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Copy pathnew method negative behaviors.R
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new method negative behaviors.R
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#New method negative behaviors
care=read.table(file="caredata.csv", header=TRUE, sep=",")
names(care)
str(care)
#Installed Packages
install.packages("nlme")
install.packages("lme4")
install.packages("geepack")
install.packages("lsmeans")
install.packages("dplyr")
install.packages("MuMIn")
install.packages("ggplot2")
install.packages("nortest")
install.packages("multcomp")
library(nlme)
library(dplyr)
library(lattice)
library(lme4)
library(geepack)
library(lsmeans)
library(dplyr)
library(MuMIn)
library(ggplot2)
library(nortest)
library(multcomp)
#####################################################################################
#Increse proximity caretaker
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxct.m = mean(iproxct, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(iproxct.mm = mean(iproxct.m, na.rm = TRUE),
iproxct.m.sd=sd(iproxct.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxct.m)), # of observations, excluding NAs.
iproxct.m.se=iproxct.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$iproxct.m.se/baseline$iproxct.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it iproxct.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$iproxct.brr=ayana.resp$iproxct.m/base.ayana$iproxct.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$iproxct.brr=stevie.resp$iproxct.m/base.stevie$iproxct.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$iproxct.brr=hope.resp$iproxct.m/base.hope$iproxct.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$iproxct.brr=patats.resp$iproxct.m/base.patats$iproxct.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxct.brr.m = mean(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxct.brr.sd=sd(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxct.brr)), # of observations, excluding NAs.
iproxct.brr.se=iproxct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxct.brr.m - iproxct.brr.se, ymax=iproxct.brr.m + iproxct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxct") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxct.brr.m = mean(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxct.brr.sd=sd(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxct.brr)), # of observations, excluding NAs.
iproxct.brr.se=iproxct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxct.brr.m - iproxct.brr.se, ymax=iproxct.brr.m + iproxct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxct") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Stevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxct.brr.m = mean(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxct.brr.sd=sd(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxct.brr)), # of observations, excluding NAs.
iproxct.brr.se=iproxct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxct.brr.m - iproxct.brr.se, ymax=iproxct.brr.m + iproxct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxct") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxct.brr.m = mean(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxct.brr.sd=sd(iproxct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxct.brr)), # of observations, excluding NAs.
iproxct.brr.se=iproxct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxct.brr.m - iproxct.brr.se, ymax=iproxct.brr.m + iproxct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxct") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#################################################################################
#Increase proximity outside
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxout.m = mean(iproxout, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(iproxout.mm = mean(iproxout.m, na.rm = TRUE),
iproxout.m.sd=sd(iproxout.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxout.m)), # of observations, excluding NAs.
iproxout.m.se=iproxout.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$iproxout.m.se/baseline$iproxout.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it iproxout.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$iproxout.brr=ayana.resp$iproxout.m/base.ayana$iproxout.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$iproxout.brr=stevie.resp$iproxout.m/base.stevie$iproxout.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$iproxout.brr=hope.resp$iproxout.m/base.hope$iproxout.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$iproxout.brr=patats.resp$iproxout.m/base.patats$iproxout.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxout.brr.m = mean(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxout.brr.sd=sd(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxout.brr)), # of observations, excluding NAs.
iproxout.brr.se=iproxout.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxout.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxout.brr.m - iproxout.brr.se, ymax=iproxout.brr.m + iproxout.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxout") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxout.brr.m = mean(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxout.brr.sd=sd(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxout.brr)), # of observations, excluding NAs.
iproxout.brr.se=iproxout.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxout.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxout.brr.m - iproxout.brr.se, ymax=iproxout.brr.m + iproxout.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxout") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#STevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxout.brr.m = mean(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxout.brr.sd=sd(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxout.brr)), # of observations, excluding NAs.
iproxout.brr.se=iproxout.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxout.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxout.brr.m - iproxout.brr.se, ymax=iproxout.brr.m + iproxout.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxout") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(iproxout.brr.m = mean(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
iproxout.brr.sd=sd(iproxout.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(iproxout.brr)), # of observations, excluding NAs.
iproxout.brr.se=iproxout.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=iproxout.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=iproxout.brr.m - iproxout.brr.se, ymax=iproxout.brr.m + iproxout.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for iproxout") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
##################################################################################
#Threat grunt
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(threatg.m = mean(threatg, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(threatg.mm = mean(threatg.m, na.rm = TRUE),
threatg.m.sd=sd(threatg.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(threatg.m)), # of observations, excluding NAs.
threatg.m.se=threatg.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$threatg.m.se/baseline$threatg.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into7
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it threatg.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$threatg.brr=ayana.resp$threatg.m/base.ayana$threatg.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$threatg.brr=stevie.resp$threatg.m/base.stevie$threatg.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$threatg.brr=hope.resp$threatg.m/base.hope$threatg.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$threatg.brr=patats.resp$threatg.m/base.patats$threatg.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(threatg.brr.m = mean(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
threatg.brr.sd=sd(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(threatg.brr)), # of observations, excluding NAs.
threatg.brr.se=threatg.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=threatg.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=threatg.brr.m - threatg.brr.se, ymax=threatg.brr.m + threatg.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for threatg") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(threatg.brr.m = mean(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
threatg.brr.sd=sd(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(threatg.brr)), # of observations, excluding NAs.
threatg.brr.se=threatg.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=threatg.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=threatg.brr.m - threatg.brr.se, ymax=threatg.brr.m + threatg.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for threatg") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#STevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(threatg.brr.m = mean(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
threatg.brr.sd=sd(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(threatg.brr)), # of observations, excluding NAs.
threatg.brr.se=threatg.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=threatg.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=threatg.brr.m - threatg.brr.se, ymax=threatg.brr.m + threatg.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for threatg") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.684, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.684, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(threatg.brr.m = mean(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
threatg.brr.sd=sd(threatg.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(threatg.brr)), # of observations, excluding NAs.
threatg.brr.se=threatg.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=threatg.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=threatg.brr.m - threatg.brr.se, ymax=threatg.brr.m + threatg.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for threatg") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.728, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0.728, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
##################################################################################
#Bark
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(bark.m = mean(bark, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(bark.mm = mean(bark.m, na.rm = TRUE),
bark.m.sd=sd(bark.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bark.m)), # of observations, excluding NAs.
bark.m.se=bark.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$bark.m.se/baseline$bark.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into7
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it bark.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$bark.brr=ayana.resp$bark.m/base.ayana$bark.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$bark.brr=stevie.resp$bark.m/base.stevie$bark.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$bark.brr=hope.resp$bark.m/base.hope$bark.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$bark.brr=patats.resp$bark.m/base.patats$bark.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(bark.brr.m = mean(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
bark.brr.sd=sd(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bark.brr)), # of observations, excluding NAs.
bark.brr.se=bark.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=bark.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=bark.brr.m - bark.brr.se, ymax=bark.brr.m + bark.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for bark") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.816, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0.816, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(bark.brr.m = mean(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
bark.brr.sd=sd(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bark.brr)), # of observations, excluding NAs.
bark.brr.se=bark.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=bark.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=bark.brr.m - bark.brr.se, ymax=bark.brr.m + bark.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for bark") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.714, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.714, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#STevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(bark.brr.m = mean(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
bark.brr.sd=sd(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bark.brr)), # of observations, excluding NAs.
bark.brr.se=bark.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=bark.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=bark.brr.m - bark.brr.se, ymax=bark.brr.m + bark.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for bark") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(bark.brr.m = mean(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
bark.brr.sd=sd(bark.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bark.brr)), # of observations, excluding NAs.
bark.brr.se=bark.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=bark.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=bark.brr.m - bark.brr.se, ymax=bark.brr.m + bark.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for bark") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
######################################################################################
#Headshake
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(headshake.m = mean(headshake, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(headshake.mm = mean(headshake.m, na.rm = TRUE),
headshake.m.sd=sd(headshake.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(headshake.m)), # of observations, excluding NAs.
headshake.m.se=headshake.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$headshake.m.se/baseline$headshake.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into7
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it headshake.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$headshake.brr=ayana.resp$headshake.m/base.ayana$headshake.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$headshake.brr=stevie.resp$headshake.m/base.stevie$headshake.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$headshake.brr=hope.resp$headshake.m/base.hope$headshake.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$headshake.brr=patats.resp$headshake.m/base.patats$headshake.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(headshake.brr.m = mean(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
headshake.brr.sd=sd(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(headshake.brr)), # of observations, excluding NAs.
headshake.brr.se=headshake.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=headshake.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=headshake.brr.m - headshake.brr.se, ymax=headshake.brr.m + headshake.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for headshake") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(headshake.brr.m = mean(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
headshake.brr.sd=sd(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(headshake.brr)), # of observations, excluding NAs.
headshake.brr.se=headshake.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=headshake.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=headshake.brr.m - headshake.brr.se, ymax=headshake.brr.m + headshake.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for headshake") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.683, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.683, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Stevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(headshake.brr.m = mean(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
headshake.brr.sd=sd(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(headshake.brr)), # of observations, excluding NAs.
headshake.brr.se=headshake.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=headshake.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=headshake.brr.m - headshake.brr.se, ymax=headshake.brr.m + headshake.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for headshake") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.258, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.258, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(headshake.brr.m = mean(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
headshake.brr.sd=sd(headshake.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(headshake.brr)), # of observations, excluding NAs.
headshake.brr.se=headshake.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=headshake.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=headshake.brr.m - headshake.brr.se, ymax=headshake.brr.m + headshake.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for headshake") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.413, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0.413, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#####################################################################################
#Yawn
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(yawn.m = mean(yawn, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(yawn.mm = mean(yawn.m, na.rm = TRUE),
yawn.m.sd=sd(yawn.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(yawn.m)), # of observations, excluding NAs.
yawn.m.se=yawn.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$yawn.m.se/baseline$yawn.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into7
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it yawn.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$yawn.brr=ayana.resp$yawn.m/base.ayana$yawn.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$yawn.brr=stevie.resp$yawn.m/base.stevie$yawn.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$yawn.brr=hope.resp$yawn.m/base.hope$yawn.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$yawn.brr=patats.resp$yawn.m/base.patats$yawn.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(yawn.brr.m = mean(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
yawn.brr.sd=sd(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(yawn.brr)), # of observations, excluding NAs.
yawn.brr.se=yawn.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=yawn.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=yawn.brr.m - yawn.brr.se, ymax=yawn.brr.m + yawn.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for yawn") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.683, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0.683, linetype=2) + #this value is 1- ayanaSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Hope
x.p <- group_by(hope.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(yawn.brr.m = mean(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
yawn.brr.sd=sd(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(yawn.brr)), # of observations, excluding NAs.
yawn.brr.se=yawn.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=yawn.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=yawn.brr.m - yawn.brr.se, ymax=yawn.brr.m + yawn.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for yawn") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.683, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.683, linetype=2) + #this value is 1- hopeSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#STevie
x.p <- group_by(stevie.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(yawn.brr.m = mean(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
yawn.brr.sd=sd(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(yawn.brr)), # of observations, excluding NAs.
yawn.brr.se=yawn.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=yawn.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=yawn.brr.m - yawn.brr.se, ymax=yawn.brr.m + yawn.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for yawn") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.220, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.220, linetype=2) + #this value is 1- stevieSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(yawn.brr.m = mean(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
yawn.brr.sd=sd(yawn.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(yawn.brr)), # of observations, excluding NAs.
yawn.brr.se=yawn.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=yawn.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=yawn.brr.m - yawn.brr.se, ymax=yawn.brr.m + yawn.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for yawn") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+1, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-1, linetype=2) + #this value is 1- patatsSE from baseline table above
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8),
axis.text.x=element_text(size=8))
################################################################################
#Agressive agains baboons
#making a table for Behavior Response Ratio
#calculate mean
#create a summary table for each behavior one at a time by individual, condition, and total day
x <- group_by(care, id, cond, tday) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(aggb.m = mean(aggb, na.rm = TRUE)) # na.rm = TRUE to remove missing values
#calculate average of baseline condition per individual from table x
avg <- group_by(x, id, cond) %>%
summarize(aggb.mm = mean(aggb.m, na.rm = TRUE),
aggb.m.sd=sd(aggb.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(aggb.m)), # of observations, excluding NAs.
aggb.m.se=aggb.m.sd/sqrt(n))
#subset avg table to remove all conditions except for 1
baseline<-subset(avg, cond==1)
#calculate standard error of condition 1 as a percentage rather than a raw number
baseline$sepct=baseline$aggb.m.se/baseline$aggb.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into7
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it aggb.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$aggb.brr=ayana.resp$aggb.m/base.ayana$aggb.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$aggb.brr=stevie.resp$aggb.m/base.stevie$aggb.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$aggb.brr=hope.resp$aggb.m/base.hope$aggb.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$aggb.brr=patats.resp$aggb.m/base.patats$aggb.mm
#create a summary table for plotting (for each individual)
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(aggb.brr.m = mean(aggb.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
aggb.brr.sd=sd(aggb.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(aggb.brr)), # of observations, excluding NAs.
aggb.brr.se=aggb.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=aggb.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=aggb.brr.m - aggb.brr.se, ymax=aggb.brr.m + aggb.brr.se), width=0.2,