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

knitr::opts_chunk$set(echo = TRUE)

Notes

Note that for aceitillar sampling only ever occured in Nov, Jan and March, and that this only occured in 1996-1997, and 1997-1998. In 1999 and beyond all sampling occurred in January. Note that sampling in January 1999 is coded as "1998-1999" for consistency witht he prior years and the Mencia data.

In contrast, Mencia sampling occurred in Nov, Jan and Feb (never March) in all years except 2008, when not sampling is indicate in March.

Load annual data

Load Mencia annual data

filename <- "mencia_cleaned.csv"
filename <- here::here("data-raw",
                  "data-raw-mencia",
                  "mencia_all_captures_by_year",
                  filename)

mencia.ann <- read.csv(file = filename, stringsAsFactors = F)

Load acceitillar annual data

Annual data was created by melting the site persistence capture histories that were originally provided to NLB

filename <- "aceit_annual.csv"
filename <- here::here("data-raw",
                  "data-raw-aceitillar_96-02",
                  filename)

aceit.ann <- read.csv(file = filename, stringsAsFactors = F)

Change names in aceitillar to match

names(aceit.ann) <- gsub("bnd","band",names(aceit.ann))
names(aceit.ann) <- gsub("col","colors",names(aceit.ann))
names(aceit.ann) <- gsub("nts","notes",names(aceit.ann))
names(aceit.ann) <- gsub("ag","age",names(aceit.ann))
names(aceit.ann) <- gsub("sx","sex",names(aceit.ann))
names(aceit.ann) <- gsub("stat.focals","status.focals",names(aceit.ann))



aceit.ann$site.age <- NA
aceit.ann$site.age.init <- NA

Compare datasets

m <- names(mencia.ann)[order(names(mencia.ann))]
a <- names(aceit.ann)[order(names(aceit.ann))]

intersect(m,a)
setdiff(m,a)
setdiff(a,m)

Look at band colum

some issues downstream occured w/ these columns. Ended up being an excel error while saving the data. See https://twitter.com/lobrowR/status/958418038705082369

# head(mencia.ann$band)
# head(aceit.ann$band)
# 
# 
# summary(factor(mencia.ann$band))
# 
# summary(factor(aceit.ann$band))

Compare columns

# head(aceit.ann$band)
# head(mencia.ann$band)
# summary(factor(aceit.ann$year))
# summary(factor(mencia.ann$year))
# summary(factor(aceit.ann$year.num))
# summary(factor(mencia.ann$year.num))
# 
# head(aceit.ann[,c("year","year.num")])
# 
# head(mencia.ann[,c("year","year.num")])

Merge

ann_caps  <- merge(mencia.ann,
                   aceit.ann,
                   all = TRUE)
dim(mencia.ann)
dim(aceit.ann)
dim(mencia.ann)[1] + dim(aceit.ann)[1]
dim(ann_caps)

Make unique annual ID

ID is unique for each individual some individuals captured multiple times w/in years some re-captured between years. Make unique ID to seperate out w/in year recaps

ann_caps$ID.yr <- with(ann_caps,
                     paste(band,
                           year,sep = "_"))

447 re-captured in different sites

length(unique(ann_caps$band))  #3143
length(unique(ann_caps$ID.yr)) #3590
length(unique(ann_caps$ID.yr)) - length(unique(ann_caps$band))

Make unique site ID

161 re-captured in different sites (w/in yr OR between yr)

ann_caps$ID.site <- with(ann_caps,
                     paste(band,
                           site),
                     sep = ".")

length(unique(ann_caps$ID.site))-length(unique(ann_caps$band))

#create unique spp-site combo
ann_caps$spp_site <- with(ann_caps,paste(spp.code, site))

#center site age variable
ann_caps$site.age.cent <- scale(ann_caps$site.age,scale = F)

Set up sex

Change NA to "U"

ann_caps$sex[is.na(ann_caps$sex)] <- "U"

Make AHY/not AHY code

Combine all AHY and beyond catetories

Has to be aged to count

summary(ann_caps$age)
ann_caps$age <- as.character(ann_caps$age)

#HY and SY = "age1"
ann_caps$age.AHY.01 <- gsub("^HY","age1", ann_caps$age)
ann_caps$age.AHY.01 <- gsub("^SY","age1",ann_caps$age.AHY.01)

ann_caps$age.AHY.01 <- gsub("^AHY","age2",ann_caps$age.AHY.01)
ann_caps$age.AHY.01 <- gsub("^ASY","age2",ann_caps$age.AHY.01)


ann_caps$age.AHY.01  <- factor(ann_caps$age.AHY.01 )

summary(ann_caps$age.AHY.01 )

Check values

summary(factor(ann_caps$sex))
summary(factor(ann_caps$age))
summary(factor(ann_caps$site))
summary(factor(ann_caps$age.AHY.01))

Isolate a single capture per year

This should extract the 1st instance that a unique band-year combo occurs within a dataset. Datasets are ordered by data so this will grab the 1st capture per year.

#ann_caps$ID.yr
i.within.yr  <- match(unique(ann_caps$ID.yr),
                      ann_caps$ID.yr)

length(i.within.yr)
dim(ann_caps)

ann_caps <- ann_caps[i.within.yr,]

save .RData files

saved to "data" folder where working data for real analyses is stored

filename <- "ann_caps.RData"
filename <-here::here("data",
            filename)
save(ann_caps, 
           file = filename,row.names = F)

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