library(reshape2)
library(dplyr)
library(ggplot2)
library(forcats)

Make tables

subset database to experiment data zero-jelly tanks

Database <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Final_Aurelia_Database_Jan11_2023.csv")

Subset to zero jelly tanks. First subset the trial type to Number, which codes for samples from experiments where the number of jellyfish in the tanks varies. Next, subset to all tanks with zero jellyfish added. This includes tanks sampled at the beginning of the experiment and tanks sampled at the end.

Exp_data<-subset(Database,Trial.Type=="Number")
Exp_data_zerojellies<-subset(Exp_data,Number.of.Jellies=="0")

remove tanks sampled at the beginning of the experiment

These tanks are removed because we decided not to include them in analyses down the line.

First, read in the file that designates different samples to tanks with jellies other, tanks with zero jellies sampled at the beginning of the experiment remove, and tanks with zero jellies sampled at the end of the experiment zero

Control_tanks <- read.csv("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/data/current_data/Control_Tanks.csv")

Exp_data_zerojellies$Control_info <- Control_tanks$c1[match(Exp_data_zerojellies$Sample.Code, Control_tanks$Sample.Code)]

Exp_data_control<-subset(Exp_data_zerojellies,Control_info!="remove")

Combine taxa

Exp_data_control$Species_lifestage <- paste(Exp_data_control$Genus.species, Exp_data_control$Life.History.Stage, sep="_")

Combine the following taxa: - Acartia and small calanoida are added to medium calanoida - Calanus pacificus Female Adult, Calanus pacificus Male Adult, and Calanus pacificus C5-adult are combined into Calanus pacficus - Ditrichocorycaeus anlgicus large and small are combined

Exp_data_control <- Exp_data_control %>%
                   mutate(Species_lifestage_combined = fct_recode(Species_lifestage, 
                                     "CALANOIDA_Medium" = "ACARTIA_Copepodite", 
                                     "CALANOIDA_Medium" = "ACARTIA_Female, Adult",
                                     "CALANUS PACIFICUS" = "CALANUS PACIFICUS_C5-adult",
                                     "CALANUS PACIFICUS" = "CALANUS PACIFICUS_Female, Adult",
                                     "CALANUS PACIFICUS" = "CALANUS PACIFICUS_Male, Adult",
                                     "DITRICHOCORYCAEUS ANGLICUS" = "DITRICHOCORYCAEUS ANGLICUS_Large",
                                     "DITRICHOCORYCAEUS ANGLICUS" = "DITRICHOCORYCAEUS ANGLICUS_Small"))

Exp_data_control_sum <-aggregate(Density....m3. ~ Sample.Code+Sample.Year+Sample.Date+Broad.Group+Species_lifestage_combined, data = Exp_data_control, sum)

Calculate means and standard error per experiment

Exp_data_control_sum <- Exp_data_control_sum %>%
  group_by(Sample.Year,Sample.Date,Broad.Group,Species_lifestage_combined) %>%
  summarise(
    mean = mean(Density....m3.),
    sd = sd(Density....m3.),
    n = n(),
    se = sd / sqrt(n)
  )

Make mean and standard error tables

#reformat table
Exp_control_mean<-dcast(Exp_data_control_sum,Broad.Group+Species_lifestage_combined~Sample.Date, value.var = "mean")

#reformat table
Exp_control_SE<-dcast(Exp_data_control_sum,Broad.Group+Species_lifestage_combined~Sample.Date, value.var = "se")

Combine mean and standard error tables and format final table

# Round dataframes to 2 decimal points
Exp_control_mean[, -c(1,2)]<-round(Exp_control_mean[, -c(1,2)],2)
Exp_control_SE[, -c(1,2)]<-round(Exp_control_SE[, -c(1,2)],2)
# Exclude first column
columns_to_combine <- names(Exp_control_mean)[-c(1,2)]

# Combine means and standard errors for selected columns
Exp_stock_densities <- data.frame(
  Exp_control_mean[, c(1,2)],
  sapply(columns_to_combine, function(col) {
   paste(Exp_control_mean[[col]], " ± ", Exp_control_SE[[col]])
  })
)

Exp_stock_densities$Species_lifestage_combined <- gsub("_", " ", Exp_stock_densities$Species_lifestage_combined)

#remove X at the beginning
names(Exp_stock_densities)[3:ncol(Exp_stock_densities)] <- substring(names(Exp_stock_densities)[3:ncol(Exp_stock_densities)], 2)
#convert periods to slashes
colnames(Exp_stock_densities) <- sub("\\.", "/", colnames(Exp_stock_densities))
colnames(Exp_stock_densities) <- sub("\\.", "/", colnames(Exp_stock_densities))
#replace first column name
colnames(Exp_stock_densities)[colnames(Exp_stock_densities) == "xp_control_mean//.1."] <- "Taxa"

Export table to csv

write.csv(Exp_stock_densities,"/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/output/Experiment_Stocking_Means.csv", row.names = FALSE)

From this point on, the table can be edited in excel to fit the correct format for the table

Stacked Bar Chart

Exp_stacked_barplot<-ggplot(Exp_data_control_sum, aes(x = Sample.Date, y = mean, fill = Species_lifestage_combined)) +
  geom_bar(stat = "identity",position = "fill") +
  labs(x = "Date", y = "Mean Density") +
  theme_minimal()
Exp_stacked_barplot

setwd("/Users/hailaschultz/Dropbox/Other studies/Aurelia project/Data Analysis/output")
ggsave(plot = Exp_stacked_barplot, width = 20, height = 10, dpi = 300, filename = "Exp_stacked_barplot.png")

01_exp_species-table