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build-se.r
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build-se.r
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bills = subset(bills, type == "PROPOSITION DE LOI")
for (k in 53:49) { # too few bills in S. 54
cat("Sénat, législature", k,
years[ as.character(k) ], "to",
years[ as.character(k + 1) ])
data = subset(bills, legislature == k & n_au > 1)
cat(":", nrow(data), "cosponsored documents, ")
# ============================================================================
# DIRECTED EDGE LIST
# ============================================================================
edges = lapply(data$authors, function(d) {
w = unlist(strsplit(d, ";")) %>% as.integer
# remove missing sponsors; if first author is missing, first nonmissing
# cosponsor is used in replacement (happens only in very few cases)
w = w[ w %in% b$sid ]
if (length(w) > 0) {
e = expand.grid(i = b$nom[ b$sid %in% w ],
j = b$nom[ b$sid == w[1]], stringsAsFactors = FALSE)
return(data.frame(e, w = length(w) - 1)) # number of cosponsors
} else {
return(NULL)
}
}) %>% bind_rows
# ============================================================================
# EDGE WEIGHTS
# ============================================================================
# first author self-loops, with counts of cosponsors
self = subset(edges, i == j)
# count number of bills per first author
n_au = table(self$j)
# remove self-loops from directed edge list
edges = subset(edges, i != j)
# count number of bills cosponsored per sponsor
n_co = table(edges$i)
# identify directed ties
edges$ij = apply(edges[, 1:2 ], 1, paste0, collapse = "///")
# raw edge counts
raw = table(edges$ij)
# Newman-Fowler weights (weighted quantity of bills cosponsored)
edges = aggregate(w ~ ij, function(x) sum(1 / x), data = edges)
# expand to edge list
edges = data_frame(i = gsub("(.*)///(.*)", "\\1", edges$ij),
j = gsub("(.*)///(.*)", "\\2", edges$ij),
raw = as.vector(raw[ edges$ij ]), # raw edge counts
nfw = edges$w)
# Gross-Shalizi weights (weighted propensity to cosponsor)
edges = merge(edges, aggregate(w ~ j, function(x) sum(1 / x), data = self))
edges$gsw = edges$nfw / edges$w
# sanity check
stopifnot(edges$gsw <= 1)
# final edge set: cosponsor, first author, weights
edges = select(edges, i, j, raw, nfw, gsw)
cat(nrow(edges), "edges, ")
# ============================================================================
# DIRECTED NETWORK
# ============================================================================
n = network(edges[, 1:2 ], directed = TRUE)
n %n% "country" = meta[ "cty" ]
n %n% "country" = meta[ "cty" ] %>% as.character
n %n% "lang" = meta[ "lang" ] %>% as.character
n %n% "years" = paste0(years[ as.character(k) ], "-", years[ as.character(k + 1) ])
n %n% "legislature" = as.character(k)
n %n% "chamber" = meta[ "se" ] %>% as.character
n %n% "type" = meta[ "type-se" ] %>% as.character
n %n% "ipu" = meta[ "ipu-se" ] %>% as.integer
n %n% "seats" = meta[ "seats-se" ] %>% as.integer
n %n% "n_cosponsored" = nrow(data)
n %n% "n_sponsors" = table(subset(bills, legislature == k)$n_au)
# ============================================================================
# VERTEX-LEVEL ATTRIBUTES
# ============================================================================
n_au = as.vector(n_au[ network.vertex.names(n) ])
n %v% "n_au" = ifelse(is.na(n_au), 0, n_au)
n_co = as.vector(n_co[ network.vertex.names(n) ])
n %v% "n_co" = ifelse(is.na(n_co), 0, n_co)
n %v% "n_bills" = n %v% "n_au" + n %v% "n_co"
cat(network.size(n), "nodes\n")
rownames(b) = b$nom
n %v% "url" = b[ network.vertex.names(n), "url" ]
n %v% "sex" = b[ network.vertex.names(n), "sex" ]
n %v% "born" = b[ network.vertex.names(n), "born" ]
n %v% "party" = b[ network.vertex.names(n), "parti" ]
n %v% "partyname" = groups[ n %v% "party" ] %>% as.character
n %v% "lr" = scores[ n %v% "party" ] %>% as.numeric
n %v% "photo" = b[ network.vertex.names(n), "photo" ]
# mandate years done up to start year of legislature
b$nyears = sapply(b$mandate, function(x) {
sum(unlist(strsplit(x, ";")) <= as.numeric(years[ as.character(k) ]))
})
n %v% "nyears" = b[ network.vertex.names(n), "nyears" ] %>% as.integer
set.edge.attribute(n, "source", as.character(edges[, 1])) # cosponsor
set.edge.attribute(n, "target", as.character(edges[, 2])) # first author
set.edge.attribute(n, "raw", edges$raw) # raw edge counts
set.edge.attribute(n, "nfw", edges$nfw) # Newman-Fowler weights
set.edge.attribute(n, "gsw", edges$gsw) # Gross-Shalizi weights
# ============================================================================
# SAVE PLOTS
# ============================================================================
if (plot) {
save_plot(n, paste0("plots/net_be_se", years[ as.character(k) ], "-", years[ as.character(k + 1) ]),
i = colors[ b[ n %e% "source", "parti" ] ],
j = colors[ b[ n %e% "target", "parti" ] ],
mode, colors)
}
# ============================================================================
# SAVE OBJECTS
# ============================================================================
assign(paste0("net_be_se", years[ as.character(k) ]), n)
assign(paste0("edges_be_se", years[ as.character(k) ]), edges)
assign(paste0("bills_be_se", years[ as.character(k) ]), data)
# ============================================================================
# SAVE GEXF
# ============================================================================
if (gexf)
save_gexf(n, paste0("net_be_se", years[ as.character(k) ], "-", years[ as.character(k + 1) ]),
mode, colors)
}
if (gexf)
zip("net_be_se.zip", files = dir(pattern = "^net_be_se\\d{4}-\\d{4}\\.gexf$"))