R/index.builder.R

Defines functions index_builder

Documented in index_builder

#' Centrality Index Builder
#'
#' This shiny gadget can be used to build centrality indices based on specific indirect relations,
#' transformations and aggregation functions.
#'
#' @return code to calculate the specified index.
#' @export
index_builder <- function() {
  if (!requireNamespace("shiny", quietly = TRUE)) {
    stop("shiny is needed for the addin to work. Please install it.", call. = FALSE)
  }
  dist_transform <- c(
    identity = "identity", `1/x` = "dist_inv", `2^-x` = "dist_2pow",
    `a^x` = "dist_powd", `x^-a` = "dist_dpow"
  )
  walk_transform <- c(
    `limit proportion` = "walks_limit_prop", `exponential` = "walks_exp",
    `even exponential` = "walks_exp_even", `odd exponential` = "walks_exp_odd",
    `attenuated walks` = "walks_attenuated",
    `up to length k` = "walks_uptok"
  )

  transforms <- list(
    adjacency = "identity",
    dist_sp = dist_transform,
    dist_resist = dist_transform,
    dist_lf = dist_transform,
    dist_walk = dist_transform,
    dist_rwalk = dist_transform,
    depend_sp = c(identity = "identity"),
    depend_netflow = c(identity = "identity"),
    depend_curflow = c(identity = "identity"),
    depend_exp = c(identity = "identity"),
    depend_rsps = c(identity = "identity"),
    depend_rspn = c(identity = "identity"),
    walks = walk_transform
  )

  indices <- list(
    "degree" = c("adjacency", "identity", "sum"),
    "ccclassic" = c("dist_sp", "identity", "invsum"),
    "bcsp" = c("depend_sp", "identity", "sum"),
    "eigen" = c("walks", "walks_limit_prop", "sum"),
    "scall" = c("walks", "walks_exp", "self"),
    "sceven" = c("walks", "walks_exp_even", "self"),
    "scodd" = c("walks", "walks_exp_odd", "self"),
    "katz" = c("walks", "walks_attenuated", "sum"),
    "comall" = c("walks", "walks_exp", "sum"),
    "comeven" = c("walks", "walks_exp_even", "sum"),
    "comodd" = c("walks", "walks_exp_odd", "sum"),
    "netflow" = c("depend_netflow", "identity", "sum"),
    "curflow" = c("depend_curflow", "identity", "sum"),
    "combet" = c("depend_exp", "identity", "sum"),
    "rsps" = c("depend_rsps", "identity", "sum"),
    "rspn" = c("depend_rspn", "identity", "sum"),
    "hcc" = c("dist_sp", "dist_inv", "sum"),
    "rcc" = c("dist_sp", "dist_2pow", "sum"),
    "inf" = c("dist_resist", "identity", "invsum"),
    "gencc" = c("dist_sp", "dist_dpow", "sum"),
    "decay" = c("dist_sp", "dist_powd", "sum"),
    "drwalk" = c("dist_rwalk", "identity", "invsum")
  )
  # ui ----
  ui <- miniUI::miniPage(
    miniUI::gadgetTitleBar("Centrality Index Builder"),
    shiny::fluidRow(
      shiny::column(6, shiny::selectInput(
        "index", "Prebuild Indices",
        list(
          "Build your own" = "buildself",
          "Classic Indices" = c(
            "Degree" = "degree", "Closeness" = "ccclassic",
            "Betweenness" = "bcsp", "Eigenvector" = "eigen"
          ),
          "Feedback" = c(
            "Subgraph" = "scall", "Subgraph even" = "sceven",
            "Subgraph odd" = "scodd", "Katz Status" = "katz",
            "Communicability" = "comall", "Communcability even" = "comeven",
            "Communcability odd" = "comodd"
          ),
          "Betweenness Type" = c(
            "Flow Betweenness" = "netflow",
            "Current Flow Betweenness" = "curflow",
            "Communicability Betweenness" = "combet",
            "Simple RSP Beteenness" = "rsps",
            "Net RSP Betweenness" = "rspn"
          ),
          "Closeness Type" = c(
            "Harmonic Closeness" = "hcc",
            "Residual Closeness" = "rcc",
            "Information Centrality" = "inf",
            "Generalized Closeness" = "gencc",
            "Decay Centrality" = "decay",
            "Random Walk Closeness" = "drwalk"
          )
        )
      )),
      shiny::column(6)
    ),
    shiny::fluidRow(
      shiny::column(3, shiny::textInput("network", "network", value = "g", width = NULL, placeholder = NULL)),
      shiny::column(3, shiny::selectInput(
        "relation", "Indirect Relation",
        list(
          "Adjacency" = c("Adjacency" = "adjacency"),
          "Distances" = c(
            "Shortest Path Distance" = "dist_sp",
            "Resistance Distance" = "dist_resist",
            "Log Forest Distance" = "dist_lf",
            "Walk Distance" = "dist_walk",
            "Random Walk Distance" = "dist_rwalk"
          ),
          "Walks" = c("Walk Counts" = "walks"),
          "Dependencies" = c(
            "Shortest Path Dep." = "depend_sp",
            "Network Flow Dep. " = "depend_netflow",
            "Current Flow Dep." = "depend_curflow",
            "Exponential Walks Dep." = "depend_exp",
            "Simple RSP Dep." = "depend_rsps",
            "Net RSP Dep." = "depend_rspn"
          )
        )
      )),
      shiny::column(3, shiny::selectInput("transformation", "Transformation", c(""))),
      shiny::column(3, shiny::selectInput(
        "aggregation", "Aggregation",
        c(
          "Sum" = "sum", "Mean" = "mean", "Max" = "max", "Min" = "min",
          "Inverse Sum" = "invsum", "Self" = "self",
          "Product" = "prod"
        )
      ))
    ),
    shiny::fluidRow(
      shiny::column(3),
      shiny::column(
        3,
        shiny::conditionalPanel(
          "input.relation=='depend_netflow'",
          shiny::selectInput("netflow", "netflow mode", c("Raw" = "raw", "Fraction" = "frac", "Normalized" = "norm"))
        ),
        shiny::conditionalPanel(
          "input.relation=='dist_lf'",
          shiny::sliderInput("lfparam", "Log Forest Parameter", 0, 500, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.relation=='dist_walk'",
          shiny::sliderInput("dwparam", "Walk Distance Parameter", 0, 500, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.relation=='depend_rsps'",
          shiny::sliderInput("rspxparam", "Randomized SP Parameter", 0, 500, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.relation=='depend_rspn'",
          shiny::sliderInput("rspxparam", "Randomized SP Parameter", 0, 500, 1, 0.1)
        )
      ),
      shiny::column(
        3,
        shiny::conditionalPanel(
          "input.transformation=='walks_exp'",
          shiny::sliderInput("alpha", "alpha", 0.001, 100, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='walks_exp_even'",
          shiny::sliderInput("alpha", "alpha", 0.001, 100, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='walks_exp_odd'",
          shiny::sliderInput("alpha", "alpha", 0.001, 100, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='walks_attenuated'",
          shiny::sliderInput("alpha", "alpha", 0.001, 0.5, 0.01, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='walks_uptok'",
          shiny::sliderInput("alpha", "alpha", 0.001, 10, 1, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='dist_dpow'",
          shiny::sliderInput("alpha", "alpha", 0, 10, 0.3, 0.1)
        ),
        shiny::conditionalPanel(
          "input.transformation=='dist_powd'",
          shiny::sliderInput("alpha", "alpha", 0, 1, 0.33, 0.1)
        )
      ),
      shiny::column(3, shiny::checkboxInput("pipe", "Use pipes", value = TRUE, width = NULL))
    ),
    shiny::fluidRow(
      shiny::column(3),
      shiny::column(3),
      shiny::column(
        3,
        shiny::conditionalPanel(
          "input.transformation=='walks_uptok'",
          shiny::sliderInput("tok", "k", 1, 10, 4, 1)
        )
      ),
      shiny::column(3)
    )
  )

  # server ----
  server <- function(input, output, session) {
    shiny::observe({
      shiny::updateSelectInput(session, "transformation",
        label   = "Transformation",
        choices = transforms[[input$relation]]
      )
    })
    shiny::observe({
      index_focus <- indices[[input$index]]
      if (input$index != "buildself") {
        shiny::updateSelectInput(session, "relation", selected = index_focus[1])
        shiny::updateSelectInput(session, "transformation",
          choices = transforms[[input$relation]],
          selected = index_focus[2]
        )
        shiny::updateSelectInput(session, "aggregation", selected = index_focus[3])
      }
    })
    shiny::observeEvent(input$done, {
      lfparam_text <- ifelse(input$relation != "dist_lf", "", paste0(", lfparam = ", input$lfparam))
      dwparam_text <- ifelse(input$relation != "dist_walk", "", paste0(", dwparam = ", input$lfparam))
      netflow_text <- ifelse(input$relation != "depend_netflow", "", paste0(", netflowmode = \"", input$netflow, "\""))
      rspx_text <- ifelse(!input$relation %in% c("depend_rsps", "depend_rspn"),
        "", paste0(", rspxparam = ", input$rspxparam)
      )

      alpha_text <- ifelse(input$transformation %in% c("identity", "dist_2pow", "dist_inv", "walks_limit_prop"),
        "", paste0(", alpha = ", input$alpha)
      )
      tok_text <- ifelse(input$transformation != "walks_uptok", "", paste0(", k = ", input$tok))
      if (input$pipe) {
        indexText <- paste0(
          "cent <- ", input$network,
          " %>% \n\t",
          "indirect_relations(",
          "type = \"",
          input$relation, "\"", lfparam_text, dwparam_text, netflow_text, rspx_text,
          ", FUN = ", input$transformation, alpha_text, tok_text, ") %>%\n\t",
          "aggregate_positions(type = \"", input$aggregation, "\")"
        )
      } else {
        indexText <- paste0(
          "rel <- indirect_relations(g,",
          "type = \"",
          input$relation, "\"", lfparam_text, dwparam_text, netflow_text, rspx_text,
          ", FUN = ", input$transformation, alpha_text, tok_text, ")\n",
          "cent <- aggregate_positions(rel,type = \"", input$aggregation, "\")"
        )
      }
      rstudioapi::insertText(indexText)
      shiny::stopApp()
    })

    shiny::observeEvent(input$cancel, {
      shiny::stopApp()
    })
  }

  viewer <- shiny::dialogViewer("Index Builder", width = 840, height = 400)
  shiny::runGadget(ui, server, viewer = viewer)
}

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netrankr documentation built on Dec. 21, 2021, 5:07 p.m.