sna and magrittr namespace bug fix + update description fields

DESCRIPTION

@@ -1,21 +1,22 @@
 Package: gwdegree
 Title: A Shiny App to Aid Interpretation of Geometrically-Weighted Degree Estimates in Exponential
     Random Graph Models
-Version: 0.1.0.9000
+Version: 0.1.1
 Authors@R: person("Michael", "Levy", email = "[email protected]", role = c("aut", "cre"))
 License: MIT + file LICENSE
 Description: This is a Shiny application intended to provide better understanding of how geometrically-weighted degree terms function in exponential random graph models of networks. It contains just one user function, gwdegree(), which launches the Shiny application.
 URL: https://github.com/michaellevy/gwdegree, https://michaellevy.shinyapps.io/gwdegree/
+BugReports: https://github.com/michaellevy/gwdegree/issues
 Depends:
     R (>= 3.2.3),
     ergm,
     ggplot2,
+    magrittr,
     shiny,
     shinydashboard
 Imports:
     dplyr,
     gtools,
-    magrittr,
     network,
     scales,
     sna,

NAMESPACE

@@ -5,14 +5,14 @@ import(dplyr)
 import(ergm)
 import(ggplot2)
 import(gtools)
+import(magrittr)
 import(scales)
 import(shiny)
 import(shinydashboard)
 import(sna)
-import(tidyr)
 importFrom(grDevices,topo.colors)
 importFrom(graphics,plot)
-importFrom(magrittr,"%>%")
 importFrom(network,network)
 importFrom(network,network.size)
 importFrom(stats,rbinom)
+importFrom(tidyr,gather)

R/functions.R

@@ -5,13 +5,13 @@ utils::globalVariables(".")  # For the magrittr pipe to circumvent cran-check no
 #' @return NULL Called for side-effect of launching shiny application
 #' @export
 #'
-#' @import ergm sna ggplot2 scales dplyr tidyr gtools shiny shinydashboard
+#' @import ergm sna ggplot2 magrittr scales dplyr gtools shiny shinydashboard
 #' @importFrom grDevices topo.colors
 #' @importFrom graphics plot
 #' @importFrom network network.size
 #' @importFrom network network
-#' @importFrom magrittr "%>%"
 #' @importFrom stats rbinom
+#' @importFrom tidyr gather
 #'
 #' @examples
 #' \dontrun{
@@ -66,18 +66,25 @@ simCCCent = function(gwdRange = c(-2, 2), gwespRange = c(-.5, .5),
 
   N = makeNetwork(netSize, density)
 
-  lapply(1:nrow(dfForSim), function(i) {
-    n = simulate.formula(N ~ gwdegree(theta_s, TRUE) + gwesp(theta_t, TRUE),
-                         coef = unlist(dfForSim[i, ]),
-                         constraints = ~ edges,
-                         nsim = nsim)
-    data.frame(
-      Centralization = mean(sna::centralization(n, 'degree', mode = 'graph')),
-      ClusteringCoef = mean(clusteringCoef(n))
-    )
-  }) %>%
-    do.call(rbind, .) %>%
-    cbind(dfForSim, .)
+  cbind(dfForSim,
+        do.call(rbind,
+                lapply(1:nrow(dfForSim), function(i) {
+                  n = simulate.formula(N ~ gwdegree(theta_s, TRUE) + gwesp(theta_t, TRUE),
+                                       coef = unlist(dfForSim[i, ]),
+                                       constraints = ~ edges,
+                                       nsim = nsim)
+                  data.frame(
+                    Centralization = mean(degCent(n)),
+                    ClusteringCoef = mean(clusteringCoef(n))
+                  )
+                })
+        )
+  )
+}
+
+degCent = function(n) {
+  degs = sna::degree(n)
+  sum(abs(max(degs) - degs)) / sna::degree(n, tmaxdev = TRUE)
 }
 
 clusteringCoef = function(net)

inst/app.R

@@ -222,12 +222,8 @@ ui = dashboardPage(header, sidebar, body)
 server =
   shinyServer(function(input, output, session) {
 
-    # lapply(c('ergm', 'network', 'sna', 'ggplot2', 'dplyr', 'scales', 'magrittr'),
-    #        require, char = TRUE)
-    # source("../R/functions.R")
     theme_set(theme_bw(base_size = 20))
 
-
     ### Change-statistic plot  ####
     deltaGWDdf = reactive({
       gwdegree:::makeDeltaGWDdF(kmin = input$degreeRange[1],
@@ -262,7 +258,7 @@ server =
         isolate({
           networks = vector('list', 2L)
           networks[[1]] = replicate(input$reps,
-                                    gwdegree:::makeNetwork(input$netSize, density = input$density),
+                                    gwdegree:::makeNetwork(input$netSize, input$density),
                                     simplify = FALSE)
           networks[[2]] =
             lapply(networks[[1]], function(basis) {  # To keep number edges same across types
@@ -279,7 +275,7 @@ server =
             # type is stack of simulated graphs of a particular type
             # Split into each simulation, tabulate degrees, and use `smartbind` to match on names (ie, degree)
             deg =
-              degree(networks[[type]], g = 1:length(networks[[type]]), gmode = 'graph') %>%
+              sna::degree(networks[[type]], g = 1:length(networks[[type]]), gmode = 'graph') %>%
               split(., 1:ncol(.)) %>%
               lapply(table) %>%
               do.call(gtools::smartbind, .) %>%
@@ -294,15 +290,10 @@ server =
             deg[is.na(deg)] = 0
             deg$type = names(networks[type])
 
-            list(deg = deg,
-                 var = data.frame(
-                   type = names(networks)[type],
-                   var = mean(sapply(networks[[type]], function(n) var(degree(n, gmode = 'graph'))))
-                 ))
+            deg
           })
 
-          vari = do.call(rbind, lapply(dd, "[[", "var"))
-          deg = do.call(rbind, lapply(dd, "[[", "deg"))
+          deg = do.call(rbind, dd)
 
           # Fill in the degrees-never-found to keep box-widths constant
           filled = expand.grid(
@@ -316,15 +307,13 @@ server =
           filled$type = factor(filled$type, levels = sort(unique(filled$type), decreasing = TRUE))
         })
 
-        list(dd = filled, variance = vari, random = networks[[1]][[1]], gwd = networks[[2]][[1]])
+      list(dd = filled, random = networks[[1]][[1]], gwd = networks[[2]][[1]])
 
 
       })
 
     #### Plot degree distributions ####
 
-    # Pretty the x-axis labels and get outlier points colored
-    # And add average variance
     output$degDistPlot = renderPlot({
 
       ggplot(degDists()[["dd"]], aes(x = factor(degree), y = count, fill = type)) +

tests/testthat/test_degCent.R

@@ -0,0 +1,15 @@
+library(sna)
+context("clustering coefficient function")
+
+networks = list(
+  nEmpty = gwdegree:::makeNetwork(100, 0),
+  nFull = gwdegree:::makeNetwork(100, 1),
+  nOther = gwdegree:::makeNetwork(100, .2)
+)
+
+test_that("Degree centralization is the same for my function as package:sna",
+          expect_equal(
+            sapply(networks, gwdegree:::degCent),
+            sapply(networks, sna::centralization, sna::degree, "graph")
+          ))
+