From 66cad0366ea46e526af9e5d879d0884dd629ebbb Mon Sep 17 00:00:00 2001
From: Loic-Lenof <loic.lenof@gmail.com>
Date: Sun, 28 Aug 2022 22:33:10 +0200
Subject: [PATCH] Added Stats on group ID

+ plots by group ID
+ KW  pairwise test by group ID
---
 CSV_data/Audio_Data_2020.csv       | 802 ++++++++++++++---------------
 CSV_data/Audio_Data_2021.csv       | 202 ++++----
 Stats/.Rhistory                    | 694 ++++++++++++-------------
 Stats/BBP-click-whistles_3models.R |  71 ++-
 4 files changed, 918 insertions(+), 851 deletions(-)

diff --git a/CSV_data/Audio_Data_2020.csv b/CSV_data/Audio_Data_2020.csv
index 0cdf268..217948c 100644
--- a/CSV_data/Audio_Data_2020.csv
+++ b/CSV_data/Audio_Data_2020.csv
@@ -1,401 +1,401 @@
-Fichier Audio,Date,Heure (UTC),T,AV,AV+D,D,D+AP,AP,AP+AV,F,SSF,NSP,CHAS,SOCI,DEPL,SONAR,SIGNAL,C-GR,FILET
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+,,,,,,,,,,,,,,,,,,,,,
+TOTAUX,,,118,27,38,144,33,35,1,280,116,0,31,49,316,14,,,,47,64
+,,,,,,,,,,,,,,,,,,,,,
+NOMBRE,NOMBRE D'AUDIOS,396,,,396,,,,,,396,,,396,,,,,,,
diff --git a/CSV_data/Audio_Data_2021.csv b/CSV_data/Audio_Data_2021.csv
index 9605bb4..968a5e4 100644
--- a/CSV_data/Audio_Data_2021.csv
+++ b/CSV_data/Audio_Data_2021.csv
@@ -1,101 +1,101 @@
-Fichier Audio,Date,Heure (UTC),T,AV,AV+D,D,D+AP,AP,AP+AV,F,SSF,NSP,CHAS,SOCI,DEPL,SONAR,SIGNAL,C-GR,FILET
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+,,,,,,,,,,,,,,,,,,,,,
+TOTAUX,,,11,11,14,32,13,14,1,12,84,0,11,0,85,63,,,,12,13
+,,,,,,,,,,,,,,,,,,,,,
+NOMBRE,NOMBRE D'AUDIOS,96,,,96,,,,,,96,,,96,,,,,,,
diff --git a/Stats/.Rhistory b/Stats/.Rhistory
index 7182207..e0b7e29 100644
--- a/Stats/.Rhistory
+++ b/Stats/.Rhistory
@@ -1,13 +1,3 @@
-numb_stats_b %>%
-ggplot(aes(x=number, y=mean, group=1)) +
-geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
-color="red", width=.1, show.legend = FALSE)+
-geom_point() + geom_line() +
-theme_classic() + theme(text=element_text(size=12)) +
-ylab("Number of BBPs per min")+
-xlab("Number of dolphins in group")
-# Clicks
-numb_stats_c <- computeStats(acoustic.dta, number, number_of_clicks)
 numb_stats_c[is.na(numb_stats_c)] <- 0
 numb_stats_c$number <- as.factor(numb_stats_c$number)
 numb_stats_c %>%
@@ -18,153 +8,6 @@ geom_point() + geom_line() +
 theme_classic() + theme(text=element_text(size=12)) +
 ylab("Mean number of clicks per min")+
 xlab("Number of echolocation clicks in group")
-#### Nets plots + KW analysis ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$net)
-# p<0.05 so post-hoc
-kruskalmc(acoustic.dta$whistling_time_per_dolphin, acoustic.dta$net)
-# DIY : letters
-myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-letter = c("a","ad","bd","cd","a"))
-barPlot(computeStats(acoustic.dta, net, whistling_time_per_dolphin/375),
-NULL,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
-xname="Fishing nets", height=.6,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8.5))
-# BBPs
-#KW test
-kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$net)
-# p<0.05 so post-hoc
-kruskalmc(acoustic.dta$BBPs_per_dolphin, acoustic.dta$net)
-# DIY : letters
-myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-letter = c("a","a","a","a","a"))
-barPlot(computeStats(acoustic.dta, net, BBPs_per_dolphin),
-NULL,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
-xname="Fishing nets", height=.8,
-ytitle="Mean number of BBPs per dolphin per min")+
-theme(axis.text.x=element_text(size=8.5))
-# Clicks
-#KW test
-kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$net)
-# p<0.05 so post-hoc
-kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
-# DIY : letters
-myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-letter = c("ae","ad","bd","cd","e"))
-barPlot(computeStats(acoustic.dta, net, clicks_per_dolphin),
-NULL,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
-xname="Fishing nets", height=120,
-ytitle="Mean number of clicks per dolphin per min")+
-theme(axis.text.x=element_text(size=8.5))
-#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon,
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-beacon
-computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
-names(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375))
-factor(acoustic.dta$beacon)
-unique(acoustic.dta$beacon)
-computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
-computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)
-computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-#### : Beacon plots + KW analysis (letters not shown for readability) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon, old_names = names, new_names = names,
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-names = list(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"])
-names
-names
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-names
-unlist(names)
-#### Beacon plots + KW analysis (letters not shown for readability) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon, old_names = unlist(names), new_names = unlist(names),
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-#### Beacon plots + KW analysis (letters not shown for readability) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
-NULL,
-beacon, old_names = unlist(names), new_names = unlist(names),
-xname="Signals from bio-inspired beacon", height=0.9, size=3,
-ytitle="Mean whistling time per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-# BBPs
-#KW test
-kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
-NULL,
-beacon, old_names = unlist(names), new_names = unlist(names),
-xname="Signals from bio-inspired beacon", height=0.5, size=3,
-ytitle="Mean number of BBPs per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
-# Clicks
-#KW test
-kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
-names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
-barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
-NULL,
-beacon, old_names = unlist(names), unlist(names),
-xname="Signals from bio-inspired beacon", height=150, size=3,
-ytitle="Mean number of clicks per dolphin per min")+
-theme(axis.text.x=element_text(size=8))+
-scale_x_discrete(guide=guide_axis(n.dodge = 2))
 ########################################################################
 #    STATISTICS
 #    Author : Loic LEHNHOFF
@@ -182,32 +25,46 @@ library(stringr)      # "gsub" function
 library(rcompanion)   # "fullPTable" function
 library(multcompView) # "multcompLetters" function
 library(ggplot2)
+library(pgirmess)
 #library(tidyquant)    # geom_ma() if rolling average needed
 ################# DATASET IMPORTS #####################################
 folder <- './../'
+whistles.dta <-read.table(file=paste0(folder,
+'Whistles/Evaluation/whistles_durations.csv'),
+sep = ',', header=TRUE)
+whistles.dta <- whistles.dta[order(whistles.dta$audio_names),]
 bbp.dta <-read.table(file=paste0(folder,
 'BBPs/Results/16-06-22_14h00_number_of_BBP.csv'),
 sep = ',', header=TRUE)
 bbp.dta <- bbp.dta[order(bbp.dta$audio_names),]
-# suppress "T" acoustic data (other groups not tested on our variables)
-bbp.dta <- bbp.dta[bbp.dta$acoustic!="T",]
+clicks.dta <-read.table(file=paste0(folder,
+'Clicks/Results/projection_updated_number_of_clicks_02052022.csv'), #number_of_clicks_02052022.csv
+sep = ',', header=TRUE)
+clicks.dta <- clicks.dta[order(clicks.dta$audio_names),]
+# Merge files into 1 dataset
+acoustic.dta <- clicks.dta
+acoustic.dta$number_of_bbp <- bbp.dta$number_of_BBP
+acoustic.dta$total_whistles_duration <- whistles.dta$total_whistles_duration
+rm(whistles.dta, bbp.dta, clicks.dta)
+# suppress "T" acoustic data (data not related to this analysis)
+acoustic.dta <- acoustic.dta[acoustic.dta$acoustic!="T",]
 # shuffle dataframe
-bbp.dta <- bbp.dta[sample(1:nrow(bbp.dta)), ]
-bbp.dta$acoustic <- factor(bbp.dta$acoustic)
+acoustic.dta <- acoustic.dta[sample(1:nrow(acoustic.dta)), ]
+acoustic.dta$acoustic <- factor(acoustic.dta$acoustic)
 #################### DATA INSPECTION  #################################
 # Data description
-names(bbp.dta)
+names(acoustic.dta)
 # self explenatory except acoustic : correspond to the activation sequence.
 # Look for obvious correlations
-plot(bbp.dta) # nothing that we can see
+plot(acoustic.dta) # nothing that we can see
 # Look for zero-inflation
-100*sum(bbp.dta$number_of_BBP == 0)/nrow(bbp.dta)
-100*sum(bbp.dta$Buzz == 0)/nrow(bbp.dta)
-100*sum(bbp.dta$Burst.pulse == 0)/nrow(bbp.dta)
-# 53.7%, 60.1% & 73.6% of data are zeroes
+100*sum(acoustic.dta$number_of_clicks == 0)/nrow(acoustic.dta)
+100*sum(acoustic.dta$number_of_bbp == 0)/nrow(acoustic.dta)
+100*sum(acoustic.dta$total_whistles_duration == 0)/nrow(acoustic.dta)
+# 3.6%, 53.7% & 24.7% of our data are zeros. Will have to be dealt with.
 # QUESTION: This study is aimed at understanding if dolphin's acoustic activity
 # is influenced bytheir behavior, the emission of a pinger or a fishing net.
-# Dependent variables (Y): number_of_BBP, Buzz & Burst.pulse
+# Dependent variables (Y): number_of_clicks, number_of_bbp, total_whistles_duration.
 # Explanatory variables (X): acoustic, fishing_net, behavior, beacon, net, number.
 # What are the H0/ H1 hypotheses ?
 # H0 : No influence of any of the explanatory variables on a dependant one.
@@ -215,47 +72,47 @@ plot(bbp.dta) # nothing that we can see
 ##################### DATA EXPLORATION ################################
 # Y Outlier detection
 par(mfrow=c(2,3))
-boxplot(bbp.dta$number_of_BBP, col='red',
-ylab='number_of_BBP')
-boxplot(bbp.dta$Buzz, col='red',
-ylab='Buzz')
-boxplot(bbp.dta$Burst.pulse, col='red',
-ylab='Burst.pulse')
-dotchart(bbp.dta$number_of_BBP, pch=16,
-xlab='number_of_BBP', col='red')
-dotchart(bbp.dta$Buzz, pch=16,
-xlab='Buzz', col='red')
-dotchart(bbp.dta$Burst.pulse, pch=16,
-xlab='Burst.pulse', col='red')
+boxplot(acoustic.dta$total_whistles_duration, col='red',
+ylab='total_whistles_duration')
+boxplot(acoustic.dta$number_of_bbp, col='red',
+ylab='number_of_bbp')
+boxplot(acoustic.dta$number_of_clicks, col='red',
+ylab='number_of_clicks')
+dotchart(acoustic.dta$total_whistles_duration, pch=16,
+xlab='total_whistles_duration', col='red')
+dotchart(acoustic.dta$number_of_bbp, pch=16,
+xlab='number_of_bbp', col='red')
+dotchart(acoustic.dta$number_of_clicks, pch=16,
+xlab='number_of_clicks', col='red')
 # Y distribution
 par(mfrow=c(2,3))
-hist(bbp.dta$number_of_BBP, col='red', breaks=8,
-xlab='number_of_BBP', ylab='number')
-hist(bbp.dta$Buzz, col='red', breaks=8,
-xlab='Buzz', ylab='number')
-hist(bbp.dta$Burst.pulse, col='red', breaks=8,
-xlab='Burst.pulse', ylab='number')
-qqnorm(bbp.dta$number_of_BBP, col='red', pch=16)
-qqline(bbp.dta$number_of_BBP)
-qqnorm(bbp.dta$Buzz, col='red', pch=16)
-qqline(bbp.dta$Buzz)
-qqnorm(bbp.dta$Burst.pulse, col='red', pch=16)
-qqline(bbp.dta$Burst.pulse)
-shapiro.test(bbp.dta$number_of_BBP)
-shapiro.test(bbp.dta$Buzz)
-shapiro.test(bbp.dta$Burst.pulse)
+hist(acoustic.dta$total_whistles_duration, col='red', breaks=8,
+xlab='total_whistles_duration', ylab='number')
+hist(acoustic.dta$number_of_bbp, col='red', breaks=8,
+xlab='number_of_bbp', ylab='number')
+hist(acoustic.dta$number_of_clicks, col='red', breaks=8,
+xlab='number_of_clicks', ylab='number')
+qqnorm(acoustic.dta$total_whistles_duration, col='red', pch=16)
+qqline(acoustic.dta$total_whistles_duration)
+qqnorm(acoustic.dta$number_of_bbp, col='red', pch=16)
+qqline(acoustic.dta$number_of_bbp)
+qqnorm(acoustic.dta$number_of_clicks, col='red', pch=16)
+qqline(acoustic.dta$number_of_clicks)
+shapiro.test(acoustic.dta$total_whistles_duration)
+shapiro.test(acoustic.dta$number_of_bbp)
+shapiro.test(acoustic.dta$number_of_clicks)
 # p-values are significant => they do not follow normal distributions
-# we will need transformations or the use of glm models
+# will need a transformation or the use of a glm model
 # X Number of individuals per level
-summary(factor(bbp.dta$acoustic))
-summary(factor(bbp.dta$fishing_net))
-summary(factor(bbp.dta$behavior))
-summary(factor(bbp.dta$beacon))
-summary(factor(bbp.dta$net))
-table(factor(bbp.dta$acoustic),factor(bbp.dta$fishing_net))
-table(factor(bbp.dta$acoustic),factor(bbp.dta$behavior))
-table(factor(bbp.dta$behavior),factor(bbp.dta$acoustic))
-ftable(factor(bbp.dta$fishing_net), factor(bbp.dta$behavior), factor(bbp.dta$acoustic))
+summary(factor(acoustic.dta$acoustic))
+summary(factor(acoustic.dta$fishing_net))
+summary(factor(acoustic.dta$behavior))
+summary(factor(acoustic.dta$beacon))
+summary(factor(acoustic.dta$net))
+table(factor(acoustic.dta$acoustic),factor(acoustic.dta$fishing_net))
+table(factor(acoustic.dta$acoustic),factor(acoustic.dta$behavior))
+table(factor(acoustic.dta$behavior),factor(acoustic.dta$acoustic))
+ftable(factor(acoustic.dta$fishing_net), factor(acoustic.dta$behavior), factor(acoustic.dta$acoustic))
 # => unbalanced, no big deal but will need more work (no orthogonality):
 # Effects can depend on the order of the variables
 # => Beacon and net have modalities with <10 individuals => analysis impossible
@@ -273,45 +130,54 @@ ftable(factor(bbp.dta$fishing_net), factor(bbp.dta$behavior), factor(bbp.dta$aco
 # (using kruskall-Wallis non-parametric test)
 # fishing_net, behavior and acoustic where tested with their interactions.
 # If a variable is it in a model, it is because it had no significant effect.
+par(mfrow=c(1,1))
+### Model for whistles
+# Residual hypotheses not verified for LM
+# Overdipsersion when using GLM (negative binomial)
+# Using ZINB:
+zero.whi <- zeroinfl(total_whistles_duration ~
+acoustic + fishing_net + behavior + offset(log(number)),
+data=acoustic.dta, dist='negbin')
+nb.whi <- glm.nb(total_whistles_duration ~
+acoustic + fishing_net + behavior + offset(log(number)),
+data=acoustic.dta)
+# comparison ZINB VS NB model
+vuong(zero.whi, nb.whi)  #(if p-value<0.05 then first model in comparison is better)
+mod.whi <- zero.whi # => zeroinflated model is indeed better suited
+car::Anova(mod.whi, type=3)
+shapiro.test(residuals(mod.whi)) # H0 : normality -> not rejected if p>0.05
+dwtest(mod.whi) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
+bptest(mod.whi) # H0 -> homoscedasticity if p<0.05
+# No normality but we do not need it
 ### Model for BBP
 # No normality of residuals for LM
 # overdispersion with GLM quasipoisson
 #try with glm NB:
-mod.bbp <- glm.nb(number_of_BBP ~ acoustic + fishing_net + behavior
+mod.bbp <- glm.nb(number_of_bbp ~ acoustic + fishing_net + behavior
 + offset(log(number)),
-data=bbp.dta)
+data=acoustic.dta)
 car::Anova(mod.bbp, type=3)
 dwtest(mod.bbp) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
 bptest(mod.bbp) # H0 -> homoscedasticity if p<0.05
 # Normality not needed in GLM, hypotheses verified !
 mod.bbp$deviance/mod.bbp$df.residual
-# slight underdispersion
-### Model for Buzzes
-# No normality of residuals for LM
-# overdispersion with GLM quasipoisson
-# underdispersion with glm NB
-# Try with ZINB:
-mod.buzz <- glm.nb(Buzz ~ behavior + fishing_net + acoustic
-+ offset(log(number)),
-data=bbp.dta)
-car::Anova(mod.buzz, type=3)
-dwtest(mod.buzz) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
-bptest(mod.buzz) # H0 -> homoscedasticity if p<0.05
-mod.buzz$df.null/mod.buzz$df.residual
-# No overdispersion
-### Model for Burst-pulses
-# No normality of residuals for LM
-# overdispersion with quasipoisson
-# underdispersion with NB
-# ZINB is working :
-mod.burst.pulse <- zeroinfl(Burst.pulse ~ fishing_net + acoustic + behavior
-+ offset(log(number)), dist="negbin",
-data=bbp.dta)
-car::Anova(mod.burst.pulse, type=3)
-dwtest(mod.burst.pulse) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
-bptest(mod.burst.pulse) # H0 -> homoscedasticity if p<0.05
-mod.burst.pulse$df.null/mod.burst.pulse$df.residual  # -> Overdispersion of != 1
-# no overdispersion
+# slight underdispersion, not improved with ZINB so we keep this
+### Model for clicks
+# Using NB model:
+mod.cli <- glm.nb(number_of_clicks ~ acoustic + fishing_net + acoustic:fishing_net + offset(log(number)),
+data=acoustic.dta)
+car::Anova(mod.cli, type=3)
+shapiro.test(residuals(mod.cli)) # H0 : normality -> cannot be rejected if p > 0.05
+dwtest(mod.cli) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
+bptest(mod.cli) # H0 -> homoscedasticity if p<0.05
+# Normality not needed in GLM, hypotheses verified !
+mod.cli$deviance/mod.cli$df.residual
+# slight overdispersion. (ZINB does not clearly improve results so we keep this)
+# FYI1: Comparison of combination of explanatory variables between models
+# were compared based on BIC criterion.
+# The model with the lowest BIC was kept (and is the one shown)
+# FYI2: log(number of dolphin per group) does have an effect on data but we have
+# no interest in investigating it, that is why we use it as an offset.
 ##################### Boxplots and comparisons #####################
 ### Functions to compute stats
 computeLetters <- function(temp, category) {
@@ -339,10 +205,10 @@ height, xname="", colours="black", legend_title="", legend_labs="",ytitle=""){
 if (!is.null(signif)){colnames(signif)[1] <- "use"}
 dta %>%
 mutate(use=fct_relevel({{category}}, old_names)) %>%
-ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}}, na.rm = TRUE)) +
+ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}})) +
 {if(length(colours)==1)geom_point(color=colours, position=position_dodge(.5))}+
-{if(length(colours)>=2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
-{if(length(colours)>=2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
+{if(length(colours)==2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
+{if(length(colours)==2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
 scale_x_discrete(breaks=old_names,
 labels=new_names)+
 ylab(ytitle)+
@@ -354,159 +220,293 @@ geom_errorbar(aes(x=use, ymin=mean-ic, ymax=mean+ic), position=position_dodge(.5
 }
 ####Introducing variables averaged per dolphins ####
 # since we introduced an offset, variables can be divided by the number of dolphins
-bbp.dta$BBPs_per_dolphin <- bbp.dta$number_of_BBP/bbp.dta$number
-bbp.dta$Buzz_per_dolphin <- bbp.dta$Buzz/bbp.dta$number
-bbp.dta$Burst.pulse_per_dolphin <- bbp.dta$Burst.pulse/bbp.dta$number
-#### Fishing nets plots  ####
-par(mfrow=c(3, 1))
-# BBPs
-table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
+acoustic.dta$whistling_time_per_dolphin <- acoustic.dta$total_whistles_duration/acoustic.dta$number
+acoustic.dta$BBPs_per_dolphin <- acoustic.dta$number_of_bbp/acoustic.dta$number
+acoustic.dta$clicks_per_dolphin <- acoustic.dta$number_of_clicks/acoustic.dta$number
+#### Fishing net  ####
+# whistles
+table <- cld(emmeans(mod.whi, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, fishing_net, BBPs_per_dolphin),
+barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375), # 375 bins = 1 sec
 myletters_df, fishing_net,
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=.6,
-ytitle="Mean number of BBP per dolphin per min")
-# Buzz
-table <- cld(emmeans(mod.buzz, pairwise~fishing_net, adjust="tukey"), Letters = letters)
+xname="Presence/Asence of fishing net", height=.5,
+ytitle="Mean whistling time per dolphin per min")
+# BBP
+table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin),
+barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin),
 myletters_df, fishing_net,
-ytitle="Mean number of Buzzes per dolphin per min",
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=.45)
-# Burst-pulse
-table <- cld(emmeans(mod.burst.pulse, pairwise~fishing_net, adjust="tukey"), Letters = letters)
+xname="Presence/Asence of fishing net", height=.6,
+ytitle="Mean number of BBPs per dolphin per min")
+# Clicks
+table <- cld(emmeans(mod.cli, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin),
+barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin),
 myletters_df, fishing_net,
-ytitle="Mean number of Burst-pulses per dolphin per min",
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=.18, )
+xname="Presence/Asence of fishing net", height=100,
+ytitle="Mean number of clicks per dolphin per min")
 #### Acoustic plots  ####
-# BBPs
-table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,
-letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, acoustic, BBPs_per_dolphin),
-myletters_df, acoustic, height=.9, ytitle="Mean number of BBPs per dolphin per min",
+# Whistles
+table <- cld(emmeans(mod.whi, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
+barPlot(computeStats(acoustic.dta, acoustic, whistling_time_per_dolphin/375),
+myletters_df, acoustic, height=0.65, ytitle="Mean whistling time per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-# Buzz
-table <- cld(emmeans(mod.buzz, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,
-letter = trimws(table$.group))myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"),                                                                                   letter = c("a","a","a","a","a"))
-#error, no acoustic in model:
-myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"),
-letter = c("a","a","a","a","a"))
-barPlot(computeStats(bbp.dta, acoustic, Buzz_per_dolphin),
-myletters_df, acoustic, height=0.45, ytitle="Mean number of Buzzes per dolphin per min",
+# BBPs
+table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
+barPlot(computeStats(acoustic.dta, acoustic, BBPs_per_dolphin),
+myletters_df, acoustic, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-# Burst-pulse
-table <- cld(emmeans(mod.burst.pulse, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,
-letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, acoustic, Burst.pulse_per_dolphin),
-myletters_df, acoustic, height=0.5, ytitle="Mean number of Burst-pulses per dolphin per min",
+# Clicks
+table <- cld(emmeans(mod.cli, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
+barPlot(computeStats(acoustic.dta, acoustic, clicks_per_dolphin),
+myletters_df, acoustic, height=155, ytitle="Mean number of clicks per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-#### Behaviour plots  ####
-# BBPs
-table <- cld(emmeans(mod.bbp, pairwise~behavior, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, behavior, BBPs_per_dolphin),
-myletters_df, behavior, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
-old_names = c("CHAS", "DEPL", "SOCI"),
-new_names = c("Foraging", "Travelling", "Socialising"),
-xname="Behaviours of dolphins")
-# Buzz
-table <- cld(emmeans(mod.buzz, pairwise~behavior, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, behavior, Buzz_per_dolphin),
-myletters_df, behavior, height=1, ytitle="Mean number of Buzzes per dolphin per min",
-old_names = c("CHAS", "DEPL", "SOCI"),
-new_names = c("Foraging", "Travelling", "Socialising"),
-xname="Behaviours of dolphins")
-# Burst-pulse
-table <- cld(emmeans(mod.burst.pulse, pairwise~behavior, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
-barPlot(computeStats(bbp.dta, behavior, Burst.pulse_per_dolphin),
-myletters_df, behavior, height=0.4, ytitle="Mean number of Burst-pulses per dolphin per min",
-old_names = c("CHAS", "DEPL", "SOCI"),
-new_names = c("Foraging", "Travelling", "Socialising"),
-xname="Behaviours of dolphins")
-#### Interaction : acoustic:fishing_net plots  ####
-# BBP
-letters_df <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
+#### Interaction fishing_net:acoustic plots  ####
+# Whistles
+letters_df <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(bbp.dta, fishing_net, BBPs_per_dolphin, two=acoustic),
+barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375, two=acoustic),
 NULL, acoustic, fill=fishing_net,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
+old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean whistling time per dolphin per min",
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence", height=c(1.6),
+xname="Activation sequence", height=c(.95,.95,.95,1,.95,1,.95,1,1,1),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-# Buzz
-letters_df <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
+# BBPs
+letters_df <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin, two=acoustic),
+barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin, two=acoustic),
 NULL, acoustic, fill=fishing_net,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Buzzes per dolphin per min",
+old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence", height=c(0.77,0.77,0.8,0.77,0.77,0.8,0.77,0.8,0.8,0.8),
+xname="Activation sequence", height=c(1.65,1.65,1.72,1.65,1.72,1.65,1.65,1.72,1.72,1.72),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-# Burst-pulse
-letters_df <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
+# Clicks
+letters_df <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin, two=acoustic),
+barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin, two=acoustic),
 NULL, acoustic, fill=fishing_net,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Burst-pulses per dolphin per min",
+old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of clicks per dolphin per min",
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence", height=c(0.9,0.85,0.9,0.9,0.85,0.9,0.85,0.9,0.85,0.85),
+xname="Activation sequence", height=c(180,180,187,187,180,187,180,180,187,187),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-#### Interaction : acoustic:behavior plots  ####
-# BBP
-barPlot(computeStats(bbp.dta, behavior, BBPs_per_dolphin, two=acoustic),
-NULL, acoustic, fill=behavior,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
-new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence",
-colours=c("#E69F00","#55c041", "#FF3814"), size=5,
-legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
-# Buzz
-barPlot(computeStats(bbp.dta, behavior, Buzz_per_dolphin, two=acoustic),
-NULL, acoustic, fill=behavior,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Buzzes per dolphin per min",
-new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence",
-colours=c("#E69F00","#55c041", "#FF3814"), size=5,
-legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
-# Burst-pulse
-barPlot(computeStats(bbp.dta, behavior, Burst.pulse_per_dolphin, two=acoustic),
-NULL, acoustic, fill=behavior,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Burst-pulses per dolphin per min",
-new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence",
-colours=c("#E69F00","#55c041", "#FF3814"), size=5,
-legend_title="Behaviour", legend_labs= c("Foraging", "Travelling", "Socialising"))
+#### Behaviour plots ####
+# Whistles
+table <- cld(emmeans(mod.whi, pairwise~behavior, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(behavior=table$behavior,letter = trimws(table$.group))
+barPlot(computeStats(acoustic.dta, behavior, whistling_time_per_dolphin/375),
+myletters_df, behavior, height=0.75, ytitle="Mean whistling time per dolphin per min",
+old_names = c("CHAS", "DEPL", "SOCI"),
+new_names = c("Foraging", "Travelling", "Socialising"),
+xname="Behaviours of dolphins")
+# BBPs
+# real effect measured in model
+table <- cld(emmeans(mod.bbp, pairwise~behavior, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$behavior,letter = trimws(table$.group))
+barPlot(computeStats(acoustic.dta, behavior, BBPs_per_dolphin),
+myletters_df, behavior, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
+old_names = c("CHAS", "DEPL", "SOCI"),
+new_names = c("Foraging", "Travelling", "Socialising"),
+xname="Behaviours of dolphins")
+# Clicks
+# no significant effect in click statistical model so all the same letters
+myletters_df <- data.frame(behavior=unique(acoustic.dta$behavior),
+letter = rep("a",length(unique(acoustic.dta$behavior))))
+barPlot(computeStats(acoustic.dta, behavior, clicks_per_dolphin),
+myletters_df,
+behavior, old_names = c("CHAS", "DEPL", "SOCI"),
+new_names = c("Foraging", "Travelling", "Socialising"),
+xname="Behaviours of dolphins", height=150,
+ytitle="Mean number of clicks per dolphin per min")
+#### Nets plots + KW analysis ####
+# Whistles
+#KW test
+kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$net)
+# p<0.05 so post-hoc
+kruskalmc(acoustic.dta$whistling_time_per_dolphin, acoustic.dta$net)
+# DIY : letters
+myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+letter = c("a","ad","bd","cd","a"))
+barPlot(computeStats(acoustic.dta, net, whistling_time_per_dolphin/375),
+NULL,
+net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
+xname="Fishing nets", height=.6,
+ytitle="Mean whistling time per dolphin per min")+
+theme(axis.text.x=element_text(size=8.5))
+# BBPs
+#KW test
+kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$net)
+# p<0.05 so post-hoc
+kruskalmc(acoustic.dta$BBPs_per_dolphin, acoustic.dta$net)
+# DIY : letters
+myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+letter = c("a","a","a","a","a"))
+barPlot(computeStats(acoustic.dta, net, BBPs_per_dolphin),
+NULL,
+net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
+xname="Fishing nets", height=.8,
+ytitle="Mean number of BBPs per dolphin per min")+
+theme(axis.text.x=element_text(size=8.5))
+# Clicks
+#KW test
+kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$net)
+# p<0.05 so post-hoc
+kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
+# DIY : letters
+myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+letter = c("ae","ad","bd","cd","e"))
+barPlot(computeStats(acoustic.dta, net, clicks_per_dolphin),
+NULL,
+net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
+new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Nylon gill net", "Long nylon gill net"),
+xname="Fishing nets", height=120,
+ytitle="Mean number of clicks per dolphin per min")+
+theme(axis.text.x=element_text(size=8.5))
+#### Beacon plots + KW analysis (letters not shown for readability) ####
+# Whistles
+#KW test
+kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
+names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
+barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
+NULL,
+beacon, old_names = unlist(names), new_names = unlist(names),
+xname="Signals from bio-inspired beacon", height=0.9, size=3,
+ytitle="Mean whistling time per dolphin per min")+
+theme(axis.text.x=element_text(size=8))+
+scale_x_discrete(guide=guide_axis(n.dodge = 2))
+# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
+# BBPs
+#KW test
+kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
+names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
+barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
+NULL,
+beacon, old_names = unlist(names), new_names = unlist(names),
+xname="Signals from bio-inspired beacon", height=0.5, size=3,
+ytitle="Mean number of BBPs per dolphin per min")+
+theme(axis.text.x=element_text(size=8))+
+scale_x_discrete(guide=guide_axis(n.dodge = 2))
+# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
+# Clicks
+#KW test
+kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
+names = computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375)["beacon"]
+barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
+NULL,
+beacon, old_names = unlist(names), unlist(names),
+xname="Signals from bio-inspired beacon", height=150, size=3,
+ytitle="Mean number of clicks per dolphin per min")+
+theme(axis.text.x=element_text(size=8))+
+scale_x_discrete(guide=guide_axis(n.dodge = 2))
+# NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
+#### Plots by group ID ####
+# Whistles
+numb_stats_w <- computeStats(acoustic.dta, number, total_whistles_duration/375)
+numb_stats_w[is.na(numb_stats_w)] <- 0
+numb_stats_w$number <- as.factor(numb_stats_w$number)
+numb_stats_w %>%
+ggplot(aes(x=number, y=mean, group=1)) +
+geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
+color="red", width=.1, show.legend = FALSE)+
+geom_point() + geom_line() +
+theme_classic() + theme(text=element_text(size=12)) +
+ylab("Mean whistling time per min")+
+xlab("Number of dolphins in group")
+# BBPs
+numb_stats_b <- computeStats(acoustic.dta, number, number_of_bbp)
+numb_stats_b[is.na(numb_stats_b)] <- 0
+numb_stats_b$number <- as.factor(numb_stats_b$number)
+numb_stats_b %>%
+ggplot(aes(x=number, y=mean, group=1)) +
+geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
+color="red", width=.1, show.legend = FALSE)+
+geom_point() + geom_line() +
+theme_classic() + theme(text=element_text(size=12)) +
+ylab("Number of BBPs per min")+
+xlab("Number of dolphins in group")
+# Clicks
+numb_stats_c <- computeStats(acoustic.dta, number, number_of_clicks)
+numb_stats_c[is.na(numb_stats_c)] <- 0
+numb_stats_c$number <- as.factor(numb_stats_c$number)
+numb_stats_c %>%
+ggplot(aes(x=number, y=mean, group=1)) +
+geom_errorbar(aes(x=number, ymin=mean-ic, ymax=mean+ic),
+color="red", width=.1)+
+geom_point() + geom_line() +
+theme_classic() + theme(text=element_text(size=12)) +
+ylab("Mean number of clicks per min")+
+xlab("Number of echolocation clicks in group")
+#### Plots by Number of dolphins in group ####
+# Whistles
+numb_stats_w <- computeStats(acoustic.dta, ID, whistling_time_per_dolphin/375)
+numb_stats_w[is.na(numb_stats_w)] <- 0
+numb_stats_w$ID <- as.factor(numb_stats_w$ID)
+numb_stats_w %>%
+ggplot(aes(x=ID, y=mean, group=1)) +
+geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd),
+color="red", width=.1, show.legend = FALSE)+
+geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+theme_light() + theme(text=element_text(size=12)) +
+ylab("Mean whistling time per min")+
+xlab("ID of dolphins group")
+# BBPs
+numb_stats_b <- computeStats(acoustic.dta, ID, BBPs_per_dolphin)
+numb_stats_b[is.na(numb_stats_b)] <- 0
+numb_stats_b$ID <- as.factor(numb_stats_b$ID)
+numb_stats_b %>%
+ggplot(aes(x=ID, y=mean, group=1)) +
+geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd),
+color="red", width=.1, show.legend = FALSE)+
+geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+theme_light() + theme(text=element_text(size=12)) +
+ylab("Number of BBPs per min")+
+xlab("ID of dolphins group")
+# Clicks
+numb_stats_c <- computeStats(acoustic.dta, ID, clicks_per_dolphin)
+numb_stats_c[is.na(numb_stats_c)] <- 0
+numb_stats_c$ID <- as.factor(numb_stats_c$ID)
+numb_stats_c %>%
+ggplot(aes(x=ID, y=mean, group=1)) +
+geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd),
+color="red", width=.1)+
+geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+theme_light() + theme(text=element_text(size=12)) +
+ylab("Mean number of clicks per min")+
+xlab("ID of dolphin group")
+# KW test on IDs
+# whistles (excluding groups "2" because 1 sample and groups because no whistles recorded )
+data_test <- acoustic.dta[acoustic.dta$ID!="2",]
+print( posthocKW(data_test$whistling_time_per_dolphin, data_test$ID))
+print( posthocKW(data_test$BBPs_per_dolphin, data_test$ID))
+print( posthocKW(data_test$clicks_per_dolphin, data_test$ID))
diff --git a/Stats/BBP-click-whistles_3models.R b/Stats/BBP-click-whistles_3models.R
index 3856be8..9d674b6 100644
--- a/Stats/BBP-click-whistles_3models.R
+++ b/Stats/BBP-click-whistles_3models.R
@@ -16,6 +16,7 @@ library(rcompanion)   # "fullPTable" function
 library(multcompView) # "multcompLetters" function
 library(ggplot2)
 library(pgirmess)
+library(postHoc)
 #library(tidyquant)    # geom_ma() if rolling average needed
 
 
@@ -40,6 +41,23 @@ acoustic.dta$number_of_bbp <- bbp.dta$number_of_BBP
 acoustic.dta$total_whistles_duration <- whistles.dta$total_whistles_duration
 rm(whistles.dta, bbp.dta, clicks.dta)
 
+# add group IDs
+id2020 <- read.table(file=paste0(folder, 'CSV_data/Audio_Data_2020.csv'),
+                     sep = ',', header=TRUE)[1:396,]
+id2021 <- read.table(file=paste0(folder, 'CSV_data/Audio_Data_2021.csv'),
+                     sep = ',', header=TRUE)[1:96,]
+id2021$ID <- id2021$ID+max(id2020$ID)
+id2021$Seq <- id2021$Seq+max(id2020$Seq)
+id.dta <- rbind(id2020, id2021)
+id.dta$Fichier.Audio <-str_sub(id.dta$Fichier.Audio, -27, -5)
+acoustic.dta$ID <- rep(-1, 490)
+for (name in acoustic.dta$audio_names){
+  acoustic.dta$ID[match(name, acoustic.dta$audio_names)] <- id.dta$ID[match(name, id.dta$Fichier.Audio)]
+}
+acoustic.dta$ID <- as.factor(acoustic.dta$ID)
+rm(id2020, id2021, id.dta)
+
+
 # suppress "T" acoustic data (other groups not tested on our variables)
 acoustic.dta <- acoustic.dta[acoustic.dta$acoustic!="T",]
 # shuffle dataframe
@@ -125,7 +143,6 @@ ftable(factor(acoustic.dta$fishing_net), factor(acoustic.dta$behavior), factor(a
 # => Beacon and net have modalities with <10 individuals => analysis impossible
 # => They will be treated apart from the rest as they are likely to be biased
 
-
 ##################### STATISTICAL MODELLING ###########################
 ### Model tested
 # LM: Linear model (residual hypothesis: normality, homoscedasticity, independant)
@@ -477,7 +494,8 @@ barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
   scale_x_discrete(guide=guide_axis(n.dodge = 2))
 # NC stands for "Unknown". Corresponding to categories where the beacon was not turned on yet ('BEF')
 
-#### WHY NOT: Number plots ####
+
+#### Plots by number of dolphins ####
 # Whistles
 numb_stats_w <- computeStats(acoustic.dta, number, total_whistles_duration/375)
 numb_stats_w[is.na(numb_stats_w)] <- 0
@@ -520,3 +538,52 @@ numb_stats_c %>%
   ylab("Mean number of clicks per min")+
   xlab("Number of echolocation clicks in group")
 
+
+#### Plots by Group ID ####
+# Whistles
+numb_stats_w <- computeStats(acoustic.dta, ID, whistling_time_per_dolphin/375)
+numb_stats_w[is.na(numb_stats_w)] <- 0
+numb_stats_w$ID <- as.factor(numb_stats_w$ID)
+numb_stats_w %>%
+  ggplot(aes(x=ID, y=mean, group=1)) +
+  geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd), 
+                color="red", width=.1, show.legend = FALSE)+
+  geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+  theme_light() + theme(text=element_text(size=12)) +
+  ylab("Mean whistling time per min")+
+  xlab("ID of dolphins group")
+
+# BBPs
+numb_stats_b <- computeStats(acoustic.dta, ID, BBPs_per_dolphin)
+numb_stats_b[is.na(numb_stats_b)] <- 0
+numb_stats_b$ID <- as.factor(numb_stats_b$ID)
+
+numb_stats_b %>%
+  ggplot(aes(x=ID, y=mean, group=1)) +
+  geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd), 
+                color="red", width=.1, show.legend = FALSE)+
+  geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+  theme_light() + theme(text=element_text(size=12)) +
+  ylab("Number of BBPs per min")+
+  xlab("ID of dolphins group")
+
+# Clicks
+numb_stats_c <- computeStats(acoustic.dta, ID, clicks_per_dolphin)
+numb_stats_c[is.na(numb_stats_c)] <- 0
+numb_stats_c$ID <- as.factor(numb_stats_c$ID)
+
+numb_stats_c %>%
+  ggplot(aes(x=ID, y=mean, group=1)) +
+  geom_errorbar(aes(x=ID, ymin=mean-sd, ymax=mean+sd), 
+                color="red", width=.1)+
+  geom_point() + scale_x_discrete(guide = guide_axis(n.dodge = 2))+
+  theme_light() + theme(text=element_text(size=12)) +
+  ylab("Mean number of clicks per min")+
+  xlab("ID of dolphin group")
+
+# KW test on IDs 
+# whistles (excluding groups "2" because 1 sample and groups because no whistles recorded )
+data_test <- acoustic.dta[acoustic.dta$ID!="2",]
+print( posthocKW(data_test$whistling_time_per_dolphin, data_test$ID))
+print( posthocKW(data_test$BBPs_per_dolphin, data_test$ID))
+print( posthocKW(data_test$clicks_per_dolphin, data_test$ID))
\ No newline at end of file
-- 
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