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ANOVA_w_Interactions.R
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# ANOVA with Interactions
# This script was written to run multiple ANOVA models including some with interactions. Correlations and plots
# were also developed depending on the results. Written by Sharn Singh
options(scipen = 999)
##----------------------------
## Load Packages -
##----------------------------
library(readxl)
library(tidyverse)
library(data.table)
library(lsmeans)
library(multcompView)
library(car)
library(ggpubr)
library(cowplot)
library(tableone)
##----------------------------
## Import Data -
##----------------------------
data <- read_excel("~/WorkingData.xlsx")
View(data)
##----------------------------
## Data Manipulation -
##----------------------------
setnames(data, 'Boat.Intensity', 'BI')
setnames (data, 'Zone', 'Z')
##----------------------------
## Data Analysis -
##----------------------------
# Exploratory Analysis
plot(data$Sighting ~ data$Year + data$Sector + data$Zone, data=data)
ggplot(data, aes(data$Sector, data$Sighting, colour = factor(data$Year))) + geom_point()
########## Univaraite ANOVA ##########
## Turtle Outcome
# By Zone
tur1 <- lm(data$Sighting ~ factor(data$Zone), data=data)
anova(tur1)
tur1means <-lsmeans(tur1, "Zone")
cld(tur1means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(tur1),col= "blue")
kruskal.test(data$Sighting ~ factor(data$Zone), data=data)
# By Time
hist(data$Time)
tur2 <- lm(data$Sighting ~ factor(data$Time), data=data)
anova(tur2)
tur2means <-lsmeans(tur2, "Time")
cld(tur2means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(tur2),col= "pink")
data$logt <- log(data$Time)
kruskal.test(data$Sighting ~ factor(data$Time), data=data)
# By Sector
hist(data$Sector)
tur3 <- lm(data$Sighting ~ factor(data$Sector), data=data)
anova(tur3)
tur3means <-lsmeans(tur3, "Sector")
cld(tur3means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(tur3),col= "pink")
tur33 <- kruskal.test(data$Sighting ~ factor(data$Sector), data=data)
tur33
# By Year
hist(data$Year)
tur4 <- lm(data$Sighting ~ factor(data$Year), data=data)
anova(tur4)
tur4means <-lsmeans(tur4, "Year")
cld(tur4means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(tur4),col= "pink")
tur44 <- kruskal.test(data$Sighting ~ factor(data$Year), data=data)
tur44
## Boat Outcome
# By Zone
boat1 <- lm(data$BI ~ factor(data$Zone), data=data)
anova(boat1)
boat1means <-lsmeans(boat1, "Zone")
cld(boat1means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(boat1),col= "pink")
kruskal.test(data$BI ~ factor(data$Zone), data=data)
# By Time
hist(data$Time)
boat2 <- lm(data$BI ~ factor(data$Time), data=data)
anova(boat2)
boat2means <-lsmeans(boat2, "Time")
cld(boat2means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(boat2),col= "pink")
kruskal.test(data$BI ~ factor(data$Time), data=data)
# By Sector
hist(data$Sector)
boat3 <- lm(data$BI ~ factor(data$Sector), data=data)
anova(boat3)
boat3means <-lsmeans(boat3, "Sector")
cld(boat3means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(boat3),col= "pink")
kruskal.test(data$BI ~ factor(data$Sector), data=data)
# By Year
hist(data$Year)
boat4 <- lm(data$BI ~ factor(data$Year), data=data)
anova(boat4)
boat4means <-lsmeans(boat4, "Year")
cld(boat4means, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(boat4),col= "pink")
kruskal.test(data$BI ~ factor(data$Year), data=data)
########## Two Way ANOVA ##########
# Turtle
turtle1 <- lm(Sighting ~ relevel (factor(Year), ref="2017") + factor(Z) + factor(Time), data=data)
summary(turtle1)
turtle1MY <- lsmeans(turtle1, "Year")
turtle1MZ <- lsmeans(turtle1, "Zone")
turtle1MT <- lsmeans(turtle1, "Time")
cld(turtle1MY, alpha=0.05, Letters=letters, adjust='tukey')
cld(turtle1MZ, alpha=0.05, Letters=letters, adjust='tukey')
cld(turtle1MT, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(turtle1),col= "pink")
# Boat
boat1 <- lm(BI ~ relevel(factor(Year),ref="2017") + factor(Z), data=data)
summary(boat1)
boat1MY <- lsmeans(boat1, "Year")
boat1MZ <- lsmeans(boat1, "Zone")
boat11MT <- lsmeans(boat1, "Time")
cld(boat1MY, alpha=0.05, Letters=letters, adjust='tukey')
cld(boat1MZ, alpha=0.05, Letters=letters, adjust='tukey')
cld(boat11MT, alpha=0.05, Letters=letters, adjust='tukey')
hist(residuals(boat1),col= "pink")
########## Two Way ANOVA w/ Interactions ##########
# Turtle;
turtle1 <- lm(data$Sighting ~ data$Year + data$Zone + data$Time + data$Year*data$Zone + data$Time*data$Zone + data$Time*data$Year + data$Time*data$Year*data$Zone, data=data)
summary(turtle1)
turtle1MY <- lsmeans(turtle1, 'data$Year|data$Zone', adjust='tukey')
turtle1MZ <- lsmeans(turtle1, "Zone")
turtle11MT <- lsmeans(turtle1, "Time")
cld(turtle1MY, alpha=0.05, Letters=letters, adjust='tukey')
cld(turtle1MZ, alpha=0.05, Letters=letters, adjust='tukey')
cld(turtle11MT, alpha=0.05, Letters=letters, adjust='tukey')
turtle1 <- lm(data$Sighting ~ data$Year + data$Zone + data$Time + data$Time*data$Zone + data$Time*data$Year, data=data)
summary(turtle1)
# Boat;
boat1 <- lm(data$BI~ data$Year + data$Zone + data$Time + data$Year*data$Zone + data$Time*data$Zone + data$Time*data$Year + data$Time*data$Year*data$Zone, data=data)
summary(boat1)
boat1 <- lm(data$BI~ data$Year + data$Zone + data$Time + data$Year*data$Zone, data=data)
summary(boat1)
########## Correlation Plot ##########
# Pearson Correlation
BTcorr <- cor.test(data$BI, data$Sighting, method = 's')
BTcorr
Year2016corr <- cor.test (data$BI, data$Sightings, subset=(Year=="2016"))
######Subset the data by year to run correlations###
mydata2016 <- subset (data, data$Year=="2016")
mydata2017 <- subset (data, data$Year=="2017")
mydata2018 <- subset (data, data$Year=="2018")
######Year Correlation#####
corr2016 <- cor.test(mydata2016$BI, mydata2016$Sighting, method = 's')
corr2016
corr2017 <- cor.test(mydata2017$BI, mydata2017$Sighting, method = 's')
corr2017
corr2018 <- cor.test(mydata2018$BI, mydata2018$Sighting, method = 's')
corr2018
# Correlation Scatterplot
splot <- ggplot(data, aes(data$Sighting, data$BI, colour = factor(data$Year))) + geom_point()
bplot <- splot + theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
legend.background = element_rect(fill="white",
size=0.5, linetype="solid",
colour ="black"),
legend.title = element_text(colour="black", size=10,
face="bold")) +
xlab("Turtle Sighings") +
ylab("Boat Intensity") +
labs(title = "Turtle Sightings vs. Boat Intensity by Year")
bplot + guides(color=guide_legend("Year")) + theme( plot.title =element_text( size=15, hjust=0.7),
axis.title=element_text(size=14))
pairs(~data$Sighting+data$BI+data$Year)
p2 <- ggplot(data, aes(data$Sighting, data$BI, colour = factor(data$Year))) +
geom_point() + facet_wrap(~ data$Year, ncol = 2, scales = "free") +
guides(colour = "none") +
theme() +
xlab("Turtle Sightings") +
ylab("Boat Intensity")
p2