# loading packages
library(ggpubr)
library(ggplot2)
library(plyr)
library(gridExtra)
library(wesanderson)
library(agricolae)
library(emmeans)
library(car)
library(multcomp)
# Define a function to summarizes data.
# Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else       length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N    = length2(xx[[col]], na.rm=na.rm),
mean = mean   (xx[[col]], na.rm=na.rm),
sd   = sd     (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
# loading data
lumino = read.csv('alldata.csv', header = T)
lumino = subset(lumino, Depth != '0' & Depth != '0.5' & Depth != '4'& Depth != '5')
lumino$Depth = as.numeric(lumino$Depth)
lumino_freq = subset(lumino, Setting != 'monitoring')
lumino_mntr = subset(lumino, Setting == 'monitoring')
lumino_freq$bioturbated_area = (lumino_freq$area_adjusted_perc)/100 * 35.23865
# for 2 frequencies
freq_summ = summarySE(lumino_freq, measurevar="bioturbated_area", groupvars=c('Setting',"Depth"), na.rm=T)
# statistics
lumino_freq$Setting = as.factor(lumino_freq$Setting) # this one!
lumino_freq$Depth = as.factor(lumino_freq$Depth)
#ANCOVA
fit2=aov(bioturbated_area ~ Setting + Depth, data = lumino_freq)
Anova(fit2, type="III")
#Post-hoc
posth=glht(fit2, linfct=mcp(Setting="Tukey"))
summary(posth)
