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Apply the Review from @bockthom and @clhunsen on the previous changes. This includes compliance of coding conventions, update of copyright headers and improvement of documentation. Move the functions for 'get.author.names.from.networks' and 'get.expanded.adjacency' to new file 'util-networks-misc.R'. Also add two functions 'get.author.names.from.data' and 'convert.adjacency.matrix.list.to.array' from the 'dev-network-growth' project to the new file. Signed-off-by: fehnkera <[email protected]>
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| Original file line number | Diff line number | Diff line change |
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| ## This file is part of coronet, which is free software: you | ||
| ## can redistribute it and/or modify it under the terms of the GNU General | ||
| ## Public License as published by the Free Software Foundation, version 2. | ||
| ## | ||
| ## This program is distributed in the hope that it will be useful, | ||
| ## but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| ## GNU General Public License for more details. | ||
| ## | ||
| ## You should have received a copy of the GNU General Public License along | ||
| ## with this program; if not, write to the Free Software Foundation, Inc., | ||
| ## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. | ||
| ## | ||
| ## Copyright 2016 by Sofie Kemper <[email protected]> | ||
| ## Copyright 2016 by Claus Hunsen <[email protected]> | ||
| ## Copyright 2016-2018 by Thomas Bock <[email protected]> | ||
| ## Copyright 2017 by Angelika Schmid <[email protected]> | ||
| ## Copyright 2019 by Jakob Kronawitter <[email protected]> | ||
| ## Copyright 2019-2020 by Anselm Fehnker <[email protected]> | ||
| ## All Rights Reserved. | ||
|
|
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| ## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | ||
| ## Get active authors ----------------------------------------------------- | ||
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| #' Get all author names that are active in at least one of the networks. | ||
| #' | ||
| #' @param networks the list of networks | ||
| #' @param globally decides if all author names are in one list or in separate for each network [default: TRUE] | ||
| #' | ||
| #' @return the list of author names | ||
| get.author.names.from.networks = function(networks, globally = TRUE) { | ||
|
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| ## for each network, get a list of authors that are in this network | ||
| active.authors.list = lapply(networks, function(network) { | ||
| active.authors = igraph::V(network)$name | ||
| return(active.authors) | ||
| }) | ||
|
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| if (globally) { | ||
| ## flatten the list of lists to one list of authors | ||
| active.authors = unlist(active.authors.list, recursive = FALSE) | ||
|
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| ## remove distracting named list members | ||
| names(active.authors) = NULL | ||
|
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| ## remove duplicates and order alphabetically ascending | ||
| active.authors = active.authors[!duplicated(active.authors)] | ||
| active.authors = active.authors[order(active.authors)] | ||
| return(active.authors) | ||
| } else { | ||
| return(active.authors.list) | ||
| } | ||
| } | ||
|
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| #' Get all author names that are active in at least one of the date ranges. | ||
| #' | ||
| #' @param data.ranges the list of the data ranges | ||
| #' @param is.mail.analysis bool if the data is a mail analysis | ||
| #' @param globally decides if all author names are in one list or in separate for each network [default: TRUE] | ||
| #' | ||
| #' @return the list of author names | ||
| get.author.names.from.data = function(dataRanges, isMailAnalysis, globally = TRUE) { | ||
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| ## for each range, get the authors who made at least one commit/mail in this range | ||
| active.authors.list = lapply(dataRanges, function(range.data) { | ||
| if (isMailAnalysis) { | ||
| active.authors = names(range.data$group.artifacts.by.data.column("mails", "author.name")) | ||
| } else { | ||
| active.authors = names(range.data$group.artifacts.by.data.column("commits", "author.name")) | ||
| } | ||
| return(active.authors) | ||
| }) | ||
|
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| if (globally) { | ||
| ## flatten the list of lists to one list of authors | ||
| active.authors = unlist(active.authors.list, recursive = FALSE) | ||
|
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| ## remove distracting named list members | ||
| names(active.authors) = NULL | ||
|
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| ## remove duplicates and order alphabetically ascending | ||
| active.authors = active.authors[!duplicated(active.authors)] | ||
| active.authors = active.authors[order(active.authors)] | ||
| return(active.authors) | ||
| } else { | ||
| return(active.authors.list) | ||
| } | ||
| } | ||
|
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| ## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / | ||
| ## Adjacency matrices ---------------------------------------------------- | ||
|
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| #' Get a sparse adjacency matrix for a network. | ||
| #' | ||
| #' @param network the given network | ||
| #' @param authors all authors that are wanted in the adjacency matrix | ||
| #' @param weighted decides if the adjacency matrix shall be weighted [default: FALSE] | ||
| #' | ||
| #' @return the sparse adjacency matrix of the network | ||
| get.expanded.adjacency = function(network, authors, weighted = FALSE) { | ||
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| ## create an empty sparse matrix with the right size | ||
| matrix = Matrix::sparseMatrix(i = c(), j = c(), dims = c(length(authors), length(authors)), giveCsparse = FALSE) | ||
| matrix = as(matrix, "dgTMatrix") | ||
|
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| ## add row and column names | ||
| rownames(matrix) = authors | ||
| colnames(matrix) = authors | ||
|
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| if (igraph::vcount(network) > 0) { | ||
|
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| if (weighted) { | ||
| ## get the weighted adjacency matrix for the current network | ||
| matrix.data = igraph::get.adjacency(network, attr = "weight") | ||
| } else { | ||
| ## get the unweighted adjacency matrix for the current network | ||
| matrix.data = igraph::get.adjacency(network) | ||
| } | ||
|
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| ## order the adjacency matrix | ||
| if (nrow(matrix.data) > 1) { # for a 1x1 matrix ordering does not work | ||
| matrix.data = matrix.data[order(rownames(matrix.data)), order(colnames(matrix.data))] | ||
| } | ||
|
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| ## save the activity data per author | ||
| if (nrow(matrix.data) > 0) { | ||
| matrix[rownames(matrix.data), colnames(matrix.data)] = matrix.data | ||
| } | ||
|
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| if (!weighted) { | ||
| matrix[matrix > 0] = 1 | ||
| } | ||
|
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| } | ||
|
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| return(matrix) | ||
| } | ||
|
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| #' Gets a list of networks, converts them to sparse adjacency matrices and ums up the adjacency matrices cumulatively. | ||
| #' This means that the first entry of the return list is just the adjacency matrix from the first network, | ||
| #' the second entry is the sum of the first and the second entry, and so on. | ||
| #' | ||
| #' @param networks list of networks | ||
| #' @param weighted decides if the adjacency matrix shall be weighted [default: FALSE] | ||
| #' | ||
| #' @return the list of cumulated adjacency matrices | ||
| get.expanded.adjacency.cumulated = function(networks, weighted = FALSE) { | ||
| ## get expanded adjacency matrices first | ||
| matrices = get.expanded.adjacency(networks, weighted) | ||
|
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| ## pair-wise sum of matrices: m.cumul(n) = m.cumul(m-1) + m | ||
| ## (intermediate results consecutively stored in matrices.cumulated) | ||
| matrices.cumulated = list(matrices[[1]]) # first one is complete already | ||
|
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| if (length(matrices) > 1) { | ||
| for (m in 2:(length(matrices))){ | ||
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| matrices.cumulated[[m]] = matrices.cumulated[[m - 1]] + matrices[[m]] | ||
| rownames(matrices.cumulated[[m]]) = rownames(matrices.cumulated[[m-1]]) | ||
| colnames(matrices.cumulated[[m]]) = colnames(matrices.cumulated[[m-1]]) | ||
|
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| if (!weighted) { | ||
| ## search for a non-zero entry and set them to an arbitray number (e.g., 42) | ||
| ## to force that all non-zero entries are correctly set to 1 afterwards | ||
| not.zero.idxs = which(matrices.cumulated[[m]] >= 1, arr.ind = TRUE) | ||
| if (nrow(not.zero.idxs) > 0) { | ||
| first.not.zero.idx = not.zero.idxs[1, ] | ||
| names(first.not.zero.idx) = c("row", "col") | ||
| matrices.cumulated[[m]][first.not.zero.idx[["row"]], first.not.zero.idx[["col"]]] = 42 | ||
| matrices.cumulated[[m]]@x = rep(1, length(matrices.cumulated[[m]]@i)) | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
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| return(matrices.cumulated) | ||
| } | ||
|
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| #' Converts a list of adjacency matrices to an array. | ||
| #' | ||
| #' @param network the list of adjacency matrices | ||
| #' | ||
| #' @return the converted array | ||
| convert.adjacency.matrix.list.to.array = function(adjacency.list){ | ||
|
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| ## create a 3-dimensional array representing the adjacency matrices (SIENA data format) as result | ||
| array = array(data = 0, dim = c(nrow(adjacency.list[[1]]), nrow(adjacency.list[[1]]), length(adjacency.list))) | ||
| rownames(array) = rownames(adjacency.list[[1]]) | ||
| colnames(array) = colnames(adjacency.list[[1]]) | ||
|
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| ## copy the activity values from the adjacency matrices in the list to the corresponding array slices | ||
| for (i in seq_along(adjacency.ist)){ | ||
| adjacency = adjacency.list[[i]] | ||
| activity.indices = which(adjacency != 0, arr.ind = TRUE) | ||
|
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| for (j in 1:nrow(activity.indices)){ | ||
| array[as.vector(activity.indices[j, 1]), as.vector(activity.indices[j,2]), i] = | ||
| adjacency[as.vector(activity.indices[j, 1]), as.vector(activity.indices[j, 2])] | ||
| } | ||
| } | ||
|
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| return(array) | ||
| } |
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