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

knitr::opts_chunk$set(echo = TRUE)

Introduction

The Mencia pasture succession project uses part of the Aceitillar data as a reference.

This file loads, cleans, and reshapes the data

Libraries

library(here)
library(reshape2)
library(car)
library(stringi)
library(stringr)
library(lubridate)

Load data

skip <- 11
filename <- "Aceitillar_96-02_for_Mencia_all_caps_CSV.csv"
filename <- here::here("data-raw",
                  "data-raw-aceitillar_96-02",
                  "CSV_Aceitillar_96-02",
                  filename)

aceit <- read.csv(filename, skip = 11,stringsAsFactors = F)

Basic cleaning

Change all headings to lower case

names(aceit) <- tolower(names(aceit))

Fix age

aceit$age[-grep("AHY|HY",aceit$age)] <- NA

summary(factor(aceit$age))

Fix sex

aceit$sex[-grep("F|M",aceit$sex)] <- NA

#summary(factor(aceit$sex))

#summary(factor(aceit$species))

Fix typos in spp

#remove space
aceit$species <- gsub("BCPT ", "BCPT", aceit$species)

aceit$species[grep("Z",aceit$species)]

aceit$species <- gsub("ZEDO", "ZEND", aceit$species)

aceit$species[grep("CGDO",aceit$species)]

aceit$species <- gsub("CGDO", "COGD", aceit$species)
aceit$species <- gsub("CGOO", "COGD", aceit$species)

HIPE Hisp Pewee

GAPE GREP

aceit$species[grep("GREP",aceit$species)] <- "HIPE"
aceit$species[grep("GAPE",aceit$species)] <- "HIPE"

Convert to factors

aceit$colors <- factor(aceit$colors)
aceit$age <- factor(aceit$age)
aceit$sex <- factor(aceit$sex)

Dates

Fix typo in name

names(aceit)[grep("data.cap.orig", names(aceit))] <- "date.cap.orig"

Chang _ to -

aceit$date.cap.raw <- aceit$date.cap.orig
aceit$date.cap.orig <- gsub("_","-",aceit$date.cap.orig)

Convert to standard R date format

aceit$date.cap.orig <- mdy(aceit$date.cap.orig)

Compare dates

#aceit[,c("date.cap.orig","date.cap.raw")]

Order by date

Order by date 1st captured

aceit <- aceit[order(aceit$date.cap.orig), ]

Code focal migrants

These are the species focused on in the original reject MS

focal.mig <- c("OVEN","BAWW","COYE","AMRE","CMWA",
               "BTBW","PAWA","PRAW")
i <- which(aceit$species %in% focal.mig)

aceit$status.focals <- NA
aceit[i, "status.focals"] <- "mig"

Focal residents

These are the species focused on in the original reject MS

focal.res <- c("HLCU" #changes to HILC
,"STOF"
,"RLTH"
,"NOMO"
,"GRWA" # changes to GTGT
,"BANA"
,"BCPT"
,"YFGR"
,"BFGR"
,"GABU")
i <- which(aceit$species %in% focal.res)


aceit[i, "status.focals"] <- "res"

Load spp meta / trait info

spp.meta <- read.csv(here::here("data","spp_list.csv"),stringsAsFactors =F)

Subset columns I want to use

spp.meta <- spp.meta[c("spp.code","spp",
                       "status2",
                       "hab1","diet")]

names(spp.meta) <- gsub("status2","status",names(spp.meta))

Change GRWA -> GTGT in main dataframe

Green tailed ground warlber and green tailed ground tanager are the same

Change column name

names(aceit) <- gsub("species","spp.code", names(aceit))

aceit$spp.code <- as.character(aceit$spp.code)
aceit$spp.code[which(aceit$spp.code == "GRWA")] <- "GTGT"

aceit$spp.code[which(aceit$spp.code == "HLCU")] <- "HILC"

MYWA = myrtyle warbler = YRWA yellow rumped warbler NUMA -> SBMU #nutmet manakin = scaly breasted munia WHQD = WFQD

aceit$spp.code[which(aceit$spp.code == "MYWA")] <- "YRWA"
aceit$spp.code[which(aceit$spp.code == "NUMA")] <- "SBMU"
aceit$spp.code[which(aceit$spp.code == "WHQD")] <- "WFQD"

Merge cleaned data w/ species meta data

Merge

#names(aceit)
#names(spp.meta)

aceit2 <- merge(aceit, spp.meta, all = T)

#dim(aceit)
#dim(aceit2)

aceit2 <-aceit2[-which(is.na(aceit2$site) == TRUE), ]
aceit <- aceit2

Name site

Change current "site" column to "sub.site"

aceit$sub.site <- aceit$site 
aceit$site <- "Aceitillar"

Shorten column names

names(aceit) <- gsub("nov","N",names(aceit))
names(aceit) <- gsub("jan","J",names(aceit))
names(aceit) <- gsub("feb","F",names(aceit))
names(aceit) <- gsub("mar","M",names(aceit))
names(aceit) <- gsub("wanderer","wndr",names(aceit))
names(aceit) <- gsub("species","spp",names(aceit))
names(aceit) <- gsub("year","yr",names(aceit))
names(aceit) <- gsub("month","mo",names(aceit))
names(aceit) <- gsub("band","bnd",names(aceit))
names(aceit) <- gsub("sex","sx",names(aceit))
names(aceit) <- gsub("colors","col",names(aceit))
names(aceit) <- gsub("notes","nts",names(aceit))
names(aceit) <- gsub("status","stat",names(aceit))
names(aceit) <- gsub("age","ag",names(aceit))
names(aceit) <- gsub("comments","nts",names(aceit))

Code site persistence

j <- grep("^[NDJFM]",names(aceit))
#names(aceit)[j]

x <- with(aceit,paste(N.96,J.97,M.97,N.97,J.98,M.98,J.99,
                 J.00,J.01,J.02,J.03),sep ="")
x <- gsub(" ","",x)

i.C <- which(x == "C")
x[i.C] <- "NP"
x[-i.C] <- "P"

aceit$persist <- x

Save cleaned data

Save .csv

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

write.csv(aceit, file = filename,row.names = F)

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