023-10-11
+
+## Package
+
+- Added more code annotations in 'tutorial4' (Community)
+- Elaborated 'tutorial5' (was named Equivalence, now Position)
+- Chunks in tutorials that are incremental now hidden upon extraction using `purl = FALSE` argument
+
+## Members
+
+- Improved printing of node_members and node_measures objects
+
 # migraph 1.1.1
 
 2023-10-11

R/class_measures.R

@@ -1,4 +1,5 @@
 make_node_measure <- function(out, .data) {
+  if(manynet::is_labelled(.data)) names(out) <- manynet::node_names(.data)
   class(out) <- c("node_measure", class(out))
   attr(out, "mode") <- manynet::node_mode(.data)
   out
@@ -154,6 +155,6 @@ print_tblvec <- function(y, names){
         print(body)
     cat(pillar::style_subtle(paste("# ... with",
                                    setup$extra_cols_total,
-                                   "more from this nodeset in the vector.")))
+                                   "more values from this nodeset not printed but in the vector.")))
       } else print(body)
 }

R/class_members.R

@@ -1,4 +1,5 @@
 make_node_member <- function(out, .data) {
+  if(manynet::is_labelled(.data)) names(out) <- manynet::node_names(.data)
   class(out) <- c("node_member", class(out))
   attr(out, "mode") <- manynet::node_mode(.data)
   out
@@ -24,13 +25,14 @@ print.node_member <- function(x, ...,
     }
   } else {
     for (i in names(table(x))) {
-      if (i == names(table(x))[1]) cat(i, "\n")
-      else cat("\n", i, "\n")
+      if (i == names(table(x))[1]) cat("Class ", i, ":", sep = "")
+      else cat("Class ", i, ":", sep = "")
       if (!is.null(names(x)))
         y <- paste(names(x[x == i]), collapse = ", ")
       else
         y <- paste(which(x == i), collapse = ", ")
-      cat("  ", y)
+      cat(" ", y)
+      if (i != names(table(x))[length(table(x))]) cat("\n")
     }
   }
 }

R/member_community.R

@@ -65,7 +65,6 @@ node_kernighanlin <- function(.data){
 
   # extract names of vertices in each group after swaps
   out <- ifelse(manynet::node_names(.data) %in% g1.newnames, 1, 2)
-  if(manynet::is_labelled(.data)) names(out) <- manynet::node_names(.data)
   make_node_member(out, .data)
 }
 

inst/tutorials/tutorial3/centrality.Rmd

@@ -26,24 +26,26 @@
 by renaming the nodal attribute "twomode_type" to just "type".
 Let's begin by graphing these datasets using `manynet::autographr()`.
 
-```{r coercion, exercise = TRUE}
+```{r coercion, exercise = TRUE, purl = FALSE}
 
 ```
 
-```{r coercion-hint-1}
+```{r coercion-hint-1, purl = FALSE}
 # Let's graph the one-mode version
 autographr(____)
 ```
 
-```{r coercion-hint-2}
+```{r coercion-hint-2, purl = FALSE}
 # Now, let's create a two-mode version 'ison_brandes2' and graph it.
 ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)
 autographr(____)
 ```
 
-```{r coercion-solution}
+```{r coercion-solution, purl = FALSE}
+# plot the one-mode version
 autographr(ison_brandes)
 ison_brandes2 <- ison_brandes %>% rename(type = twomode_type)
+# plot the two-mode version
 autographr(ison_brandes2)
 ```
 
@@ -52,17 +54,18 @@
 Luckily, `{manynet}` has a function for this.
 This makes plotting the network just a wee bit more accessible and interpretable:
 
-```{r addingnames, exercise = TRUE}
+```{r addingnames, exercise = TRUE, purl = FALSE}
 ison_brandes <- to_named(ison_brandes)
 ```
 
-```{r addingnames-hint-1}
+```{r addingnames-hint-1, purl = FALSE}
 # Now, let's graph using the object names: "ison_brandes"
 autographr(____)
 ```
 
 ```{r addingnames-solution}
 ison_brandes <- to_named(ison_brandes)
+# plot network with names
 autographr(ison_brandes)
 ```
 
@@ -74,36 +77,37 @@
 Let's start with calculating degree, as it is easy to calculate yourself.
 Just sum the rows or columns of the matrix!
 
-```{r degreesum, exercise = TRUE, exercise.setup = "addingnames"}
+```{r degreesum, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
-```{r degreesum-hint-1}
+```{r degreesum-hint-1, purl = FALSE}
 # We can calculate degree centrality like this:
 (mat <- as_matrix(ison_brandes))
 (degrees <- rowSums(mat))
 rowSums(mat) == colSums(mat)
 ```
 
-```{r degreesum-hint-2}
+```{r degreesum-hint-2, purl = FALSE}
 # Or by using a built in command in migraph like this:
 node_degree(ison_brandes, normalized = FALSE)
 ```
 
 ```{r degreesum-solution}
+# manually calculate degree centrality
 mat <- as_matrix(ison_brandes)
 degrees <- rowSums(mat)
 rowSums(mat) == colSums(mat)
 # You can also just use a built in command in migraph though:
 node_degree(ison_brandes, normalized = FALSE)
 ```
 
-```{r degreesum-Q, echo=FALSE}
+```{r degreesum-Q, echo=FALSE, purl = FALSE}
 question("Are the row sums the same as the column sums?",
   answer("Yes",
          correct = TRUE,
          message = "That's right, that's because this is an undirected network."),
   answer("No"),
   allow_retry = FALSE
 )
 ```
@@ -112,11 +116,12 @@
 `{migraph}` offers a way to get a pretty good first look at this distribution,
 though there are more elaborate ways to do this in base and grid graphics.
 
-```{r distrib, exercise = TRUE, exercise.setup = "addingnames"}
+```{r distrib, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
 ```{r distrib-solution}
+# distribution of degree centrality scores of nodes
 plot(node_degree(ison_brandes))
 ```
 
@@ -131,24 +136,24 @@
 betweenness, closeness, and eigenvector centralities for each node in the network.
 Let's collect the vectors of these centralities for the `ison_brandes` dataset:
 
-```{r micent, exercise = TRUE, exercise.setup = "addingnames"}
+```{r micent, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
-```{r micent-hint-1}
+```{r micent-hint-1, purl = FALSE}
 # Use the node_betweenness() function to calculate the
 # betweenness centralities of nodes in a network
 node_betweenness(ison_brandes)
 ```
 
-```{r micent-hint-2}
+```{r micent-hint-2, purl = FALSE}
 # Use the node_closeness() function to calculate the 
 # closeness centrality of nodes in a network
 node_closeness(ison_brandes)
 ```
 
-```{r micent-hint-3}
+```{r micent-hint-3, purl = FALSE}
 # Use the node_eigenvector() function to calculate 
 # the eigenvector centrality of nodes in a network
 node_eigenvector(ison_brandes)
 ```
@@ -186,17 +191,18 @@
 By passing this attribute to the `autographr()` argument "node_color"
 we can highlight which node or nodes hold the maximum score in red.
 
-```{r ggid, exercise = TRUE, exercise.setup = "addingnames"}
+```{r ggid, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
 ```{r ggid-solution}
+# plot the network, highlighting the node with the highest centrality score with a different colour
 ison_brandes %>%
   add_node_attribute("color", node_is_max(node_degree(ison_brandes))) %>%
   autographr(node_color = "color")
 
 ison_brandes %>%
   add_node_attribute("color", node_is_max(node_betweenness(ison_brandes))) %>%
   autographr(node_color = "color")
 
 ison_brandes %>%
@@ -211,7 +217,7 @@
 How neat! Try it with the two-mode version.
 What can you see?
 
-```{r ggid_twomode, exercise = TRUE}
+```{r ggid_twomode, exercise = TRUE, purl = FALSE}
 # Instead of "ison_brandes", use "ison_brandes2"
 
 ```
@@ -234,7 +240,7 @@
   autographr(node_color = "color")
 ```
 
-```{r brandes2quiz}
+```{r brandes2quiz, purl = FALSE}
 question("Select all that are true for the two-mode Brandes network.",
          answer("Only one node is selected in each plot."),
          answer("The maximum degree square has a higher degree than the maximum degree circle(s).",
@@ -251,7 +257,7 @@
 but in the level of the whole network, 
 so the syntax replaces `node_` with `network_`:
 
-```{r centzn, exercise = TRUE, exercise.setup = "addingnames"}
+```{r centzn, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
@@ -277,7 +283,7 @@
 This can be a little tricky with gg-based plots,
 but fortunately the `{patchwork}` package is here to help.
 
-```{r multiplot, exercise = TRUE, exercise.setup = "addingnames"}
+```{r multiplot, exercise = TRUE, exercise.setup = "addingnames", purl = FALSE}
 
 ```
 
@@ -304,7 +310,7 @@
 ```
 
 
-```{r centzdq}
+```{r centzdq, purl = FALSE}
 question("How centralized is the ison_brandes network? Select all that apply.",
          answer("It is more degree centralised than betweenness centralised.",
                 message = "Degree centralisation is at 0.18 for this network whereas betweenness centralisation is at 0.32. In other words, the network is better characterised as having 1 or 2 nodes lying on the shortest paths between others than one where 1 or 2 nodes have many more ties than the others."),
@@ -317,7 +323,7 @@
          allow_retry = TRUE)
 ```
 
-```{r centvcent, echo=FALSE}
+```{r centvcent, echo=FALSE, purl = FALSE}
 question("What is the difference between centrality and centralisation according to the literature?",
   answer("Centrality is for nodes and centralisation is for networks",
          correct = TRUE),