Thermal Stress on the Zebra Finch ; which deshydratation marker is the best?
HOGNON-PELLETIER Loan
26 mai 2024
Summary
Package, Library & Data upload
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1. Package, Library & Data upload
1.A] Packages & Library
####### Pour le formatage spécifique à RMarkdown #######
#install.packages("knitr")
library(knitr)
####### Pour le formatage des données #######
#install.packages("tidyverse", dependencies = TRUE)
library(tidyverse)## Warning: package 'ggplot2' was built under R version 4.3.2#install.packages("tidyr")
library(tidyr)
#install.packages("dplyr")
library(dplyr)
#install.packages("xtable")
library(xtable)
#install.packages("RMark")
library(kableExtra)
#install.packages("webshot")
library(webshot)
####### Pour les statistiques #######
#install.packages("rstatix")
library(rstatix)
#install.packages("car")
library(car)
#install.packages("rcompanion")
library(rcompanion)
####### Pour les représentation graphiques #######
#install.packages("ggplot2")
library(ggplot2)
#install.packages("ggpubr")
library(ggpubr)
#install.packages("gridExtra")
library(gridExtra)1.B] Data importation
## cloac temperature & body mass
file_path <- "/Users/Lj/Documents/Documents Loan HP/ Études/Master EEE [2023-25]/Master_EEE_S2/Internship = IDSE - Initiation à la démarche scientifique en écophysiologie et éthologie/F] Results_ANALYSE_R/Internship_project/csv_data_files_(27.05.24)/T_BM_data.csv"
T_BM_df <- read.csv2(file_path, header = TRUE)
## Respirometry
file_path <- "/Users/Lj/Documents/Documents Loan HP/ Études/Master EEE [2023-25]/Master_EEE_S2/Internship = IDSE - Initiation à la démarche scientifique en écophysiologie et éthologie/F] Results_ANALYSE_R/Internship_project/csv_data_files_(27.05.24)/RESPIRO_data.csv"
RESPIRO_df <- read.csv2(file_path, header = TRUE)
## Stress oxydatif post calo
file_path <- "/Users/Lj/Documents/Documents Loan HP/ Études/Master EEE [2023-25]/Master_EEE_S2/Internship = IDSE - Initiation à la démarche scientifique en écophysiologie et éthologie/F] Results_ANALYSE_R/Internship_project/csv_data_files_(27.05.24)/StressOX_data.csv"
StressOx_df <- read.csv2(file_path, header = TRUE)
## Cell concentration
file_path <- "/Users/Lj/Documents/Documents Loan HP/ Études/Master EEE [2023-25]/Master_EEE_S2/Internship = IDSE - Initiation à la démarche scientifique en écophysiologie et éthologie/F] Results_ANALYSE_R/Internship_project/csv_data_files_(27.05.24)/Cell_Ct_data.csv"
Cellc_df <- read.csv2(file_path, header = TRUE)2. Data exploration
2.A] Data synthetisation & reorganisation
## 'data.frame': 780 obs. of 22 variables:
## $ ID_ZF : chr "22003" "22003" "22003" "22003" ...
## $ Birthdate_ZF : int 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 ...
## $ Age_ZF : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Sex_ZF : chr "M" "M" "M" "M" ...
## $ Session_calo : chr "S1" "S1" "S1" "S1" ...
## $ Treatment_calo : chr "30" "30" "30" "30" ...
## $ Time : chr "t0" "t1" "t2" "t3" ...
## $ Temperature_time : chr "Tc_t0" "Tc_t1" "Tc_t2" "Tc_t3" ...
## $ MB_time : chr "Mb_t0" "Mb_t1" "Mb_t2" "Mb_t3" ...
## $ ID_ZF_Session : chr "22003_S1" "22003_S1" "22003_S1" "22003_S1" ...
## $ ID_Ses_Treatement: chr "22003_S1_30" "22003_S1_30" "22003_S1_30" "22003_S1_30" ...
## $ ID_Ses_Treat_Temp: chr "22003_S1_30_t0" "22003_S1_30_t1" "22003_S1_30_t2" "22003_S1_30_t3" ...
## $ Ses_Treat : chr "S1_30" "S1_30" "S1_30" "S1_30" ...
## $ Ses_Treat_time : chr "S1_30_t0" "S1_30_t1" "S1_30_t2" "S1_30_t3" ...
## $ INTER_temperature: chr "#N/A" "2" "3" "4" ...
## $ Temperature : num NA 42.6 39.2 41.2 NA ...
## $ INTER_BM : int 6 7 8 9 10 11 6 7 8 9 ...
## $ Body_mass : num 16.2 15.5 15 15 NA ...
## $ Experiment_date : chr "20/03/2024" "20/03/2024" "20/03/2024" "20/03/2024" ...
## $ Commentaires : logi NA NA NA NA NA NA ...
## $ Manipulateurs : chr "Loan HP" "Loan HP" "Loan HP" "Loan HP" ...
## $ N. : int 1 2 3 4 5 6 7 8 9 10 ...## 'data.frame': 780 obs. of 12 variables:
## $ ID_ZF : chr "22003" "22003" "22003" "22003" ...
## $ Age_ZF : Factor w/ 4 levels "1","2","5","6": 2 2 2 2 2 2 2 2 2 2 ...
## $ Sex_ZF : chr "M" "M" "M" "M" ...
## $ Ses_Treat : chr "S1_30" "S1_30" "S1_30" "S1_30" ...
## $ Session_calo : chr "S1" "S1" "S1" "S1" ...
## $ Treatment_calo : chr "30" "30" "30" "30" ...
## $ Time : chr "t0" "t1" "t2" "t3" ...
## $ Ses_Treat_time : chr "S1_30_t0" "S1_30_t1" "S1_30_t2" "S1_30_t3" ...
## $ Temperature : num NA 42.6 39.2 41.2 NA ...
## $ Body_mass : num 16.2 15.5 15 15 NA ...
## $ Experiment_date: chr "20/03/2024" "20/03/2024" "20/03/2024" "20/03/2024" ...
## $ GroupAge : chr "Young" "Young" "Young" "Young" ...## ID_ZF Age_ZF Sex_ZF Ses_Treat
## Length:780 1 :132 Length:780 Length:780
## Class :character 2 :204 Class :character Class :character
## Mode :character 5 :156 Mode :character Mode :character
## 6 :276
## NA's: 12
##
##
## Session_calo Treatment_calo Time Ses_Treat_time
## Length:780 Length:780 Length:780 Length:780
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Temperature Body_mass Experiment_date GroupAge
## Min. :32.10 Min. :11.26 Length:780 Length:780
## 1st Qu.:40.30 1st Qu.:13.46 Class :character Class :character
## Median :41.40 Median :14.24 Mode :character Mode :character
## Mean :41.19 Mean :14.38
## 3rd Qu.:42.30 3rd Qu.:15.10
## Max. :44.20 Max. :21.94
## NA's :281 NA's :109## 'data.frame': 485 obs. of 12 variables:
## $ ID_ZF : chr "22003" "22003" "22003" "22003" ...
## $ Age_ZF : Factor w/ 4 levels "1","2","5","6": 2 2 2 2 2 2 2 2 2 2 ...
## $ Sex_ZF : chr "M" "M" "M" "M" ...
## $ Ses_Treat : chr "S1_30" "S1_30" "S1_30" "S1_30" ...
## $ Session_calo : chr "S1" "S1" "S1" "S1" ...
## $ Treatment_calo : chr "30" "30" "30" "30" ...
## $ Time : chr "t1" "t2" "t3" "t5" ...
## $ Ses_Treat_time : chr "S1_30_t1" "S1_30_t2" "S1_30_t3" "S1_30_t5" ...
## $ Temperature : num 42.6 39.2 41.2 41.2 42.7 ...
## $ Body_mass : num 15.5 15 15 14.8 15.5 ...
## $ Experiment_date: chr "20/03/2024" "20/03/2024" "20/03/2024" "20/03/2024" ...
## $ GroupAge : chr "Young" "Young" "Young" "Young" ...## ID_ZF Age_ZF Sex_ZF Ses_Treat
## Length:485 1 : 83 Length:485 Length:485
## Class :character 2 :135 Class :character Class :character
## Mode :character 5 : 92 Mode :character Mode :character
## 6 :174
## NA's: 1
##
##
## Session_calo Treatment_calo Time Ses_Treat_time
## Length:485 Length:485 Length:485 Length:485
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Temperature Body_mass Experiment_date GroupAge
## Min. :38.10 Min. :11.26 Length:485 Length:485
## 1st Qu.:40.50 1st Qu.:13.29 Class :character Class :character
## Median :41.45 Median :14.10 Mode :character Mode :character
## Mean :41.32 Mean :14.19
## 3rd Qu.:42.30 3rd Qu.:14.87
## Max. :44.20 Max. :21.24
## NA's :2## 'data.frame': 127 obs. of 15 variables:
## $ Date_Exp : chr "04/03/2024" "04/03/2024" "04/03/2024" "04/03/2024" ...
## $ ID_ZF : chr "K18031" "K19179" "22056" "S18006" ...
## $ Sex : chr "M" "M" "F" "M" ...
## $ Category_age : chr "Vieux" "Vieux" "Jeunes" "Vieux" ...
## $ Age : int 6 5 2 6 1 6 2 6 5 2 ...
## $ Session : chr "S1" "S1" "S1" "S1" ...
## $ Treatement : int 30 30 30 30 40 40 40 30 30 30 ...
## $ ID_Session_Treatment: chr "K18031_S1_30" "K19179_S1_30" "22056_S1_30" "S18006_S1_30" ...
## $ VO2..mL.min. : num 0.64 0.72 1 0.78 1.09 1 1.06 1.09 0.69 0.96 ...
## $ VCO2..mL.min. : num 0.39 0.44 0.61 0.46 0.54 0.52 0.54 0.7 0.43 0.61 ...
## $ VCO2_corrige : num 0.45 0.5 0.7 0.54 0.77 0.7 0.74 0.76 0.48 0.67 ...
## $ VH2O..mg.min. : num 0.68 0.38 0.67 0.64 1.84 1.53 1.52 0.47 0.38 0.41 ...
## $ EE : logi NA NA NA NA NA NA ...
## $ QR : num 0.6 0.61 0.61 0.59 0.49 0.52 0.51 0.65 0.62 0.64 ...
## $ QR_corrige : num 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ...## 'data.frame': 127 obs. of 15 variables:
## $ Date_Exp : chr "04/03/2024" "04/03/2024" "04/03/2024" "04/03/2024" ...
## $ ID_ZF : chr "K18031" "K19179" "22056" "S18006" ...
## $ Sex : chr "M" "M" "F" "M" ...
## $ Age : Factor w/ 4 levels "1","2","5","6": 4 3 2 4 1 4 2 4 3 2 ...
## $ Ses_Treat : chr "S1_30" "S1_30" "S1_30" "S1_30" ...
## $ Session : chr "S1" "S1" "S1" "S1" ...
## $ Treatement : Factor w/ 2 levels "30","40": 1 1 1 1 2 2 2 1 1 1 ...
## $ VO2..mL.min. : num 0.64 0.72 1 0.78 1.09 1 1.06 1.09 0.69 0.96 ...
## $ VCO2..mL.min.: num 0.39 0.44 0.61 0.46 0.54 0.52 0.54 0.7 0.43 0.61 ...
## $ VCO2_corrige : num 0.45 0.5 0.7 0.54 0.77 0.7 0.74 0.76 0.48 0.67 ...
## $ VH2O..mg.min.: num 0.68 0.38 0.67 0.64 1.84 1.53 1.52 0.47 0.38 0.41 ...
## $ EE : logi NA NA NA NA NA NA ...
## $ QR : num 0.6 0.61 0.61 0.59 0.49 0.52 0.51 0.65 0.62 0.64 ...
## $ QR_corrige : num 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 ...
## $ GroupAge : chr "Old" "Old" "Young" "Old" ...## Date_Exp ID_ZF Sex Age
## Length:127 Length:127 Length:127 1 :22
## Class :character Class :character Class :character 2 :34
## Mode :character Mode :character Mode :character 5 :24
## 6 :46
## NA's: 1
##
## Ses_Treat Session Treatement VO2..mL.min.
## Length:127 Length:127 30:63 Min. :0.6400
## Class :character Class :character 40:64 1st Qu.:0.8450
## Mode :character Mode :character Median :0.9400
## Mean :0.9672
## 3rd Qu.:1.0600
## Max. :1.8400
## VCO2..mL.min. VCO2_corrige VH2O..mg.min. EE
## Min. :0.3900 Min. :0.4500 Min. :0.320 Mode:logical
## 1st Qu.:0.4900 1st Qu.:0.5900 1st Qu.:0.670 NA's:127
## Median :0.5400 Median :0.6600 Median :1.530
## Mean :0.5607 Mean :0.6772 Mean :1.575
## 3rd Qu.:0.6100 3rd Qu.:0.7400 3rd Qu.:2.305
## Max. :1.0700 Max. :1.2900 Max. :3.560
## QR QR_corrige GroupAge
## Min. :0.4600 Min. :0.7 Length:127
## 1st Qu.:0.5500 1st Qu.:0.7 Class :character
## Median :0.5900 Median :0.7 Mode :character
## Mean :0.5824 Mean :0.7
## 3rd Qu.:0.6200 3rd Qu.:0.7
## Max. :0.6600 Max. :0.72.B] Data representation
2.B] 1. T_BM_df (Temperature & Body mass data)
Il existe des valeurs abberantes pour la température : toutes valeurs <= 38°C ont été removed. La température à prendre est T_BM_df_38sup.
Ne pas prendre en compte le poids dans ce cas, car les valeurs abberrantes du poids peuvent ne pas l’être pour la masse. La masse a prendre est donc du tableau = T_BM_df
2.B] 2. RESPIRO_df : Data on the respirometry experiments