FoxMonitoring_Temperature_Activity/03_Descriptive_analyses_temp.R at main · jtshapiro/FoxMonitoring_Temperature_Activity · GitHub

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####################################################################################################################
# Analysis of implant data from foxes for:
# Shapiro et al.
# Potential for real-time health and welfare monitoring in experimental rabies infection in red fox (Vulpes vulpes)
# using implants
#
# Script 3: For each fox, calculates summary statistics for temperature and performs t-tests to compare
#           calibration and  monitoring periods
#
#
# Uses the following data frames created in Script 01_Load_data.R :
#      df_0fae2, df_0faf2, df_0fb1.2, df_0fb0.2
#
# Script tested for R version 4.1.2
####################################################################################################################


####################################################################################################################
# Load packages ####
####################################################################################################################
# Load packages (install if necessary)

library(tidyverse)
####################################################################################################################


####################################################################################################################
# Fox 0fae ####
####################################################################################################################

#  15min intervals ####
df.0fae.temp.descr <- df_0fae2 %>%
  mutate(timePeriod = floor_date(date_time_min, "15minutes")) %>%
  group_by(timePeriod) %>%
  filter(timePeriod >= "2022-07-9") %>%
  # Note : data collected by implant for some days after death, need to filter out
  # Note : probably due to timezone issue in R or tidyverse must specifiy 19:00 to keep data points up to 17h on 18-08
  filter(timePeriod <= "2022-08-18 19:00:00") %>%
  summarise(Temp = mean(Temp))

# Summary overall
summary(df.0fae.temp.descr)

# Split data into calibration and monitoring periods :
avant_0fae.temp.descr <- df.0fae.temp.descr %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fae.temp.descr <- df.0fae.temp.descr %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each :
summary(avant_0fae.temp.descr)

summary(apres_0fae.temp.descr)

# Daily (24h) ####
df.0fae.temp.descr.24h <- df_0fae2 %>%
  mutate(timePeriod = floor_date(date_time_min, "1day")) %>%
  group_by(timePeriod) %>%
  # Note : data collected by implant for some days after death, need to filter out
  # Note : probably due to timezone issue in R or tidyverse must specifiy 19:00 to keep data points up to 17h on 18-08
  filter(timePeriod <= "2022-08-18 19:00:00") %>%
  summarise(Temp = mean(Temp))

# Summary
summary(df.0fae.temp.descr.24h)

# Split data into calibration and monitoring periods :
avant_0fae.temp.descr.24h <- df.0fae.temp.descr.24h %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fae.temp.descr.24h <- df.0fae.temp.descr.24h %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each
summary(avant_0fae.temp.descr.24h)

summary(apres_0fae.temp.descr.24h)

# t-test (daily 24h)
t.test(avant_0fae.temp.descr.24h$Temp, apres_0fae.temp.descr.24h$Temp)
####################################################################################################################


####################################################################################################################
# Fox 0fb0 ####
####################################################################################################################

#  15min intervals ####
df.0fb0.temp.descr <- df_0fb0.2 %>%
  mutate(timePeriod = floor_date(date_time_min, "15minutes")) %>%
  group_by(timePeriod) %>%
  summarise(Temp = mean(Temp))

# Summary overall
summary(df.0fb0.temp.descr)

# Split data into calibration and monitoring periods :
avant_0fb0.temp.descr <- df.0fb0.temp.descr %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fb0.temp.descr <- df.0fb0.temp.descr %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each
summary(avant_0fb0.temp.descr)

summary(apres_0fb0.temp.descr)

# Daily (24h) ####
df.0fb0.temp.descr.24h <- df_0fb0.2 %>%
  mutate(timePeriod = floor_date(date_time_min, "1day")) %>%
  group_by(timePeriod) %>%
  summarise(Temp = mean(Temp))

# Daily summary
summary(df.0fb0.temp.descr.24h)

# Split data into calibration and monitoring periods :
avant_0fb0.temp.descr.24h <- df.0fb0.temp.descr.24h %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fb0.temp.descr.24h <- df.0fb0.temp.descr.24h %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each
summary(avant_0fb0.temp.descr.24h)

summary(apres_0fb0.temp.descr.24h)

# t-test (daily 24h)
t.test(avant_0fb0.temp.descr.24h$Temp, apres_0fb0.temp.descr.24h$Temp)
####################################################################################################################


####################################################################################################################
# Fox 0faf ####
####################################################################################################################

#  15min intervals ####
df.0faf.temp.descr <- df_0faf2 %>%
  mutate(timePeriod = floor_date(date_time_min, "15minutes")) %>%
  group_by(timePeriod) %>%
  summarise(Temp = mean(Temp))

# Summary overall
summary(df.0faf.temp.descr)

# Split data into calibration and monitoring periods :
avant_0faf.temp.descr <- df.0faf.temp.descr %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0faf.temp.descr <- df.0faf.temp.descr %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each :
summary(avant_0faf.temp.descr)

summary(apres_0faf.temp.descr)

# Daily (24h) ####
df.0faf.temp.descr.24h <- df_0faf2 %>%
  mutate(timePeriod = floor_date(date_time_min, "1day")) %>%
  group_by(timePeriod) %>%
  filter(timePeriod <= "2022-08-18") %>%
  summarise(Temp = mean(Temp))

# Daily summary
summary(df.0faf.temp.descr.24h)

# Split data into calibration and monitoring periods :
avant_0faf.temp.descr.24h <- df.0faf.temp.descr.24h %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0faf.temp.descr.24h <- df.0faf.temp.descr.24h %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each
summary(avant_0faf.temp.descr.24h)

summary(apres_0faf.temp.descr.24h)

# t-test
t.test(avant_0faf.temp.descr.24h$Temp, apres_0faf.temp.descr.24h$Temp)
####################################################################################################################


####################################################################################################################
# Fox 0fb1 ####
####################################################################################################################

#  15min intervals ####
df.0fb1.temp.descr <- df_0fb1.2 %>%
  mutate(timePeriod = floor_date(date_time_min, "15minutes")) %>%
  group_by(timePeriod) %>%
  summarise(Temp = mean(Temp))

# Summary overall
summary(df.0fb1.temp.descr)

# Split data into calibration and monitoring periods :
avant_0fb1.temp.descr <- df.0fb1.temp.descr %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fb1.temp.descr <- df.0fb1.temp.descr %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each :
summary(avant_0fb1.temp.descr)

summary(apres_0fb1.temp.descr)

# Daily (24h) ####
df.0fb1.temp.descr.24h <- df_0fb1.2 %>%
  mutate(timePeriod = floor_date(date_time_min, "1day")) %>%
  group_by(timePeriod) %>%
  summarise(Temp = mean(Temp))

# Daily summary
summary(df.0fb1.temp.descr.24h)

# Split data into calibration and monitoring periods :
avant_0fb1.temp.descr.24h <- df.0fb1.temp.descr.24h %>%
  filter(timePeriod >= "2022-07-9") %>%
  filter(timePeriod <= "2022-07-19")

apres_0fb1.temp.descr.24h <- df.0fb1.temp.descr.24h %>%
  filter(timePeriod > "2022-07-19")

# Summaries for each :
summary(avant_0fb1.temp.descr.24h)

summary(apres_0fb1.temp.descr.24h)

# t-test
t.test(avant_0fb1.temp.descr.24h$Temp, apres_0fb1.temp.descr.24h$Temp)
####################################################################################################################