Update READMES

Stats/.Rhistory

-kruskal.test(acoustic.dta$BBPs_per_dolphin ~ acoustic.dta$beacon)
+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
-#KW test
-kruskal.test(acoustic.dta$clicks_per_dolphin ~ acoustic.dta$beacon)
-#### : Beacon plots + KW analysis (letters not shown for lisibility) ####
-# Whistles
-#KW test
-kruskal.test(acoustic.dta$whistling_time_per_dolphin ~ acoustic.dta$beacon)
+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")
 #### Nets plots + KW analysis ####
 # Whistles
 #KW test
@@ -16,9 +28,9 @@ kruskalmc(acoustic.dta$whistling_time_per_dolphin, acoustic.dta$net)
 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),
-myletters_df,
+NULL,
 net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
+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))
@@ -26,14 +38,14 @@ theme(axis.text.x=element_text(size=8.5))
 #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,)
+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),
-myletters_df,
+NULL,
 net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
+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))
@@ -46,14 +58,113 @@ kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
 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),
-myletters_df,
+NULL,
 net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long gill net"),
+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))
-# p<0.05 so post-hoc
-kruskalmc(acoustic.dta$clicks_per_dolphin, acoustic.dta$net)
+#### : 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
@@ -71,46 +182,32 @@ 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),]
-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$total_whistles_duration
-rm(whistles.dta, bbp.dta, clicks.dta)
 # suppress "T" acoustic data (other groups not tested on our variables)
-acoustic.dta <- acoustic.dta[acoustic.dta$acoustic!="T",]
+bbp.dta <- bbp.dta[bbp.dta$acoustic!="T",]
 # shuffle dataframe
-acoustic.dta <- acoustic.dta[sample(1:nrow(acoustic.dta)), ]
-acoustic.dta$acoustic <- factor(acoustic.dta$acoustic)
+bbp.dta <- bbp.dta[sample(1:nrow(bbp.dta)), ]
+bbp.dta$acoustic <- factor(bbp.dta$acoustic)
 #################### DATA INSPECTION  #################################
 # Data description
-names(acoustic.dta)
+names(bbp.dta)
 # self explenatory except acoustic : correspond to the activation sequence.
 # Look for obvious correlations
-plot(acoustic.dta) # nothing that we can see
+plot(bbp.dta) # nothing that we can see
 # Look for zero-inflation
-100*sum(acoustic.dta$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.
+100*sum(bbp.dta$number_of_BBP == 0)/nrow(bbp.dta)
+100*sum(bbp.dta$Buzz == 0)/nrow(bbp.dta)
+100*sum(bbp.dta$Burst.pulse == 0)/nrow(bbp.dta)
+# 53.7%, 60.1% & 73.6% of data are zeroes
 # QUESTION: This study is aimed at understanding if dolphin's acoustic activity
 # is influenced bytheir behavior, the emission of a pinger or a fishing net.
-# Dependent variables (Y): number_of_clicks, number_of_bbp, total_whistles_duration.
+# Dependent variables (Y): number_of_BBP, Buzz & Burst.pulse
 # Explanatory variables (X): acoustic, fishing_net, behavior, beacon, net, number.
 # What are the H0/ H1 hypotheses ?
 # H0 : No influence of any of the explanatory variables on a dependant one.
@@ -118,47 +215,47 @@ plot(acoustic.dta) # nothing that we can see
 ##################### DATA EXPLORATION ################################
 # Y Outlier detection
 par(mfrow=c(2,3))
-boxplot(acoustic.dta$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$number_of_clicks, pch=16,
-xlab='number_of_clicks', col='red')
+boxplot(bbp.dta$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$Burst.pulse, pch=16,
+xlab='Burst.pulse', col='red')
 # Y distribution
 par(mfrow=c(2,3))
-hist(acoustic.dta$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$number_of_clicks)
+hist(bbp.dta$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$Burst.pulse)
 # p-values are significant => they do not follow normal distributions
-# will need a transformation or the use of a glm model
+# we will need transformations or the use of glm models
 # X Number of individuals per level
-summary(factor(acoustic.dta$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$acoustic))
+summary(factor(bbp.dta$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$acoustic))
 # => unbalanced, no big deal but will need more work (no orthogonality):
 # Effects can depend on the order of the variables
 # => Beacon and net have modalities with <10 individuals => analysis impossible
@@ -176,54 +273,45 @@ ftable(factor(acoustic.dta$fishing_net), factor(acoustic.dta$behavior), factor(a
 # (using kruskall-Wallis non-parametric test)
 # fishing_net, behavior and acoustic where tested with their interactions.
 # If a variable is it in a model, it is because it had no significant effect.
-par(mfrow=c(1,1))
-### Model for whistles
-# Residual hypotheses not verified for LM
-# Overdipsersion when using GLM (negative binomial)
-# Using ZINB:
-zero.whi <- zeroinfl(total_whistles_duration ~
-acoustic + fishing_net + behavior + offset(log(number)),
-data=acoustic.dta, dist='negbin')
-nb.whi <- glm.nb(total_whistles_duration ~
-acoustic + fishing_net + behavior + offset(log(number)),
-data=acoustic.dta)
-# comparison ZINB VS NB model
-vuong(zero.whi, nb.whi)  #(if p-value<0.05 then first model in comparison is better)
-mod.whi <- zero.whi # => zeroinflated model is indeed better suited
-car::Anova(mod.whi, type=3)
-shapiro.test(residuals(mod.whi)) # H0 : normality -> not rejected if p>0.05
-dwtest(mod.whi) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
-bptest(mod.whi) # H0 -> homoscedasticity if p<0.05
-# No normality but we do not need it
 ### Model for BBP
 # No normality of residuals for LM
 # overdispersion with GLM quasipoisson
 #try with glm NB:
-mod.bbp <- glm.nb(number_of_bbp ~ acoustic + fishing_net + behavior
+mod.bbp <- glm.nb(number_of_BBP ~ acoustic + fishing_net + behavior
 + offset(log(number)),
-data=acoustic.dta)
+data=bbp.dta)
 car::Anova(mod.bbp, type=3)
 dwtest(mod.bbp) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
 bptest(mod.bbp) # H0 -> homoscedasticity if p<0.05
 # Normality not needed in GLM, hypotheses verified !
 mod.bbp$deviance/mod.bbp$df.residual
-# slight underdispersion, not improved with ZINB so we keep this
-### Model for clicks
-# Using NB model:
-mod.cli <- glm.nb(number_of_clicks ~ acoustic + fishing_net + acoustic:fishing_net + offset(log(number)),
-data=acoustic.dta)
-car::Anova(mod.cli, type=3)
-shapiro.test(residuals(mod.cli)) # H0 : normality -> cannot be rejected if p > 0.05
-dwtest(mod.cli) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
-bptest(mod.cli) # H0 -> homoscedasticity if p<0.05
-# Normality not needed in GLM, hypotheses verified !
-mod.cli$deviance/mod.cli$df.residual
-# slight overdispersion. (ZINB does not clearly improve results so we keep this)
-# FYI1: Comparison of combination of explanatory variables between models
-# were compared based on BIC criterion.
-# The model with the lowest BIC was kept (and is the one shown)
-# FYI2: log(number of dolphin per group) does have an effect on data but we have
-# no interest in investigating it, that is why we use it as an offset.
+# slight underdispersion
+### Model for Buzzes
+# No normality of residuals for LM
+# overdispersion with GLM quasipoisson
+# underdispersion with glm NB
+# Try with ZINB:
+mod.buzz <- glm.nb(Buzz ~ behavior + fishing_net + acoustic
++ offset(log(number)),
+data=bbp.dta)
+car::Anova(mod.buzz, type=3)
+dwtest(mod.buzz) # H0 -> independent if p>0.05 (autocorrelation if p<0.05)
+bptest(mod.buzz) # H0 -> homoscedasticity if p<0.05
+mod.buzz$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$df.residual  # -> Overdispersion of != 1
+# no overdispersion
 ##################### Boxplots and comparisons #####################
 ### Functions to compute stats
 computeLetters <- function(temp, category) {
@@ -251,10 +339,10 @@ height, xname="", colours="black", legend_title="", legend_labs="",ytitle=""){
 if (!is.null(signif)){colnames(signif)[1] <- "use"}
 dta %>%
 mutate(use=fct_relevel({{category}}, old_names)) %>%
-ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}})) +
+ggplot(aes(x=use, y=mean, group={{fill}}, fill={{fill}},color={{fill}}, na.rm = TRUE)) +
 {if(length(colours)==1)geom_point(color=colours, position=position_dodge(.5))}+
-{if(length(colours)==2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
-{if(length(colours)==2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
+{if(length(colours)>=2)geom_point(position=position_dodge(.5), show.legend = FALSE)}+
+{if(length(colours)>=2)scale_color_manual(values=colours, name=legend_title, labels=legend_labs)}+
 scale_x_discrete(breaks=old_names,
 labels=new_names)+
 ylab(ytitle)+
@@ -266,247 +354,159 @@ geom_errorbar(aes(x=use, ymin=mean-ic, ymax=mean+ic), position=position_dodge(.5
 }
 ####Introducing variables averaged per dolphins ####
 # since we introduced an offset, variables can be divided by the number of dolphins
-acoustic.dta$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)
+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$number
+#### Fishing nets plots  ####
+par(mfrow=c(3, 1))
+# BBPs
+table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375), # 375 bins = 1 sec
+barPlot(computeStats(bbp.dta, fishing_net, BBPs_per_dolphin),
 myletters_df, fishing_net,
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=.5,
-ytitle="Mean whistling time per dolphin per min")
-# BBP
-table <- cld(emmeans(mod.bbp, pairwise~fishing_net, adjust="tukey"), Letters = letters)
+xname="Presence/Asence of fishing net", height=.6,
+ytitle="Mean number of BBP per dolphin per min")
+# Buzz
+table <- cld(emmeans(mod.buzz, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin),
+barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin),
 myletters_df, fishing_net,
+ytitle="Mean number of Buzzes per dolphin per min",
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=.6,
-ytitle="Mean number of BBPs per dolphin per min")
-# Clicks
-table <- cld(emmeans(mod.cli, pairwise~fishing_net, adjust="tukey"), Letters = letters)
+xname="Presence/Asence of fishing net", height=.45)
+# Burst-pulse
+table <- cld(emmeans(mod.burst.pulse, pairwise~fishing_net, adjust="tukey"), Letters = letters)
 myletters_df <- data.frame(fishing_net=table$fishing_net,
 letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin),
+barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin),
 myletters_df, fishing_net,
+ytitle="Mean number of Burst-pulses per dolphin per min",
 old_names = c("SSF","F"), new_names = c("Absent", "Present"),
-xname="Presence/Asence of fishing net", height=100,
-ytitle="Mean number of clicks per dolphin per min")
+xname="Presence/Asence of fishing net", height=.18, )
 #### Acoustic plots  ####
-# Whistles
-table <- cld(emmeans(mod.whi, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, acoustic, whistling_time_per_dolphin/375),
-myletters_df, acoustic, height=0.65, ytitle="Mean whistling time per dolphin per min",
+# BBPs
+table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,
+letter = trimws(table$.group))
+barPlot(computeStats(bbp.dta, acoustic, BBPs_per_dolphin),
+myletters_df, acoustic, height=.9, ytitle="Mean number of BBPs per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-# BBPs
-table <- cld(emmeans(mod.bbp, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, acoustic, BBPs_per_dolphin),
-myletters_df, acoustic, height=1.2, ytitle="Mean number of BBPs per dolphin per min",
+# Buzz
+table <- cld(emmeans(mod.buzz, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,
+letter = trimws(table$.group))myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"),                                                                                   letter = c("a","a","a","a","a"))
+#error, no acoustic in model:
+myletters_df <- data.frame(acoustic=c("AP","AV","AV+D","D","D+AP"),
+letter = c("a","a","a","a","a"))
+barPlot(computeStats(bbp.dta, acoustic, Buzz_per_dolphin),
+myletters_df, acoustic, height=0.45, ytitle="Mean number of Buzzes per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-# Clicks
-table <- cld(emmeans(mod.cli, pairwise~acoustic, adjust="tukey"), Letters = letters)
-myletters_df <- data.frame(acoustic=table$acoustic,letter = trimws(table$.group))
-barPlot(computeStats(acoustic.dta, acoustic, clicks_per_dolphin),
-myletters_df, acoustic, height=155, ytitle="Mean number of clicks per dolphin per min",
+# Burst-pulse
+table <- cld(emmeans(mod.burst.pulse, pairwise~acoustic, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$acoustic,
+letter = trimws(table$.group))
+barPlot(computeStats(bbp.dta, acoustic, Burst.pulse_per_dolphin),
+myletters_df, acoustic, height=0.5, ytitle="Mean number of Burst-pulses per dolphin per min",
 old_names = c("AV","AV+D","D","D+AP","AP"),
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
 xname="Activation sequence")
-#### Interaction fishing_net:acoustic plots  ####
-# Whistles
-letters_df <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
+#### Behaviour plots  ####
+# BBPs
+table <- cld(emmeans(mod.bbp, pairwise~behavior, adjust="tukey"), Letters = letters)
+myletters_df <- data.frame(acoustic=table$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$.group))
+barPlot(computeStats(bbp.dta, behavior, Burst.pulse_per_dolphin),
+myletters_df, behavior, height=0.4, ytitle="Mean number of Burst-pulses per dolphin per min",
+old_names = c("CHAS", "DEPL", "SOCI"),
+new_names = c("Foraging", "Travelling", "Socialising"),
+xname="Behaviours of dolphins")
+#### Interaction : acoustic:fishing_net plots  ####
+# BBP
+letters_df <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.whi, pairwise~fishing_net:acoustic, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~acoustic:fishing_net, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(acoustic.dta, fishing_net, whistling_time_per_dolphin/375, two=acoustic),
+barPlot(computeStats(bbp.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 whistling time 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(.95,.95,.95,1,.95,1,.95,1,1,1),
+xname="Activation sequence", height=c(1.6),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-# BBPs
-letters_df <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
+# Buzz
+letters_df <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.bbp, pairwise~fishing_net:acoustic, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.buzz, pairwise~acoustic:fishing_net, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(acoustic.dta, fishing_net, BBPs_per_dolphin, two=acoustic),
+barPlot(computeStats(bbp.dta, fishing_net, Buzz_per_dolphin, two=acoustic),
 NULL, acoustic, fill=fishing_net,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of BBPs per dolphin per min",
+old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of Buzzes per dolphin per min",
 new_names = c("BEF","BEF+DUR","DUR", "DUR+AFT", "AFT"),
-xname="Activation sequence", height=c(1.65,1.65,1.72,1.65,1.72,1.65,1.65,1.72,1.72,1.72),
+xname="Activation sequence", height=c(0.77,0.77,0.8,0.77,0.77,0.8,0.77,0.8,0.8,0.8),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-# Clicks
-letters_df <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
+# Burst-pulse
+letters_df <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
 "fishing_net")
-letters_df$acoustic <- computeLetters(emmeans(mod.cli, pairwise~fishing_net:acoustic, adjust="tukey"),
+letters_df$acoustic <- computeLetters(emmeans(mod.burst.pulse, pairwise~acoustic:fishing_net, adjust="tukey"),
 "acoustic")$acoustic
 letters_df <- letters_df[, c("acoustic","fishing_net","letter")]
 letters_df$letter <- gsub(" ", "", letters_df$letter)
-barPlot(computeStats(acoustic.dta, fishing_net, clicks_per_dolphin, two=acoustic),
+barPlot(computeStats(bbp.dta, fishing_net, Burst.pulse_per_dolphin, two=acoustic),
 NULL, acoustic, fill=fishing_net,
-old_names = c("AV","AV+D","D","D+AP","AP"), ytitle="Mean number of clicks per dolphin per min",
+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", height=c(180,180,187,187,180,187,180,180,187,187),
+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),
 colours=c("#E69F00","#999999"), size=5,
 legend_title="Fishing net", legend_labs=c("Present", "Absent"))
-#### 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),
-myletters_df,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long 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),
-myletters_df,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long 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),
-myletters_df,
-net, old_names = c("SSF", "chalut_blanc", "chalut_vert", "tremail", "grand_filet"),
-new_names = c("Absent", "Nylon trawl net", "PE trawl net", "Gill net", "Long 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))
-# 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)
-barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
-NULL,
-beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
-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)
-barPlot(computeStats(acoustic.dta, beacon, clicks_per_dolphin),
-NULL,
-beacon, old_names = names(letters$Letters), new_names = names(letters$Letters),
-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')
-#### WHY NOT: Number plots ####
-# 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")
+#### 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"))

Stats/BBP-click-whistles_3models.R

@@ -399,7 +399,7 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
 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", "Gill net", "Long gill net"),
+        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))
@@ -415,7 +415,7 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
 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", "Gill net", "Long gill net"),
+        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))
@@ -431,19 +431,20 @@ myletters_df <- data.frame(net=c("SSF", "chalut_blanc", "chalut_vert", "tremail"
 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", "Gill net", "Long gill net"),
+        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) ####
+#### 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(letters$Letters), new_names = names(letters$Letters),
+        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))+
@@ -453,9 +454,10 @@ barPlot(computeStats(acoustic.dta, beacon, whistling_time_per_dolphin/375),
 # 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 = names(letters$Letters), new_names = names(letters$Letters),
+        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))+
@@ -465,9 +467,10 @@ barPlot(computeStats(acoustic.dta, beacon, BBPs_per_dolphin),
 # 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 = names(letters$Letters), new_names = names(letters$Letters),
+        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))+