version 0.2.3

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: manynet
 Title: Many Ways to Make, Manipulate, and Map Myriad Networks
-Version: 0.2.1
-Date: 2023-08-11
+Version: 0.2.3
+Date: 2023-09-17
 Description: A set of tools for making, manipulating, and mapping many different types of networks.
    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.
    The package includes functions for importing and exporting, creating and generating networks,
    molding and manipulating networks and node and tie attributes,
    and describing and visualizing networks with sensible defaults.
-URL: https://github.com/snlab-ch/manynet
+URL: https://snlab-ch.github.io/manynet/
 BugReports: https://github.com/snlab-ch/manynet/issues
 Depends: R (>= 3.6.0)
 License: MIT + file LICENSE
@@ -34,10 +34,10 @@ Authors@R:
              comment = c("IHEID", ORCID = "0000-0003-3420-6085"))
     )
 NeedsCompilation: no
-Packaged: 2023-08-11 13:07:02 UTC; hollway
+Packaged: 2023-09-17 13:57:21 UTC; hollway
 Author: James Hollway [cre, aut, ctb] (IHEID,
     <https://orcid.org/0000-0002-8361-9647>),
   Henrique Sposito [ctb] (IHEID, <https://orcid.org/0000-0003-3420-6085>)
 Maintainer: James Hollway <james.hollway@graduateinstitute.ch>
 Repository: CRAN
-Date/Publication: 2023-08-11 13:40:06 UTC
+Date/Publication: 2023-09-17 17:10:02 UTC

MD5

@@ -1,12 +1,12 @@
-5682971c475d8f91198989f247d133f8 *DESCRIPTION
+e3190dd093af516c23ca4fb0a72a4b11 *DESCRIPTION
 361d71c6379f4ddf09ca9c8540b8fea1 *LICENSE
 3ab8ea40ca6fbb3728177c8ca0f1255c *NAMESPACE
-53e5898f3d3fc9f59be9822e0bdd9bda *NEWS.md
-65446f8db7ac66243fe0e2bd445d6076 *R/data_ison.R
+12671caf0c3008c3fc369a17afcd63c0 *NEWS.md
+6bc2af6eaf65d3416ba0e39510b015d8 *R/data_ison.R
 9022cd74ad3f3e6b31705c7555e23567 *R/make_as.R
 280195557bbe3c28bd67cde1f5423490 *R/make_create.R
 a59dcc247a1aa29833f174ec2529cb10 *R/make_generate.R
-50f2dd264d63cab1914f5fbaaecdf5fc *R/make_read.R
+ae917bc4b4e693789611337e6c17f0b3 *R/make_read.R
 eaf9a4357387f0e949880e03221472dd *R/manip_add.R
 53c02b0642c4c2bd507849f3a6f67eb3 *R/manip_from.R
 e9aad6f23cbc1657f3281237185c8908 *R/manip_miss.R
@@ -16,7 +16,7 @@ c86f435854b50d81a7d318ba60ca7897 *R/manip_reformat.R
 9fe01dbfe1df9c4b0e060de9713c6e2a *R/manip_transform.R
 e6904ca7fa87c64684580f5b36e5896d *R/manynet-utils.R
 5a485b8100c0667e5fdd249c345a4bff *R/map_attributes.R
-f672ad16ae8ecdf382b6c2bfdcb2f065 *R/map_autographr.R
+51cb43b3f9f00e069bbe2eecb61193f8 *R/map_autographr.R
 262ac6411f46c6a8eea206024cfc863c *R/map_is.R
 27ed31b66230cc7bdea828a439f57b6a *R/map_layout_partition.R
 97a8d4d39163df53cbafb9a63b3d3610 *R/map_palettes.R
@@ -27,21 +27,22 @@ f672ad16ae8ecdf382b6c2bfdcb2f065 *R/map_autographr.R
 33162022353ceac96f9282149c710d90 *R/reexports_classes.R
 5b6bdf57abcbb9776d5b05ae9d0c1192 *R/reexports_ggplot2.R
 c32b17bf67aaa82f9497832d48ffc8b1 *R/sysdata.rda
-20c5d0ac9aba30fa26730845f310eb3a *README.md
-439bf689fa27cf9affd0335332142165 *build/partial.rdb
+9b86447ce1e63febc51e5c86eed764ce *README.md
+7080893e02c49cd296d4424b9be55069 *build/partial.rdb
 165a53aa74d16bf17d9476a4cdca3567 *data/ison_adolescents.rda
 e5b3237a16d54a8fc774b8d8128fed39 *data/ison_algebra.rda
 b20354b3d6f9fb3b4e65c511ed5db78a *data/ison_brandes.rda
 41a221361a130d269ba1a91788ab2922 *data/ison_karateka.rda
 8250e638e7ae6c2299e8f831eab0a82e *data/ison_konigsberg.rda
 ffe9cca1f76c1fe8bc16265b57da49f7 *data/ison_laterals.rda
+505933fd1de5c9d14040c28628fbc829 *data/ison_lawfirm.rda
 93e366676084deeb1faa88333b54a6a3 *data/ison_lotr.rda
-3d49ca0a38656ff2f38a9f8cb5bd3ef8 *data/ison_marvel_relationships.rda
-0283024d033581ff0e9dcab5f05d0e54 *data/ison_marvel_teams.rda
+c670cc2e0d2c9d2fb0f7be97c56ed735 *data/ison_marvel_relationships.rda
+daa202d696a0ff4374a92d1927eb8572 *data/ison_marvel_teams.rda
 e327b78dbf21f748a1fe54322c93e496 *data/ison_networkers.rda
 b45178a091e0cdb0ab2ba12abaf63e4e *data/ison_southern_women.rda
 a8b18fd3012e113ac1533d3e43e28d02 *inst/manynet.png
-8316307638b5e9cbf37d88e864ab2b78 *inst/tutorials/tutorial1/data.Rmd
+1e4ad271de7b80858e500d28faac01da *inst/tutorials/tutorial1/data.Rmd
 fb00940f1411f89fe46a9c062a62fb81 *inst/tutorials/tutorial1/data.html
 e6611f534f1efb2d9df7fb51777f93d9 *inst/tutorials/tutorial1/data/adols.csv
 52bb5bccd9a3b7d3dbbc28794a9b64f8 *inst/tutorials/tutorial1/data/adols.net
@@ -52,31 +53,32 @@ dbd302bd9c1e2c921c6255d740ca5c0b *inst/tutorials/tutorial1/data/flonode.csv
 2ed79662b1c293c0be2edceb47c1aa5d *inst/tutorials/tutorial2/visualisation_files/figure-html/maxbet-1.png
 577785c00b8026d2e5400086537611cb *man/add.Rd
 daadf1d5c685eba1af39e765c9a13ae6 *man/as.Rd
-07182262c8dd174e4ba6657bc589d6cb *man/attributes.Rd
-a00c21113d63e8f5b8ade9ba189a70db *man/auto_graph.Rd
+560ca9f988383ba3ae099e8359255f33 *man/attributes.Rd
+6e93fdc19b29d31cc6ee345db3d55190 *man/autographing.Rd
 d820a2ec1e34da685561d025a0185fd0 *man/create.Rd
 d2f6ba1c56e762bdd6893dd3e87b3386 *man/figures/README-coercion-graph-1.png
 34076488ddf8d5fcab83b9b25dffc725 *man/figures/README-import-graph-1.png
 4aa3a28863f403109d42ab15aa6534da *man/figures/README-layout-comparison-1.png
 ca331d67c1914f18ff55433134498306 *man/figures/logo.png
 955258e5212728cea3c637a208ce7fb6 *man/from.Rd
 ec6f767d4a16ce9affc08e855ab399bd *man/generate.Rd
-be0945ab0eea0c4e2efd09161317a5e8 *man/is.Rd
+a6c9e89dbeeff154c581349b0d2fce0b *man/is.Rd
 c87ee7ae255f81a25c605d7a592e411a *man/ison_adolescents.Rd
 b23963ab1fa2d335889796aff99766df *man/ison_algebra.Rd
 abb742a15b0d42220fdf1a9842343908 *man/ison_brandes.Rd
 641f9e92c6a5a93938cb11af8f6680a2 *man/ison_karateka.Rd
 3d3dae69f6654d22e5f1a3d769e95186 *man/ison_konigsberg.Rd
 d2b34f7444de9f5f84b4194c4be507c4 *man/ison_laterals.Rd
+6c3d2156439908d884fefe32f5cb8198 *man/ison_lawfirm.Rd
 363a5584ba45f3d348b54de3ec6e37f1 *man/ison_lotr.Rd
-872d6ffc4b2e0e3593bfff2c44ecf12d *man/ison_marvel.Rd
+39018588f2ad52520528dcd51b837989 *man/ison_marvel.Rd
 8af25118b798ee1918429e28c169580f *man/ison_networkers.Rd
 489b7ee3f4807dc597cd78f6fa5d92a7 *man/ison_southern_women.Rd
 f5cd8e08542281e8877896bb2f1e2679 *man/miss.Rd
-f64eb245d4747839e5d137af2f8ffd1b *man/partition_layouts.Rd
-389ed0f7a8c898a6f4e51eae2e4dedb6 *man/properties.Rd
-83afbd68939135cccb533a3d78dfe13c *man/read.Rd
-74cd9b0a7752d1047ee138a8936950bd *man/reexports.Rd
+1c2ce582b3692c13cdee9ccde3c19ce4 *man/partition_layouts.Rd
+d578057ec2a5db8039aa26764d2b3718 *man/properties.Rd
+ee22c63fcf1ba72a300617b658cdcd69 *man/read.Rd
+c8e1ec4e677f76dea30d29effffdcc95 *man/reexports.Rd
 76dfe722f52ddcf2ba64725e261dfdf2 *man/reformat.Rd
 98a4acacc3b543d909a6982eb1533054 *man/split.Rd
 5aa570e908958357e0b8f0be069f4e5b *man/themes.Rd

NEWS.md

@@ -1,3 +1,32 @@
+# manynet 0.2.3
+
+2023-09-17
+
+## Package
+
+* Fixed URL in read_
+
+# manynet 0.2.2
+
+2023-09-17
+
+## Package
+
+* README now points to `{migraph}` earlier
+
+## Tutorials
+
+* Fixed various bugs in first (data) tutorial
+
+## Map
+
+* Fixed explicit node_shape bug
+
+## Data
+
+* Added `ison_lawfirm` data from Lazega, see documentation for more details
+* Upgraded ison_marvel data to latest igraph specification
+
 # manynet 0.2.1
 
 2023-08-11

R/data_ison.R

@@ -249,3 +249,31 @@
 #'   ison_southern_women
 #'   ```
 "ison_southern_women"
+
+# Lawfirm ####
+
+#' One-mode lawfirm (Lazega 2001)
+#'
+#' @description
+#' One-mode network dataset collected by Lazega (2001)
+#' on the relations between partners in a corporate law firm called SG&R in New England 1988-1991.
+#' This particular subset includes the 36 partners among the 71 attorneys of this firm.
+#' Nodal attributes include seniority, formal status, office in which they work, gender, lawschool they attended,
+#' their age, and how many years they had been at the firm.
+#' @details
+#' The larger data from which this subset comes includes also individual performance measurements (hours worked, fees brought in)
+#' and attitudes concerning various management policy options (see also `{sand}`),
+#' their strong-coworker network, advice network, friendship network, and indirect control network.
+#' @docType data
+#' @keywords datasets
+#' @name ison_lawfirm
+#' @usage data(ison_lawfirm)
+#' @references
+#'   Lazega, Emmanuel. 2001.
+#'   \emph{The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership}.
+#'   Oxford: Oxford University Press.
+#' @format
+#'   ```{r, echo = FALSE}
+#'   ison_lawfirm
+#'   ```
+"ison_lawfirm"

R/make_read.R

@@ -40,7 +40,7 @@
 #'
 #' See also:
 #'
-#' - [networkdata](http://networkdata.schochastics.net/)
+#' - [networkdata](https://schochastics.github.io/networkdata/)
 #' - [GML datasets](http://www-personal.umich.edu/~mejn/netdata/)
 #' - UCIrvine Network Data Repository
 #' - [KONECT project](http://konect.cc/)

R/map_autographr.R

@@ -34,10 +34,10 @@
 #' @importFrom ggraph geom_edge_link geom_node_text geom_conn_bundle
 #' get_con geom_node_point scale_edge_width_continuous geom_node_label
 #' @importFrom ggplot2 aes arrow unit scale_color_brewer scale_fill_brewer
-#' @name auto_graph
+#' @name autographing
 NULL
 
-#' @describeIn auto_graph Graphs a network with sensible defaults
+#' @describeIn autographing Graphs a network with sensible defaults
 #' @examples
 #' #ison_adolescents %>% 
 #' #   mutate(shape = rep(c("circle", "square"), times = 4),
@@ -62,18 +62,18 @@ autographr <- function(.data,
   #     mutate(node_group = node_group)
   # }
   # Add layout ----
-  layout <- .infer_layout(g, layout)
-  p <- .graph_layout(g, layout, labels, ...)
+  g_layout <- .infer_layout(g, layout)
+  p <- .graph_layout(g, g_layout, labels, ...)
   # Add edges ----
   p <- .graph_edges(p, g, edge_color)
   # Add nodes ----
   p <- .graph_nodes(p, g, node_color, node_shape, node_size)
   p
 }
 
-#' @describeIn auto_graph Graphs a list of networks 
+#' @describeIn autographing Graphs a list of networks 
 #'   with sensible defaults
-#' @param netlist A list of migraph-compatible networks.
+#' @param netlist A list of manynet-compatible networks.
 #' @source http://blog.schochastics.net/post/animating-network-evolutions-with-gganimate/
 #' @examples
 #' #autographs(to_egos(ison_adolescents))
@@ -91,7 +91,7 @@ autographs <- function(netlist, ...) {
   do.call(patchwork::wrap_plots, gs)
 }
 
-#' @describeIn auto_graph Graphs an dynamic (animated) network
+#' @describeIn autographing Graphs an dynamic (animated) network
 #'   with sensible defaults
 #' @param tlist The same migraph-compatible network listed according to
 #'   a time attribute, waves, or slices.
@@ -186,12 +186,12 @@ autographd <- function(tlist, keep_isolates = TRUE, layout = "stress",
     ggplot2::theme_void()
 }
 
-.infer_layout <- function(g, layout){
-  if(is.null(layout)){
-      if (is_twomode(g)) layout <- "hierarchy" else 
-      layout <- "stress"
+.infer_layout <- function(g, g_layout){
+  if(is.null(g_layout)){
+      if (is_twomode(g)) g_layout <- "hierarchy" else 
+        g_layout <- "stress"
   }
-  layout
+  g_layout
 }
 
 .infer_nsize <- function(g, node_size){
@@ -419,8 +419,10 @@ autographd <- function(tlist, keep_isolates = TRUE, layout = "stress",
   nsize <- .infer_nsize(g, node_size)
 
   if (!is.null(node_shape)) {
-    node_shape <- as.factor(node_attribute(g, node_shape))
-    node_shape <- c("circle","square","triangle")[node_shape]
+    if(length(node_shape) == 1) node_shape <- rep(node_shape, network_nodes(g)) else {
+      node_shape <- as.factor(node_attribute(g, node_shape))
+      node_shape <- c("circle","square","triangle")[node_shape]
+    }
   } else if (is_twomode(g)) {
     node_shape <- ifelse(igraph::V(g)$type,
                          "square",

README.md

@@ -39,6 +39,9 @@ them.
 *That’s why `{manynet}` aims to help researchers with Making,
 Manipulating, and Mapping networks.*
 
+For Measures, Memberships, or Models, see
+[`{migraph}`](https://snlab-ch.github.io/migraph/).
+
 ## Making
 
 First, `{manynet}` provides a set of tools that can be used to make

inst/tutorials/tutorial1/data.Rmd

@@ -15,7 +15,7 @@ knitr::opts_chunk$set(echo = FALSE)
 
 # Introduction
 
-Network analysis is best with actual networks.
+Most network analysts want to use network analysis to better understand empirical networks.
 `{manynet}` offers several options for creating or generating,
 importing or coercing networks into formats that you can use.
 This tutorial is going to cover several ways to get data into the package:
@@ -116,7 +116,7 @@ question("How many nodes does this network have?",
 ```{r tblgraph-ties, echo = FALSE}
 question("How many ties does this network have?",
   answer(4,
-         message = "There are four more ties beyond what is listed."),
+         message = "There are four more ties beyond the six that are listed."),
   answer(10,
          correct = TRUE,
          message = "There are 10 ties."),
@@ -132,7 +132,7 @@ question("How many ties does this network have?",
 ```
 
 ```{r tblgraph-type, echo = FALSE}
-question("From what is stated here, what kind of network is this?",
+question("What kind of network is this? Choose all that apply.",
   answer("Undirected",
          correct = TRUE),
   answer("Labelled",
@@ -395,12 +395,12 @@ To assign and immediately print the result, wrap the line in parentheses.
 
 ```{r southproj-solution}
 ison_southern_women
-(women <- to_mode1(ison_southern_women))
-(events <- to_mode2(ison_southern_women))
+(s_women <- to_mode1(ison_southern_women))
+(s_events <- to_mode2(ison_southern_women))
 ```
 
 ```{r namemode1}
-question("The first mode consists of...",
+question("The first mode of the two-mode network consists of...",
   answer("women named Evelyn, Laura, Theresa, etc",
          correct = TRUE),
   answer("events called E1, E2, E3, etc"),
@@ -454,17 +454,22 @@ to find out a) how many nodes there are in each of these networks,
 b) what the names of the nodes are,
 and c) what tie attributes there are in the networks.
 
-```{r projprop, exercise = TRUE, exercise.setup = "southproj"}
+```{r projprop-setup}
+s_women <- to_mode1(ison_southern_women)
+s_events <- to_mode2(ison_southern_women)
+```
+
+```{r projprop, exercise = TRUE}
 
 ```
 
 ```{r projprop-solution}
-network_nodes(women)
-node_names(women)
-network_tie_attributes(women)
-network_nodes(events)
-node_names(events)
-network_tie_attributes(events)
+network_nodes(s_women)
+network_nodes(s_events)
+node_names(s_women)
+node_names(s_events)
+network_tie_attributes(s_women)
+network_tie_attributes(s_events)
 ```
 
 So we can see that the `to_mode*()` functions have created a network of only one of the modes in the network.
@@ -484,7 +489,7 @@ Retrieve the tie weights from your women projection.
 Find the average (mean) of this vector of tie weights,
 and the average (mean) tie weight overall.
 
-```{r womenweights, exercise = TRUE}
+```{r womenweights, exercise = TRUE, exercise.setup = "projprop-setup"}
 
 ```
 
@@ -501,9 +506,9 @@ mean(as_matrix(_____))
 ```
 
 ```{r womenweights-solution}
-tie_weights(women)
-mean(tie_weights(women))
-mean(as_matrix(women))
+tie_weights(s_women)
+mean(tie_weights(s_women))
+mean(as_matrix(s_women))
 ```
 
 ```{r womenweightsq}