added my R scripts

1 files changed, 223 insertions, 0 deletions

diff --git a/R Scripts/data-prep.R b/R Scripts/data-prep.R
new file mode 100755
index 0000000..3104ea2
--- /dev/null
+++ b/R Scripts/data-prep.R
@@ -0,0 +1,223 @@
+library(igraph)
+setwd('~/Documents/Cascade Project/Raw Data/')
+
+#================
+# (1) load data
+#================
+
+#load all three sets of data
+arrests <- read.csv("2006to2014arrests2.csv", header=T, colClass=c("character"))
+
+#I need to add the "ir" for this to make sense when I "project"
+arrests$ir2 <- paste("ir", arrests$ir_no)
+
+## Match arrests based on date, time, and location
+a = arrests[arrests$rd_no=='',]
+dtab = table(a$arrest_date)
+dates = attr(dtab,'name')[dtab>1]
+for (date in dates){
+  if (which(date==dates)%%10000==0) print(which(date==dates))
+  ids = which(a$arrest_date==date)
+  grp = a[ids,]
+  stab = table(grp$street_nme)
+  streets = attr(stab,'name')[stab>1]
+  for (street in streets){
+    arr_ids = as.numeric(rownames(grp[grp$street_nme==street,]))
+    arrests$rd_no[arr_ids] = paste('rd',arr_ids[1])
+  }
+}
+# now make unique rd_nos for the other people arrested alone
+null_rds = which(arrests$rd_no=='')
+arrests$rd_no[null_rds] = paste('rd',null_rds)
+save(arrests,file='arrests.RData')
+
+#=====================
+# (2) Structure Data
+#=====================
+
+#get the fields we need for all three: incidents, mni, and "type"
+sub.arrests <- subset(arrests, select=c(as.character("rd_no"), as.character('ir2')))
+colnames(sub.arrests) <- c("events", "individuals")
+
+#=============================
+# (3) Prep for making graphs
+#=============================
+
+individuals <- unique(sub.arrests$individuals)
+
+events <- unique(sub.arrests$events)
+
+if (any(individuals %in% events))
+  
+  stop('vertex name collision')
+
+vertices <- data.frame(name=c(events, individuals),
+                       type=c(rep(FALSE, length(events)),
+                              rep(TRUE, length(individuals))),
+                       stringsAsFactors=FALSE)
+
+#===================================================================
+# (4) Make the GRAPH file
+#===================================================================
+g <- graph.data.frame(sub.arrests, vertices=vertices)
+
+
+#===================================================================
+# Sanity check the resulting igraph object
+stopifnot(ecount(g) == nrow(sub.arrests))
+stopifnot(vcount(g) == nrow(vertices))
+
+names <- V(g)$name
+stopifnot(isTRUE(identical(sort(names), sort(vertices$name))))
+
+inames <- V(g)[V(g)$type]$name
+stopifnot(isTRUE(identical(sort(inames), sort(individuals))))
+
+enames <- V(g)[! V(g)$type]$name
+stopifnot(isTRUE(identical(sort(enames), sort(events))))
+
+#===================================================================
+#now do the converstion into a single network 
+net1 <- bipartite.projection(g)
+person <- net1[[2]]
+
+#===================================================================
+# (5) Define attributes on nodes
+#===================================================================
+# set attributes from arrests file
+
+attribs <- arrests
+match_vector = match(V(person)$name, attribs$ir2)
+V(person)$sex <- as.character(attribs$sex_code_cd[match_vector])
+V(person)$race <- as.character(attribs$race_code_cd[match_vector])
+# V(person)$age <- as.character(attribs$age[match_vector])
+V(person)$dob <- as.character(as.Date(attribs$birth_date[match_vector],format='%m/%d/%Y'))
+
+# January 1, 2006 is Day 1 of the study period
+start_date = as.Date("2005-12-31")
+
+## Get first arrest date in the study period for each person
+sub.arrests$dates = as.Date(arrests$arrest_date,format='%m/%d/%Y')
+sub.arrests = sub.arrests[order(sub.arrests$dates),]
+sub.arrests = sub.arrests[match(unique(sub.arrests$individuals),sub.arrests$individuals),]
+arrest.dates = as.Date(sub.arrests$dates,format='%m/%d/%y')
+arrest.days = as.numeric(arrest.dates-start_date)
+V(person)$arrest.day = arrest.days[match(V(person)$name, sub.arrests$individuals)]
+
+V(person)$age.arrest = floor(difftime(arrest.dates[match(V(person)$name, sub.arrests$individuals)],
+                                      V(person)$dob,
+                                      units='days')/365.25)
+
+#===================================================================
+
+
+# get victim attributes
+shootings <- read.csv("shooting-data-withdate2.csv", header = T)
+victims = shootings[shootings$INV_PARTY_TYPE_CD=="VIC",]
+victims = victims[!is.na(victims$IR_NO),]
+victims$ir2 <- paste("ir", victims$IR_NO)
+
+# get murder victim attributes
+murders = read.csv("murder-victims-13nov.csv", header=T)
+murders = murders[!is.na(murders$VICTIM_IR_NO),]
+murders = murders[murders$INJURY_DESCR=="SHOT",]
+murders = murders[match(unique(murders$VICTIM_IR_NO),murders$VICTIM_IR_NO),]
+murders = murders[as.Date(murders$INJURY_DATE,format='%m/%d/%y')>=start_date,]
+murders$ir2 = paste("ir", murders$VICTIM_IR_NO)
+
+# set victim data in network
+vtab = as.data.frame(table(victims$ir2))
+match_vector = match(V(person)$name,vtab$Var1)
+V(person)$vic.nonfatal = vtab$Freq[match_vector]
+V(person)$vic.nonfatal[is.na(V(person)$vic.nonfatal)] = 0
+V(person)$vic.fatal = V(person)$name %in% murders$ir2
+V(person)$vic = V(person)$name %in% union(victims$ir2,murders$ir2)
+
+# add fatal shooting dates to the network
+match_vector = match(V(person)$name, murders$ir2)
+fatal_dates = murders$INJURY_DATE[match_vector]
+fatal_dates = as.character(as.Date(fatal_dates,format='%m/%d/%y'))
+V(person)$fatal_date = fatal_dates
+
+# add nonfatal shooting dates to the network
+match_vector = match(victims$ir2,V(person)$name)
+vics = which(V(person)$vic.nonfatal>0)
+nfd1 = nfd2 = nfd3 = nfd4 = nfd5 = rep(0,length(vics))
+for(i in 1:length(vics)){
+  if (i%%3000==0) print(i)
+  name = vics[i]
+  ids = which(match_vector==name)
+  dates = sort(as.Date(victims$INCIDENT_DATE[ids],format='%m/%d/%y'))
+  nfd1[i] = as.character(dates[1])
+  nfd2[i] = as.character(dates[2])
+  nfd3[i] = as.character(dates[3])
+  nfd4[i] = as.character(dates[4])
+  nfd5[i] = as.character(dates[5])
+}
+V(person)$nonfatal_date_1 = NA
+V(person)$nonfatal_date_2 = NA
+V(person)$nonfatal_date_3 = NA
+V(person)$nonfatal_date_4 = NA
+V(person)$nonfatal_date_5 = NA
+V(person)$nonfatal_date_1[vics] = nfd1
+V(person)$nonfatal_date_2[vics] = nfd2
+V(person)$nonfatal_date_3[vics] = nfd3
+V(person)$nonfatal_date_4[vics] = nfd4
+V(person)$nonfatal_date_5[vics] = nfd5
+
+# convert dates into numeric values ("days")
+start_date
+V(person)$fatal_day = as.numeric(as.Date(V(person)$fatal_date)-start_date)
+V(person)$nonfatal_day_1 = as.numeric(as.Date(V(person)$nonfatal_date_1)-start_date)
+V(person)$nonfatal_day_2 = as.numeric(as.Date(V(person)$nonfatal_date_2)-start_date)
+V(person)$nonfatal_day_3 = as.numeric(as.Date(V(person)$nonfatal_date_3)-start_date)
+V(person)$nonfatal_day_4 = as.numeric(as.Date(V(person)$nonfatal_date_4)-start_date)
+V(person)$nonfatal_day_5 = as.numeric(as.Date(V(person)$nonfatal_date_5)-start_date)
+
+#===================================================================
+# set gang attributes
+gangs <- read.csv("Sept2014-ganglist.csv", header=T)
+gangs = gangs[match(unique(gangs$IR_NO),gangs$IR_NO),]
+gangs$ir2 <- paste("ir", gangs$IR_NO)
+
+V(person)$gang.member <- V(person)$name %in% gangs$ir2
+
+match_vector = match(V(person)$name, gangs$ir2)
+gnames = gangs$GANG_NAME[match_vector]
+gnames = as.character(gnames)
+gnames[V(person)$gang.member==''] = 'Unknown'
+gnames[V(person)$gang.member==F] = 'None'
+V(person)$gang.name <- as.character(gnames)
+V(person)$faction.name <- as.character(gangs$FACTION_NAME[match_vector])
+
+#===================================================================
+# create id number
+V(person)$id = rank(V(person)$name)
+
+# save data
+# person = remove.edge.attribute(person,'weight')
+# person_data = get.data.frame(person,'both')
+save(person, file="chi-19mar2015.RData")
+
+#===================================================================
+# get LCC of the network
+lcc = induced.subgraph(person,which(clusters(person)$membership==which.max(clusters(person)$csize)))
+V(lcc)$ir_no = V(lcc)$name
+vic_ids = which(V(lcc)$vic)
+lcc_verts = get.data.frame(lcc,'vertices')
+lcc_edges = get.data.frame(lcc,'edges')
+save(lcc, lcc_verts, lcc_edges, vic_ids, file="lcc.RData")
+
+lcc_data = get.data.frame(lcc,'both')
+lcc = set.vertex.attribute(graph=lcc, name='name', value=V(lcc)$id)
+row.names(lcc_data$vertices) = lcc_data$vertices$id
+vertices = lcc_data$vertices[c('name','vic','vic.fatal','vic.nonfatal',
+                               'fatal_day','nonfatal_day_1','nonfatal_day_2',
+                               'nonfatal_day_3','nonfatal_day_4','nonfatal_day_5')]
+
+write.csv(vertices,file='lcc_vertices.csv')
+write.csv(lcc_data$edges,file='lcc_edges.csv')
+
+lcc = graph.data.frame(lcc_edges, directed=FALSE, 
+                       vertices=lcc_verts[,c('name','vic','vic.fatal','vic.nonfatal')])
+write.graph(lcc,'lcc.gml','gml')