In brouwern/DRmencia: Data and code to replicate analyses for Latta, Brouwer, et al. 2018. PeerJ.

knitr::opts_chunk$set(echo = TRUE)

x <- dcast(data= ann_caps[,],
      formula = spp.code + band + status2 ~ sex + year,
      value.var = "site",
      fun.aggregate = length)

x[,-c(1:3)]

Cast by year and look at duplicates

Creates capture histories (but not w/ re-sights)

By look at duplicates, it becomes evident that a fair number of birds move between sites and within sites. This could account for some of the differene between Steve's work up of the data and mine. I was originally only counting a bird the very first time it was captured. Steve possibly was counting a bird every time it was captured.

x <- dcast(data= ann_caps[,],
      formula = spp.code + band + status2 +
                age + sex + site ~ year,
      value.var = "site",
      fun.aggregate = length)


length(unique(ann_caps$band))
length(unique(x$band))
dim(x)[1]-length(unique(ann_caps$band))

x[duplicated(x$band), ]

Cast for demographic summaries

Identify first instance of bird occuring. (I checked - all birds have the same age code even if recaptured; recaps rare regardless)

i.unique <- match(unique(ann_caps$band),ann_caps$band)

length(unique(ann_caps$band))
length(i.unique)

length(which(duplicated(ann_caps$band) == TRUE))

summary(ann_caps$age)

Subset by focal species

focal.mig <- c("OVEN","BAWW","COYE","AMRE","CMWA",
               "BTBW","PAWA","PRAW")

focal.res <- c("HLCU"
,"STOF"
,"RLTH"
,"NOMO"
,"GRWA"
,"BANA"
,"BCPT"
,"YFGR"
,"BFGR"
,"GABU")

focals <- c(focal.mig,focal.res)

i.focals <- which(ann_caps$species %in% focals)

ann_caps.foc <- ann_caps[i.focals,]

Find uniques w/in focals subset

i.unique <- match(unique(ann_caps.foc$band),ann_caps.foc$band)

Subset just uniques

ann_caps.foc2 <- ann_caps.foc[i.unique,]

Calcualte sample sizes

i.is.aged <- which(ann_caps.foc2$age %in% c("AHY","ASY","HY","SY"))
i.is.sexed <- which(ann_caps.foc2$sex %in% c("F","M"))

age.cast <- dcast(data= ann_caps.foc2[i.is.sexed,],
      formula = species + status ~ site,
      value.var = "site",
      fun.aggregate = length)


age.cast
#write.csv(age.cast, file = "temp.csv")

cast by sex

sex.cast <- dcast(data= ann_caps[i.unique,],
      formula = species + status  ~ site + sex,
      value.var = "site",
      fun.aggregate = length)


sex.cast[which(sex.cast$status == "mig"), ]

Save output

save .csv files

save to raw data folder

# focal spp and summary output
# filename <- "focals_herbdat_16.csv"
# filename <-here("data-raw","data-raw-herb","herb_veg_16",
#       filename)
# write.csv(focals.herbdat.16, 
#           file = filename,row.names = F)
# 
# 
# #cover by species
# filename <- "cover_by_spp_herbdat_16.csv"
# filename <-here("data-raw","data-raw-herb","herb_veg_16",
#       filename)
# write.csv(cover.by.spp.herbdat.16, 
#           file = filename,row.names = F)
# 
# 
# 
# #pres abs by spp
# filename <- "presabs_by_spp_herbdat_16.csv"
# filename <-here("data-raw","data-raw-herb","herb_veg_16",
#       filename)
# write.csv(presabs.by.spp.herbdat.16, 
#           file = filename,row.names = F)

brouwern/DRmencia documentation built on May 6, 2019, 12:24 p.m.