###Cleaning Experiment-S magnus###
# Data type: presence (1) - absence (0)
#Packages:
library(plyr)
library(dplyr)
library(ggplot2)
library(vegan)
library(multcomp)
library(ggplot2)
library(tidyverse)
library(ggthemes)
library(multcompView)


##############Data preparation-------
SM<-read.csv2("Sm_results.csv")

percent<-read.csv2("Sm_results_percent.csv")

plot(Chae~Name,data=SM)

SM$Name<-as.factor(SM$Name)


### Stats in Plot ####
## Source: Rosane Rech
## https://statdoe.com/cld-customisation/#adding-the-letters-indicating-significant-differences
## https://www.youtube.com/watch?v=Uyof3S1gx3M



# analysis of variance
anova <- aov(Chae ~ Name, data = SM)
anova
summary(anova)

# Tukey's test
tukey <- TukeyHSD(anova)
tukey
# compact letter display
cld <- multcompLetters4(anova, tukey)
cld



# table with factors and 3rd quantile
dt <- group_by(percent, Name) %>%
  #summarise(cg=mean(percent$Chae_detection), sd = sd(percent$Chae_detection)) %>%
  arrange(desc(percent$Chae_detection))

# extracting the compact letter display and adding to the Tk table
cld <- as.data.frame.list(cld$Name)
dt$cld <- cld$Letters
print(dt$cld)
print(dt)



Chae_detection_grey <- ggplot(dt, aes(x = reorder(Name, - Chae_detection), y = Chae_detection), 
                              fill = type)+ 
  geom_bar(stat = "identity", color="black", fill="White",  show.legend = FALSE) +
  #geom_errorbar(aes(ymin = Chae-sd, ymax=Chae+sd), width = 0.2) +
  labs(x = "Method", y = expression(paste(italic("Chaetomium" )," detection %"))) +
  geom_text(aes(label = cld), vjust = -0.5) +
  ylim(0,100) +
  theme(axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14), 
        axis.text.x = element_text("Name", angle = 45, size = 12, hjust = 1, vjust = 1))


Chae_detection_grey

ggsave("Chae_detection_grey.png", width = 190, height = 160, units = "mm", plot = Chae_detection_grey )

##Plectus detection ####


SM <- read.csv2("Sm_results.csv")

plec<-read.csv2("Sm_results_plec.csv")



SM$Name<-as.factor(SM$Name)



## Plectus detection stats ####
# analysis of variance
anova_plec <- aov(Plec ~ Name, data = SM)
anova_plec
summary(anova_plec)

# Tukey's test
tukey_plec <- TukeyHSD(anova_plec)
tukey_plec
# compact letter display
cld_plec <- multcompLetters4(anova_plec, tukey_plec)
cld_plec



# table with factors and 3rd quantile
dt <- group_by(plec, Name) %>%
  #summarise(cg=mean(percent$Chae_detection), sd = sd(percent$Chae_detection)) %>%
  arrange(desc(plec$Plec_detection))

# extracting the compact letter display and adding to the Tk table
cld_plec <- as.data.frame.list(cld_plec$Name)
dt$cld_plec <- cld_plec$Letters
print(dt$cld_plec)
print(dt)

Plec_detection <- ggplot(dt, aes(x = reorder(Name, - Plec_detection), y = Plec_detection), 
                         fill = type)+ 
  geom_bar(stat = "identity", color="black", fill="White",  show.legend = FALSE) +
  #geom_errorbar(aes(ymin = Chae-sd, ymax=Chae+sd), width = 0.2) +
  labs(x = "Method", y = expression(paste(italic("Plectus" )," sp. detection %"))) +
  geom_text(aes(label = cld_plec), vjust = -0.5) +
  ylim(0,60) +
  theme(axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14), 
        axis.text.x = element_text("Name", angle = 45, size = 12, hjust = 1, vjust = 1))
#theme_bw()
#theme_few()

Plec_detection

ggsave("Plectus_detection.png", width = 190, height = 160, units = "mm", plot = Plec_detection )