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New method positive.R
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New method positive.R
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#New method Posive 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)
#####################################################################################
#Food 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(foodgrunt.m = mean(foodgrunt, 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(foodgrunt.mm = mean(foodgrunt.m, na.rm = TRUE),
foodgrunt.m.sd=sd(foodgrunt.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(foodgrunt.m)), # of observations, excluding NAs.
foodgrunt.m.se=foodgrunt.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$sepct=baseline$foodgrunt.m.se/baseline$foodgrunt.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it foodgrunt.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$foodgrunt.brr=ayana.resp$foodgrunt.m/base.ayana$foodgrunt.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$foodgrunt.brr=stevie.resp$foodgrunt.m/base.stevie$foodgrunt.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$foodgrunt.brr=hope.resp$foodgrunt.m/base.hope$foodgrunt.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$foodgrunt.brr=patats.resp$foodgrunt.m/base.patats$foodgrunt.mm
#create a summary table for plotting (for each individual)
#this example for "ayana"...change this variable for each
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(foodgrunt.brr.m = mean(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
foodgrunt.brr.sd=sd(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(foodgrunt.brr)), # of observations, excluding NAs.
foodgrunt.brr.se=foodgrunt.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=foodgrunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=foodgrunt.brr.m - foodgrunt.brr.se, ymax=foodgrunt.brr.m + foodgrunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for foodgrunt") +
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(foodgrunt.brr.m = mean(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
foodgrunt.brr.sd=sd(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(foodgrunt.brr)), # of observations, excluding NAs.
foodgrunt.brr.se=foodgrunt.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=foodgrunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=foodgrunt.brr.m - foodgrunt.brr.se, ymax=foodgrunt.brr.m + foodgrunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for foodgrunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.944, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.944, 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(foodgrunt.brr.m = mean(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
foodgrunt.brr.sd=sd(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(foodgrunt.brr)), # of observations, excluding NAs.
foodgrunt.brr.se=foodgrunt.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=foodgrunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=foodgrunt.brr.m - foodgrunt.brr.se, ymax=foodgrunt.brr.m + foodgrunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for foodgrunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.537, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.537, 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(foodgrunt.brr.m = mean(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
foodgrunt.brr.sd=sd(foodgrunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(foodgrunt.brr)), # of observations, excluding NAs.
foodgrunt.brr.se=foodgrunt.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=foodgrunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=foodgrunt.brr.m - foodgrunt.brr.se, ymax=foodgrunt.brr.m + foodgrunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for foodgrunt") +
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))
####################################################################################
#Grunt
#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(grunt.m = mean(grunt, 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(grunt.mm = mean(grunt.m, na.rm = TRUE),
grunt.m.sd=sd(grunt.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(grunt.m)), # of observations, excluding NAs.
grunt.m.se=grunt.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$grunt.m.se/baseline$grunt.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 grunt.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$grunt.brr=ayana.resp$grunt.m/base.ayana$grunt.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$grunt.brr=stevie.resp$grunt.m/base.stevie$grunt.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$grunt.brr=hope.resp$grunt.m/base.hope$grunt.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$grunt.brr=patats.resp$grunt.m/base.patats$grunt.mm
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(grunt.brr.m = mean(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
grunt.brr.sd=sd(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(grunt.brr)), # of observations, excluding NAs.
grunt.brr.se=grunt.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=grunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=grunt.brr.m - grunt.brr.se, ymax=grunt.brr.m + grunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for grunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.273, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0.273, 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(grunt.brr.m = mean(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
grunt.brr.sd=sd(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(grunt.brr)), # of observations, excluding NAs.
grunt.brr.se=grunt.brr.sd/sqrt(n))
ggplot(data=x.p, aes(x=cond, y=grunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=grunt.brr.m - grunt.brr.se, ymax=grunt.brr.m + grunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for grunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.0902, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.0902, 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(grunt.brr.m = mean(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
grunt.brr.sd=sd(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(grunt.brr)), # of observations, excluding NAs.
grunt.brr.se=grunt.brr.sd/sqrt(n))
ggplot(data=x.p, aes(x=cond, y=grunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=grunt.brr.m - grunt.brr.se, ymax=grunt.brr.m + grunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for grunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.0270, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.0270, 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(grunt.brr.m = mean(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
grunt.brr.sd=sd(grunt.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(grunt.brr)), # of observations, excluding NAs.
grunt.brr.se=grunt.brr.sd/sqrt(n))
ggplot(data=x.p, aes(x=cond, y=grunt.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=grunt.brr.m - grunt.brr.se, ymax=grunt.brr.m + grunt.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for grunt") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.0274, linetype=2) + #this value is 1+ patasSE from baseline table above
geom_hline(yintercept = 1-0.0274, linetype=2) + #this value is 1- patasSE 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))
########################################################################################
#Lipsmack
#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(lipsmack.m = mean(lipsmack, 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(lipsmack.mm = mean(lipsmack.m, na.rm = TRUE),
lipsmack.m.sd=sd(lipsmack.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(lipsmack.m)), # of observations, excluding NAs.
lipsmack.m.se=lipsmack.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$sepct=baseline$lipsmack.m.se/baseline$lipsmack.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it lipsmack.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$lipsmack.brr=ayana.resp$lipsmack.m/base.ayana$lipsmack.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$lipsmack.brr=stevie.resp$lipsmack.m/base.stevie$lipsmack.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$lipsmack.brr=hope.resp$lipsmack.m/base.hope$lipsmack.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$lipsmack.brr=patats.resp$lipsmack.m/base.patats$lipsmack.mm
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(lipsmack.brr.m = mean(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
lipsmack.brr.sd=sd(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(lipsmack.brr)), # of observations, excluding NAs.
lipsmack.brr.se=lipsmack.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=lipsmack.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=lipsmack.brr.m - lipsmack.brr.se, ymax=lipsmack.brr.m + lipsmack.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for lipsmack") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.258, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0.258, 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(lipsmack.brr.m = mean(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
lipsmack.brr.sd=sd(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(lipsmack.brr)), # of observations, excluding NAs.
lipsmack.brr.se=lipsmack.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=lipsmack.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=lipsmack.brr.m - lipsmack.brr.se, ymax=lipsmack.brr.m + lipsmack.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for lipsmack") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.142, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.142, 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(lipsmack.brr.m = mean(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
lipsmack.brr.sd=sd(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(lipsmack.brr)), # of observations, excluding NAs.
lipsmack.brr.se=lipsmack.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=lipsmack.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=lipsmack.brr.m - lipsmack.brr.se, ymax=lipsmack.brr.m + lipsmack.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for lipsmack") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.409, linetype=2) + #this value is 1+ stevieSE from baseline table above
geom_hline(yintercept = 1-0.409, 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(lipsmack.brr.m = mean(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
lipsmack.brr.sd=sd(lipsmack.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(lipsmack.brr)), # of observations, excluding NAs.
lipsmack.brr.se=lipsmack.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=lipsmack.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=lipsmack.brr.m - lipsmack.brr.se, ymax=lipsmack.brr.m + lipsmack.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for lipsmack") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.215, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0.215, 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))
#Presentations to baboon
#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(pbaboon.m = mean(pbaboon, 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(pbaboon.mm = mean(pbaboon.m, na.rm = TRUE),
pbaboon.m.sd=sd(pbaboon.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(pbaboon.m)), # of observations, excluding NAs.
pbaboon.m.se=pbaboon.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$pbaboon.m.se/baseline$pbaboon.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 pbaboon.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$pbaboon.brr=ayana.resp$pbaboon.m/base.ayana$pbaboon.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$pbaboon.brr=stevie.resp$pbaboon.m/base.stevie$pbaboon.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$pbaboon.brr=hope.resp$pbaboon.m/base.hope$pbaboon.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$pbaboon.brr=patats.resp$pbaboon.m/base.patats$pbaboon.mm
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(pbaboon.brr.m = mean(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
pbaboon.brr.sd=sd(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(pbaboon.brr)), # of observations, excluding NAs.
pbaboon.brr.se=pbaboon.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=pbaboon.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=pbaboon.brr.m - pbaboon.brr.se, ymax=pbaboon.brr.m + pbaboon.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for pbaboon") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.305, linetype=2) + #this value is 1+ ayanaSE from baseline table above
geom_hline(yintercept = 1-0.305, 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(pbaboon.brr.m = mean(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
pbaboon.brr.sd=sd(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(pbaboon.brr)), # of observations, excluding NAs.
pbaboon.brr.se=pbaboon.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=pbaboon.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=pbaboon.brr.m - pbaboon.brr.se, ymax=pbaboon.brr.m + pbaboon.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for pbaboon") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.231, linetype=2) + #this value is 1+ hopeSE from baseline table above
geom_hline(yintercept = 1-0.231, 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(pbaboon.brr.m = mean(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
pbaboon.brr.sd=sd(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(pbaboon.brr)), # of observations, excluding NAs.
pbaboon.brr.se=pbaboon.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=pbaboon.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=pbaboon.brr.m - pbaboon.brr.se, ymax=pbaboon.brr.m + pbaboon.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for pbaboon") +
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(pbaboon.brr.m = mean(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
pbaboon.brr.sd=sd(pbaboon.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(pbaboon.brr)), # of observations, excluding NAs.
pbaboon.brr.se=pbaboon.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=pbaboon.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=pbaboon.brr.m - pbaboon.brr.se, ymax=pbaboon.brr.m + pbaboon.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for pbaboon") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.447, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0.447, 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))
#####################################################################################
#Presentation to humans
#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(phuman.m = mean(phuman, 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(phuman.mm = mean(phuman.m, na.rm = TRUE),
phuman.m.sd=sd(phuman.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(phuman.m)), # of observations, excluding NAs.
phuman.m.se=phuman.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$sepct=baseline$phuman.m.se/baseline$phuman.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it phuman.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$phuman.brr=ayana.resp$phuman.m/base.ayana$phuman.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$phuman.brr=stevie.resp$phuman.m/base.stevie$phuman.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$phuman.brr=hope.resp$phuman.m/base.hope$phuman.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$phuman.brr=patats.resp$phuman.m/base.patats$phuman.mm
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(phuman.brr.m = mean(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
phuman.brr.sd=sd(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(phuman.brr)), # of observations, excluding NAs.
phuman.brr.se=phuman.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=phuman.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=phuman.brr.m - phuman.brr.se, ymax=phuman.brr.m + phuman.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for phuman") +
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(phuman.brr.m = mean(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
phuman.brr.sd=sd(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(phuman.brr)), # of observations, excluding NAs.
phuman.brr.se=phuman.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=phuman.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=phuman.brr.m - phuman.brr.se, ymax=phuman.brr.m + phuman.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for phuman") +
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(phuman.brr.m = mean(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
phuman.brr.sd=sd(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(phuman.brr)), # of observations, excluding NAs.
phuman.brr.se=phuman.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=phuman.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=phuman.brr.m - phuman.brr.se, ymax=phuman.brr.m + phuman.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for phuman") +
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(phuman.brr.m = mean(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
phuman.brr.sd=sd(phuman.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(phuman.brr)), # of observations, excluding NAs.
phuman.brr.se=phuman.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=phuman.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=phuman.brr.m - phuman.brr.se, ymax=phuman.brr.m + phuman.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for phuman") +
ylim(0,2) +
theme_bw() +
geom_hline(yintercept = 1) +
geom_hline(yintercept = 1+0.683, linetype=2) + #this value is 1+ patatsSE from baseline table above
geom_hline(yintercept = 1-0.683, 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))
####################################################################################
#human groom count
#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(hgroomct.m = mean(hgroomct, 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(hgroomct.mm = mean(hgroomct.m, na.rm = TRUE),
hgroomct.m.sd=sd(hgroomct.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomct.m)), # of observations, excluding NAs.
hgroomct.m.se=hgroomct.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$hgroomct.m.se/baseline$hgroomct.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 hgroomct.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$hgroomct.brr=ayana.resp$hgroomct.m/base.ayana$hgroomct.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$hgroomct.brr=stevie.resp$hgroomct.m/base.stevie$hgroomct.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$hgroomct.brr=hope.resp$hgroomct.m/base.hope$hgroomct.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$hgroomct.brr=patats.resp$hgroomct.m/base.patats$hgroomct.mm
#Ayana
x.p <- group_by(ayana.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(hgroomct.brr.m = mean(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
hgroomct.brr.sd=sd(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomct.brr)), # of observations, excluding NAs.
hgroomct.brr.se=hgroomct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=hgroomct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=hgroomct.brr.m - hgroomct.brr.se, ymax=hgroomct.brr.m + hgroomct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for hgroomct") +
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(hgroomct.brr.m = mean(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
hgroomct.brr.sd=sd(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomct.brr)), # of observations, excluding NAs.
hgroomct.brr.se=hgroomct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=hgroomct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=hgroomct.brr.m - hgroomct.brr.se, ymax=hgroomct.brr.m + hgroomct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for hgroomct") +
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))
#Patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(hgroomct.brr.m = mean(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
hgroomct.brr.sd=sd(hgroomct.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomct.brr)), # of observations, excluding NAs.
hgroomct.brr.se=hgroomct.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=hgroomct.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=hgroomct.brr.m - hgroomct.brr.se, ymax=hgroomct.brr.m + hgroomct.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for hgroomct") +
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))
##################################################################################
#Human groom duration
#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(hgroomd.m = mean(hgroomd, 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(hgroomd.mm = mean(hgroomd.m, na.rm = TRUE),
hgroomd.m.sd=sd(hgroomd.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomd.m)), # of observations, excluding NAs.
hgroomd.m.se=hgroomd.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$sepct=baseline$hgroomd.m.se/baseline$hgroomd.mm
baseline#after seeing what the standard error is by pct, you'll need to manually enter this into
#the graphic commands below
#subset table x to remove condition 1
responses<-subset(x, cond!=1)
#subset responses to get each individual
ayana.resp<-subset(responses, id=="ayana")
stevie.resp<-subset(responses, id=="stevie")
hope.resp<-subset(responses, id=="hope ")#hope has an accidental space saved after her name so this needs to be
#typed to include the space, or remove the spaces and update this
patats.resp<-subset(responses, id=="patats")
#for each baboon, divide the daily average response by the average response in the "baseline" table and
#call it hgroomd.brr (behavior response ratio)
base.ayana<-subset(baseline, id=="ayana")
ayana.resp$hgroomd.brr=ayana.resp$hgroomd.m/base.ayana$hgroomd.mm
base.stevie<-subset(baseline, id=="stevie")
stevie.resp$hgroomd.brr=stevie.resp$hgroomd.m/base.stevie$hgroomd.mm
base.hope<-subset(baseline, id=="hope ")
hope.resp$hgroomd.brr=hope.resp$hgroomd.m/base.hope$hgroomd.mm
base.patats<-subset(baseline, id=="patats")
patats.resp$hgroomd.brr=patats.resp$hgroomd.m/base.patats$hgroomd.mm
#patats
x.p <- group_by(patats.resp, cond) %>% # Grouping function causes subsequent functions to aggregate by season and reach
summarize(hgroomd.brr.m = mean(hgroomd.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
hgroomd.brr.sd=sd(hgroomd.brr, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(hgroomd.brr)), # of observations, excluding NAs.
hgroomd.brr.se=hgroomd.brr.sd/sqrt(n))
#create plot
library(ggplot2)
ggplot(data=x.p, aes(x=cond, y=hgroomd.brr.m)) +
geom_bar(stat="identity", position=position_dodge(), color = "black") +
geom_errorbar(aes(ymin=hgroomd.brr.m - hgroomd.brr.se, ymax=hgroomd.brr.m + hgroomd.brr.se), width=0.2,
position=position_dodge(0.9)) +
scale_fill_manual(values=c("black")) +
xlab("Condition") +
ylab("Behavior Response Ratio for hgroomd") +
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))
###############################################################################
#Baboon groom count
#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(bgroomct.m = mean(bgroomct, 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(bgroomct.mm = mean(bgroomct.m, na.rm = TRUE),
bgroomct.m.sd=sd(bgroomct.m, na.rm = TRUE), # na.rm = TRUE to remove missing values
n = sum(!is.na(bgroomct.m)), # of observations, excluding NAs.
bgroomct.m.se=bgroomct.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$bgroomct.m.se/baseline$bgroomct.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")