SS simultaneous edits

Merge branch 'master' of https://github.com/zipkinlab/timberdoodle

# Conflicts:
#	Code/AMWO_submodels/Data manip AMWO marray SR.R

Author: saund123

Code/AMWO_submodels/Data manip AMWO marray SR.R

@@ -97,6 +97,7 @@ colnames(clean)<-c("bSeason","bYear","rYear","region","age","class","dummy")
 Year<-unique(clean$bYear)
 Year<-sort(Year)  #SS added because needs to be chronological or else will have values below diagnol?
 NbYear<-length(Year)
+NYear<-length(Year)
 Season<-unique(clean$bSeason)
 NSeason<-length(Season)
 Class<-unique(clean$class)
@@ -106,12 +107,15 @@ Region<-unique(clean$region)
 Region<-sort(Region)       #SS sorted
 NRegion<-length(Region)
 
-awc<-array(NA,dim=c(NbYear,NbYear,NSeason,NClass,NRegion))
+awc<-array(NA,dim=c(NYear,NYear,NSeason,NClass,NRegion),
+           dimnames =list(Year, Year, c("spring","not_spring"),
+                       c("local","Hatch_Year","Adult_Male","Adult_Female"),
+                       c("Eastern","Central")))
 for (s in 1:NSeason){
   for (cc in 1:NClass){
     for (i in 1:NRegion){
-      for (b in 1:NbYear){
-        for (r in 1:NbYear){
+      for (b in 1:NYear){
+        for (r in 1:NYear){
                 awc[b,r,s,cc,i]<-sum(clean[clean$bYear==Year[b]&clean$rYear==Year[r]&clean$class==Class[cc]&clean$region==Region[i],7])
                 }}}}}
 
@@ -120,3 +124,4 @@ awc[1:20,1:20,1,1,1]
 
 #are these the correct dimensions that we want?? or do we want to merge all age classes together into same marray? 2 separate
 #marrays for the 2 seasons, right? and regions, right?
+

Data manip AMWO marray SS.R

@@ -0,0 +1,122 @@
+#------------------------------------------------------------------------------
+# Data manipulation of banding data to create m-arrays for band recovery models
+#------------------------------------------------------------------------------
+rm(list=ls())
+# Read in dataset
+#raw.data<-read.csv(file.choose())
+#raw<-read.csv("~/Google Drive/AMWO IPM/Datasets/M array table AMWO CSV.csv")
+raw<-read.csv("AMWO recoveries.csv")  #reading in CSV from GitHub-linked timberdoodle folder
+
+########################################################################
+#cleaning data
+########################################################################
+#SS added following conditions to subset raw data:
+
+#only use status 3 birds
+raw<-subset(raw,Status==3)
+
+#only use how obtained category 1 (shot)
+raw<-subset(raw,How.Obt==1)
+
+#only use B.Year from 1963 onwards
+raw<-subset(raw,B.Year>=1963)
+
+#bring in B.month, convert to season
+clean<-matrix(NA,nrow=length(raw$B.Month),ncol=1)
+clean<-data.frame(clean)
+clean[raw$B.Month<=6,]<-1  #shouldn't we change this to between 4 and 6?
+clean[raw$B.Month>6,]<-2   #shouldn't we change this to between 7 and 9?
+
+#Bring in B.year
+clean[,2]<-raw$B.Year  #but we only want to start with 1963!
+
+#bring in recovery year and account for recoveries occurring in Jan-March
+clean[,3]<-NA
+clean[raw$R.Month>=4,3]<-raw[raw$R.Month>=4,"R.Year"]
+#adjust if you don't want to include birds recovered in March
+clean[raw$R.Month<4,3]<-raw[raw$R.Month<4,"R.Year"]-1
+
+#bring in B region
+clean[,4]<-0
+clean[raw$B.Flyway==1,4]<-1
+clean[raw$B.Flyway%in%2:3,4]<-2
+clean[raw$B.Flyway==6&raw$BRegion..STA%in%c("QC","NS","NB","PE","NF","PQ"),4]<-1
+clean[raw$B.Flyway==6&raw$BRegion..STA%in%c("ONT"),4]<-2
+
+#bring in R region, this is only to exlude region crossers
+clean[,5]<-0 #specify different number from previous step to flag it in the next step
+clean[raw$B.Flyway==1,5]<-1
+clean[raw$B.Flyway%in%2:3,5]<-2
+clean[raw$B.Flyway==6&raw$BRegion..STA%in%c("QC","NS","NB","PE","NF","PQ"),5]<-1
+clean[raw$B.Flyway==6&raw$BRegion..STA%in%c("ONT"),5]<-2
+
+# pull out places you don't care about
+raw<-raw[clean$V4!=0|clean$V5!=0,]
+clean<-clean[clean$V4!=0|clean$V5!=0,]
+clean<-clean[,1:4] #remove R.state becuase it is redundant 
+
+#bring in age
+# local = 1, hatch year = 2, adult = 3
+clean[,5]<-NA
+clean[raw$Age..VAGE=="After Hatch Year",5]<-3
+clean[raw$Age..VAGE=="After Second Year",5]<-3
+clean[raw$Age..VAGE=="After Third Year",5]<-3
+clean[raw$Age..VAGE=="Second Year",5]<-3
+clean[raw$Age..VAGE=="Unknown",5]<-NA
+clean[raw$Age..VAGE=="Hatch Year",5]<-2
+clean[raw$Age..VAGE=="Local",5]<-1
+#remove unknowns
+raw<-raw[!is.na(clean[,5]),]
+clean<-clean[!is.na(clean[,5]),]
+
+# get rid of hatch years in months 5 and 6
+clean <- clean[!(raw$Age..VAGE=="Hatch Year"&raw$B.Month%in%c(5:6)),]
+raw <- raw[!(raw$Age..VAGE=="Hatch Year"&raw$B.Month%in%c(5:6)),]
+
+#bring in sex and convert to age class
+# 1=local, 2=juv, 3=male, 4=female
+clean[,6]<-NA
+clean[clean[,5]%in%1:2,6]<-clean[clean[,5]%in%1:2,5]
+clean[raw$Sex..VSEX%in%c("Male","Male; from subsequent encounter")&clean[,5]==3,6]<-3
+clean[raw$Sex..VSEX%in%c("Female","Female; from subsequent encounter")&clean[,5]==3,6]<-4
+
+#remove unknown adults for now? Can treat unknowns via mixtures acc. to Todd
+raw<-raw[!(is.na(clean[,6])&clean[,5]==3),]
+clean<-clean[!(is.na(clean[,6])&clean[,5]==3),]
+
+# remove unwanted banding periods (Oct-Dec) for now
+clean<-clean[!raw$B.Month%in%c(10:12,1:3),]
+raw<-raw[!raw$B.Month%in%c(10:12,1:3),]
+
+
+clean[,7]<-1
+colnames(clean)<-c("bSeason","bYear","rYear","region","age","class","dummy")
+########################################################################
+#create the marray
+########################################################################
+Year<-unique(clean$bYear)
+Year<-sort(Year)  #SS added because needs to be chronological or else will have values below diagnol?
+NbYear<-length(Year)
+Season<-unique(clean$bSeason)
+NSeason<-length(Season)
+Class<-unique(clean$class)
+Class<-sort(Class)       #SS sorted
+NClass<-length(Class)
+Region<-unique(clean$region)
+Region<-sort(Region)       #SS sorted
+NRegion<-length(Region)
+
+awc<-array(NA,dim=c(NbYear,NbYear,NSeason,NClass,NRegion))
+for (s in 1:NSeason){
+  for (cc in 1:NClass){
+    for (i in 1:NRegion){
+      for (b in 1:NbYear){
+        for (r in 1:NbYear){
+          awc[b,r,s,cc,i]<-sum(clean[clean$bYear==Year[b]&clean$rYear==Year[r]&clean$class==Class[cc]&clean$region==Region[i],7])
+        }}}}}
+
+#take a look at subset of giant marray
+awc[1:20,1:20,1,1,1]
+
+#are these the correct dimensions that we want?? or do we want to merge all age classes together into same marray? 2 separate
+#marrays for the 2 seasons, right? and regions, right?