scrap.R

In arnoblalam/risk_sharing: Risk Sharing Networks in Tanzania

for (r in 1:nrow(adj_mat)) {
    for (c in 1:ncol(adj_mat)) {
        if(r == c) {
            adj_mat[r, c] <- 0}
        else {
            adj_mat[r,c] <- as.numeric(
                tanzania_data$willingness_link1[tanzania_data$hh1==r &
                                                    tanzania_data$hh2==c]|
                    tanzania_data$willingness_link2[tanzania_data$hh1 == r &
                                                        tanzania_data$hh2 == c])
            }}}


# for (r in 1:nrow(adj_mat)) {
#     for (c in 1:ncol(adj_mat)) {
#         if(r == c) {
#             adj_mat[r, c] <- 0}
#         else {
#             adj_mat[r,c] <- as.numeric(
#                 tanzania_data$willingness_link1[tanzania_data$hh1==r &
#                                                     tanzania_data$hh2==c])
#         }}}


length(V(tanzania_graph))

connectedness <- degree(tanzania_graph)
closeness_structure <- closeness(tanzania_graph)
betweenness_structure <- betweenness(tanzania_graph)

deg <- centr_degree(tanzania_graph)$centralization
clos <- centr_clo(tanzania_graph)$centralization
bet <- centr_betw(tanzania_graph)$centralization
eig <- centr_eigen(tanzania_graph)$centralization

# Print out the stats to screen
message("The Tanzanaia data has ",
        length(V(tanzania_graph)),
        " nodes and ",
        length(E(tanzania_graph)),
        " connections (edges) between these nodes")
message("The most connected household is (household ID) ",
        which.max(connectedness),
        " which has ", max(connectedness),
        " connections")
message("The household that is most frequently in the shortest path between two other households is household ",
        which.max(betweenness_structure))
message("The household that is adjacent to most other households is household ",
        which.max(closeness_structure))

message("
        Measures of graph centralization (normalized to theoretical maximum of a 115 node network)")

message("\tDegree Centralization: ", round(deg, 2))
message("\tCloseness Centralization: ", round(clos, 2))
message("\tBetweenness Centralization: ", round(bet), 2)
message("\tEigenvector Centralization: ", round(eig), 2)

message("Some measure of redunandancy:")

mst <- minimum.spanning.tree(tanzania_graph)

message("The minimum spanning tree for this graph has ", length(E(mst)),
        " edges (compared to the " , length(E(tanzania_graph)),
        " in the original graph).")


flog.info("Plotting the graph and histogram of degree")

plot(tanzania_graph, layout = layout_nicely)
hist(connectedness,
     main = "Histogram of degree distribution of the Tanzania Graph",
     xlab = "Degrees")
hist(closeness_structure,
     main = "Histogram of closeness (1/distance) of the Tanzania Graph",
     xlab = "Closeness")
hist(betweenness_structure,
     main = "Histogram of betweenness of the Tanzania Graph",
     xlab = "Betweenness")