diff --git a/DESCRIPTION b/DESCRIPTION index c98d772..7b0d774 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: manynet -Title: Many Ways to Make, Manipulate, and Map Myriad Networks -Version: 0.2.8 -Date: 2023-11-02 -Description: A set of tools for making, manipulating, and mapping many different types of networks. +Title: Many Ways to Make, Modify, and Map Myriad Networks +Version: 0.3.0 +Date: 2023-12-15 +Description: A set of tools for making, modifying, 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, @@ -16,11 +16,12 @@ Language: en-GB Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 -Imports: dplyr (>= 1.1.0), ggplot2, ggraph, igraph (>= 1.5.0.1), - network, tidygraph -Suggests: gganimate, methods, readxl, roxygen2, RSiena, testthat, xml2, - patchwork, rmarkdown, learnr, grDevices, BiocManager, minMSE, - graphlayouts, ggforce, multiplex, concaveman, migraph, knitr +Imports: dplyr (>= 1.1.0), ggplot2, ggraph, igraph (>= 1.6.0), network, + tidygraph +Suggests: BiocManager, concaveman, gganimate, ggforce, gifski, + graphlayouts, grDevices, knitr, learnr, methods, migraph, + minMSE, multiplex, patchwork, png, readxl, rmarkdown, roxygen2, + RSiena, testthat, xml2 Enhances: Rgraphviz Authors@R: c(person(given = "James", @@ -34,10 +35,10 @@ Authors@R: comment = c("IHEID", ORCID = "0000-0003-3420-6085")) ) NeedsCompilation: no -Packaged: 2023-11-02 08:49:47 UTC; hollway +Packaged: 2023-12-15 18:39:06 UTC; hollway Author: James Hollway [cre, aut, ctb] (IHEID, ), Henrique Sposito [ctb] (IHEID, ) Maintainer: James Hollway Repository: CRAN -Date/Publication: 2023-11-02 10:10:08 UTC +Date/Publication: 2023-12-15 19:10:02 UTC diff --git a/MD5 b/MD5 index 43a483e..998fac5 100644 --- a/MD5 +++ b/MD5 @@ -1,35 +1,35 @@ -8a806c803cd7e177a22f162f94918164 *DESCRIPTION +c606d31e46407716819a093f20223ec0 *DESCRIPTION 361d71c6379f4ddf09ca9c8540b8fea1 *LICENSE -bd6cfd5ca3bb7fc6cead1bd6cb97febb *NAMESPACE -c5ce4b06d504aea0c10c0d6803963de0 *NEWS.md -02bc41f69d220553f1ed24eb2cd97bd7 *R/data_ison.R -69aad26ca508f40dc04a3c2012be020d *R/make_as.R -280195557bbe3c28bd67cde1f5423490 *R/make_create.R +83e5fc83a4f4158589dc43cda7fb0dde *NAMESPACE +27f0ee5527ecc55c8cbde34bddf0417b *NEWS.md +cf2435748a8b6b7f626e8d9662841bf4 *R/data_ison.R +f7cdeef0484aa4b2e6d0e5446dbe6cb8 *R/make_as.R +e1a167904dc146b0e7d74ce6465b4f54 *R/make_create.R 80edd6b5d8b5adf4b6df0477cfc5ba08 *R/make_generate.R 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*tests/testthat/sheets/testCSVComma.csv @@ -109,15 +109,16 @@ bdd9ad82944ff4c6cc1171e853071012 *tests/testthat/sheets/ucinettest.##h bdd9ad82944ff4c6cc1171e853071012 *tests/testthat/sheets/ucinettest1.##h 6e36522f661de1ec7c05f2b5d7493f34 *tests/testthat/test-make_create.R 9fa727b7cffe964ca8637cb47cd8341d *tests/testthat/test-make_generate.R -cdbac54b8a56d7007e5696222d913847 *tests/testthat/test-make_read.R +e22ad1a8fac1e9d13be6baf2bcd4b3cb *tests/testthat/test-make_read.R 75c447fe8a95ea6707ced6fd999b4853 *tests/testthat/test-manip_add.R -e704b7958c4e61614d5f3d6ca466fd46 *tests/testthat/test-manip_as.R +eb7f2ac7ea90a38175b65e3f587ad6c8 *tests/testthat/test-manip_as.R b51f8cdc30203b9774ece4f36ec6bcea *tests/testthat/test-manip_grab.R b644d01d099d612cc806972a4ad28c84 *tests/testthat/test-manip_miss.R d7a9db995bdb8e37043eec3b2614d23b *tests/testthat/test-manip_reformat.R -6a03770649c99095fb7a58a9ba04fd53 *tests/testthat/test-manip_split.R +58192e6585ca84ca30c52e05f5208800 *tests/testthat/test-manip_split.R 47b2d9d25c1b81d27a4439995ac5f7c6 *tests/testthat/test-manip_transform.R -11134ac6e6d21ecfbe25bcba9ef4f128 *tests/testthat/test-map_autographr.R +dd7c2e52c0d2d8691c5e1421263a2817 *tests/testthat/test-map_autographr.R d699c2b2e6326cd476b4f1539b22b53a *tests/testthat/test-map_is.R +24f91d41ff044ce69330391a1bcfc9bb *tests/testthat/test-map_theme.R 805f7fd21fcb36696bce345246b6c741 *tests/testthat/test-print_class.R 04d5192ce082546e899ea7bb5250e7e4 *tests/testthat/test_plots.R diff --git a/NAMESPACE b/NAMESPACE index 867c772..866fb10 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +S3method("+",ggplot) S3method(add_nodes,igraph) S3method(add_nodes,network) S3method(add_nodes,tbl_graph) @@ -42,7 +43,9 @@ S3method(as_network,network.goldfish) S3method(as_network,siena) S3method(as_network,tbl_graph) S3method(as_siena,igraph) +S3method(as_siena,tbl_graph) S3method(as_tidygraph,data.frame) +S3method(as_tidygraph,diff_model) S3method(as_tidygraph,igraph) S3method(as_tidygraph,list) S3method(as_tidygraph,matrix) @@ -90,6 +93,7 @@ S3method(is_twomode,data.frame) S3method(is_twomode,igraph) S3method(is_twomode,matrix) S3method(is_twomode,network) +S3method(is_twomode,numeric) S3method(is_twomode,tbl_graph) S3method(is_weighted,data.frame) S3method(is_weighted,igraph) @@ -239,13 +243,13 @@ S3method(to_unweighted,matrix) S3method(to_unweighted,network) S3method(to_unweighted,tbl_graph) S3method(to_waves,data.frame) +S3method(to_waves,diff_model) S3method(to_waves,igraph) S3method(to_waves,tbl_graph) export("%>%") export(.E) export(.G) export(.N) -export(activate) export(add_node_attribute) export(add_nodes) export(add_tie_attribute) @@ -289,13 +293,11 @@ export(ggplot) export(ggsave) export(ggtitle) export(guides) -export(iheid_palette) export(is.igraph) export(is.network) export(is.tbl_graph) export(is_acyclic) export(is_aperiodic) -export(is_bipartite) export(is_complex) export(is_connected) export(is_directed) @@ -319,10 +321,12 @@ export(layout_tbl_graph_alluvial) export(layout_tbl_graph_concentric) export(layout_tbl_graph_hierarchy) export(layout_tbl_graph_ladder) +export(layout_tbl_graph_lineage) export(layout_tbl_graph_multilevel) export(layout_tbl_graph_quad) export(layout_tbl_graph_railway) export(layout_tbl_graph_triad) +export(many_palettes) export(mutate) export(mutate_ties) export(na_to_mean) @@ -337,6 +341,7 @@ export(node_mode) export(node_names) export(read_dynetml) export(read_edgelist) +export(read_graphml) export(read_matrix) export(read_nodelist) export(read_pajek) @@ -345,17 +350,29 @@ export(rename) export(rename_ties) export(run_tute) export(scale_color_centres) +export(scale_color_ethz) export(scale_color_iheid) +export(scale_color_rug) export(scale_color_sdgs) +export(scale_color_uzh) export(scale_colour_centres) +export(scale_colour_ethz) export(scale_colour_iheid) +export(scale_colour_rug) export(scale_colour_sdgs) +export(scale_colour_uzh) export(scale_fill_centres) +export(scale_fill_ethz) export(scale_fill_iheid) +export(scale_fill_rug) export(scale_fill_sdgs) +export(scale_fill_uzh) export(select_ties) export(summarise_ties) +export(theme_ethz) export(theme_iheid) +export(theme_rug) +export(theme_uzh) export(tie_attribute) export(tie_signs) export(tie_weights) @@ -392,6 +409,7 @@ export(to_unweighted) export(to_waves) export(with_graph) export(write_edgelist) +export(write_graphml) export(write_matrix) export(write_nodelist) export(write_pajek) @@ -440,10 +458,6 @@ importFrom(ggraph,geom_node_text) importFrom(ggraph,get_con) importFrom(ggraph,ggraph) importFrom(ggraph,scale_edge_width_continuous) -importFrom(graphics,image) -importFrom(graphics,par) -importFrom(graphics,rect) -importFrom(graphics,text) importFrom(igraph,E) importFrom(igraph,V) importFrom(igraph,add_edges) @@ -452,8 +466,8 @@ importFrom(igraph,any_multiple) importFrom(igraph,as.directed) importFrom(igraph,as.undirected) importFrom(igraph,as_adjacency_matrix) +importFrom(igraph,as_biadjacency_matrix) importFrom(igraph,as_data_frame) -importFrom(igraph,as_incidence_matrix) importFrom(igraph,bipartite.projection) importFrom(igraph,complementer) importFrom(igraph,decompose) @@ -468,8 +482,8 @@ importFrom(igraph,eulerian_path) importFrom(igraph,get.edgelist) importFrom(igraph,get.vertex.attribute) importFrom(igraph,graph_from_adjacency_matrix) +importFrom(igraph,graph_from_biadjacency_matrix) importFrom(igraph,graph_from_data_frame) -importFrom(igraph,graph_from_incidence_matrix) importFrom(igraph,gsize) importFrom(igraph,induced_subgraph) importFrom(igraph,is.connected) @@ -488,6 +502,7 @@ importFrom(igraph,make_line_graph) importFrom(igraph,make_star) importFrom(igraph,make_tree) importFrom(igraph,max_bipartite_match) +importFrom(igraph,read_graph) importFrom(igraph,reverse_edges) importFrom(igraph,sample_bipartite) importFrom(igraph,sample_gnm) diff --git a/NEWS.md b/NEWS.md index 1c5d037..f291eea 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,84 @@ +# manynet 0.3.0 + +2023-12-15 + +## Package + +* Overhaul of the README to summarise many of the unique aspects of the package + +## Making + +* Added `as_tidygraph()` method for diff_model objects +* Added `as_siena()` method for tidygraph objects + +## Modifying + +* Manipulating functions now called Modifying +* `to_waves()` now works on diff_model objects, add attributes and names +* `is_multiplex()` now recognises a tie/edge 'type' attribute as evidence of multiplexity +* Deleted unnecessary reexports from other packages + - `igraph::is_bipartite()` is superseded by `is_twomode()` + - `tidygraph::activate()` is superseded by `mutate_ties()` and similar functions +* Fixed bug by replacing older `igraph::as_incidence_matrix()` and `igraph::graph_from_incidence_matrix()` +with `igraph::as_biadjacency_matrix()` and `igraph::graph_from_biadjacency_matrix()` + +## Mapping + +* `autographr()` now plots diff_model objects, showing the diffusion as a heatmap on the vertices +* `autographs()` and `autographd()` now utilise network information in diff_model objects to provide better layouts +* Fixed bug with specifying `node_size` in `autographd()` +* `many_palettes` replaces `iheid_palette` +* Added new palettes, themes and scales for graphs + - `theme_ethz()`, `scale_color_ethz()`/`scale_colour_ethz()`, and `scale_fill_ethz()` for ETH Zürich + - `theme_uzh()`, `scale_color_uzh()`/`scale_colour_uzh()`, and `scale_fill_uzh()` for Uni Zürich + - `theme_rug()`, `scale_color_rug()`/`scale_colour_rug()`, and `scale_fill_rug()` for Uni Gröningen + +## Data + +* Added `ison_physicians` data that includes four, multiplex networks with adoption data + +# manynet 0.2.10 + +2023-12-06 + +## Package + +* Fixed documentation issues with `run_tute()` + +## Making + +* Added `read_graphml()` and `write_graphml()` for importing and exporting graphml objects, mostly wrappers for igraph functions. + +## Mapping + +* `autographd()` and `autographs()` can now be used for plotting diffusion models. + * Updates to `to_waves()` and `autographd()` to account for 'exposed' nodes in diffusion models. +* Updates to layouts + * Updates to `hierarchical` layout so that node name can be specified for centering the layout + * Updated `theme_heid()` layout + +## Data + +* Added faction attribute to `ison_starwars` data, thanks to coding by Yichen Shen and Tiphaine Aeby + +# manynet 0.2.9 + +2023-11-15 + +## Package + +* Updated `run_tute()`function to "fuzzy" match tutorial names + +## Mapping + +* Added `+.ggplot()` method for visualising multiple plots in the same pane +* Added custom theme `theme_iheid` for plots +* Added custom `scale_` family of functions for changing colour scales in plots +* Updated `autographr()`: + * Added "center" option for hierarchy layout that places events or actors in the middle alike "bip" and "bip3" layouts + * Added "lineage" layout that fixes node positions according to values in y axis +* Fixed bugs with `autographd()` function + # manynet 0.2.8 2023-11-02 diff --git a/R/data_ison.R b/R/data_ison.R index 44c992d..5818461 100644 --- a/R/data_ison.R +++ b/R/data_ison.R @@ -292,6 +292,10 @@ #' For all networks, characters are named (eg. R2-D2, Anakin, Chewbacca) #' and the following node attributes are provided where available: #' height, mass, hair color, skin color, eye color, birth year, sex, homeworld, and species. +#' The node attribute 'faction' has also been added, +#' denoting the faction (eg. Jedi, Rebel Alliance, etc) +#' that Star Wars characters belong to in each episode +#' (coding completed with help of Yichen Shen and Tiphaine Aeby). #' Weighted ties represent the number of times characters speak #' within the same scene of the film. #' @details @@ -340,3 +344,56 @@ #' ison_friends #' ``` "ison_friends" + + +# Physicians #### + +#' Four multiplex one-mode physician diffusion data (Coleman, Katz, and Menzel, 1966) +#' +#' @description +#' Ron Burt prepared this data from +#' Coleman, Katz and Menzel's 1966 study on medical innovation. +#' They had collected data from physicians in four towns in Illinois: +#' Peoria, Bloomington, Quincy and Galesburg. +#' These four networks are held as separate networks in a list. +#' +#' Coleman, Katz and Menzel were concerned with the impact of network ties +#' on the physicians' adoption of a new drug, tetracycline. +#' Data on three types of ties were collected in response to three questions: +#' +#' - advice: "When you need information or advice about questions of therapy +#' where do you usually turn?" +#' - discussion: "And who are the three or four physicians with whom you most often find yourself +#' discussing cases or therapy in the course of an ordinary week – last week for instance?" +#' - friendship: "Would you tell me the first names of your three friends +#' whom you see most often socially?" +#' +#' Additional questions and records of prescriptions provided additional information: +#' - recorded date of tetracycline `adoption` date +#' - years in `practice` +#' (note that these are `{messydates}`-compatible dates) +#' - `conferences` attended +#' (those that attended "Specialty" conferences presumably also attended "General" conferences) +#' - regular subscriptions to medical `journals` +#' - `free_time` spent associating with doctors +#' - `discussions` on medical matters when with other doctors sociallyy +#' - memberships in `clubs` with other doctores +#' - number of top 3 `friends` that are doctors +#' - time practicing in current `community` +#' - `patients` load (ordinal) +#' - physical `proximity` to other physicians (in building/sharing office) +#' - medical `specialty` (GP/Internist/Pediatrician/Other) +#' @docType data +#' @keywords datasets +#' @name ison_physicians +#' @usage data(ison_physicians) +#' @references +#' Coleman, James, Elihu Katz, and Herbert Menzel. 1966. +#' \emph{Medical innovation: A diffusion study}. +#' Indianapolis: The Bobbs-Merrill Company. +#' @source `{networkdata}` +#' @format +#' ```{r, echo = FALSE} +#' ison_physicians +#' ``` +"ison_physicians" diff --git a/R/make_as.R b/R/make_as.R index ad81336..d9d0e20 100644 --- a/R/make_as.R +++ b/R/make_as.R @@ -147,7 +147,7 @@ as_edgelist.siena <- function(.data, #' @rdname as #' @importFrom dplyr arrange -#' @importFrom igraph edge_attr_names as_adjacency_matrix as_incidence_matrix +#' @importFrom igraph edge_attr_names as_adjacency_matrix as_biadjacency_matrix #' @importFrom network is.bipartite list.edge.attributes as.matrix.network #' @export as_matrix <- function(.data, @@ -202,10 +202,10 @@ as_matrix.igraph <- function(.data, twomode = NULL) { if ((!is.null(twomode) && twomode) | (is.null(twomode) & is_twomode(.data))) { if (is_weighted(.data) | is_signed(.data)) { - mat <- igraph::as_incidence_matrix(.data, sparse = FALSE, + mat <- igraph::as_biadjacency_matrix(.data, sparse = FALSE, attr = igraph::edge_attr_names(.data)[[1]]) } else { - mat <- igraph::as_incidence_matrix(.data, sparse = FALSE, + mat <- igraph::as_biadjacency_matrix(.data, sparse = FALSE, attr = NULL) } } else { @@ -293,7 +293,7 @@ as_matrix.siena <- function(.data, # igraph #### #' @rdname as -#' @importFrom igraph graph_from_data_frame graph_from_incidence_matrix +#' @importFrom igraph graph_from_data_frame graph_from_biadjacency_matrix #' graph_from_adjacency_matrix delete_vertex_attr V vertex_attr #' edge_attr delete_edge_attr set_edge_attr #' @importFrom network list.edge.attributes as.sociomatrix @@ -329,11 +329,11 @@ as_igraph.matrix <- function(.data, twomode = FALSE) { if (nrow(.data) != ncol(.data) | twomode) { if (!(all(.data %in% c(0, 1)))) { - graph <- igraph::graph_from_incidence_matrix(.data, + graph <- igraph::graph_from_biadjacency_matrix(.data, weighted = TRUE, directed = FALSE) } else { - graph <- igraph::graph_from_incidence_matrix(.data, + graph <- igraph::graph_from_biadjacency_matrix(.data, directed = FALSE) } } else { @@ -374,10 +374,10 @@ as_igraph.network <- function(.data, if (network::is.bipartite(.data)) { if ("weight" %in% network::list.edge.attributes(.data)) { graph <- network::as.sociomatrix(.data, attrname = "weight") - graph <- igraph::graph_from_incidence_matrix(graph, weighted = TRUE) + graph <- igraph::graph_from_biadjacency_matrix(graph, weighted = TRUE) } else { graph <- network::as.sociomatrix(.data) - graph <- igraph::graph_from_incidence_matrix(graph) + graph <- igraph::graph_from_biadjacency_matrix(graph) } } else { if ("weight" %in% network::list.edge.attributes(.data)) { @@ -656,6 +656,17 @@ as_tidygraph.siena <- function(.data, twomode = FALSE) { as_tidygraph(as_igraph.siena(.data, twomode = FALSE)) } +#' @export +as_tidygraph.diff_model <- function(.data, twomode = FALSE) { + out <- as_tidygraph(attr(.data, "network")) + attr(out, "diff_model") <- .data + if (!"name" %in% names(node_attribute(out))) { + out <- add_node_attribute(out, "name", + as.character(seq_len(igraph::vcount(out)))) + } + out +} + # Network #### #' @rdname as @@ -775,6 +786,11 @@ as_siena.igraph <- function(.data, twomode = FALSE) { RSiena::sienaDataCreate(list("depnet" = depnet)) } +#' @export +as_siena.tbl_graph <- function(.data, twomode = FALSE) { + as_siena.igraph(.data, twomode = twomode) +} + # graphAM #### #' @rdname as diff --git a/R/make_create.R b/R/make_create.R index a5c6403..e04b9f6 100644 --- a/R/make_create.R +++ b/R/make_create.R @@ -44,7 +44,7 @@ #' using `as_edgelist()`, `as_matrix()`, #' `as_tidygraph()`, or `as_network()`. #' @importFrom tidygraph as_tbl_graph -#' @importFrom igraph graph_from_incidence_matrix +#' @importFrom igraph graph_from_biadjacency_matrix NULL #' @describeIn create Creates an empty graph of the given dimensions. @@ -135,7 +135,7 @@ create_ring <- function(n, directed = FALSE, width = 1, ...) { #' @describeIn create Creates a graph of the given dimensions #' that has a maximally central node. -#' @importFrom igraph graph_from_adjacency_matrix graph_from_incidence_matrix +#' @importFrom igraph graph_from_adjacency_matrix graph_from_biadjacency_matrix #' make_star #' @examples #' create_star(12) @@ -464,7 +464,7 @@ create_explicit <- function(...){ # #' Will construct an affiliation matrix, # #' with decreasing fill across n2. # #' @importFrom tidygraph as_tbl_graph -# #' @importFrom igraph graph_from_incidence_matrix +# #' @importFrom igraph graph_from_biadjacency_matrix # #' @examples # #' create_nest(10, 12) # #' @export @@ -474,7 +474,7 @@ create_explicit <- function(...){ # out <- matrix(0, n1, n2) # out[(row(out) - col(out)) >= 0] <- 1 # if(as == "tidygraph") out <- tidygraph::as_tbl_graph(out) -# if(as == "igraph") out <- igraph::graph_from_incidence_matrix(out) +# if(as == "igraph") out <- igraph::graph_from_biadjacency_matrix(out) # out # } # diff --git a/R/make_read.R b/R/make_read.R index 531c1f1..39f9dfa 100644 --- a/R/make_read.R +++ b/R/make_read.R @@ -589,3 +589,24 @@ read_dynetml <- function(file = file.choose()) { edgelist <- dplyr::filter(edgelist, value != 0) as_tidygraph(list(nodes = nodes, ties = edgelist)) } + +#' @describeIn read Reading GraphML files +#' @importFrom igraph read_graph +#' @export +read_graphml <- function(file = file.choose()) { + as_tidygraph(igraph::read_graph(file, format = "graphml")) +} + +#' @describeIn read Writing GraphML files +#' @importFrom igraph write_graph +#' @export +write_graphml <- function(.data, + filename, + name, + ...) { + if (missing(name)) name <- deparse(substitute(.data)) + if (missing(filename)) filename <- paste0(getwd(), "/", name, ".graphml") + igraph::write_graph(.data, + filename, + format = "graphml") +} diff --git a/R/manip_from.R b/R/manip_from.R index 9138a93..6ff9b2e 100644 --- a/R/manip_from.R +++ b/R/manip_from.R @@ -42,7 +42,6 @@ from_subgraphs <- function(.data) { #' @importFrom dplyr distinct #' @examples #' ison_adolescents %>% -#' activate(edges) %>% #' to_egos() %>% #' from_egos() #' @export diff --git a/R/manip_split.R b/R/manip_split.R index 0015c90..cef839f 100644 --- a/R/manip_split.R +++ b/R/manip_split.R @@ -175,7 +175,6 @@ to_waves <- function(.data, attribute = "wave", panels = NULL) UseMethod("to_wav #' @importFrom tidygraph to_subgraph as_tbl_graph #' @export to_waves.tbl_graph <- function(.data, attribute = "wave", panels = NULL) { - wp <- unique(tie_attribute(.data, attribute)) if(!is.null(panels)) wp <- intersect(panels, wp) @@ -201,17 +200,33 @@ to_waves.igraph <- function(.data, attribute = "wave", panels = NULL) { #' @export to_waves.data.frame <- function(.data, attribute = "wave", panels = NULL) { wp <- unique(tie_attribute(.data, attribute)) - if(!is.null(panels)) - wp <- intersect(panels, wp) + if(!is.null(panels)) wp <- intersect(panels, wp) if(length(wp)>1){ out <- lapply(wp, function(l) .data[,attribute == l]) names(out) <- wp - } else if(length(wp)>1){ + } else if(length(wp)>1) { out <- .data[,attribute == wp] } out } +#' @export +to_waves.diff_model <- function(.data, attribute = "t", panels = NULL) { + if(!is.null(panels)) .data <- .data[.data[[attribute]] %in% panels,] + if (length(unique(.data[["n"]])) > 1) + stop("Please make sure diffusion has the same number of nodes for all time points.") + diffusion <- as_tidygraph(.data) + out <- list() + for (k in .data[[attribute]]) { + out[[paste0("Time: ", k)]] <- diffusion %>% + add_node_attribute("Infected", c(rep("Recovered", .data$R[k + 1]), + rep("Infected", .data$I[k + 1]), + rep("Exposed", .data$E[k + 1]), + rep("Susceptible", .data$S[k + 1]))) + } + out +} + #' @describeIn split Returns a list of a network #' with some continuous time variable at some time slice(s). #' @param attribute One or two attributes used to slice data. diff --git a/R/manip_tidy.R b/R/manip_tidy.R index 1ba7629..977dadb 100644 --- a/R/manip_tidy.R +++ b/R/manip_tidy.R @@ -46,7 +46,7 @@ join_ties <- function(.data, object2, attr_name) { edges <- from <- to <- NULL el <- c(t(as.matrix(as_edgelist(object2)))) obj <- as_tidygraph(.data) %>% - activate(edges) + tidygraph::activate(edges) if(ncol(as.data.frame(obj)) < 3){ obj <- obj %>% igraph::set_edge_attr("orig", value = 1) } @@ -59,7 +59,7 @@ join_ties <- function(.data, object2, attr_name) { select_ties(-object2) } edges <- out %>% - activate(edges) %>% + tidygraph::activate(edges) %>% as.data.frame() %>% dplyr::group_by(from, to) %>% dplyr::summarise(dplyr::across(dplyr::everything(), @@ -69,7 +69,7 @@ join_ties <- function(.data, object2, attr_name) { out }), .groups = "keep") %>% dplyr::ungroup() - nodes <- out %>% activate(nodes) %>% as.data.frame() + nodes <- out %>% tidygraph::activate(nodes) %>% as.data.frame() tidygraph::tbl_graph(nodes, edges, directed = is_directed(.data)) } @@ -81,11 +81,12 @@ mutate.igraph <- function(.data, ...){ } #' @describeIn tidy Tidy way to add vector as tie attributes. +#' @importFrom tidygraph activate #' @export mutate_ties <- function(.data, ...){ nodes <- edges <- NULL out <- as_tidygraph(.data) - out %>% activate(edges) %>% mutate(...) %>% activate(nodes) + out %>% tidygraph::activate(edges) %>% mutate(...) %>% activate(nodes) } #' @describeIn tidy Tidy way to select tie attributes. @@ -94,7 +95,7 @@ mutate_ties <- function(.data, ...){ select_ties <- function(.data, ...){ nodes <- edges <- NULL out <- as_tidygraph(.data) - out %>% activate(edges) %>% dplyr::select(...) %>% activate(nodes) + out %>% tidygraph::activate(edges) %>% dplyr::select(...) %>% activate(nodes) } #' @describeIn tidy Tidy way to filter ties based on a logical statement with @@ -104,7 +105,7 @@ select_ties <- function(.data, ...){ filter_ties <- function(.data, ...){ nodes <- edges <- NULL out <- as_tidygraph(.data) - out %>% activate(edges) %>% dplyr::filter(...) %>% activate(nodes) + out %>% tidygraph::activate(edges) %>% dplyr::filter(...) %>% activate(nodes) } #' @describeIn tidy Tidy way to rename tie attributes. @@ -113,7 +114,7 @@ filter_ties <- function(.data, ...){ rename_ties <- function(.data, ...){ nodes <- edges <- NULL out <- as_tidygraph(.data) - out %>% activate(edges) %>% dplyr::rename(...) %>% activate(nodes) + out %>% tidygraph::activate(edges) %>% dplyr::rename(...) %>% activate(nodes) } #' @describeIn tidy Tidy way to summarise tie attributes. diff --git a/R/manip_transform.R b/R/manip_transform.R index 5b1569b..c890f19 100644 --- a/R/manip_transform.R +++ b/R/manip_transform.R @@ -529,8 +529,7 @@ to_anti.network <- function(.data){ #' @importFrom dplyr filter #' @examples #' ison_adolescents %>% -#' activate(edges) %>% -#' mutate(wave = sample(1995:1998, 10, replace = TRUE)) %>% +#' mutate_ties(wave = sample(1995:1998, 10, replace = TRUE)) %>% #' to_waves(attribute = "wave") %>% #' to_no_isolates() #' @export @@ -540,7 +539,7 @@ to_no_isolates <- function(.data) UseMethod("to_no_isolates") to_no_isolates.tbl_graph <- function(.data) { nodes <- NULL # Delete edges not present vertices - .data %>% activate(nodes) %>% dplyr::filter(!tidygraph::node_is_isolated()) + .data %>% tidygraph::activate(nodes) %>% dplyr::filter(!tidygraph::node_is_isolated()) } #' @export @@ -548,7 +547,7 @@ to_no_isolates.list <- function(.data) { nodes <- NULL # Delete edges not present vertices in each list lapply(.data, function(x) { - x %>% activate(nodes) %>% dplyr::filter(!tidygraph::node_is_isolated()) + x %>% tidygraph::activate(nodes) %>% dplyr::filter(!tidygraph::node_is_isolated()) }) } diff --git a/R/manynet-utils.R b/R/manynet-utils.R index 86f425d..701dd2d 100644 --- a/R/manynet-utils.R +++ b/R/manynet-utils.R @@ -31,3 +31,9 @@ thisRequiresBio <- function(pkgname) { BiocManager::install(pkgname) }} } + +#' @export +`+.ggplot` <- function(e1, e2, ...) { + thisRequires("patchwork") + patchwork::wrap_plots(e1, e2, ...) +} diff --git a/R/map_autographr.R b/R/map_autographr.R index 6616141..aa53a42 100644 --- a/R/map_autographr.R +++ b/R/map_autographr.R @@ -15,6 +15,9 @@ #' "quad" for networks with 4 nodes, #' "stress" for all other one mode networks, #' or "hierarchy" for two mode networks. +#' For "hierarchy" layout, one can further split graph by +#' declaring the "center" argument as the "events", "actors", +#' or by declaring a node name. #' For "concentric" layout algorithm please declare the "membership" as an #' extra argument. #' The "membership" argument expects either a quoted node attribute present @@ -25,6 +28,11 @@ #' in data or vector with the same length as nodes to hierarchically #' order categories. #' If "level" is missing, function will look for 'lvl' node attribute in data. +#' The "lineage" layout ranks nodes in Y axis according to values. +#' For "lineage" layout algorithm please declare the "rank" +#' as extra argument. +#' The "rank" argument expects either a quoted node attribute present +#' in data or vector with the same length as nodes. #' @param labels Logical, whether to print node names #' as labels if present. #' @param node_shape Node variable to be used for shaping the nodes. @@ -68,6 +76,9 @@ NULL #' @describeIn autographing Graphs a network with sensible defaults #' @examples #' #autographr(ison_adolescents) +#' #ison_adolescents %>% +#' # mutate(year = rep(c(1985, 1990, 1995, 2000), times = 2)) %>% +#' # autographr(layout = "lineage", rank = "year") #' #autographr(ison_algebra, layout = "circle", #' # node_size = 8, node_color = "orange", node_shape = "square", #' # edge_color = "blue", edge_size = 2) @@ -84,20 +95,13 @@ NULL #' # edge_size = migraph::tie_closeness(ison_karateka)) #' #autographr(ison_southern_women, layout = "concentric", #' # node_color = "type", membership = "type") +#' #autographr(ison_southern_women, layout = "hierarchy", center = "events") #' #autographr(ison_lotr, layout = "multilevel", -#' # node_color = "Race", node_shape = , level = "Race") +#' # node_color = "Race", level = "Race") #' @export -autographr <- function(.data, - layout, - labels = TRUE, - node_color, - node_shape, - node_size, - node_group, - edge_color, - edge_size, - ...) { - name <- weight <- nodes <- label <- NULL # avoid CMD check notes +autographr <- function(.data, layout, labels = TRUE, + node_color, node_shape, node_size, node_group, + edge_color, edge_size, ...) { g <- as_tidygraph(.data) if (missing(layout)) { if (length(g) == 3) { @@ -117,7 +121,7 @@ autographr <- function(.data, } if (missing(node_group)) node_group <- NULL else { node_group <- as.character(substitute(node_group)) - g <- activate(g, nodes) %>% + g <- activate(g, "nodes") %>% mutate(node_group = reduce_categories(g, node_group)) } if (missing(edge_color)) edge_color <- NULL else @@ -140,9 +144,13 @@ autographr <- function(.data, #' @source http://blog.schochastics.net/post/animating-network-evolutions-with-gganimate/ #' @examples #' #autographs(to_egos(ison_adolescents)) +#' #autographs(migraph::play_diffusion(ison_adolescents)) #' @export autographs <- function(netlist, ...) { thisRequires("patchwork") + if (any(class(netlist) == "diff_model")) { + netlist <- to_waves(netlist) + } if(!is.null(names(netlist))){ gs <- lapply(1:length(netlist), function(x) autographr(netlist[[x]], ...) + @@ -178,18 +186,48 @@ autographs <- function(netlist, ...) { #' # color = rep(c("blue", "red"), times = 4), #' # size = sample(4:16, 8, replace = TRUE)) %>% #' # mutate_ties(year = sample(1995:1998, 10, replace = TRUE), -#' # e_color = sample(c("yellow", "green"), 10, replace = TRUE)) %>% +#' # e_color = sample(c("yellow", "green"), 10, replace = TRUE)) %>% #' # to_waves(attribute = "year") %>% #' # autographd(keep_isolates = FALSE, layout = "circle", node_shape = "shape", -#' # node_color = "color", node_size = "size", -#' # edge_color = "e_color") +#' # node_color = "color", node_size = "size", edge_color = "e_color") +#' #autographd(play_diffusion(ison_adolescents, seeds = 4), layout = "circle") +#' #autographd(play_diffusion(ison_adolescents, seeds = 4, recovery = 0.2), +#' # layout = "circle") #' @export -autographd <- function(tlist, keep_isolates = TRUE, layout = "stress", - labels = TRUE, node_color = NULL, node_shape = NULL, - node_size = NULL, edge_color = NULL) { - # Todo: add (...) arguments passed on to `ggraph()`/`ggplot()`/`gganimate()` +autographd <- function(tlist, layout, labels = TRUE, + node_color, node_shape, node_size, + edge_color, edge_size, keep_isolates = TRUE, ...) { thisRequires("gganimate") + thisRequires("gifski") + thisRequires("png") + thisRequires("migraph") x <- y <- name <- status <- frame <- NULL + # Check arguments + if (missing(layout)) { + if (length(tlist[[1]]) == 3) { + layout <- "triad" + } else if (length(tlist[[1]]) == 4) { + layout <- "quad" + } else if (is_twomode(tlist[[1]])) { + layout <- "hierarchy" + } else layout <- "stress" + } + if (missing(node_color)) node_color <- NULL else + node_color <- as.character(substitute(node_color)) + if (missing(node_shape)) node_shape <- NULL else + node_shape <- as.character(substitute(node_shape)) + if (missing(node_size)) node_size <- NULL else if (!is.numeric(node_size)) { + node_size <- as.character(substitute(node_size)) + } + if (missing(edge_color)) edge_color <- NULL else + edge_color <- as.character(substitute(edge_color)) + if (missing(edge_size)) edge_size <- NULL else if (!is.numeric(edge_size)) { + edge_size <- as.character(substitute(edge_size)) + } + # Check if diffusion model + if (any(class(tlist) == "diff_model")) { + tlist <- to_waves(tlist) + } # Check if object is a list of lists if (!is.list(tlist[[1]])) { stop("Please declare a migraph-compatible network listed according @@ -199,18 +237,21 @@ autographd <- function(tlist, keep_isolates = TRUE, layout = "stress", tlist <- Filter(function(x) igraph::gsize(x) > 0, tlist) # Create an edge list edges_lst <- lapply(1:length(tlist), function(i) - cbind(igraph::as_data_frame(tlist[[i]], "edges"), frame = i)) + cbind(igraph::as_data_frame(tlist[[i]], "edges"), + frame = ifelse(is.null(names(tlist)), i, names(tlist)[i]))) # Check if all names are present in all lists if (length(unique(unlist(unname(lapply(tlist, length))))) != 1) { - tlist <- to_waves(do.call("rbind", edges_lst), attribute = "frame") + tlist <- to_waves(as_tidygraph(do.call("rbind", edges_lst)), + attribute = "frame") } # Add separate layouts for each time point lay <- lapply(1:length(tlist), function(i) - ggraph::create_layout(tlist[[i]], layout)) + ggraph::create_layout(tlist[[i]], layout, ...)) # Create a node list for each time point nodes_lst <- lapply(1:length(tlist), function(i) { cbind(igraph::as_data_frame(tlist[[i]], "vertices"), - x = lay[[i]][, 1], y = lay[[i]][, 2], frame = i) + x = lay[[i]][, 1], y = lay[[i]][, 2], + frame = ifelse(is.null(names(tlist)), i, names(tlist)[i])) }) # Create an edge list for each time point edges_lst <- time_edges_lst(tlist, edges_lst, nodes_lst, edge_color) @@ -227,26 +268,22 @@ autographd <- function(tlist, keep_isolates = TRUE, layout = "stress", if (isFALSE(keep_isolates)) { nodes_out <- remove_isolates(edges_out, nodes_out) } else { - if(nrow(nodes_out)/length(unique(nodes_out$frame)) > 20) { + if(nrow(nodes_out)/length(unique(nodes_out$frame)) > 30 & + any(unlist(lapply(tlist, migraph::node_is_isolate)) == TRUE)) { message("Please considering deleting isolates to improve visualisation.") } nodes_out$status <- TRUE } - # Plot with ggplot2 and gganimate + # Plot with ggplot2/ggraph and animate with gganimate p <- map_dynamic(edges_out, nodes_out, edge_color, node_shape, - node_color, node_size) - # Add labels, if declared - if (isTRUE(labels)) { - p <- p + ggplot2::geom_text(data = nodes_out, - aes(x, y, label = name, alpha = status), - hjust = -0.2, vjust = -0.2, - show.legend = FALSE) - } - # Animate - p + ggplot2::scale_alpha_manual(values = c(0, 1)) + - gganimate::transition_states(frame, state_length = 1) + - ggplot2::labs(title = "{closest_state}") + - ggplot2::theme_void() + node_color, node_size, edge_size, labels) + + gganimate::transition_states(states = frame, transition_length = 5, + state_length = 10, wrap = FALSE) + + gganimate::enter_fade() + + gganimate::exit_fade() + + ggplot2::labs(title = "{closest_state}") + gganimate::animate(p, duration = 2*length(tlist), start_pause = 5, + end_pause = 10, renderer = gganimate::gifski_renderer()) } reduce_categories <- function(g, node_group) { @@ -300,6 +337,12 @@ reduce_categories <- function(g, node_group) { } } } + if (layout == "stress" & is_diamond(g)) { + turn <- matrix(c(cos(0.71), -sin(0.71), sin(0.71), cos(0.71)), 2, 2) + coord <- matrix(cbind(lo[,1], lo[,2]), ncol = 2) %*% turn + lo[,1] <- coord[,1] + lo[,2] <- coord[,2] + } p <- ggraph::ggraph(lo) + ggplot2::theme_void() if (labels & is_labelled(g)) { if (layout == "circle") { @@ -334,7 +377,7 @@ reduce_categories <- function(g, node_group) { } p <- p + ggraph::geom_node_text(ggplot2::aes(label = name), size = 3, hjust = hj, - vjust = vj) + + vjust = vj, check_overlap = TRUE) + ggplot2::coord_cartesian(xlim=c(-1.2,1.2), ylim=c(-1.2,1.2)) } else if (layout %in% c("bipartite", "railway") | (layout == "hierarchy" & length(unique(lo[,2])) <= 2)) { @@ -343,7 +386,10 @@ reduce_categories <- function(g, node_group) { nudge_y = ifelse(lo[,2] == 1, 0.05, -0.05), # vjust = ifelse(node_mode(object), -1, 1), angle = 90) + - ggplot2::coord_cartesian(ylim=c(-0.2,1.2)) + ggplot2::coord_cartesian(ylim=c(-0.2, 1.2)) + } else if (layout == "hierarchy" & length(unique(lo[,2])) > 2) { + p <- p + ggraph::geom_node_text(ggplot2::aes(label = name), size = 2, + hjust = "inward", vjust = -0.4) } else if (!is_twomode(g)) { # Plot one mode p <- p + ggraph::geom_node_label(ggplot2::aes(label = name), label.padding = 0.15, label.size = 0, @@ -568,33 +614,63 @@ reduce_categories <- function(g, node_group) { .graph_nodes <- function(p, g, node_color, node_shape, node_size){ nsize <- .infer_nsize(g, node_size) nshape <- .infer_shape(g, node_shape) - if (is_twomode(g)) { - if (!is.null(node_color)) { - if (node_color %in% names(node_attribute(g))) { - node_color <- as.factor(node_attribute(g, node_color)) - p <- p + ggraph::geom_node_point(ggplot2::aes(color = node_color), - size = nsize, shape = nshape) + - ggplot2::scale_colour_manual(values = colorsafe_palette, guide = "none") + if ("Infected" %in% names(node_attribute(g))) { + node_color <- as.factor(node_attribute(g, "Infected")) + p <- p + ggraph::geom_node_point(ggplot2::aes(color = node_color), + size = nsize, shape = nshape) + + ggplot2::scale_color_manual(name = NULL, guide = "none", + values = c("Infected" = "red", + "Susceptible" = "blue", + "Exposed" = "orange", + "Recovered" = "darkgreen")) + } else if (any("diff_model" %in% names(attributes(g)))) { + node_color <- infection_rate(attr(g, "diff_model")) + nshape <- ifelse(node_color == 0, "Not Adopted", + ifelse(node_color == 1, "Seed(s)", "Adopted")) + node_color <- ifelse(node_color == 0, max(node_color) + 1, node_color) + p <- p + ggraph::geom_node_point(ggplot2::aes(shape = nshape, + color = node_color), + size = nsize) + + ggplot2::scale_color_gradient(low = "red", high = "blue", + breaks=c(1, max(node_color)), + labels=c("Early adoption", "Late adoption"), + name = "Time of\nAdoption") + + ggplot2::scale_shape_manual(name = "", + breaks = c("Seed(s)", "Adopted", "Not Adopted"), + values = c("Seed(s)" = "triangle", + "Adopted" = "circle", + "Not Adopted" = "square")) + + ggplot2::guides(color = ggplot2::guide_colorbar(order = 1, reverse = TRUE), + shape = ggplot2::guide_legend(order = 2)) + } else { + if (is_twomode(g)) { + if (!is.null(node_color)) { + if (node_color %in% names(node_attribute(g))) { + node_color <- as.factor(node_attribute(g, node_color)) + p <- p + ggraph::geom_node_point(ggplot2::aes(color = node_color), + size = nsize, shape = nshape) + + ggplot2::scale_colour_manual(values = colorsafe_palette, guide = "none") + } else { + p <- p + ggraph::geom_node_point(color = node_color, + size = nsize, shape = nshape) + } } else { - p <- p + ggraph::geom_node_point(color = node_color, - size = nsize, shape = nshape) + p <- p + ggraph::geom_node_point(size = nsize, shape = nshape) } } else { - p <- p + ggraph::geom_node_point(size = nsize, shape = nshape) - } - } else { - if (!is.null(node_color)) { - if (node_color %in% names(node_attribute(g))) { - node_color <- as.factor(node_attribute(g, node_color)) - p <- p + ggraph::geom_node_point(aes(color = node_color), - size = nsize, shape = nshape) + - ggplot2::scale_colour_manual(values = colorsafe_palette, guide = "none") + if (!is.null(node_color)) { + if (node_color %in% names(node_attribute(g))) { + node_color <- as.factor(node_attribute(g, node_color)) + p <- p + ggraph::geom_node_point(aes(color = node_color), + size = nsize, shape = nshape) + + ggplot2::scale_colour_manual(values = colorsafe_palette, guide = "none") + } else { + p <- p + ggraph::geom_node_point(color = node_color, + size = nsize, shape = nshape) + } } else { - p <- p + ggraph::geom_node_point(color = node_color, - size = nsize, shape = nshape) + p <- p + ggraph::geom_node_point(size = nsize, shape = nshape) } - } else { - p <- p + ggraph::geom_node_point(size = nsize, shape = nshape) } } p @@ -672,6 +748,32 @@ reduce_categories <- function(g, node_group) { out } +is_diamond <- function(x) { + x <- as_matrix(x) + if (is.numeric(x)) { + if (length(x) == 100 | length(x) == 10000 & + suppressWarnings(all(unique(rowSums(x)) == c(3, 5, 8)))) { + TRUE + } else FALSE + } else FALSE +} + +infection_rate <- function(x) { + if ("I_new" %in% names(x)) { + out <- c(x$I[1], x$I_new[-1]) + } else { + out <- c(x$I[1], diff(x$I)[-(length(x$I))]) + out <- ifelse(out < 0, 0, out) + } + a <- list() + for (k in seq_len(length(out))) { + a[[k]] <- rep(x$t[k], out[k]) + } + out <- unlist(a) + if (length(out) < unique(x$n)) out <- c(out, rep(0, (unique(x$n)-length(out)))) + out +} + cart2pol <- function(xyz){ stopifnot(is.numeric(xyz)) if (is.vector(xyz) && (length(xyz) == 2 || length(xyz) == @@ -757,14 +859,14 @@ time_edges_lst <- function(tlist, edges_lst, nodes_lst, edge_color) { transition_edge_lst <- function(tlist, edges_lst, nodes_lst, all_edges) { x <- lapply(1:length(tlist), function(i) { idx <- which(!all_edges[, 3] %in% edges_lst[[i]]$id) - if (length(idx != 0)) { + if (length(idx) != 0) { tmp <- data.frame(from = all_edges[idx, 1], to = all_edges[idx, 2], id = all_edges[idx, 3]) tmp$x <- nodes_lst[[i]]$x[match(tmp$from, nodes_lst[[i]]$name)] tmp$y <- nodes_lst[[i]]$y[match(tmp$from, nodes_lst[[i]]$name)] tmp$xend <- nodes_lst[[i]]$x[match(tmp$to, nodes_lst[[i]]$name)] tmp$yend <- nodes_lst[[i]]$y[match(tmp$to, nodes_lst[[i]]$name)] - tmp$frame <- i + tmp$frame <- ifelse(is.null(names(tlist)), i, names(tlist)[i]) tmp$status <- FALSE edges_lst[[i]] <- dplyr::bind_rows(edges_lst[[i]], tmp) } @@ -773,17 +875,18 @@ transition_edge_lst <- function(tlist, edges_lst, nodes_lst, all_edges) { } remove_isolates <- function(edges_out, nodes_out) { - status <- frame <- from <- to <- NULL + status <- frame <- from <- to <- framen <- NULL # Create node metadata for node presence in certain frame meta <- edges_out %>% dplyr::filter(status == TRUE) %>% - dplyr::mutate(meta = ifelse(frame > 1, - paste0(from, (frame - 1)), from)) %>% + dplyr::mutate(framen = match(frame, unique(frame)), + meta = ifelse(framen > 1, paste0(from, (framen - 1)), from)) %>% dplyr::select(meta, status) %>% dplyr::distinct() metab <- edges_out %>% dplyr::filter(status == TRUE) %>% - dplyr::mutate(meta = ifelse(frame > 1, paste0(to, (frame - 1)), to)) %>% + dplyr::mutate(framen = match(frame, unique(frame)), + meta = ifelse(framen > 1, paste0(to, (framen - 1)), to)) %>% dplyr::select(meta, status) %>% rbind(meta) %>% dplyr::distinct() @@ -796,14 +899,15 @@ remove_isolates <- function(edges_out, nodes_out) { } map_dynamic <- function(edges_out, nodes_out, edge_color, node_shape, - node_color, node_size) { - x <- xend <- y <- yend <- id <- status <- name <- NULL - p <- ggplot2::ggplot() + node_color, node_size, edge_size, labels) { + x <- xend <- y <- yend <- id <- status <- Infected <- name <- NULL + alphad <- ifelse(nodes_out$status == TRUE, 1, 0) + alphae <- ifelse(edges_out$status == TRUE, 1, 0) + if (all(unique(alphae) == 1)) alphae <- 0.8 # Plot edges if (!is.null(edge_color)) { # Remove NAs in edge color, if declared - edge_color <- ifelse(is.na(edges_out[[edge_color]]), "black", - edges_out[[edge_color]]) + edge_color <- ifelse(is.na(edges_out[[edge_color]]), "black", edges_out[[edge_color]]) color <- grDevices::colors() color <- color[!color %in% "black"] if(!any(grepl(paste(color, collapse = "|"), edge_color)) | @@ -814,25 +918,26 @@ map_dynamic <- function(edges_out, nodes_out, edge_color, node_shape, } } } - } else { - edge_color <- rep("black", nrow(edges_out)) - } - p <- p + ggplot2::geom_segment(data = edges_out, - aes(x = x, xend = xend, y = y, yend = yend, - group = id, alpha = status), - color = edge_color, show.legend = FALSE) + } else edge_color <- rep("black", nrow(edges_out)) + if (!is.null(edge_size)) { + edge_size <- as.numeric(edges_out[[edge_size]]) + } else edge_size <- 0.5 + p <- ggplot2::ggplot() + + ggplot2::geom_segment(aes(x = x, xend = xend, y = y, yend = yend, group = id), + alpha = alphae, data = edges_out, color = edge_color, + linewidth = edge_size, show.legend = FALSE) # Set node shape, color, and size if (!is.null(node_shape)) { node_shape <- as.factor(nodes_out[[node_shape]]) - node_shape <- c("circle","square","triangle")[node_shape] - } else { - node_shape <- rep("circle", nrow(nodes_out)) - } + node_shape <- c("circle", "square", "triangle")[node_shape] + } else node_shape <- rep("circle", nrow(nodes_out)) if (!is.null(node_color)) { - node_color <- nodes_out[[node_color]] + if (node_color %in% names(nodes_out)) { + node_color <- nodes_out[[node_color]] + } color <- grDevices::colors() color <- color[!color %in% "black"] - if(!any(grepl(paste(color, collapse = "|"), node_color)) | + if (!any(grepl(paste(color, collapse = "|"), node_color)) | any(grepl("#", node_color))) { for(i in unique(node_color)) { if (i != "black") { @@ -840,18 +945,41 @@ map_dynamic <- function(edges_out, nodes_out, edge_color, node_shape, } } } - } else { - node_color <- rep("gray", nrow(nodes_out)) - } + } else if (is.null(node_color) & "Infected" %in% names(nodes_out)) { + node_color <- ifelse(nodes_out[["Infected"]] == "Infected", "red", + ifelse(nodes_out[["Infected"]] == "Susceptible", "blue", + ifelse(nodes_out[["Infected"]] == "Exposed", "orange", "darkgreen"))) + } else node_color <- "darkgray" if (!is.null(node_size)) { - node_size <- as.numeric(nodes_out[[node_size]]) - } else { - node_size <- rep(nrow(nodes_out)/length(unique(nodes_out$frame)), - nrow(nodes_out)) + if (node_size %in% names(nodes_out)) { + node_size <- nodes_out[[node_size]] + } + } else if (nrow(nodes_out) > 100) { + node_size <- 3 + } else node_size <- nrow(nodes_out)/length(unique(nodes_out$frame)) + # Add labels + if (isTRUE(labels)) { + p <- p + ggplot2::geom_text(aes(x, y, label = name), + alpha = alphad, + data = nodes_out, color = "black", + hjust = -0.2, vjust = -0.2, show.legend = FALSE) } # Plot nodes - p <- p + ggplot2::geom_point(data = nodes_out, - aes(x, y, group = name, alpha = status), - size = node_size, color = node_color, - shape = node_shape, show.legend = FALSE) + if ("Infected" %in% names(nodes_out)) { + p <- p + ggplot2::geom_point(aes(x, y, group = name, color = Infected), + size = node_size, shape = node_shape, data = nodes_out) + + ggplot2::scale_color_manual(name = NULL, values = c("Infected" = "red", + "Susceptible" = "blue", + "Exposed" = "orange", + "Recovered" = "darkgreen")) + + ggplot2::theme_void() + + ggplot2::theme(legend.position = "bottom") + } else { + p <- p + ggplot2::geom_point(aes(x, y, group = name), alpha = alphad, + size = node_size, data = nodes_out, + color = node_color, shape = node_shape, + show.legend = FALSE) + + ggplot2::theme_void() + } + p } diff --git a/R/map_is.R b/R/map_is.R index 7fd6ef6..7c4477d 100644 --- a/R/map_is.R +++ b/R/map_is.R @@ -122,6 +122,11 @@ is_twomode.data.frame <- function(.data) { length(intersect(.data[,1], .data[,2])) == 0 } +#' @export +is_twomode.numeric <- function(.data) { + return(FALSE) +} + #' @describeIn is Tests whether network is weighted #' @importFrom igraph is.weighted #' @examples @@ -305,13 +310,15 @@ is_multiplex.matrix <- function(.data) { #' @export is_multiplex.tbl_graph <- function(.data) { igraph::any_multiple(.data) | - length(igraph::edge_attr_names(as_igraph(.data))) > 1 + length(igraph::edge_attr_names(as_igraph(.data))) > 1 | + "type" %in% igraph::edge_attr_names(.data) } #' @export is_multiplex.igraph <- function(.data) { igraph::any_multiple(.data) | - length(igraph::edge_attr_names(as_igraph(.data))) > 1 + length(igraph::edge_attr_names(.data)) > 1 | + "type" %in% igraph::edge_attr_names(.data) } #' @export diff --git a/R/map_layout_partition.R b/R/map_layout_partition.R index 34b909d..c1cf863 100644 --- a/R/map_layout_partition.R +++ b/R/map_layout_partition.R @@ -11,7 +11,8 @@ #' #' The "hierarchy" layout layers the first node set along the bottom, #' and the second node set along the top, -#' sequenced and spaced as necessary to minimise edge overlap. +#' sequenced and spaced as necessary to minimise edge overlap. +#' These node sets can be arranged using the "center" argument. #' The "alluvial" layout is similar to "hierarchy", #' but places successive layers horizontally rather than vertically. #' The "railway" layout is similar to "hierarchy", @@ -21,10 +22,17 @@ #' The "concentric" layout places a "hierarchy" layout #' around a circle, with successive layers appearing as concentric circles. #' The "multilevel" layout places successive layers as multiple levels. +#' The "lineage" layout ranks nodes in Y axis according to values. +#' These values for ranking the nodes should be declared as a node attribute +#' in the "rank" argument. #' @name partition_layouts #' @inheritParams transform #' @param circular Should the layout be transformed into a radial representation. -#' Only possible for some layouts. Defaults to FALSE +#' Only possible for some layouts. Defaults to FALSE. +#' @param center Further split "hierarchical" layouts by +#' declaring the "center" argument as the "events", "actors", +#' or by declaring a node name. +#' Defaults to NULL. #' @param times Maximum number of iterations, where appropriate #' @param radius A vector of radii at which the concentric circles #' should be located. @@ -37,6 +45,8 @@ #' the number of edge crossings. #' @param level A node attribute or a vector to hierarchically order levels for #' "multilevel" layout. +#' @param rank A numerical node attribute to place nodes in Y axis +#' according to values. #' @family mapping #' @source #' Diego Diez, Andrew P. Hutchins and Diego Miranda-Saavedra. 2014. @@ -47,22 +57,64 @@ NULL #' @rdname partition_layouts #' @export -layout_tbl_graph_hierarchy <- function(.data, - circular = FALSE, times = 1000){ - thisRequiresBio("Rgraphviz") - prep <- as_matrix(.data, twomode = FALSE) - if(anyDuplicated(rownames(prep))){ - rownames(prep) <- seq_len(nrow(prep)) - colnames(prep) <- seq_len(ncol(prep)) +layout_tbl_graph_hierarchy <- function(.data, center = NULL, + circular = FALSE, times = 1000) { + if (is.null(center)) { + thisRequiresBio("Rgraphviz") + prep <- as_matrix(.data, twomode = FALSE) + if(anyDuplicated(rownames(prep))){ + rownames(prep) <- seq_len(nrow(prep)) + colnames(prep) <- seq_len(ncol(prep)) + } + if(any(prep<0)) prep[prep<0] <- 0 + out <- as_graphAM(prep) + out <- suppressMessages(Rgraphviz::layoutGraph(out, layoutType = 'dot', + attrs = list(graph = list(rankdir = "BT")))) + nodeX <- .rescale(out@renderInfo@nodes$nodeX) + nodeY <- .rescale(out@renderInfo@nodes$nodeY) + # nodeY <- abs(nodeY - max(nodeY)) + out <- .to_lo(cbind(nodeX, nodeY)) + } else { + if (!is_twomode(.data)) stop("Please declare a two-mode network.") + net <- as_matrix(.data) + nn <- dim(net)[1] + mm <- dim(net)[2] + if (center == "actors") { + Act <- cbind(rep(1, nrow(net)), nrm(rng(nn))) + Evt1 <- cbind(rep(0, ceiling(ncol(net)/2)), nrm(rng(ceiling(mm/2)))) + Evt2 <- cbind(rep(2, floor(ncol(net)/2)), nrm(rng(floor(mm/2)))) + crd <- rbind(Act, Evt1, Evt2) + crd[which(is.nan(crd))] <- 0.5 + crd[, 2] <- crd[, 2] * cos(pi) - crd[, 1] * sin(pi) + rownames(crd) <- c(dimnames(net)[[1]], dimnames(net)[[2]]) + } else if (center == "events") { + Act1 <- cbind(rep(0, ceiling(nrow(net)/2)), nrm(rng(ceiling(nn/2)))) + Act2 <- cbind(rep(2, floor(nrow(net)/2)), nrm(rng(floor(nn/2)))) + Evt <- cbind(rep(1, ncol(net)), nrm(rng(mm))) + crd <- rbind(Act1, Act2, Evt) + crd[which(is.nan(crd))] <- 0.5 + crd[, 2] <- crd[, 2] * cos(pi) - crd[, 1] * sin(pi) + rownames(crd) <- c(dimnames(net)[[1]], dimnames(net)[[2]]) + } else { + if (center %in% node_names(.data)) { + side1 <- suppressWarnings(cbind(rep(0, nrow(net)), nrm(rng(nn)))) + side2 <- suppressWarnings(cbind(rep(2, ncol(net)), nrm(rng(mm)))) + if (any(rownames(net) == center)) { + side1[,1] <- ifelse(rownames(net) == center, 1, side1[,1]) + side1[,2] <- ifelse(rownames(net) == center, 0.5, side1[,2]) + } else { + side2[,1] <- ifelse(rownames(net) == center, 1, side2[,1]) + side2[,2] <- ifelse(rownames(net) == center, 0.5, side2[,2]) + } + crd <- rbind(side1, side2) + crd[which(is.nan(crd))] <- 0.5 + crd[, 2] <- crd[, 2] * cos(pi) - crd[, 1] * sin(pi) + rownames(crd) <- c(dimnames(net)[[1]], dimnames(net)[[2]]) + } else stop("Please declare actors, events, or a node name as center.") + } + out <- .to_lo(crd) } - if(any(prep<0)) prep[prep<0] <- 0 - out <- as_graphAM(prep) - out <- suppressMessages(Rgraphviz::layoutGraph(out, layoutType = 'dot', - attrs = list(graph = list(rankdir = "BT")))) - nodeX <- .rescale(out@renderInfo@nodes$nodeX) - nodeY <- .rescale(out@renderInfo@nodes$nodeY) - # nodeY <- abs(nodeY - max(nodeY)) - .to_lo(cbind(nodeX, nodeY)) + out } #' @rdname partition_layouts @@ -136,7 +188,7 @@ layout_tbl_graph_concentric <- function(.data, membership = NULL, radius = NULL, order.values <- lapply(order.by, function(b) node_attribute(.data, b)) } else { - if (is_twomode(.data)) { + if (is_twomode(.data) & length(membership) == 2) { for(k in 2:length(membership)) { xnet <- as_matrix(to_multilevel(.data))[membership[[k-1]], membership[[k]]] @@ -174,7 +226,7 @@ layout_tbl_graph_multilevel <- function(.data, level = NULL, circular = FALSE) { if (length(level) > 1 & length(level) != length(.data)) { stop("Please pass the function a `level` node attribute or a vector.") } else if (length(level) != length(.data)) { - level <- node_attribute(.data, level) + level <- as.factor(node_attribute(.data, level)) } } out <- igraph::set_vertex_attr(.data, "lvl", value = level) @@ -183,10 +235,57 @@ layout_tbl_graph_multilevel <- function(.data, level = NULL, circular = FALSE) { .to_lo(out) } +#' @rdname partition_layouts +#' @export +layout_tbl_graph_lineage <- function(.data, rank, circular = FALSE) { + if (length(rank) > 1 & length(rank) != length(.data)) { + stop("Please pass the function a `rank` node attribute or a vector.") + } else if (length(rank) != length(.data)) { + rank <- node_attribute(.data, rank) + if (!is.numeric(rank)) + stop("Please declare a numeric attribute to `rank` nodes.") + } + thisRequiresBio("Rgraphviz") + prep <- as_matrix(.data, twomode = FALSE) + if(anyDuplicated(rownames(prep))){ + rownames(prep) <- seq_len(nrow(prep)) + colnames(prep) <- seq_len(ncol(prep)) + } + if(any(prep<0)) prep[prep<0] <- 0 + out <- as_graphAM(prep) + out <- suppressMessages(Rgraphviz::layoutGraph(out, layoutType = 'dot', + attrs = list(graph = list(rankdir = "BT")))) + nodeX <- .rescale(out@renderInfo@nodes$nodeX) + names <- names(nodeX) + nodeY <- .rescale(rank*(-1)) + .to_lo(.adjust(nodeX, nodeY, names)) +} + .rescale <- function(vector){ (vector - min(vector)) / (max(vector) - min(vector)) } +.adjust <- function(x, y, names) { + out <- data.frame(cbind(x, y, names)) + adj <- data.frame() + for (k in levels(as.factor(y))) { + a <- subset(out, y == k) + if (length(a[,1]) == 1) { + a[,1] <- ifelse(a[,1] > 0.8, as.numeric(a[,1])*0.8, + ifelse(a[,1] < 0.2, as.numeric(a[,1])*1.2, + as.numeric(a[,1]))) + } else if (length(a[,1]) > 2) { + a[,1] <- seq(min(a[,1]), max(a[,1]), len = length(a[,1])) + } + adj <- rbind(adj, a) + } + name <- data.frame(names = out[,3]) + out <- dplyr::left_join(name, adj, by = "names") + out <- apply(out[,2:3], 2, as.numeric) + rownames(out) <- name$names + out +} + .to_lo <- function(mat){ res <- as.data.frame(mat) names(res) <- c("x","y") @@ -237,7 +336,7 @@ getNNvec <- function(.data, members){ }) } -getCoordinates <- function(x, r){ +getCoordinates <- function(x, r) { l <- length(x) d <- 360/l c1 <- seq(0, 360, d) @@ -250,3 +349,29 @@ getCoordinates <- function(x, r){ rownames(tmp) <- x tmp } + +rng <- function(r) { + if (r == 1L) return(0) + if (r > 1L) { + x <- vector() + x <- append(x, (-1)) + for (i in 1:(r - 1)) x <- append(x, ((-1) + (2L/(r - 1L)) * i)) + return(x * (r/50L)) + } else stop("no negative values") +} + +nrm <- function(x, digits = 3) { + if (isTRUE(length(x) == 1L) == TRUE) return(x) + if (is.array(x) == TRUE) { + xnorm <- (x[, 1] - min(x[, 1]))/(max(x[, 1]) - min(x[, 1])) + rat <- (max(x[, 1]) - min(x[, 1]))/(max(x[, 2]) - min(x[, 2])) + ynorm <- ((x[, 2] - min(x[, 2]))/(max(x[, 2]) - min(x[, 2]))) * (rat) + ifelse(isTRUE(rat > 0) == FALSE, + ynorm <- ((x[, 2] - min(x[, 2]))/(max(x[, 2]) - + min(x[, 2]))) * (1L/rat), NA) + return(round(data.frame(X = xnorm, Y = ynorm), digits)) + } + else if (is.vector(x) == TRUE) { + return(round((x - min(x))/(max(x) - min(x)), digits)) + } +} diff --git a/R/map_palettes.R b/R/map_palettes.R index bd6426e..4ee3a1a 100644 --- a/R/map_palettes.R +++ b/R/map_palettes.R @@ -1,22 +1,22 @@ -#' An IHEID palette generator -#' -#' These are a few color palettes useful for members of the Geneva Graduate Institute. -#' This function calls palettes for the Institute, for the Centres, and for the SDGs. -#' @param n Number of colors desired. If omitted, uses all colours. +#' Many Palettes generator +#' #' @param palette Name of desired palette. Current choices are: -#' \code{IHEID}, \code{Centres}, and \code{SDGs}. +#' \code{IHEID}, \code{Centres}, \code{SDGs}, \code{ETHZ}, \code{RUG}, +#' and \code{UZH}. +#' @param n Number of colors desired. If omitted, uses all colours. #' @param type Either "continuous" or "discrete". Use continuous if you want #' to automatically interpolate between colours. -#' @importFrom graphics rect par image text -#' @return A vector of colours. +#' @return A graphic display of colours in palette. #' @source Adapted from \url{https://github.com/karthik/wesanderson/blob/master/R/colors.R} -#' @keywords colors #' @examples -#' #iheid_palette("IHEID") +#' #many_palettes("IHEID") +#' #many_palettes() #' @export -iheid_palette <- function(palette, n, type = c("discrete", "continuous")) { +many_palettes <- function(palette, n, type = c("discrete", "continuous")) { type <- match.arg(type) - pal <- iheid_corp_palette(palette) + if (missing(palette)) { + pal <- corp_palette(c("IHEID", "SDGs", "Centres", "ETHZ", "RUG", "UZH")) + } else pal <- corp_palette(palette) if (is.null(pal)) stop("Palette not found.") if (missing(n)) { @@ -25,26 +25,16 @@ iheid_palette <- function(palette, n, type = c("discrete", "continuous")) { if (type == "discrete" && n > length(pal)) { stop("Number of requested colors greater than what palette can offer") } - #thisRequires("grDevices") - out <- switch(type, - continuous = grDevices::colorRampPalette(pal)(n), - discrete = pal[1:n] - ) - structure(out, class = "palette", name = palette) -} - -print.palette <- function(x, ...) { - #thisRequires("grDevices") - #thisRequires("graphics") - n <- length(x) + x <- switch(type, continuous = grDevices::colorRampPalette(pal)(n), + discrete = pal[1:n]) old <- graphics::par(mar = c(0.5, 0.5, 0.5, 0.5)) on.exit(graphics::par(old)) graphics::image(1:n, 1, as.matrix(1:n), col = x, ylab = "", xaxt = "n", yaxt = "n", bty = "n") graphics::rect(0, 0.9, n + 1, 1.1, col = grDevices::rgb(1, 1, 1, 0.8), border = NA) - graphics::text((n + 1) / 2, 1, labels = attr(x, "name"), - cex = 1, family = "serif") + graphics::text(c(seq_len(n)), 1, labels = attr(x, "name"), + cex = 0.9, family = "serif", srt = 90) } colorsafe_palette <- c("#d73027", "#4575b4", "#1B9E77","#D95F02","#7570B3", diff --git a/R/map_theme.R b/R/map_theme.R index 3840c32..e08348b 100644 --- a/R/map_theme.R +++ b/R/map_theme.R @@ -6,116 +6,179 @@ #' @name themes #' @param base_size Font size, by default 12. #' @param base_family Font family, by default "sans". -#' @return Themes the current ggraph to current IHEID guidelines. -NULL - -#' @rdname themes #' @examples #' # autographr(to_mentoring(ison_brandes)) + #' # labs(title = "Who leads and who follows?") + #' # theme_iheid() +NULL + +#' @rdname themes #' @export -theme_iheid <- function(base_size = 12, base_family = "sans") { - element_blank <- element_line <- element_rect <- element_text <- rel <- NULL - colors <- iheid_corp_palette("IHEID") +theme_iheid <- function(base_size = 12, base_family = "Times") { + colors <- corp_palette("IHEID") (ggplot2::theme_minimal(base_size = base_size, base_family = base_family) + ggplot2::theme( - line = element_line(colors["IHEIDBlack"]), - rect = element_rect(fill = colors["IHEIDGrey"], - linetype = 0, colour = NA), - text = element_text(colour = colors["IHEIDRed"]), - axis.title = element_blank(), - axis.text = element_text(), - axis.ticks = element_blank(), - axis.line = element_blank(), - axis.title.x = element_blank(), - axis.text.x = element_blank(), - axis.ticks.x = element_blank(), - axis.title.y = element_blank(), - axis.text.y = element_blank(), - axis.ticks.y = element_blank(), - panel.grid.major = element_blank(), - panel.grid.minor = element_blank(), - legend.background = element_rect(), + line = ggplot2::element_line(colors["IHEIDBlack"]), + rect = ggplot2::element_rect(fill = "#FFFFFF", linetype = 1, + linewidth = 0.6, colour = colors["IHEIDGrey"]), + title = ggplot2::element_text(colour = colors["IHEIDRed"], + size = base_size, + face = "bold"), + plot.subtitle = ggplot2::element_text(colors["IHEIDGrey"], + size = base_size*0.85, family = "Times", + face = "bold"), + plot.caption = ggplot2::element_text(colors["IHEIDBlack"], + size = base_size*0.75, + family = "Times", + face = "italic"), + axis.title = ggplot2::element_blank(), + axis.text = ggplot2::element_text(), + axis.ticks = ggplot2::element_blank(), + axis.line = ggplot2::element_blank(), + axis.title.x = ggplot2::element_blank(), + axis.text.x = ggplot2::element_blank(), + axis.ticks.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + axis.ticks.y = ggplot2::element_blank(), + panel.grid.major = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + legend.background = ggplot2::element_rect(), legend.position = "bottom", legend.direction = "horizontal", legend.box = "vertical", - plot.title = element_text(hjust = 0, size = rel(1.5), face = "bold"), - strip.background = element_rect())) -} - -iheid_corp_color <- function(...) { - iheid_corp_colors <- c(`IHEIDRed` = "#E20020", `IHEIDBlack` = "#5c666f", - `IHEIDGrey` = "#6f7072", `AHCD` = "#622550", - `CFFD` = "#0094D8", `CIES` = "#268D2B", - `CTEI` = "#008F92", `CGEN` = "#820C2B", - `CCDP` = "#3E2682", `GLGC` = "#006564", - `GLHC` = "#A8086E", `GLMC` = "#006EAA", - `NoPoverty` = "#e5243b", `ZeroHunger` = "#DDA63A", - `GoodHealth` = "#4C9F38", - `QualityEducation` = "#C5192D", - `GenderEquality` = "#FF3A21", `CleanWater` = "#26BDE2", - `CleanEnergy` = "#FCC30B", - `EconomicGrowth` = "#A21942", - `Innovation` = "#FD6925", - `ReducedInequalities` = "#DD1367", - `SustainableCities` = "#FD9D24", - `ResponsibleConsumption` = "#BF8B2E", - `ClimateAction` = "#3F7E44", `BelowWater` = "#0A97D9", - `OnLand` = "#56C02B", `StrongInstitutions` = "#00689D", - `GoalPartnerships` = "#19486A") - cols <- c(...) - if (is.null(cols)) - return (iheid_corp_colors) - iheid_corp_colors[cols] -} - -iheid_corp_palette <- function(palette = "IHEID", ...) { - iheid_corp_palettes <- list(`IHEID` = iheid_corp_color("IHEIDRed", - "IHEIDBlack", - "IHEIDGrey"), - `Centres` = iheid_corp_color("AHCD", "CFFD", - "CIES", "CTEI", - "CGEN", "CCDP", - "GLGC", "GLHC", - "GLMC"), - `SDGs` = iheid_corp_color("NoPoverty", - "ZeroHunger", - "GoodHealth", - "QualityEducation", - "GenderEquality", - "CleanWater", - "CleanEnergy", - "EconomicGrowth", - "Innovation", - "ReducedInequalities", - "SustainableCities", - "ResponsibleConsumption", - "ClimateAction", - "BelowWater", - "OnLand", - "StrongInstitutions", - "GoalPartnerships")) - iheid_corp_palettes[[palette]] -} - -palette_gen <- function(palette = "IHEID", direction = 1) { - function(n) { - if (n > length(iheid_corp_palette(palette))) - warning("Not enough colors in this palette!") - else { - all_colors <- iheid_corp_palette(palette) - all_colors <- unname(unlist(all_colors)) - all_colors <- if (direction >= 0) all_colors else rev(all_colors) - color_list <- all_colors[1:n] - } - } + plot.title = ggplot2::element_text(hjust = 0, size = ggplot2::rel(1.5), + face = "bold"), + strip.background = ggplot2::element_rect())) +} + +#' @rdname themes +#' @export +theme_ethz <- function(base_size = 12, base_family = "serif") { + colors <- corp_palette("ETHZ") + (ggplot2::theme_minimal(base_size = base_size, base_family = base_family) + + ggplot2::theme( + line = ggplot2::element_line(colors["ETHZ_Green"]), + rect = ggplot2::element_rect(fill = "#FFFFFF", linetype = 1, + linewidth = 0.6, colour = colors["ETHZ_Petrol"]), + title = ggplot2::element_text(colour = colors["ETHZ_Blue"], + size = base_size, + face = "bold"), + plot.subtitle = ggplot2::element_text(colors["ETHZ_Bronze"], + size = base_size*0.85, + family = "serif", + face = "bold"), + plot.caption = ggplot2::element_text(colors["ETHZ_Red"], + size = base_size*0.75, + family = "serif", + face = "italic"), + axis.title = ggplot2::element_blank(), + axis.text = ggplot2::element_text(), + axis.ticks = ggplot2::element_blank(), + axis.line = ggplot2::element_blank(), + axis.title.x = ggplot2::element_blank(), + axis.text.x = ggplot2::element_blank(), + axis.ticks.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + axis.ticks.y = ggplot2::element_blank(), + panel.grid.major = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + legend.background = ggplot2::element_rect(), + legend.position = "bottom", + legend.direction = "horizontal", + legend.box = "vertical", + plot.title = ggplot2::element_text(hjust = 0, size = ggplot2::rel(1.5), + face = "bold"), + strip.background = ggplot2::element_rect())) +} + +#' @rdname themes +#' @export +theme_uzh <- function(base_size = 12, base_family = "sans") { + colors <- corp_palette("UZH") + (ggplot2::theme_minimal(base_size = base_size, base_family = base_family) + + ggplot2::theme( + line = ggplot2::element_line(colors["UZH_Blue"]), + rect = ggplot2::element_rect(fill = "#FFFFFF", linetype = 1, + linewidth = 0.6, colour = colors["UZH_Grey"]), + title = ggplot2::element_text(colour = colors["UZH_Orange"], + size = base_size, + face = "bold"), + plot.subtitle = ggplot2::element_text(colors["UZH_Blue"], + size = base_size*0.85, + family = "sans", + face = "bold"), + plot.caption = ggplot2::element_text(colors["UZH_Grey"], + size = base_size*0.75, + family = "sans", + face = "italic"), + axis.title = ggplot2::element_blank(), + axis.text = ggplot2::element_text(), + axis.ticks = ggplot2::element_blank(), + axis.line = ggplot2::element_blank(), + axis.title.x = ggplot2::element_blank(), + axis.text.x = ggplot2::element_blank(), + axis.ticks.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + axis.ticks.y = ggplot2::element_blank(), + panel.grid.major = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + legend.background = ggplot2::element_rect(), + legend.position = "bottom", + legend.direction = "horizontal", + legend.box = "vertical", + plot.title = ggplot2::element_text(hjust = 0, size = ggplot2::rel(1.5), + face = "bold"), + strip.background = ggplot2::element_rect())) +} + +#' @rdname themes +#' @export +theme_rug <- function(base_size = 12, base_family = "mono") { + colors <- corp_palette("RUG") + (ggplot2::theme_minimal(base_size = base_size, base_family = base_family) + + ggplot2::theme( + line = ggplot2::element_line(colors["RUG_Red"]), + rect = ggplot2::element_rect(fill = "#FFFFFF", linetype = 1, + linewidth = 0.6, colour = colors["RUG_Black"]), + title = ggplot2::element_text(colour = colors["RUG_Red"], + size = base_size, + face = "bold"), + plot.subtitle = ggplot2::element_text(colors["RUG_Black"], + size = base_size*0.85, + family = "mono", + face = "bold"), + plot.caption = ggplot2::element_text(colors["RUG_Red"], + size = base_size*0.75, + family = "mono", + face = "italic"), + axis.title = ggplot2::element_blank(), + axis.text = ggplot2::element_text(), + axis.ticks = ggplot2::element_blank(), + axis.line = ggplot2::element_blank(), + axis.title.x = ggplot2::element_blank(), + axis.text.x = ggplot2::element_blank(), + axis.ticks.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + axis.ticks.y = ggplot2::element_blank(), + panel.grid.major = ggplot2::element_blank(), + panel.grid.minor = ggplot2::element_blank(), + legend.background = ggplot2::element_rect(), + legend.position = "bottom", + legend.direction = "horizontal", + legend.box = "vertical", + plot.title = ggplot2::element_text(hjust = 0, size = ggplot2::rel(1.5), + face = "bold"), + strip.background = ggplot2::element_rect())) } -#' IHEID color scales +#' Many scales +#' +#' @description These functions enable to add color scales to be graphs. #' @name scales -#' @param palette Name of the palette. Current choices are: -#' \code{IHEID}, \code{Centres}, and \code{SDGs}. #' @param direction Direction for using palette colors. #' @param ... Extra arguments passed to `ggplot2::discrete_scale()`. #' @examples @@ -127,16 +190,18 @@ NULL #' @rdname scales #' @export -scale_fill_iheid <- function(palette = "IHEID", direction = 1, ...) { +scale_fill_iheid <- function(direction = 1, ...) { ggplot2::discrete_scale("fill", "IHEID", - palette_gen(palette, direction), ...) + palette_gen(palette = "IHEID", direction), + na.value = "black", ...) } #' @rdname scales #' @export -scale_colour_iheid <- function(palette = "IHEID", direction = 1, ...) { +scale_colour_iheid <- function(direction = 1, ...) { ggplot2::discrete_scale("colour", "IHEID", - palette_gen(palette, direction), ...) + palette_gen(palette = "IHEID", direction), + na.value = "black", ...) } #' @rdname scales @@ -147,16 +212,18 @@ scale_color_iheid <- scale_colour_iheid #' #' @rdname scales #' @export -scale_fill_centres <- function(palette = "Centres", direction = 1, ...) { +scale_fill_centres <- function(direction = 1, ...) { ggplot2::discrete_scale("fill", "Centres", - palette_gen(palette, direction), ...) + palette_gen(palette = "Centres", direction), + na.value = "black", ...) } #' @rdname scales #' @export -scale_colour_centres <- function(palette = "Centres", direction = 1, ...) { +scale_colour_centres <- function(direction = 1, ...) { ggplot2::discrete_scale("colour", "Centres", - palette_gen(palette, direction), ...) + palette_gen(palette = "Centres", direction), + na.value = "black", ...) } #' @rdname scales @@ -167,18 +234,166 @@ scale_color_centres <- scale_colour_centres #' #' @rdname scales #' @export -scale_fill_sdgs <- function(palette = "SDGs", direction = 1, ...) { +scale_fill_sdgs <- function(direction = 1, ...) { ggplot2::discrete_scale("fill", "SDGs", - palette_gen(palette, direction), ...) + palette_gen(palette = "SDGs", direction), + na.value = "black", ...) } #' @rdname scales #' @export -scale_colour_sdgs <- function(palette = "SDGs", direction = 1, ...) { +scale_colour_sdgs <- function(direction = 1, ...) { ggplot2::discrete_scale("colour", "SDGs", - palette_gen(palette, direction), ...) + palette_gen(palette = "SDGs", direction), + na.value = "black", ...) } #' @rdname scales #' @export scale_color_sdgs <- scale_colour_sdgs + +#' ETHZ color scales +#' +#' @rdname scales +#' @export +scale_fill_ethz <- function(direction = 1, ...) { + ggplot2::discrete_scale("fill", "ETHZ", + palette_gen(palette = "ETHZ", direction), + na.value = "black", ...) +} + +#' @rdname scales +#' @export +scale_colour_ethz <- function(direction = 1, ...) { + ggplot2::discrete_scale("colour", "ETHZ", + palette_gen(palette = "ETHZ", direction), + na.value = "black", ...) +} + +#' @rdname scales +#' @export +scale_color_ethz <- scale_colour_ethz + +#' UZH color scales +#' +#' @rdname scales +#' @export +scale_fill_uzh <- function(direction = 1, ...) { + ggplot2::discrete_scale("fill", "UZH", + palette_gen(palette = "UZH", direction), + na.value = "black", ...) +} + +#' @rdname scales +#' @export +scale_colour_uzh <- function(direction = 1, ...) { + ggplot2::discrete_scale("colour", "UZH", + palette_gen(palette = "UZH", direction), + na.value = "black", ...) +} + +#' @rdname scales +#' @export +scale_color_uzh <- scale_colour_uzh + +#' RUG color scales +#' +#' @rdname scales +#' @export +scale_fill_rug <- function(direction = 1, ...) { + ggplot2::discrete_scale("fill", "RUG", + palette_gen(palette = "RUG", direction), + na.value = "grey", ...) +} + +#' @rdname scales +#' @export +scale_colour_rug <- function(direction = 1, ...) { + ggplot2::discrete_scale("colour", "RUG", + palette_gen(palette = "RUG", direction), + na.value = "grey", ...) +} + +#' @rdname scales +#' @export +scale_color_rug <- scale_colour_rug + +# Helper functions +corp_color <- function(...) { + corp_colors <- c(`IHEIDRed` = "#E20020", `IHEIDBlack` = "#5c666f", + `IHEIDGrey` = "#6f7072", `AHCD` = "#622550", + `CFFD` = "#0094D8", `CIES` = "#268D2B", + `CTEI` = "#008F92", `CGEN` = "#820C2B", + `CCDP` = "#3E2682", `GLGC` = "#006564", + `GLHC` = "#A8086E", `GLMC` = "#006EAA", + `NoPoverty` = "#e5243b", `ZeroHunger` = "#DDA63A", + `GoodHealth` = "#4C9F38", + `QualityEducation` = "#C5192D", + `GenderEquality` = "#FF3A21", `CleanWater` = "#26BDE2", + `CleanEnergy` = "#FCC30B", + `EconomicGrowth` = "#A21942", + `Innovation` = "#FD6925", + `ReducedInequalities` = "#DD1367", + `SustainableCities` = "#FD9D24", + `ResponsibleConsumption` = "#BF8B2E", + `ClimateAction` = "#3F7E44", `BelowWater` = "#0A97D9", + `OnLand` = "#56C02B", `StrongInstitutions` = "#00689D", + `GoalPartnerships` = "#19486A", + `ETHZ_Blue` = "#215CAF", `ETHZ_Petrol` = "#007894", + `ETHZ_Green` = "#627313", `ETHZ_Bronze` = "#8E6713", + `ETHZ_Red`= "#B7352D", `ETHZ_Purple` = "#A7117A", + `ETHZ_Grey` = "#6F6F6F", `UZH_Blue` = "#0028a5", + `UZH_Grey` = "#a3adb7", `UZH_Orange` = "#dc6027", + `RUG_Red` = "#dc002d", `RUG_Black` = "#000000") + cols <- c(...) + if (is.null(cols)) + return (corp_colors) + corp_colors[cols] +} + +corp_palette <- function(palette, ...) { + corp_palettes <- list(`IHEID` = corp_color("IHEIDRed", + "IHEIDBlack", + "IHEIDGrey"), + `Centres` = corp_color("AHCD", "CFFD", + "CIES", "CTEI", + "CGEN", "CCDP", + "GLGC", "GLHC", + "GLMC"), + `SDGs` = corp_color("NoPoverty", + "ZeroHunger", + "GoodHealth", + "QualityEducation", + "GenderEquality", + "CleanWater", + "CleanEnergy", + "EconomicGrowth", + "Innovation", + "ReducedInequalities", + "SustainableCities", + "ResponsibleConsumption", + "ClimateAction", + "BelowWater", + "OnLand", + "StrongInstitutions", + "GoalPartnerships"), + `ETHZ` = corp_color("ETHZ_Blue", "ETHZ_Petrol", + "ETHZ_Green", "ETHZ_Bronze", + "ETHZ_Red", "ETHZ_Purple", "ETHZ_Grey"), + `UZH` = corp_color("UZH_Blue", "UZH_Grey", "UZH_Orange"), + `RUG` = corp_color("RUG_Red", "RUG_Black")) + unlist(unname(corp_palettes[c(palette)])) +} + +palette_gen <- function(palette, direction = 1) { + function(n) { + if (n > length(corp_palette(palette))) + warning("Not enough colors in this palette!") + else { + all_colors <- corp_palette(palette) + all_colors <- unname(unlist(all_colors)) + all_colors <- if (direction >= 0) all_colors else rev(all_colors) + color_list <- all_colors[1:n] + } + } +} diff --git a/R/pkg_tutorials.R b/R/pkg_tutorials.R index 5072192..0c5855e 100644 --- a/R/pkg_tutorials.R +++ b/R/pkg_tutorials.R @@ -25,6 +25,22 @@ run_tute <- function(tute) { } else { try(learnr::run_tutorial(tute, "manynet"), silent = TRUE) try(learnr::run_tutorial(tute, "migraph"), silent = TRUE) + cat("Didn't find a direct match, so looking for close matches...") + t1 <- dplyr::as_tibble(learnr::available_tutorials(package = "manynet"), + silent = TRUE) %>% dplyr::select(1:3) + t2 <- dplyr::as_tibble(learnr::available_tutorials(package = "migraph"), + silent = TRUE) %>% dplyr::select(1:3) + avails <- rbind(t1, t2) + inftit <- grepl(tute, avails$title, ignore.case = TRUE) + if(!any(inftit) | sum(inftit)>1) + inftit <- which.min(utils::adist(tute, avails$title, ignore.case = TRUE)) + if(any(inftit) & sum(inftit)==1){ + cat(" and found one!") + try(learnr::run_tutorial(avails$name[inftit], avails$package[inftit]), silent = TRUE) + } else{ + cat(" and couldn't find which one you meant. Please specify one of these titles:\n") + print(avails) + } } } diff --git a/R/reexports_classes.R b/R/reexports_classes.R index 2518d00..2818113 100644 --- a/R/reexports_classes.R +++ b/R/reexports_classes.R @@ -2,10 +2,6 @@ #' @export igraph::is.igraph -#' @importFrom igraph is_bipartite -#' @export -igraph::is_bipartite - #' @importFrom network is.network #' @export network::is.network @@ -46,10 +42,6 @@ tidygraph::mutate #' @export tidygraph::rename -#' @importFrom tidygraph activate -#' @export -tidygraph::activate - #' @importFrom tidygraph bind_edges #' @export tidygraph::bind_edges diff --git a/README.md b/README.md index c66c027..7abbd5b 100644 --- a/README.md +++ b/README.md @@ -25,170 +25,281 @@ coverage](https://codecov.io/gh/snlab-ch/manynet/branch/main/graph/badge.svg)](h ## About the package -Learning and using network analysis tools in R can be challenging. There -are many great packages, all with their own offerings and advantages, -but also all with their own vocabulary, syntax, and expected formats for -data inputs and analytic outputs. Moreover, many of these packages only -work on *some* types of networks (usually one-mode, simple, directed or -undirected networks); if you have a different type of network, a -different package is needed. Lastly, even packages with excellent -graphical capabilities can make visualising networks slow and cumbersome -by using poor defaults and many, incomprehensible options to change -them. - -*That’s why `{manynet}` aims to help researchers with Making, -Manipulating, and Mapping networks.* - -For Measures, Memberships, or Models, see +While many awesome packages for network analysis exist for R, all with +their own offerings and advantages, they also all have their own +vocabulary, syntax, and expected formats for data inputs and analytic +outputs. Many of these packages only work on *some* types of networks +(usually one-mode, simple, directed or undirected networks) for *some* +types of analysis; if you want to analyse a different type of network or +try a different analysis, a different package is needed. And they can +rely on a very different visual language (and sometimes plotting +engine), which can mess up your pretty presentation or paper. This can +make learning and using network analysis tools in R challenging. + +By contrast, we build packages that offer *many* analytic tools that +work on *many* (if not most) types of networks of all kinds. `{manynet}` +is the first package that helps researchers with Making, Modifying, and +Mapping networks. For Measures, Memberships, or Models, see [`{migraph}`](https://snlab-ch.github.io/migraph/). +- [Making](#making) + - [Importing network data](#importing-network-data) + - [Identifying network data](#identifying-network-data) + - [Inventing network data](#inventing-network-data) + - [Translating network data](#translating-network-data) +- [Modifying](#modifying) + - [Reformatting](#reformatting) + - [Transforming](#transforming) + - [Splitting and Joining](#splitting-and-joining) + - [Extracting](#extracting) +- [Mapping](#mapping) + - [autographr](#autographr) + - [More options](#more-options) + - [More layouts](#more-options) + - [More themes and scales](#more-themes-and-scales) + - [autographs](#autographs) + - [autographd](#autographd) +- [Installation](#installation) + - [Stable](#stable) + - [Development](#development) + - [Tutorials](#tutorials) +- [Relationship to other packages](#relationship-to-other-packages) +- [Funding details](#funding-details) + ## Making -First, `{manynet}` provides a set of tools that can be used to make -networks in any number of common formats. Networks can come from many -sources, some can be imported from outside R, some can be found in this -or other packages and coerced into some common format, and many types of -networks can be created or generated using functions in this package. +Networks can come from many sources and be found in many different +formats: some can be found in this or other packages, some can be +created or generated using functions in this package, and others can be +downloaded from the internet and imported from your file system. +`{manynet}` provides tools to make networks from all these sources in +any number of common formats. + +#### Importing network data + +`{manynet}` offers a number of options for importing network data found +in other repositories. Besides importing and exporting to Excel +edgelists, nodelists, and (bi)adjacency matrices, there are specific +routines included for +[UCINET](http://www.analytictech.com/archive/ucinet.htm), +[Pajek](http://mrvar.fdv.uni-lj.si/pajek/), +[GraphML](http://graphml.graphdrawing.org), and +[DynetML](http://casos.cs.cmu.edu/projects/dynetml/) files, e.g.: + +![](https://www.jameshollway.com/post/manynet/README-import-graph-1.png) + +If you cannot remember the file name/path, then just run `read_*()` with +the parentheses empty, and a file selection popup will open so that you +can browse through your file system to find the file. Usually both +`read_*()` and `write_*()` are offered to make sure that `{manynet}` is +compatible with your larger project and analytic workflow. + +- `read_dynetml()`, `read_edgelist()`, `read_graphml()`, + `read_matrix()`, `read_nodelist()`, `read_pajek()`, `read_ucinet()` +- `write_edgelist()`, `write_graphml()`, `write_matrix()`, + `write_nodelist()`, `write_pajek()`, `write_ucinet()` + +#### Identifying network data + +There may be no need to import network data though, if that network data +already exists in a package in R. To facilitate testing and to +contribute to an ecosystem of easily accessible network data, +particularly for pedagogical purposes, we include a number of classical +and instructional network datasets, all thoroughly documented and ready +for analysis. Here are just a few examples, all available in +`{manynet}`: + +![](https://www.jameshollway.com/post/manynet/README-ison_egs-1.png) + +Here are some others: `ison_adolescents`, `ison_algebra`, +`ison_brandes`, `ison_friends`, `ison_karateka`, `ison_konigsberg`, +`ison_laterals`, `ison_lawfirm`, `ison_lotr`, +`ison_marvel_relationships`, `ison_marvel_teams`, `ison_networkers`, +`ison_physicians`, `ison_southern_women`, `ison_starwars` #### Inventing network data -`{manynet}` includes algorithms for making networks with particular -properties. The `create_*` group of functions create networks with a -particular structure, and will always create the same format from the -same inputs, e.g.: +`{manynet}` includes functions for making networks algorithmically. The +`create_*` group of functions create networks with a particular +structure, and will always create the same format from the same inputs, +e.g.: + +![](https://www.jameshollway.com/post/manynet/README-create_egs-1.png) -- `create_components()`, `create_core()`, `create_empty()`, - `create_explicit()`, `create_filled()`, `create_lattice()`, - `create_ring()`, `create_star()`, `create_tree()` +See also `create_components()`, `create_core()`, `create_empty()`, +`create_explicit()`, `create_filled()`, `create_lattice()`, +`create_ring()`, `create_star()`, `create_tree()`. The `generate_*` group of functions generate networks from generative mechanisms that may include some random aspect, and so will return a different output each time they are run, e.g.: -- `generate_permutation()`, `generate_random()`, `generate_scalefree()`, - `generate_smallworld()`, `generate_utilities()` +![](https://www.jameshollway.com/post/manynet/README-generate_egs-1.png) + +See also `generate_permutation()`, `generate_random()`, +`generate_scalefree()`, `generate_smallworld()`, `generate_utilities()`. Note that all these functions can create directed or undirected, one-mode or two-mode networks. Creating two-mode networks is as easy as passing the first argument (`n`) a vector of two integers instead of -one. For example, `n = 10` will create a one-mode network of 10 nodes, -whereas `n = c(10,5)` will create a two-mode network of 10 nodes in the -first mode, and 5 nodes in the second mode. Some of these functions wrap -existing algorithms in other packages, while others are unique offerings -or add additional formats, e.g. two-mode networks. - -#### Importing network data - -`{manynet}` offers a number of options for importing network data found -in other repositories. Besides importing and exporting to Excel -edgelists and nodelists, there are specific routines included for -[UCINET](http://www.analytictech.com/archive/ucinet.htm), -[Pajek](http://mrvar.fdv.uni-lj.si/pajek/), and -[DynetML](http://casos.cs.cmu.edu/projects/dynetml/) files, e.g.: - - +one. For example, while `n = 15` will create a one-mode network of 10 +nodes, whereas `n = c(10,5)` will create a two-mode network of 10 nodes +in the first mode, and 5 nodes in the second mode. Some of these +functions wrap existing algorithms in other packages, while others are +unique offerings or add additional formats, e.g. two-mode networks. -- `read_dynetml()`, `read_edgelist()`, `read_matrix()`, - `read_nodelist()`, `read_pajek()`, `read_ucinet()` -- `write_edgelist()`, `write_matrix()`, `write_nodelist()`, - `write_pajek()`, `write_ucinet()` +![](https://www.jameshollway.com/post/manynet/README-generate_tm-1.png) -#### Coercing network data +#### Translating network data -There is also a lot of network data in addition to analytic functions -that already reside in various packages in R. Once network data is in R, -`{manynet}`’s `as_*()` functions can be used to translate objects from -one of the above classes into any other, and include: +Once you have imported network data, identified network data in this or +other packages in R, or invented your own, you may need to translate +this data into another class for analysis. `{manynet}`’s `as_*()` +functions can be used to coerce objects from one of many common classes +into any other. Below is a directed graph showing the currently +available options: - +![](https://www.jameshollway.com/post/manynet/README-coercion-graph-1.png) These functions are designed to be as intuitive and lossless as possible, outperforming many other class-coercion packages. -We use these functions internally in every `{manynet}` function to (1) -allow them to be run on any compatible network format and (2) use the -most efficient algorithm available. This makes `{manynet}` compatible -with your existing workflow, whether you use base R matrices or -edgelists as data frames, [`{igraph}`](https://igraph.org/r/), -[`{network}`](https://statnet.org), or -[`{tidygraph}`](https://tidygraph.data-imaginist.com/index.html), and +We use these functions internally in every `{manynet}` and `{migraph}` +function to (1) allow them to be run on any compatible network format +and (2) use the most efficient algorithm available. This makes +`{manynet}` and `{migraph}` compatible with your existing workflow, +whether you use base R matrices or edgelists as data frames, +[`{igraph}`](https://igraph.org/r/), [`{network}`](https://statnet.org), +or [`{tidygraph}`](https://tidygraph.data-imaginist.com/index.html), and extensible by developments in those other packages too. -## Manipulating +## Modifying -`{manynet}`’s `to_*()` functions can be used on any class object to -reformat, transform, or split networks into networks with other -properties, e.g.: - -- `to_acyclic()`, `to_anti()`, `to_blocks()`, `to_components()`, - `to_directed()`, `to_egos()`, `to_eulerian()`, `to_galois()`, - `to_giant()`, `to_matching()`, `to_mode1()`, `to_mode2()`, - `to_multilevel()`, `to_named()`, `to_no_isolates()`, `to_onemode()`, - `to_reciprocated()`, `to_redirected()`, `to_simplex()`, `to_slices()`, - `to_subgraph()`, `to_subgraphs()`, `to_ties()`, `to_twomode()`, - `to_undirected()`, `to_uniplex()`, `to_unnamed()`, `to_unsigned()`, - `to_unweighted()`, `to_waves()` +Before or during analysis, you may need to modify the network you are +analysing in various ways. Different packages have different syntaxes +and vocabulary for such actions; `{manynet}`’s `to_*()` functions can be +used on any class object to reformat, transform, or split networks into +networks with other properties. ### Reformatting Reformatting means changing the format of the network, e.g. from directed to undirected via `to_undirected()`. +![](https://www.jameshollway.com/post/manynet/README-directed_egs-1.png) + ### Transforming Transforming means changing the dimensions of the network, e.g. from a two-mode network to a one-mode projection via `to_mode1()`. +![](https://www.jameshollway.com/post/manynet/README-projection_egs-1.png) + ### Splitting and Joining Splitting means separating a network, e.g. from a whole network to the various ego networks via `to_egos()`. -#### Changing network data +![](https://www.jameshollway.com/post/manynet/README-splitting_egs-1.png) Those functions that split a network into a list of networks are distinguishable as those `to_*()` functions that are named in the -plural. +plural. Split data can be rejoined using the `from_*()` family of +functions. + +See also `to_acyclic()`, `to_anti()`, `to_blocks()`, `to_components()`, +`to_directed()`, `to_egos()`, `to_eulerian()`, `to_galois()`, +`to_giant()`, `to_matching()`, `to_mentoring()`, `to_mode1()`, +`to_mode2()`, `to_multilevel()`, `to_named()`, `to_no_isolates()`, +`to_onemode()`, `to_reciprocated()`, `to_redirected()`, `to_simplex()`, +`to_slices()`, `to_subgraph()`, `to_subgraphs()`, `to_ties()`, +`to_twomode()`, `to_undirected()`, `to_uniplex()`, `to_unnamed()`, +`to_unsigned()`, `to_unweighted()`, `to_waves()` and `from_egos()`, +`from_slices()`, `from_subgraphs()`, `from_waves()`. ### Extracting -`{manynet}` offers a range of measures and models with sensible -defaults. Many wrap existing functions in common packages for use with -one-mode networks, but extend these to treat and/or normalise for -two-mode (and sometimes three-mode) networks correctly. Functions are -given intuitive and succinct names that avoid conflicts with existing -function names wherever possible. - `{manynet}`’s `*is_*()` functions offer fast logical tests of various -network properties. These can be used to create new +network properties. -- `is_acyclic()`, `is_aperiodic()`, `is_bipartite()`, `is_complex()`, - `is_connected()`, `is_directed()`, `is_dynamic()`, `is_edgelist()`, - `is_eulerian()`, `is_graph()`, `is_labelled()`, `is_longitudinal()`, - `is_manynet()`, `is_multiplex()`, `is_perfect_matching()`, - `is_signed()`, `is_twomode()`, `is_uniplex()`, `is_weighted()` +- `is_acyclic()`, `is_aperiodic()`, `is_complex()`, `is_connected()`, + `is_directed()`, `is_dynamic()`, `is_edgelist()`, `is_eulerian()`, + `is_graph()`, `is_labelled()`, `is_longitudinal()`, `is_manynet()`, + `is_multiplex()`, `is_perfect_matching()`, `is_signed()`, + `is_twomode()`, `is_uniplex()`, `is_weighted()` ## Mapping `{manynet}` includes three one-line graphing functions with sensible -defaults based on the network’s properties. `autographr()` is used to -graph networks in any of the `{manynet}` formats. It includes sensible -defaults so that researchers can view their network’s structure or -distribution with a minimum of fuss. `{manynet}` uses the excellent -`{ggraph}` package (and thus `{ggplot2}`) as a plotting engine. This -also makes extending and theming default output easy, and `{patchwork}` -is used to help arrange individual plots together. - -There are two other graphing functions too. `autographs()` is used to -graph multiple networks together, which can be useful for ego networks -or network panels. `autographd()` is used to graph dynamic networks. - -In addition, `{manynet}` offers some additional layout algorithms for -snapping layouts to a grid or visualising partitions horizontally, -vertically, or concentrically. The following figures illustrate the -difference in results over `{igraph}`: - - +defaults based on the network’s properties. + +### autographr + +First, `autographr()` is used to graph networks in any of the +`{manynet}` formats. It includes sensible defaults so that researchers +can view their network’s structure or distribution quickly with a +minimum of fuss. Compare the output from `{manynet}` with a similar +default from `{igraph}`: + +![](https://www.jameshollway.com/post/manynet/README-layout-comparison-1.png) + +Here the `{manynet}` function recognises that the network is a two-mode +network and uses a bipartite layout by default, and recognises that the +network contains names for the nodes and prints them vertically so that +they are legible in this layout. Other ‘clever’ features include +automatic node sizing and more. By contrast, `{igraph}` requires the +bipartite layout to be specified, has cumbersome node size defaults for +all but the smallest graphs, and labels also very often need resizing +and adjustment to avoid overlap. All of `{manynet}`’s adjustments can be +overridden, however… + +#### More options + +Changing the size and colors of nodes and ties is as easy as specifying +the function’s relevant argument with a replacement, or indicating from +which attribute it should inherit this information. + +![](https://www.jameshollway.com/post/manynet/README-more-options-1.png) + +#### More layouts + +`{manynet}` can use all the layout algorithms offered by packages such +as `{igraph}`, `{ggraph}`, and `{graphlayouts}`, and offers some +additional layout algorithms for snapping layouts to a grid, visualising +partitions horizontally, vertically, or concentrically, or conforming to +configurational coordinates. + +![](https://www.jameshollway.com/post/manynet/README-more-layouts-1.png) + +#### More themes and scales + +Lastly, `autographr()` is highly extensible in terms of the overall look +of your plots. `{manynet}` uses the excellent `{ggraph}` package (and +thus `{ggplot2}`) as a plotting engine. This enables alterations such as +the application of themes to be applied upon the defaults. If you want +to quickly make sure your plots conform to your institution or taste, +then it is easy to do with themes and scales that update the basic look +and color palette used in your plots. + +![](https://www.jameshollway.com/post/manynet/README-more-themes-1.png) + +More themes are on their way, and we’re happy to take suggestions. + +### autographs + +Second, `autographs()` is used to graph multiple networks together, +which can be useful for ego networks or network panels. `{patchwork}` is +used to help arrange individual plots together. + +![](https://www.jameshollway.com/post/manynet/README-autographs-1.png) + +### autographd + +Third, `autographd()` is used to visualise dynamic networks. It uses +`{gganimate}` and `{gifski}` to create a gif that visualises network +changes over time. It really couldn’t be easier. + +![](https://www.jameshollway.com/post/manynet/README-autographd-1.gif) diff --git a/data/ison_physicians.rda b/data/ison_physicians.rda new file mode 100644 index 0000000..9eb39e8 Binary files /dev/null and b/data/ison_physicians.rda differ diff --git a/data/ison_starwars.rda b/data/ison_starwars.rda index 560a273..871388c 100644 Binary files a/data/ison_starwars.rda and b/data/ison_starwars.rda differ diff --git a/man/autographing.Rd b/man/autographing.Rd index a65d20c..5548b58 100644 --- a/man/autographing.Rd +++ b/man/autographing.Rd @@ -29,13 +29,15 @@ autographs(netlist, ...) autographd( tlist, - keep_isolates = TRUE, - layout = "stress", + layout, labels = TRUE, - node_color = NULL, - node_shape = NULL, - node_size = NULL, - edge_color = NULL + node_color, + node_shape, + node_size, + edge_color, + edge_size, + keep_isolates = TRUE, + ... ) } \arguments{ @@ -46,6 +48,9 @@ If not declared, defaults to "triad" for networks with 3 nodes, "quad" for networks with 4 nodes, "stress" for all other one mode networks, or "hierarchy" for two mode networks. +For "hierarchy" layout, one can further split graph by +declaring the "center" argument as the "events", "actors", +or by declaring a node name. For "concentric" layout algorithm please declare the "membership" as an extra argument. The "membership" argument expects either a quoted node attribute present @@ -55,7 +60,12 @@ as extra argument. The "level" argument expects either a quoted node attribute present in data or vector with the same length as nodes to hierarchically order categories. -If "level" is missing, function will look for 'lvl' node attribute in data.} +If "level" is missing, function will look for 'lvl' node attribute in data. +The "lineage" layout ranks nodes in Y axis according to values. +For "lineage" layout algorithm please declare the "rank" +as extra argument. +The "rank" argument expects either a quoted node attribute present +in data or vector with the same length as nodes.} \item{labels}{Logical, whether to print node names as labels if present.} @@ -133,6 +143,9 @@ with sensible defaults }} \examples{ #autographr(ison_adolescents) +#ison_adolescents \%>\% +# mutate(year = rep(c(1985, 1990, 1995, 2000), times = 2)) \%>\% +# autographr(layout = "lineage", rank = "year") #autographr(ison_algebra, layout = "circle", # node_size = 8, node_color = "orange", node_shape = "square", # edge_color = "blue", edge_size = 2) @@ -149,9 +162,11 @@ with sensible defaults # edge_size = migraph::tie_closeness(ison_karateka)) #autographr(ison_southern_women, layout = "concentric", # node_color = "type", membership = "type") +#autographr(ison_southern_women, layout = "hierarchy", center = "events") #autographr(ison_lotr, layout = "multilevel", -# node_color = "Race", node_shape = , level = "Race") +# node_color = "Race", level = "Race") #autographs(to_egos(ison_adolescents)) +#autographs(migraph::play_diffusion(ison_adolescents)) #ison_adolescents \%>\% # mutate_ties(year = sample(1995:1998, 10, replace = TRUE)) \%>\% # to_waves(attribute = "year") \%>\% @@ -161,11 +176,13 @@ with sensible defaults # color = rep(c("blue", "red"), times = 4), # size = sample(4:16, 8, replace = TRUE)) \%>\% # mutate_ties(year = sample(1995:1998, 10, replace = TRUE), -# e_color = sample(c("yellow", "green"), 10, replace = TRUE)) \%>\% +# e_color = sample(c("yellow", "green"), 10, replace = TRUE)) \%>\% # to_waves(attribute = "year") \%>\% # autographd(keep_isolates = FALSE, layout = "circle", node_shape = "shape", -# node_color = "color", node_size = "size", -# edge_color = "e_color") +# node_color = "color", node_size = "size", edge_color = "e_color") +#autographd(play_diffusion(ison_adolescents, seeds = 4), layout = "circle") +#autographd(play_diffusion(ison_adolescents, seeds = 4, recovery = 0.2), +# layout = "circle") } \seealso{ Other mapping: diff --git a/man/configuration_layouts.Rd b/man/configuration_layouts.Rd index 2474842..3affb8b 100644 --- a/man/configuration_layouts.Rd +++ b/man/configuration_layouts.Rd @@ -21,7 +21,7 @@ layout_tbl_graph_quad(.data, circular = FALSE, times = 1000) }} \item{circular}{Should the layout be transformed into a radial representation. -Only possible for some layouts. Defaults to FALSE} +Only possible for some layouts. Defaults to FALSE.} \item{times}{Maximum number of iterations, where appropriate} } diff --git a/man/figures/README-coercion-graph-1.png b/man/figures/README-coercion-graph-1.png deleted file mode 100644 index 9d57f4a..0000000 Binary files a/man/figures/README-coercion-graph-1.png and /dev/null differ diff --git a/man/figures/README-import-graph-1.png b/man/figures/README-import-graph-1.png deleted file mode 100644 index 74dff68..0000000 Binary files a/man/figures/README-import-graph-1.png and /dev/null differ diff --git a/man/figures/README-layout-comparison-1.png b/man/figures/README-layout-comparison-1.png deleted file mode 100644 index 9c448df..0000000 Binary files a/man/figures/README-layout-comparison-1.png and /dev/null differ diff --git a/man/from.Rd b/man/from.Rd index fc562bc..0232dcc 100644 --- a/man/from.Rd +++ b/man/from.Rd @@ -59,7 +59,6 @@ ison_adolescents \%>\% to_subgraphs(attribute = "unicorn") \%>\% from_subgraphs() ison_adolescents \%>\% - activate(edges) \%>\% to_egos() \%>\% from_egos() ison_adolescents \%>\% diff --git a/man/ison_physicians.Rd b/man/ison_physicians.Rd new file mode 100644 index 0000000..5f4280c --- /dev/null +++ b/man/ison_physicians.Rd @@ -0,0 +1,157 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data_ison.R +\docType{data} +\name{ison_physicians} +\alias{ison_physicians} +\title{Four multiplex one-mode physician diffusion data (Coleman, Katz, and Menzel, 1966)} +\format{ +\if{html}{\out{
}}\preformatted{#> $Peoria +#> # A multiplex, directed network with 117 nodes and 543 arcs +#> # A tibble: 117 x 12 +#> adoption specialty conferences journals practice community patients +#> +#> 1 1 Pediatrician Specialty 9 1920..1929 20+yrs 101-150 +#> 2 12 GP None 5 1945.. -1yr 76-100 +#> 3 8 Internist General 7 1935..1939 10-20yrs 76-100 +#> 4 9 GP General 6 1940..1944 5-10yrs 51-75 +#> 5 9 GP General 4 1935..1939 10-20yrs 51-75 +#> 6 10 Internist None 7 1930..1934 10-20yrs 101-150 +#> # i 111 more rows +#> # i 5 more variables: doc_freetime , doc_discuss , doc_friends , +#> # doc_club , doc_proximity +#> # A tibble: 543 x 3 +#> from to type +#> +#> 1 1 8 friendship +#> 2 1 58 friendship +#> 3 1 87 advice +#> 4 1 90 advice +#> 5 1 110 advice +#> 6 1 112 friendship +#> # i 537 more rows +#> +#> $Bloomington +#> # A multiplex, directed network with 50 nodes and 211 arcs +#> # A tibble: 50 x 12 +#> adoption specialty conferences journals practice community patients +#> +#> 1 98 Internist Specialty 8 1930..1934 10-20yrs 101-150 +#> 2 1 GP General 3 1945.. 5-10yrs 76-100 +#> 3 98 GP Specialty 4 1930..1934 10-20yrs 101-150 +#> 4 7 Internist None 3 1945.. -1yr 26-50 +#> 5 6 Internist General 9 1935..1939 5-10yrs 76-100 +#> 6 1 GP Specialty 5 1935..1939 10-20yrs 101-150 +#> # i 44 more rows +#> # i 5 more variables: doc_freetime , doc_discuss , doc_friends , +#> # doc_club , doc_proximity +#> # A tibble: 211 x 3 +#> from to type +#> +#> 1 1 3 friendship +#> 2 1 10 discussion +#> 3 1 24 advice +#> 4 1 44 advice +#> 5 2 4 advice +#> 6 2 6 advice +#> # i 205 more rows +#> +#> $Quincy +#> # A multiplex, directed network with 44 nodes and 174 arcs +#> # A tibble: 44 x 12 +#> adoption specialty conferences journals practice community patients +#> +#> 1 2 Internist None 6 1935..1939 10-20yrs 151+ +#> 2 18 GP General 3 1920..1929 20+yrs 151+ +#> 3 18 Internist None 5 1945.. -1yr -25 +#> 4 4 GP General 3 1930..1934 20+yrs 151+ +#> 5 18 GP Specialty 4 1935..1939 10-20yrs 151+ +#> 6 5 Internist General 5 ..1919 20+yrs 51-75 +#> # i 38 more rows +#> # i 5 more variables: doc_freetime , doc_discuss , doc_friends , +#> # doc_club , doc_proximity +#> # A tibble: 174 x 3 +#> from to type +#> +#> 1 1 8 advice +#> 2 1 9 advice +#> 3 1 10 discussion +#> 4 1 13 friendship +#> 5 1 15 advice +#> 6 1 22 discussion +#> # i 168 more rows +#> +#> $Galesburg +#> # A multiplex, directed network with 35 nodes and 171 arcs +#> # A tibble: 35 x 12 +#> adoption specialty conferences journals practice community patients +#> +#> 1 18 GP General 4 1935..1939 5-10yrs 101-150 +#> 2 18 GP None 4 1935..1939 -1yr 151+ +#> 3 4 GP General 6 1945.. 2-5yrs 51-75 +#> 4 5 GP None 4 1935..1939 10-20yrs 101-150 +#> 5 8 Internist General 6 1935..1939 5-10yrs 151+ +#> 6 4 Internist Specialty 8 ..1919 20+yrs 76-100 +#> # i 29 more rows +#> # i 5 more variables: doc_freetime , doc_discuss , doc_friends , +#> # doc_club , doc_proximity +#> # A tibble: 171 x 3 +#> from to type +#> +#> 1 1 5 advice +#> 2 1 6 advice +#> 3 1 20 discussion +#> 4 1 23 discussion +#> 5 1 30 friendship +#> 6 1 31 friendship +#> # i 165 more rows +}\if{html}{\out{
}} +} +\source{ +\code{{networkdata}} +} +\usage{ +data(ison_physicians) +} +\description{ +Ron Burt prepared this data from +Coleman, Katz and Menzel's 1966 study on medical innovation. +They had collected data from physicians in four towns in Illinois: +Peoria, Bloomington, Quincy and Galesburg. +These four networks are held as separate networks in a list. + +Coleman, Katz and Menzel were concerned with the impact of network ties +on the physicians' adoption of a new drug, tetracycline. +Data on three types of ties were collected in response to three questions: +\itemize{ +\item advice: "When you need information or advice about questions of therapy +where do you usually turn?" +\item discussion: "And who are the three or four physicians with whom you most often find yourself +discussing cases or therapy in the course of an ordinary week – last week for instance?" +\item friendship: "Would you tell me the first names of your three friends +whom you see most often socially?" +} + +Additional questions and records of prescriptions provided additional information: +\itemize{ +\item recorded date of tetracycline \code{adoption} date +\item years in \code{practice} +(note that these are \code{{messydates}}-compatible dates) +\item \code{conferences} attended +(those that attended "Specialty" conferences presumably also attended "General" conferences) +\item regular subscriptions to medical \code{journals} +\item \code{free_time} spent associating with doctors +\item \code{discussions} on medical matters when with other doctors sociallyy +\item memberships in \code{clubs} with other doctores +\item number of top 3 \code{friends} that are doctors +\item time practicing in current \code{community} +\item \code{patients} load (ordinal) +\item physical \code{proximity} to other physicians (in building/sharing office) +\item medical \code{specialty} (GP/Internist/Pediatrician/Other) +} +} +\references{ +Coleman, James, Elihu Katz, and Herbert Menzel. 1966. +\emph{Medical innovation: A diffusion study}. +Indianapolis: The Bobbs-Merrill Company. +} +\keyword{datasets} diff --git a/man/ison_starwars.Rd b/man/ison_starwars.Rd index cb79a10..fd36f70 100644 --- a/man/ison_starwars.Rd +++ b/man/ison_starwars.Rd @@ -7,7 +7,7 @@ \format{ \if{html}{\out{
}}\preformatted{#> $`Episode I` #> # A labelled, weighted, undirected network with 38 nodes and 135 ties -#> # A tibble: 38 x 10 +#> # A tibble: 38 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -17,7 +17,7 @@ #> 5 TC-14 NA NA NA #> 6 OBI-W~ 182 77 auburn, w~ fair blue-gray 57 male Stewjon #> # i 32 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 135 x 3 #> from to weight #> @@ -31,7 +31,7 @@ #> #> $`Episode II` #> # A labelled, weighted, undirected network with 33 nodes and 101 ties -#> # A tibble: 33 x 10 +#> # A tibble: 33 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -41,7 +41,7 @@ #> 5 ORN F~ NA NA NA #> 6 MACE ~ 188 84 none dark brown 72 male Haruun K~ #> # i 27 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 101 x 3 #> from to weight #> @@ -55,7 +55,7 @@ #> #> $`Episode III` #> # A labelled, weighted, undirected network with 24 nodes and 65 ties -#> # A tibble: 24 x 10 +#> # A tibble: 24 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -65,7 +65,7 @@ #> 5 GENER~ 216 159 none brown, wh~ green, y~ NA male Kalee #> 6 EMPER~ 170 75 grey pale yellow 82 male Naboo #> # i 18 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 65 x 3 #> from to weight #> @@ -79,7 +79,7 @@ #> #> $`Episode IV` #> # A labelled, weighted, undirected network with 21 nodes and 60 ties -#> # A tibble: 21 x 10 +#> # A tibble: 21 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -89,7 +89,7 @@ #> 5 DARTH~ 202 136 none white yellow 41.9 male Tatooine #> 6 CAMIE NA NA NA #> # i 15 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 60 x 3 #> from to weight #> @@ -103,7 +103,7 @@ #> #> $`Episode V` #> # A labelled, weighted, undirected network with 21 nodes and 55 ties -#> # A tibble: 21 x 10 +#> # A tibble: 21 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -113,7 +113,7 @@ #> 5 RIEEK~ NA NA NA #> 6 LEIA 150 49 brown light brown 19 fema~ Alderaan #> # i 15 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 55 x 3 #> from to weight #> @@ -127,7 +127,7 @@ #> #> $`Episode VI` #> # A labelled, weighted, undirected network with 20 nodes and 55 ties -#> # A tibble: 20 x 10 +#> # A tibble: 20 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 R2-D2 96 32 white, bl~ red 33 none Naboo @@ -137,7 +137,7 @@ #> 5 C-3PO 167 75 gold yellow 112 none Tatooine #> 6 BIB F~ 180 NA none pale pink NA male Ryloth #> # i 14 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 55 x 3 #> from to weight #> @@ -151,7 +151,7 @@ #> #> $`Episode VII` #> # A labelled, weighted, undirected network with 27 nodes and 92 ties -#> # A tibble: 27 x 10 +#> # A tibble: 27 x 11 #> name height mass hair_color skin_color eye_color birth_year sex homeworld #> #> 1 LUKE 172 77 blond fair blue 19 male Tatooine @@ -161,7 +161,7 @@ #> 5 LOR S~ NA NA NA #> 6 POE NA NA brown light brown NA male #> # i 21 more rows -#> # i 1 more variable: species +#> # i 2 more variables: species , faction #> # A tibble: 92 x 3 #> from to weight #> @@ -187,6 +187,10 @@ The network for each episode varies in the number of nodes and ties. For all networks, characters are named (eg. R2-D2, Anakin, Chewbacca) and the following node attributes are provided where available: height, mass, hair color, skin color, eye color, birth year, sex, homeworld, and species. +The node attribute 'faction' has also been added, +denoting the faction (eg. Jedi, Rebel Alliance, etc) +that Star Wars characters belong to in each episode +(coding completed with help of Yichen Shen and Tiphaine Aeby). Weighted ties represent the number of times characters speak within the same scene of the film. } diff --git a/man/iheid_palette.Rd b/man/many_palettes.Rd similarity index 54% rename from man/iheid_palette.Rd rename to man/many_palettes.Rd index 3dff75a..2d04ba8 100644 --- a/man/iheid_palette.Rd +++ b/man/many_palettes.Rd @@ -1,17 +1,18 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/map_palettes.R -\name{iheid_palette} -\alias{iheid_palette} -\title{An IHEID palette generator} +\name{many_palettes} +\alias{many_palettes} +\title{Many Palettes generator} \source{ Adapted from \url{https://github.com/karthik/wesanderson/blob/master/R/colors.R} } \usage{ -iheid_palette(palette, n, type = c("discrete", "continuous")) +many_palettes(palette, n, type = c("discrete", "continuous")) } \arguments{ \item{palette}{Name of desired palette. Current choices are: -\code{IHEID}, \code{Centres}, and \code{SDGs}.} +\code{IHEID}, \code{Centres}, \code{SDGs}, \code{ETHZ}, \code{RUG}, +and \code{UZH}.} \item{n}{Number of colors desired. If omitted, uses all colours.} @@ -19,13 +20,12 @@ iheid_palette(palette, n, type = c("discrete", "continuous")) to automatically interpolate between colours.} } \value{ -A vector of colours. +A graphic display of colours in palette. } \description{ -These are a few color palettes useful for members of the Geneva Graduate Institute. -This function calls palettes for the Institute, for the Centres, and for the SDGs. +Many Palettes generator } \examples{ -#iheid_palette("IHEID") +#many_palettes("IHEID") +#many_palettes() } -\keyword{colors} diff --git a/man/partition_layouts.Rd b/man/partition_layouts.Rd index 0339403..80f6fa8 100644 --- a/man/partition_layouts.Rd +++ b/man/partition_layouts.Rd @@ -8,6 +8,7 @@ \alias{layout_tbl_graph_ladder} \alias{layout_tbl_graph_concentric} \alias{layout_tbl_graph_multilevel} +\alias{layout_tbl_graph_lineage} \title{Layout algorithms based on bi- or other partitions} \source{ Diego Diez, Andrew P. Hutchins and Diego Miranda-Saavedra. 2014. @@ -16,7 +17,12 @@ protein-protein interaction networks". \emph{Nucleic Acids Research}, 42 (1) e6. } \usage{ -layout_tbl_graph_hierarchy(.data, circular = FALSE, times = 1000) +layout_tbl_graph_hierarchy( + .data, + center = NULL, + circular = FALSE, + times = 1000 +) layout_tbl_graph_alluvial(.data, circular = FALSE, times = 1000) @@ -34,6 +40,8 @@ layout_tbl_graph_concentric( ) layout_tbl_graph_multilevel(.data, level = NULL, circular = FALSE) + +layout_tbl_graph_lineage(.data, rank, circular = FALSE) } \arguments{ \item{.data}{An object of a \code{{manynet}}-consistent class: @@ -45,8 +53,13 @@ layout_tbl_graph_multilevel(.data, level = NULL, circular = FALSE) \item tbl_graph, from the \code{{tidygraph}} package }} +\item{center}{Further split "hierarchical" layouts by +declaring the "center" argument as the "events", "actors", +or by declaring a node name. +Defaults to NULL.} + \item{circular}{Should the layout be transformed into a radial representation. -Only possible for some layouts. Defaults to FALSE} +Only possible for some layouts. Defaults to FALSE.} \item{times}{Maximum number of iterations, where appropriate} @@ -65,6 +78,9 @@ the number of edge crossings.} \item{level}{A node attribute or a vector to hierarchically order levels for "multilevel" layout.} + +\item{rank}{A numerical node attribute to place nodes in Y axis +according to values.} } \description{ These algorithms layout networks based on two or more partitions, @@ -78,6 +94,7 @@ these functions are used though. The "hierarchy" layout layers the first node set along the bottom, and the second node set along the top, sequenced and spaced as necessary to minimise edge overlap. +These node sets can be arranged using the "center" argument. The "alluvial" layout is similar to "hierarchy", but places successive layers horizontally rather than vertically. The "railway" layout is similar to "hierarchy", @@ -87,6 +104,9 @@ but places successive layers horizontally rather than vertically. The "concentric" layout places a "hierarchy" layout around a circle, with successive layers appearing as concentric circles. The "multilevel" layout places successive layers as multiple levels. +The "lineage" layout ranks nodes in Y axis according to values. +These values for ranking the nodes should be declared as a node attribute +in the "rank" argument. } \seealso{ Other mapping: diff --git a/man/read.Rd b/man/read.Rd index 689e958..2c7f770 100644 --- a/man/read.Rd +++ b/man/read.Rd @@ -13,6 +13,8 @@ \alias{read_ucinet} \alias{write_ucinet} \alias{read_dynetml} +\alias{read_graphml} +\alias{write_graphml} \title{Make networks from/to external formats} \source{ \code{read_ucinet()} and \code{write_ucinet()} kindly supplied by Christian Steglich, @@ -40,6 +42,10 @@ read_ucinet(file = file.choose()) write_ucinet(.data, filename, name) read_dynetml(file = file.choose()) + +read_graphml(file = file.choose()) + +write_graphml(.data, filename, name, ...) } \arguments{ \item{file}{A character string with the system path to the file to import. @@ -148,6 +154,10 @@ you will need to unpack them and convert them one by one. \item \code{read_dynetml()}: Reading DynetML files +\item \code{read_graphml()}: Reading GraphML files + +\item \code{write_graphml()}: Writing GraphML files + }} \seealso{ \link{as} diff --git a/man/reexports.Rd b/man/reexports.Rd index 427104d..bb01330 100644 --- a/man/reexports.Rd +++ b/man/reexports.Rd @@ -4,7 +4,6 @@ \name{reexports} \alias{reexports} \alias{is.igraph} -\alias{is_bipartite} \alias{is.network} \alias{as.network} \alias{\%>\%} @@ -15,7 +14,6 @@ \alias{.E} \alias{mutate} \alias{rename} -\alias{activate} \alias{bind_edges} \alias{ggplot} \alias{ggtitle} @@ -36,10 +34,10 @@ below to see their documentation. \item{ggplot2}{\code{\link[ggplot2]{aes}}, \code{\link[ggplot2]{ggplot}}, \code{\link[ggplot2]{ggsave}}, \code{\link[ggplot2:labs]{ggtitle}}, \code{\link[ggplot2]{guides}}, \code{\link[ggplot2]{labs}}, \code{\link[ggplot2:labs]{xlab}}, \code{\link[ggplot2:labs]{ylab}}} - \item{igraph}{\code{\link[igraph:is_igraph]{is.igraph}}, \code{\link[igraph:make_bipartite_graph]{is_bipartite}}} + \item{igraph}{\code{\link[igraph:is_igraph]{is.igraph}}} \item{network}{\code{\link[network:network]{as.network}}, \code{\link[network:network]{is.network}}} - \item{tidygraph}{\code{\link[tidygraph:context_accessors]{.E}}, \code{\link[tidygraph:context_accessors]{.G}}, \code{\link[tidygraph:context_accessors]{.N}}, \code{\link[tidygraph]{activate}}, \code{\link[tidygraph:bind_graphs]{bind_edges}}, \code{\link[tidygraph:tbl_graph]{is.tbl_graph}}, \code{\link[tidygraph:reexports]{mutate}}, \code{\link[tidygraph:reexports]{rename}}, \code{\link[tidygraph]{with_graph}}} + \item{tidygraph}{\code{\link[tidygraph:context_accessors]{.E}}, \code{\link[tidygraph:context_accessors]{.G}}, \code{\link[tidygraph:context_accessors]{.N}}, \code{\link[tidygraph:bind_graphs]{bind_edges}}, \code{\link[tidygraph:tbl_graph]{is.tbl_graph}}, \code{\link[tidygraph:reexports]{mutate}}, \code{\link[tidygraph:reexports]{rename}}, \code{\link[tidygraph]{with_graph}}} }} diff --git a/man/scales.Rd b/man/scales.Rd index 5a758d0..042d523 100644 --- a/man/scales.Rd +++ b/man/scales.Rd @@ -11,40 +11,60 @@ \alias{scale_fill_sdgs} \alias{scale_colour_sdgs} \alias{scale_color_sdgs} -\title{IHEID color scales} +\alias{scale_fill_ethz} +\alias{scale_colour_ethz} +\alias{scale_color_ethz} +\alias{scale_fill_uzh} +\alias{scale_colour_uzh} +\alias{scale_color_uzh} +\alias{scale_fill_rug} +\alias{scale_colour_rug} +\alias{scale_color_rug} +\title{Many scales} \usage{ -scale_fill_iheid(palette = "IHEID", direction = 1, ...) +scale_fill_iheid(direction = 1, ...) -scale_colour_iheid(palette = "IHEID", direction = 1, ...) +scale_colour_iheid(direction = 1, ...) -scale_color_iheid(palette = "IHEID", direction = 1, ...) +scale_color_iheid(direction = 1, ...) -scale_fill_centres(palette = "Centres", direction = 1, ...) +scale_fill_centres(direction = 1, ...) -scale_colour_centres(palette = "Centres", direction = 1, ...) +scale_colour_centres(direction = 1, ...) -scale_color_centres(palette = "Centres", direction = 1, ...) +scale_color_centres(direction = 1, ...) -scale_fill_sdgs(palette = "SDGs", direction = 1, ...) +scale_fill_sdgs(direction = 1, ...) -scale_colour_sdgs(palette = "SDGs", direction = 1, ...) +scale_colour_sdgs(direction = 1, ...) -scale_color_sdgs(palette = "SDGs", direction = 1, ...) +scale_color_sdgs(direction = 1, ...) + +scale_fill_ethz(direction = 1, ...) + +scale_colour_ethz(direction = 1, ...) + +scale_color_ethz(direction = 1, ...) + +scale_fill_uzh(direction = 1, ...) + +scale_colour_uzh(direction = 1, ...) + +scale_color_uzh(direction = 1, ...) + +scale_fill_rug(direction = 1, ...) + +scale_colour_rug(direction = 1, ...) + +scale_color_rug(direction = 1, ...) } \arguments{ -\item{palette}{Name of the palette. Current choices are: -\code{IHEID}, \code{Centres}, and \code{SDGs}.} - \item{direction}{Direction for using palette colors.} \item{...}{Extra arguments passed to \code{ggplot2::discrete_scale()}.} } \description{ -IHEID color scales - -Centres color scales - -SDGs color scales +These functions enable to add color scales to be graphs. } \examples{ #ison_brandes \%>\% diff --git a/man/split.Rd b/man/split.Rd index 90d8b06..f8696c6 100644 --- a/man/split.Rd +++ b/man/split.Rd @@ -55,13 +55,13 @@ These functions offer tools for splitting manynet-consistent objects (matrices, igraph, tidygraph, or network objects) into lists of networks. Not all functions have methods available for all object classes. -Below are the currently implemented S3 methods:\tabular{lrrrrr}{ - \tab data.frame \tab igraph \tab matrix \tab network \tab tbl_graph \cr - to_components \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr - to_egos \tab 1 \tab 1 \tab 1 \tab 1 \tab 1 \cr - to_slices \tab 0 \tab 1 \tab 0 \tab 0 \tab 1 \cr - to_subgraphs \tab 0 \tab 1 \tab 0 \tab 1 \tab 1 \cr - to_waves \tab 1 \tab 1 \tab 0 \tab 0 \tab 1 \cr +Below are the currently implemented S3 methods:\tabular{lrrrrrr}{ + \tab data.frame \tab diff_model \tab igraph \tab matrix \tab network \tab tbl_graph \cr + to_components \tab 1 \tab 0 \tab 1 \tab 1 \tab 1 \tab 1 \cr + to_egos \tab 1 \tab 0 \tab 1 \tab 1 \tab 1 \tab 1 \cr + to_slices \tab 0 \tab 0 \tab 1 \tab 0 \tab 0 \tab 1 \cr + to_subgraphs \tab 0 \tab 0 \tab 1 \tab 0 \tab 1 \tab 1 \cr + to_waves \tab 1 \tab 1 \tab 1 \tab 0 \tab 0 \tab 1 \cr } } \section{Functions}{ diff --git a/man/themes.Rd b/man/themes.Rd index d8ac5b1..9cc1b2f 100644 --- a/man/themes.Rd +++ b/man/themes.Rd @@ -3,18 +3,24 @@ \name{themes} \alias{themes} \alias{theme_iheid} +\alias{theme_ethz} +\alias{theme_uzh} +\alias{theme_rug} \title{Many themes} \usage{ -theme_iheid(base_size = 12, base_family = "sans") +theme_iheid(base_size = 12, base_family = "Times") + +theme_ethz(base_size = 12, base_family = "serif") + +theme_uzh(base_size = 12, base_family = "sans") + +theme_rug(base_size = 12, base_family = "mono") } \arguments{ \item{base_size}{Font size, by default 12.} \item{base_family}{Font family, by default "sans".} } -\value{ -Themes the current ggraph to current IHEID guidelines. -} \description{ These functions enable graphs to be easily and quickly themed, e.g. changing the default colour of the graph's vertices and edges. diff --git a/man/transform.Rd b/man/transform.Rd index 373810f..4a8c008 100644 --- a/man/transform.Rd +++ b/man/transform.Rd @@ -196,8 +196,7 @@ autographr(to_mentoring(ison_adolescents)) to_anti(ison_southern_women) #autographr(to_anti(ison_southern_women)) ison_adolescents \%>\% - activate(edges) \%>\% - mutate(wave = sample(1995:1998, 10, replace = TRUE)) \%>\% + mutate_ties(wave = sample(1995:1998, 10, replace = TRUE)) \%>\% to_waves(attribute = "wave") \%>\% to_no_isolates() } diff --git a/tests/testthat/sheets/nassau.graphml b/tests/testthat/sheets/nassau.graphml new file mode 100644 index 0000000..55372cd --- /dev/null +++ b/tests/testthat/sheets/nassau.graphml @@ -0,0 +1,197718 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 25.0811556 + -77.3214892 + 4 + 6 + 8 + 6 + + + 25.0751229 + -77.3221122 + traffic_signals + 4 + 8 + 22 + 8 + + + 25.0809593 + -77.3199061 + 3 + 7 + 7 + 9 + + + 25.0753294 + -77.323114 + 3 + 9 + 13 + 9 + + + 25.0731748 + -77.3226832 + 3 + 10 + 13 + 10 + + + 25.0728887 + -77.3227813 + 4 + 9 + 14 + 9 + + + 25.0765251 + -77.3310758 + 4 + 7 + 14 + 7 + + + 25.0754436 + -77.3239159 + 3 + 5 + 9 + 5 + + + 25.0771793 + -77.3310071 + 1 + 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b/tests/testthat/test-make_read.R @@ -113,6 +113,23 @@ test_that("read_ucinet and write_ucinet works", { on.exit(unlink(file)) # Unlink file }) +test_that("read_graphml and write_graphml works", { + testgml <- read_graphml(testthat::test_path("sheets", "nassau.graphml")) + expect_true(is.tbl_graph(testgml)) + expect_equal(nrow(as_edgelist(testgml)), 15598) + expect_equal(ncol(as_edgelist(testgml)), 17) + expect_length(network_node_attributes(testgml), 8) + expect_length(network_tie_attributes(testgml), 15) + expect_error(read_graphml(testthat::test_path("sheets", "nassau"))) + path <- tempfile() # Create file + write_graphml(testgml, path) + testgml2 <- read_graphml(paste0(path)) + expect_true(is.tbl_graph(testgml2)) + edgegml <- as_edgelist(testgml2) + expect_length(edgegml$from, length(as_edgelist(testgml)$from)) + on.exit(unlink(path)) # Unlink file +}) + test_that("read_ and write_ matrix works", { Abruzzo <- Campania <- Calabria <- Puglia <- NULL Abruzzo <- c(1, 0.76, 0.8, 0.90) diff --git a/tests/testthat/test-manip_as.R b/tests/testthat/test-manip_as.R index 4bc4cb3..e231b23 100644 --- a/tests/testthat/test-manip_as.R +++ b/tests/testthat/test-manip_as.R @@ -81,3 +81,14 @@ test_that("as_network converts correctly",{ # expect_equal(network_nodes(as_igraph(sienadata)), network_nodes(as_matrix(sienadata))) # expect_equal(network_nodes(as_igraph(sienadata)), length(sienadata[["nodeSets"]][["Actors"]])) # }) + +test_that("conversion of diff_model object works correctly", { + skip_on_cran() + skip_on_ci() + diff <- migraph::play_diffusion(ison_brandes) + tidy_diff <- as_tidygraph(diff) + expect_equal(network_nodes(tidy_diff), network_nodes(ison_brandes)) + expect_equal(network_ties(tidy_diff), network_ties(ison_brandes)) + # expect_true(is_twomode(tidy_diff)) + expect_equal(network_nodes(tidy_diff), max(diff$I)) +}) diff --git a/tests/testthat/test-manip_split.R b/tests/testthat/test-manip_split.R index d240629..4064acc 100644 --- a/tests/testthat/test-manip_split.R +++ b/tests/testthat/test-manip_split.R @@ -46,6 +46,23 @@ test_that("to_waves works", { expect_s3_class(from_waves(to_waves(wave)), "tbl_graph") }) +test_that("to_waves works for diff_model objects", { + skip_on_cran() + skip_on_ci() + wave_diff <- migraph::play_diffusion(ison_brandes) + expect_length(to_waves(wave_diff), length(wave_diff$t)) + expect_equal(network_nodes(to_waves(wave_diff)[[1]]), + network_nodes(to_waves(wave_diff)[[12]])) + expect_equal(network_ties(to_waves(wave_diff)[[1]]), + network_ties(to_waves(wave_diff)[[12]])) + expect_equal(network_nodes(to_waves(wave_diff)[[1]]), + network_nodes(ison_brandes)) + expect_equal(node_attribute(to_waves(wave_diff)[[11]], "Infected"), + c(rep("Infected", 11))) + expect_equal(node_attribute(to_waves(wave_diff)[[1]], "Infected"), + c("Infected", rep("Susceptible", 10))) +}) + slice <- ison_adolescents %>% mutate_ties(time = 1:10, increment = 1) %>% add_ties(c(1,2), list(time = 3, increment = -1)) diff --git a/tests/testthat/test-map_autographr.R b/tests/testthat/test-map_autographr.R index 93ea118..c8d4797 100644 --- a/tests/testthat/test-map_autographr.R +++ b/tests/testthat/test-map_autographr.R @@ -137,3 +137,28 @@ test_that("concentric and circular layouts graph correctly", { envir = test_conc$plot_env)$membership, "Gender") }) + +test_that("hierarchy and lineage layouts graph correctly", { + skip_on_cran() + test_lin <- ison_adolescents %>% + mutate(year = rep(c(1985, 1990, 1995, 2000), times = 2)) %>% + autographr(layout = "lineage", rank = "year") + test_hie <- autographr(ison_southern_women, + layout = "hierarchy", center = "events") + expect_equal(test_lin$plot_env$layout, "lineage") + expect_equal((eval(quote(pairlist(...)), + envir = test_lin[["plot_env"]])[["rank"]]), + "year") + expect_equal(test_hie$plot_env$layout, "hierarchy") + expect_equal((eval(quote(pairlist(...)), + envir = test_hie[["plot_env"]])[["center"]]), + "events") +}) + +test_that("autographr works for diff_model objects", { + skip_on_cran() + skip_on_ci() + test_diff <- autographr(migraph::play_diffusion(ison_brandes)) + expect_equal(test_diff[["guides"]][["shape"]][["name"]], "legend") + expect_equal(test_diff[["guides"]][["colour"]][["name"]], "colorbar") +}) diff --git a/tests/testthat/test-map_theme.R b/tests/testthat/test-map_theme.R new file mode 100644 index 0000000..099431f --- /dev/null +++ b/tests/testthat/test-map_theme.R @@ -0,0 +1,63 @@ +##### test themes +test_that("themes graph correctly", { + test_iheid <- autographr(to_mentoring(ison_brandes)) + + labs(title = "Who leads and who follows?", + subtitle = "A mentoring network", + caption = "ison_brandes network") + + theme_iheid() + test_ethz <- autographr(to_mentoring(ison_brandes)) + + labs(title = "Who leads and who follows?", + subtitle = "A mentoring network", + caption = "ison_brandes network") + + theme_ethz() + test_uzh <- autographr(to_mentoring(ison_brandes)) + + labs(title = "Who leads and who follows?", + subtitle = "A mentoring network", + caption = "ison_brandes network") + + theme_uzh() + expect_equal(names(test_iheid[["theme"]][["title"]][["colour"]]), "IHEIDRed") + expect_equal(names(test_ethz[["theme"]][["title"]][["colour"]]), "ETHZ_Blue") + expect_equal(names(test_uzh[["theme"]][["title"]][["colour"]]), "UZH_Orange") + expect_equal(names(test_iheid[["theme"]][["plot.subtitle"]][["colour"]]), + "IHEIDGrey") + expect_equal(names(test_ethz[["theme"]][["plot.subtitle"]][["colour"]]), + "ETHZ_Bronze") + expect_equal(names(test_uzh[["theme"]][["plot.subtitle"]][["colour"]]), + "UZH_Blue") + expect_equal(test_iheid[["theme"]][["plot.caption"]][["family"]], "Times") + expect_equal(test_ethz[["theme"]][["plot.caption"]][["family"]], "serif") + expect_equal(test_uzh[["theme"]][["plot.caption"]][["family"]], "sans") + expect_equal(test_iheid[["theme"]][["plot.caption"]][["face"]], "italic") + expect_equal(test_ethz[["theme"]][["plot.caption"]][["face"]], "italic") + expect_equal(test_uzh[["theme"]][["plot.caption"]][["face"]], "italic") +}) + +##### test scales +test_that("scales graph correctly", { + test_sdg <- ison_brandes %>% + mutate(color = c(rep(c(1,2,3,4,5), 2), 1)) %>% + autographr(node_color = color) + + scale_color_sdgs() + test_iheid <- ison_brandes %>% + mutate(color = c(rep(c(1,2), 5), 3)) %>% + autographr(node_color = color) + + scale_color_iheid() + test_ethz <- ison_brandes %>% + mutate(color = c(rep(c(1,2,3), 3), 4, 5)) %>% + autographr(node_color = color) + + scale_color_ethz() + test_uzh <- ison_brandes %>% + mutate(color = c(rep(c(1,2,3), 3), 1, 2)) %>% + autographr(node_color = color) + + scale_color_uzh() + test_rug <- ison_brandes %>% + mutate(color = c(rep(c(1,2,3), 3), 1, 2)) %>% + autographr(node_color = color) + + scale_color_rug() + expect_equal(test_sdg[["scales"]][["scales"]][[1]][["call"]][["scale_name"]], "SDGs") + expect_equal(test_sdg[["scales"]][["scales"]][[1]][["call"]][["palette"]][["palette"]], "SDGs") + expect_equal(test_iheid[["scales"]][["scales"]][[1]][["call"]][["scale_name"]], "IHEID") + expect_equal(test_ethz[["scales"]][["scales"]][[1]][["call"]][["scale_name"]], "ETHZ") + expect_equal(test_uzh[["scales"]][["scales"]][[1]][["call"]][["scale_name"]], "UZH") + expect_equal(test_rug[["scales"]][["scales"]][[1]][["call"]][["scale_name"]], "RUG") +})