# 1 
install.packages("RColorBrewer")
library(RColorBrewer)

display.brewer.all(n=NULL, type="all", select=NULL, exact.n=TRUE, 
colorblindFriendly=FALSE)# Displaying the Color Brewer scales. 
# colorblindFriendly set to T selects only those easy to read by color-blind persons
# n affects the number of colors in each scale to display. Try setting some numbers in n.


#2
library(readxl)
library(ggplot2)

lettuce<-read_excel("lettuce.xlsx") # Importing the data
summary(lettuce)

# Barplots with leaf color on x-axis and color-coded taste classes
ggplot(lettuce, aes(x=leaf.col, fill=as.character(taste)))+ # Definition of aesthetics; note the missing y-axis
  geom_bar()+ # Barplot plotting - stacked barplot is the default here
  theme_classic()+ #Setting the classic theme 
  scale_fill_brewer(palette="RdYlBu", name="Taste class")+ # Setting the Brewer palette (and legend title)
  # This one is a diverging palette
labs(x="Leaf color", y="Frequency")#Defining the axis labels


ggplot(lettuce, aes(x=leaf.col, fill=as.character(taste)))+
  geom_bar(position="dodge")+# Dodged barplot
  theme_classic()+
  scale_fill_brewer(palette="RdYlBu", name="Taste class")+  # Setting the Brewer palette (and legend title)
labs(x="Leaf color", y="Frequency")#Defining the axis labels


# Barplots with taste classes on x-axis and color-coded leaf color  
ggplot(lettuce, aes(fill=leaf.col, x=as.character(taste)))+
  geom_bar()+ # Stacked barplot
  theme_classic()+
  scale_fill_manual(values=c("darkgreen", "red"), name="Leaf color")+#Manual setting of the colors
  # is efficient for just two colors
  labs(x="Taste class", y="Frequency")#Defining the axis labels


ggplot(lettuce, aes(fill=leaf.col, x=as.character(taste)))+
  geom_bar(position="dodge")+ # Dodged barplot
  theme_classic()+
  scale_fill_manual(values=c("darkgreen", "red"), name="Leaf color")+
  labs(x="Taste class", y="Frequency")#Defining the axis labels

# 3 
ggplot(beer, aes(x=subtype, y=ABV))+
  stat_summary(fun.data=mean_se, geom="pointrange")+#computation an plotting the mean +- SE intervals 
  theme_classic()+
  labs(x="Beer subtype", y="Alcohol by volume (%)")#Defining the axis labels

ggplot(beer, aes(x=subtype, y=IBU))+
  stat_summary(fun.data=mean_se, geom="pointrange")+#computation an plotting the mean +- SE intervals 
  theme_classic()+
  labs(x="Beer subtype", y="IBU")#Defining the axis labels

# Arrangement of the two plots in a single two-panel plot 

library(ggpubr) # this package contains the ggarrange function 

p1<-ggplot(beer, aes(x=subtype, y=ABV))+ # saving the plot in the p1 object
  stat_summary(fun.data=mean_se, geom="pointrange")+
  theme_classic()+
  labs(x=NULL, y="Alcohol by volume (%)")#Defining the axis labels. Note, the skipped x-axis label

p2<-ggplot(beer, aes(x=subtype, y=IBU))+ # saving the plot in the p2 object
  stat_summary(fun.data=mean_se, geom="pointrange")+
  theme_classic()+
  labs(x="Beer subtype", y="IBU")#Defining the axis labels

# 4 
ggarrange(p1, p2, ncol=1, align='hv') # Combining the plots together as panels
# the ncol/nrow parameter defines how the plots are arranged
# the align='hv' ensures that the plot areas are aligned and of the same size despite 
# differences in x-axis label presence. Try plotting the same plot without the align paramter to see the difference

# Note the difference between 
# facetted plots (= multipanel plots with the SAME variables on x and y axes and facets fedined by a factor)
# and
# arrangament of two (generic) plots of even different types.

# 5 

p1<-ggplot(beer, aes(x=subtype, y=ABV))+ # saving the plot in the p1 object
  stat_summary(fun.data=mean_se, geom="pointrange")+
  theme_classic()+
  labs(x=NULL, y="Alcohol by volume (%)")#Defining the axis labels. Note, the skipped x-axis label

p2<-ggplot(beer, aes(x=subtype, y=ABV))+ # saving the plot in the p2 object
  geom_boxplot(fill="gold")+
  theme_classic()+
  labs(x="Beer subtype", y="IBU")#Defining the axis labels

ggarrange(p1, p2, ncol=1, align='hv') # Combining the plots together as panels

# 6
neobiota<-read_excel("neobiota-090-097-s002.xlsx", sheet=2, range="A3:H135", na="NA")

# a
ggplot(neobiota, aes(x=parasite, y=host_n))+
  geom_boxplot(fill="grey")+
  facet_wrap(~host)+
  theme_bw()+
  labs(x="Hemiparasite treatment", y="Number of host shoots")

ggplot(neobiota, aes(x=parasite, y=host_n))+
  geom_boxplot(fill="grey")+
  facet_wrap(~host)+
  scale_y_log10()+# log10-scaling of the y axis
  theme_bw()+
  labs(x="Hemiparasite treatment", y="Number of host shoots")

# b
ggplot(neobiota[neobiota$parasite!="Control",], aes(x=host_g, y=parasite_g, shape=host))+
  geom_point()+
  facet_wrap(~parasite)+
  # scale_y_log10()+
  theme_bw()+
  labs(x="Host biomass (g)", y="Hemiparasite biomass  (g)")+
  scale_shape_manual(values=c(1, 16), name="Host species")# The shapes are handled similarly to colors.
# numeric coding of the shapes can be obtained in the help for the base R function points, i.e.
?points
# Go to the 'pch' values section in the help.