#####################################################
# Marco FW Gauger
# 2021/02/23
#
# This is the script to obtain fig S 3 of the supplementary material of the manuscript submitted to PeerJ Gauger et al 2021 Diel influences on bottlenose dolphin acoustic detection in a coastal lagoon in the southwestern Gulf of California
#####################################################
ifelse("corrplot" %in% rownames(installed.packages()) == FALSE, 
       install.packages("corrplot", dependencies = T),
       ("corrplot is already installed"))
ifelse("GGally" %in% rownames(installed.packages()) == FALSE, 
       install.packages("GGally", dependencies = T),
       ("GGally is already installed"))
ifelse("ggcorrplot" %in% rownames(installed.packages()) == FALSE, 
       install.packages("ggcorrplot", dependencies = T),
       ("ggcorrplot is already installed"))
ifelse("dplyr" %in% rownames(installed.packages()) == FALSE, 
       install.packages("dplyr", dependencies = T),
       ("dplyr is already installed"))
require(corrplot)
require(GGally)
require(ggcorrplot)
require(dplyr)

########
Sys.setlocale("LC_ALL","English")
#Sys.setenv(TZ='UTC')

analysed_data_hour <- read.csv("Supplemental Data S1.csv", sep=";")
analysed_data_hour$cluster_hcpc <- as.factor(analysed_data_hour$cluster_hcpc)
unique(analysed_data_hour$month_campaign)




cor_data <- as.data.frame(cbind(analysed_data_hour$lunar_phase,
                                analysed_data_hour$tide,
                                analysed_data_hour$flow,
                                analysed_data_hour$derivate_tide,
                                analysed_data_hour$SST_BLAP,
                                analysed_data_hour$SST,
                                analysed_data_hour$delta_SST,
                                analysed_data_hour$CHL_BLAP,
                                analysed_data_hour$distance_BLAP,
                                analysed_data_hour$distance_coast,
                                analysed_data_hour$distance_mangrove,
                                analysed_data_hour$depth,
                                analysed_data_hour$effort,
                                analysed_data_hour$h))
names(cor_data) <- c("moon","tide","flow","derivate tide","SST_BLAP","SST","delta_SST","CHL","BLAP","coast","mangrove","depth", "effort", "hour")
#names(cor_data) <- c("luna","marea","corriente","derivada corriente","SST_BLAP","SST","delta_SST","CHL","BLAP","costa","manglar","profundidad", "esfuerzo", "hora")
head(cor_data)
unique(is.na(cor_data))

M <- cor(cor_data, method=c("spearman"))
res1 <- cor.mtest(cor_data, conf.level = 0.95)
res2 <- cor.mtest(cor_data, conf.level = 0.99)

p.mat <- cor_pmat(cor_data)
head(p.mat[, 1:4])
rownames(p.mat) <- as.numeric(rownames(p.mat) )
p.mat$SST <- as.numeric(p.mat$SST)
(ggcorrplot_dp10m <- 
  ggcorrplot(M, hc.order = TRUE, type = "lower",
             p.mat = p.mat, insig = "blank",
             lab = TRUE, lab_size=2))

ggsave("Figure S3.png", plot=ggcorrplot_dp10m, dpi=300, width = 15, height = 15, units = "cm")