tracker: tracker

# Clear environment
rm(list=ls())

# Load packages
library(dplyr)
library(ggplot2)
library(MASS)

# Source all functions
sapply(list.files('R', full.names = T), source)

# Create list of test dates
test_dates <- c('2019-11-04')
test_species <- c('Asellus')

# # Loop through test dates
# for(n in 1:length(test_dates)){
#   
#   # List files
#   date <- test_dates[[n]]
#   species <- test_species[n]
#   files <- list.files('data/raw data', full.names = T)
#   files <- files[grep(date, files, value = F)]
#   files <- files[grep(species, files, value = F)]
#   #files <- files[grep('raw_0001.csv', files, value = F)]
#   
#   # Create empty list to collect output data
#   output <- list(0)
#   
#   # Loop through all files
#   for(file.nr in 1:length(files)){
#     # Import data
#     data <- import(files[[file.nr]])
#     # Apply next steps to all locations, so make list of locations
#     list_locations <- as.list(unique(data$location))
#     # Filter on location
#     data <- lapply(list_locations, function(x){ filter_data(data, filter_location = x) })
#     # Append timebins
#     #data <- lapply(data, function(x){ append_time_bins(x) }) # TODO only for Gammarus
#     # Append dynamics to all locations
#     data <- lapply(data, function(x){ append_dynamics(x) })
#     # Add experimental data
#     data <- lapply(data, function(x){ append_exp_info(x, files[[file.nr]]) })
#     # Append polar coordinates and make histogram of radius distribution
#     #data <- lapply(data, function(x){ append_polar_coordinates(x) })
#     # Collect all data together
#     data <- do.call('rbind', data)
#     # Store in output list
#     output[[file.nr]] <- data
#   }
# }
#   
# # Combine all data together
# output_data <- do.call('rbind', output)
# # Save data
# save(output_data, file = paste('output/', species, '_', date, '.Rda', sep = ''))
# And re-load quickly
load('output/asellus_2019-11-04.Rda')


data <- output_data
# Convert time to seconds 
data$time <- data$time/1e6

# Create timebins
bins <- seq(0, 200, 10)
# Add timebins to data
data$group <- cut(data$time, bins, labels = FALSE)
# Remove NAs (time > 480)
data <- data[!is.na(data$group),]
# Add interaction between timebin and treatment group
#data$combined_group <- interaction(data$group, data$Treatment_conc)
# Add an interaction between the three factors, individual, time, and treatment
data$combined_group <- interaction(data$ind, # take ind out to average per cosm
                                   data$group) 

# Calculate average speed at all time bins over all cosms/individuals
data_summarised <- data %>% group_by(combined_group) %>%
  summarise(avaspeed = mean(aspeed),
            sdaspeed = sd(aspeed),
            group = mean(group),
            #time_bin = mean(time_bin),
            ind = mean(ind))
#data_summarised$group <- data_summarised$group*5

# Make plot (Super large variation, so turn errorbars on or off)
p <- ggplot(data_summarised, aes(x=group, y=avaspeed, group = ind, color = as.factor(ind))) + 
  geom_line() +
  geom_point() +
geom_errorbar(aes(ymin=avaspeed-sdaspeed, ymax=avaspeed+sdaspeed), width=.2,
              position=position_dodge(0.05))
print(p)