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HoboTempLogger_DataImport.R
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library(remotes)
library(dplyr)
library(tidyr)
library(ggplot2)
library(lubridate)
# read raw data
logger.a <- read.csv("C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/1_abalone_physiology_Control_13degrees.csv",
header = T)
logger.a <- logger.a %>%
dplyr::rename(date.time = 2, temperature = 3) %>%
mutate(date.time = as.POSIXct(date.time, format="%y-%m-%d %H:%M:%S", tz = 'Australia/Tasmania'),
treatment = 'control') %>%
select(date.time, temperature, treatment)
logger.b <- read.csv("C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/1_abalone_physiology_TreatmentA_16degrees.csv",
header = T)
logger.b <- logger.b %>%
dplyr::rename(date.time = 2, temperature = 3) %>%
mutate(date.time = as.POSIXct(date.time, format="%y-%m-%d %H:%M:%S", tz = 'Australia/Tasmania'),
treatment = 'medium') %>%
select(date.time, temperature, treatment)
logger.c <- read.csv("C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/1_abalone_physiology_TreatmentB_19degrees.csv",
header = T)
logger.c <- logger.c %>%
dplyr::rename(date.time = 2, temperature = 3) %>%
mutate(date.time = as.POSIXct(date.time, format="%y-%m-%d %H:%M:%S", tz = 'Australia/Tasmania'),
treatment = 'high') %>%
select(date.time, temperature, treatment)
logger.dat <- bind_rows(logger.a, logger.b, logger.c) %>%
mutate(treatment = factor(treatment, levels = c('high', 'medium', 'control')))
logger_plot <- logger.dat %>%
filter(date.time > ymd_hms("2022-12-12 12:40:00") & date.time < ymd_hms("2022-12-19 05:00:00") & treatment == 'high'|
date.time > ymd_hms("2022-12-12 12:40:00") & date.time < ymd_hms("2022-12-19 14:00:00") & treatment %in% c('control', 'medium')) %>%
ggplot(aes(x = date.time, y = temperature))+
geom_line(aes(color = treatment))+
geom_vline(aes(xintercept = ymd_hms("2022-12-14 00:00:00")), colour = 'red', linetype = 'dashed')+
geom_vline(aes(xintercept = ymd_hms("2022-12-16 00:00:00")), colour = 'blue', linetype = 'dashed')+
theme_bw()+
xlab('Date.Time')+
ylab('Temperature (\u00B0C)')+
scale_color_discrete(name = "Treatment", labels = c("High", "Medium", "Control"))+
scale_y_continuous(breaks = seq(12,20,1))+
# geom_hline(aes(yintercept = 13.8), linetype = 'dashed', colour = '#F8766D')+
# geom_hline(aes(yintercept = 15.4), linetype = 'dashed', colour = '#00BA38')+
# geom_hline(aes(yintercept = 18.2), linetype = 'dashed', colour = '#619CFF')
geom_segment(aes(x = ymd_hms("2022-12-16 00:00:00"), xend = ymd_hms("2022-12-19 05:00:00"), y = 13.8, yend = 13.8), linetype = 'dashed', colour = '#619CFF')+
geom_segment(aes(x = ymd_hms("2022-12-16 00:00:00"), xend = ymd_hms("2022-12-19 05:00:00"), y = 15.4, yend = 15.4), linetype = 'dashed', colour = '#00BA38')+
geom_segment(aes(x = ymd_hms("2022-12-16 00:00:00"), xend = ymd_hms("2022-12-19 05:00:00"), y = 18.2, yend = 18.2), linetype = 'dashed', colour = '#F8766D')+
geom_text(label = '13.8 \u00B0C', x = ymd_hms("2022-12-19 15:00:00"), y = 13.8, colour = '#619CFF', size = 3, stat = 'identity')+
geom_text(label = '15.4 \u00B0C', x = ymd_hms("2022-12-19 15:00:00"), y = 15.4, colour = '#00BA38', size = 3, stat = 'identity')+
geom_text(label = '18.2 \u00B0C', x = ymd_hms("2022-12-19 15:00:00"), y = 18.2, colour = '#F8766D', size = 3, stat = 'identity')+
geom_text(label = 'Aclimatisation', x = ymd_hms("2022-12-13 00:00:00"), y = 12.5, size = 3, stat = 'identity')+
geom_text(label = 'Manipulation', x = ymd_hms("2022-12-15 00:00:00"), y = 12.5, size = 3, stat = 'identity')+
geom_text(label = 'Heat Stress', x = ymd_hms("2022-12-18 00:00:00"), y = 12.5, size = 3, stat = 'identity')+
theme(legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.background = element_rect(fill = "white", color = "black", linewidth = 0.2))
logger.dat %>%
filter(date.time > ymd_hms("2022-12-16 00:00:00") & date.time < ymd_hms("2022-12-19 05:00:00")) %>%
group_by(treatment) %>%
summarise(average.temp = mean(temperature),
max.temp = max(temperature),
min.temp = min(temperature))
ggsave(filename = paste('C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/HeatStressExperiment_LoggerTemperature', '.pdf', sep = ''),
plot = logger_plot, units = 'mm', width = 190, height = 200)
ggsave(filename = paste('C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/HeatStressExperiment_LoggerTemperature', '.png', sep = ''),
plot = logger_plot, units = 'mm', width = 190, height = 200)
##---------------------------------------------------------------------------##
# Formulated feed experiment temperature data
# read raw data
logger_dat <- read.csv("C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/AbStressTest_Capture-Harvest_Temperature_2021-12-10_clean.csv",
header = T)
logger_dat <- logger_dat %>%
dplyr::rename(temperature = 6) %>%
mutate(date_time = as.POSIXct(date_time, format="%d-%m-%y %H:%M:%S", tz = 'Australia/Tasmania')) %>%
select(date_time, temperature)
logger_dat %>%
filter(date_time > ymd_hms("2021-10-28 11:18:00") & date_time < ymd_hms("2021-12-08 12:00:00")) %>%
ggplot(aes(x = date_time, y = temperature))+
geom_line()
logger_dat %>%
filter(date_time > ymd_hms("2021-10-28 11:18:00") & date_time < ymd_hms("2021-12-08 12:00:00")) %>%
summarise(across(where(is.numeric), .fns =
list(Median = median,
Mean = mean,
n = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75)
))) %>%
pivot_longer(everything(), names_sep = "_", names_to = c( "variable", ".value"))
##---------------------------------------------------------------------------##
# Live transport experiment
live_temp_dat <- read.csv("C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/AbStressTest_TemperatureRH_2021-12-08_clean.csv",
header = T)
live_temp_dat_clean <- live_temp_dat %>%
dplyr::rename(temperature = 3,
humidity = 4,
date_time = 2) %>%
mutate(date_time = as.POSIXct(date_time, format="%m-%d-%Y %H:%M:%S", tz = 'Australia/Tasmania')) %>%
select(date_time, temperature, humidity)
trans_exp_dat <- live_temp_dat_clean %>%
filter(date_time > ymd_hms("2021-12-07 12:15:00", tz = 'Australia/Tasmania') &
date_time < ymd_hms("2021-12-08 12:15:00", tz = 'Australia/Tasmania'))
trans_exp_plot <- trans_exp_dat %>%
ggplot(aes(x = date_time))+
geom_line(aes(y = temperature), colour = 'red')+
geom_line(aes(y = humidity / 6), colour = 'blue')+
scale_y_continuous(name = "Temperature (\u00B0C)", sec.axis = sec_axis(~.*6, name = "Humidity (rH%)"))+
geom_vline(aes(xintercept = ymd_hms("2021-12-07 13:30:00", tz = 'Australia/Tasmania')), colour = 'black', linetype = 'dashed')+
geom_vline(aes(xintercept = ymd_hms("2021-12-08 11:30:00", tz = 'Australia/Tasmania')), colour = 'black', linetype = 'dashed')+
theme_bw()+
xlab('Date Time')+
# ylab('Temperature (\u00B0C)')+
geom_text(label = 'Packing', x = ymd_hms("2021-12-07 12:15:00", tz = 'Australia/Tasmania'), y = 12.5, size = 3, stat = 'identity')+
geom_text(label = 'Live Transport', x = ymd_hms("2021-12-08 00:00:00", tz = 'Australia/Tasmania'), y = 12.5, size = 3, stat = 'identity')+
geom_text(label = 'Processing', x = ymd_hms("2021-12-08 12:25:00", tz = 'Australia/Tasmania'), y = 12.5, size = 3, stat = 'identity', angle = 90)
ggsave(filename = paste('C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/TransportExperiment_LoggerTemperature', '.pdf', sep = ''),
plot = trans_exp_plot, units = 'mm', width = 190, height = 150)
ggsave(filename = paste('C:/Users/jaimem/OneDrive - University of Tasmania/Documents/AB_proteomics/TransportExperiment_LoggerTemperature', '.png', sep = ''),
plot = trans_exp_plot, units = 'mm', width = 190, height = 150)
trans_exp_dat %>%
filter(date_time > ymd_hms("2021-12-07 13:30:00", tz = 'Australia/Tasmania') &
date_time < ymd_hms("2021-12-08 11:30:00", tz = 'Australia/Tasmania')) %>%
summarise(across(where(is.numeric), .fns =
list(Median = median,
Mean = mean,
n = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75)
))) %>%
pivot_longer(everything(), names_sep = "_", names_to = c( "variable", ".value"))