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00_1_monilinia.post_data-retrieve.R
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## ******************************************************************* ##
## ***** DATA LOAD / PRE-PROCESSING AND STATISTICAL ANALYSIS ******** ##
## ******************************************************************* ##
## libraries
{
library(plyr); library(dplyr)
library(tidyr); library(ggplot2); library(broom)
library(Rmisc)
library(graphics)
library(svglite)
library(stats); library(lme4)
library(yaml)
library(deSolve)
library(FME)
library(minpack.lm)
library(GA)
library(directlabels)
library(ggforce)
library(popbio)
library(gridExtra)
library(MASS); library(wesanderson)
library(survival); library(flexsurv); library(survminer); library(mpr); library(survAUC); library(Hmisc) #library(tidysurv)
library(ranger); library(GGally)
library(treemap)
library(pec)
library(data.table)
library(splitstackshape)
#library(simPH)
library(rsample)
library(ROSE)
library(caret)
library(parallel)
library(paletteer); library(wesanderson)
library(splitstackshape)
library(multcompView)
}
rm(list = ls())
gc()
cat('\014')
source("user-defined_functions/f_get_dd.R") # calculate degree-days
source("user-defined_functions/f_get_cdd.R") # calculate cumulative degree-days
## ************************************************************************
# 1) Let's consider the first 2 days at 2°C ? (storage.start = 0, otherwise = 2)
storage.start = 0
# 2) I do not consider the last days of experiments in 2019 ? storage.length2019 = 14, otherwise = 21)
storage.length2019 = 21
# 3) In ** monilinia2018 ** only Syst = S2 (ECO) is considered
# 4) caliber: strong or mild regroup?
strong.cal = T
## *************************************************************************
# full bloom date : 17-03-2018
# 1st harvest date : 12-07-2018
# 2nd harvest date : 19-07-2018
# 3rd harvest date : 27-07-2018
# thinning : 22-05-2018
# irrigation differentiation : 18-06-2018
date_bloom2018_julian = as.numeric( format(as.Date("2018-03-17"),"%j") ) # julian date of full blooming day
dafb_thinning2018 = as.numeric( format(as.Date("2018-05-22"),"%j") ) - date_bloom2018_julian # thinning day (in DAFB)
dafb_irrigation2018 = as.numeric( format(as.Date("2018-06-18"),"%j") ) - date_bloom2018_julian # start of differentiation in irrigation regimes (in DAFB)
dafb_1harvest2018 = as.numeric( format(as.Date("2018-07-12"),"%j") ) - date_bloom2018_julian
dafb_2harvest2018 = as.numeric( format(as.Date("2018-07-19"),"%j") ) - date_bloom2018_julian
dafb_3harvest2018 = as.numeric( format(as.Date("2018-07-27"),"%j") ) - date_bloom2018_julian
# full bloom date : 10-03-2019
# 1st harvest date : 22-07-2019
# 2nd harvest date : 29-07-2019
date_bloom2019 = as.Date("19-03-10")
date_bloom2019_julian = as.numeric( format(as.Date("2019-03-10"),"%j") )
dafb_1harvest2019 = as.numeric( format(as.Date("2019-07-22"),"%j") ) - date_bloom2019_julian
dafb_2harvest2019 = as.numeric( format(as.Date("2019-07-29"),"%j") ) - date_bloom2019_julian
## ***************************************************
## PRE-HARVEST ENVIRONMENTAL CONDITIONS
## ***************************************************
{
environconditions_pre_2018 = read.table("data/Avignon2018_daily_INRA_STATION_84007004.csv", dec=".", sep=";", header=TRUE, skip = 13)%>%
mutate( date_j = as.numeric( format(as.Date(paste0(year,"-",month,"-",day)),"%j") ),
date_dafb = date_j - date_bloom2018_julian,
date = format(as.Date(paste0( day,'/',month,'/',substr(year,3,4) ), format = "%d/%m/%y" ), "%d/%m/%Y"), date=as.character(date) ) %>%
filter(date_dafb>=0)
dd18 = vector()
for (i in 1:dim(environconditions_pre_2018)[1]) { dd18[i] = f_get_dd(Tmax = environconditions_pre_2018$t_max[i], Tmin=environconditions_pre_2018$t_min[i]) }
environconditions_pre_2018 = cbind(environconditions_pre_2018, deg_day = dd18)%>%
mutate( cum_deg_day = cumsum(dd18) )
# create different meteorological index (with different lag times) to test for the statistical models
environconditions_pre_2018 = environconditions_pre_2018 %>%
mutate(
l.wd2=lag(wetness_duration_h, 2),
l.wd3=lag(wetness_duration_h, 3),
l.wd4=lag(wetness_duration_h, 4),
l.wd5=lag(wetness_duration_h, 5),
l.wd6=lag(wetness_duration_h, 6),
l.wd7=lag(wetness_duration_h, 7),
wd1 = lag(wetness_duration_h, 1),
wd2 = (wd1 + l.wd2)/2,
wd3 = (wd1 + l.wd2 + l.wd3)/3,
wd4 = (wd1 + l.wd2 + l.wd3 + l.wd4)/4,
wd5 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5)/5,
wd6 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5 + l.wd6)/6,
wd7 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5 + l.wd6 + l.wd7 )/7,
rh1 = (lag(RH, 1)),
rh2 = (rh1 + lag(RH, 2))/2,
rh3 = (rh2 + lag(RH, 3))/2,
rh4 = (rh3 + lag(RH, 4))/2,
rh5 = (rh4 + lag(RH, 5))/2,
rh6 = (rh5 + lag(RH, 6))/2,
rh7 = (rh6 + lag(RH, 7))/2,
rain1 = lag(rain_mm, 1),
# rain2 = rain1 + lag(rain_mm, 2),
# rain3 = rain2 + lag(rain_mm, 3),
# rain4 = rain3 + lag(rain_mm, 4),
# rain5 = rain4 + lag(rain_mm, 5),
# rain6 = rain5 + lag(rain_mm, 6),
# rain7 = rain6 + lag(rain_mm, 7),
rain2 = (rain1 + lag(rain_mm, 2))/2,
rain3 = (rain2 + lag(rain_mm, 3))/2,
rain4 = (rain3 + lag(rain_mm, 4))/2,
rain5 = (rain4 + lag(rain_mm, 5))/2,
rain6 = (rain5 + lag(rain_mm, 6))/2,
rain7 = (rain6 + lag(rain_mm, 7))/2,
t1 = (lag(t_mean, 1)),
t2 = (t1 + lag(t_mean, 2))/2,
t3 = (t2 + lag(t_mean, 3))/2,
t4 = (t3 + lag(t_mean, 4))/2,
t5 = (t4 + lag(t_mean, 5))/2,
t6 = (t5 + lag(t_mean, 6))/2,
t7 = (t6 + lag(t_mean, 7))/2 )
temp.max.list = shift(environconditions_pre_2018$t_max, n=1:7, type="lag")
temp.min.list = shift(environconditions_pre_2018$t_min, n=1:7, type="lag")
environconditions_pre_2018$tmin1 = apply( t(matrix(unlist(temp.min.list[1]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin2 = apply( t(matrix(unlist(temp.min.list[1:2]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin3 = apply( t(matrix(unlist(temp.min.list[1:3]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin4 = apply( t(matrix(unlist(temp.min.list[1:4]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin5 = apply( t(matrix(unlist(temp.min.list[1:5]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin6 = apply( t(matrix(unlist(temp.min.list[1:6]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmin7 = apply( t(matrix(unlist(temp.min.list), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2018$tmax1 = apply( t(matrix(unlist(temp.max.list[1]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax2 = apply( t(matrix(unlist(temp.max.list[1:2]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax3 = apply( t(matrix(unlist(temp.max.list[1:3]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax4 = apply( t(matrix(unlist(temp.max.list[1:4]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax5 = apply( t(matrix(unlist(temp.max.list[1:5]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax6 = apply( t(matrix(unlist(temp.max.list[1:6]), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2018$tmax7 = apply( t(matrix(unlist(temp.max.list), ncol = dim(environconditions_pre_2018)[1], byrow = TRUE)),
1, max, na.rm=T)
## ****** 2019
environconditions_pre_2019 = read.table("data/Avignon2019_daily_INRA_STATION_84007004.csv", dec=".", sep=";", header=TRUE, skip = 13)%>%
mutate( date_j = as.numeric( format(as.Date(paste0(year,"-",month,"-",day)),"%j") ),
date_dafb = date_j - date_bloom2019_julian,
date = format(as.Date(paste0( day,'/',month,'/',substr(year,3,4) ), format = "%d/%m/%y" ), "%d/%m/%Y"), date=as.character(date) )%>%
filter(date_dafb>=0)
dd19 = vector()
for (i in 1:dim(environconditions_pre_2019)[1]) { dd19[i] = f_get_dd(Tmax = environconditions_pre_2019$t_max[i], Tmin=environconditions_pre_2019$t_min[i]) }
environconditions_pre_2019 = cbind(environconditions_pre_2019, deg_day = dd19)%>%
mutate( cum_deg_day = cumsum(dd19) )
# create different index to test for the statistical models
environconditions_pre_2019 = environconditions_pre_2019 %>%
mutate(
l.wd2=lag(wetness_duration_h, 2),
l.wd3=lag(wetness_duration_h, 3),
l.wd4=lag(wetness_duration_h, 4),
l.wd5=lag(wetness_duration_h, 5),
l.wd6=lag(wetness_duration_h, 6),
l.wd7=lag(wetness_duration_h, 7),
wd1 = lag(wetness_duration_h, 1),
wd2 = (wd1 + l.wd2)/2,
wd3 = (wd1 + l.wd2 + l.wd3)/3,
wd4 = (wd1 + l.wd2 + l.wd3 + l.wd4)/4,
wd5 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5)/5,
wd6 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5 + l.wd6)/6,
wd7 = (wd1 + l.wd2 + l.wd3 + l.wd4 + l.wd5 + l.wd6 + l.wd7 )/7,
rh1 = (lag(RH, 1)),
rh2 = (rh1 + lag(RH, 2))/2,
rh3 = (rh2 + lag(RH, 3))/2,
rh4 = (rh3 + lag(RH, 4))/2,
rh5 = (rh4 + lag(RH, 5))/2,
rh6 = (rh5 + lag(RH, 6))/2,
rh7 = (rh6 + lag(RH, 7))/2,
rain1 = lag(rain_mm, 1),
# rain2 = rain1 + lag(rain_mm, 2),
# rain3 = rain2 + lag(rain_mm, 3),
# rain4 = rain3 + lag(rain_mm, 4),
# rain5 = rain4 + lag(rain_mm, 5),
# rain6 = rain5 + lag(rain_mm, 6),
# rain7 = rain6 + lag(rain_mm, 7),
rain2 = (rain1 + lag(rain_mm, 2))/2,
rain3 = (rain2 + lag(rain_mm, 3))/2,
rain4 = (rain3 + lag(rain_mm, 4))/2,
rain5 = (rain4 + lag(rain_mm, 5))/2,
rain6 = (rain5 + lag(rain_mm, 6))/2,
rain7 = (rain6 + lag(rain_mm, 7))/2,
t1 = (lag(t_mean, 1)),
t2 = (t1 + lag(t_mean, 2))/2,
t3 = (t2 + lag(t_mean, 3))/2,
t4 = (t3 + lag(t_mean, 4))/2,
t5 = (t4 + lag(t_mean, 5))/2,
t6 = (t5 + lag(t_mean, 6))/2,
t7 = (t6 + lag(t_mean, 7))/2
)
temp.max.list = shift(environconditions_pre_2019$t_max, n=1:7, type="lag")
temp.min.list = shift(environconditions_pre_2019$t_min, n=1:7, type="lag")
environconditions_pre_2019$tmin1 = apply( t(matrix(unlist(temp.min.list[1]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin2 = apply( t(matrix(unlist(temp.min.list[1:2]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin3 = apply( t(matrix(unlist(temp.min.list[1:3]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin4 = apply( t(matrix(unlist(temp.min.list[1:4]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin5 = apply( t(matrix(unlist(temp.min.list[1:5]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin6 = apply( t(matrix(unlist(temp.min.list[1:6]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmin7 = apply( t(matrix(unlist(temp.min.list), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, min, na.rm=T)
environconditions_pre_2019$tmax1 = apply( t(matrix(unlist(temp.max.list[1]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax2 = apply( t(matrix(unlist(temp.max.list[1:2]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax3 = apply( t(matrix(unlist(temp.max.list[1:3]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax4 = apply( t(matrix(unlist(temp.max.list[1:4]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax5 = apply( t(matrix(unlist(temp.max.list[1:5]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax6 = apply( t(matrix(unlist(temp.max.list[1:6]), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
environconditions_pre_2019$tmax7 = apply( t(matrix(unlist(temp.max.list), ncol = dim(environconditions_pre_2019)[1], byrow = TRUE)),
1, max, na.rm=T)
rm('temp.max.list','temp.min.list')
}
## ***************************************************
## STORAGE CONDITIONS
## ***************************************************
{
## --------------- 2018 --------------- ##
environconditions_post_2018 <- read.table("data/environconditions_post_2018.csv", dec=".", sep=";", header=TRUE) %>%
mutate( harvest_date = as.character(as.Date(harvest_date, "%Y-%m-%d" )) )
## --------------- 2019 --------------- ##
capteurs <- read.table("data/capteurs_total_postharvest2019.csv", dec=".", sep=";", header=TRUE) %>%
mutate( date_time_gmt = as.POSIXct(as.character(date_time_gmt_plus2), format= "%d/%m/%y %H:%M:%S", tz="GMT+2"),
date= as.Date(date),
sensor_HOBO = as.factor(sensor_HOBO),
T_factor=as.factor(T_factor) )
# filter( (date >= date_1harvest19 & date <= j10_harvest1) )
## ------------------- DAILY MEAN VALUES ------------------- ##
{
capteurs_daily = capteurs %>%
group_by( date, T_factor ) %>% dplyr::summarize( T_C_mean = mean(T_C, na.rm=T), T_C_sd = sd(T_C, na.rm=T), Ha_mean = mean(Ha, na.rm=T), Ha_sd = sd(Ha, na.rm=T) )%>%
ungroup()%>% droplevels()
capteurs_hour = capteurs %>%
group_by( date, time, T_factor ) %>% dplyr::summarize( T_C = mean(T_C, na.rm=T), Ha = mean(Ha, na.rm=T) )%>%
ungroup()%>% droplevels()
}
# start and end date of the storage period (2019 PH)
storage_start_date = as.Date("19-07-22")
storage_differentiation_date = as.Date("19-07-24")
storage_end_date = as.Date("19-08-01")
# set specific conditions
clim_2C_beforedifferentiation = capteurs_daily %>% filter( date < storage_differentiation_date & T_factor=="2")
clim_15_afterdifferentiation = capteurs_daily %>% filter( (date <= storage_end_date & date >= storage_differentiation_date ) & T_factor=="15")
clim_25_afterdifferentiation = capteurs_daily %>% filter( (date <= storage_end_date & date >= storage_differentiation_date ) & T_factor=="25")
## --------storage conditions (3 temperature levels) --------------------- ##
clim_02C = capteurs_daily %>% filter( date <= storage_end_date & T_factor=="2") %>% mutate(temp = "02", T_factor=NULL, T_cumul = cumsum(T_C_mean))
clim_15C = rbind(clim_2C_beforedifferentiation, clim_15_afterdifferentiation ) %>% mutate(temp = "15", T_factor=NULL, T_cumul = cumsum(T_C_mean))
clim_25C = rbind(clim_2C_beforedifferentiation, clim_25_afterdifferentiation ) %>% mutate(temp = "25", T_factor=NULL, T_cumul = cumsum(T_C_mean))
## daily mean storage conditions for 2019 postharvest biochemical experimentations
environconditions_post_2019 = rbind(clim_02C, clim_15C, clim_25C) %>% mutate(date=as.character(date))
## ----------------------------------------------------------------------- ##
rm("clim_02C", "clim_15C", "clim_25C", "clim_15_afterdifferentiation", "clim_25_afterdifferentiation", "clim_2C_beforedifferentiation",
"capteurs", "capteurs_hour")
}
## ***************************************************
## PREHARVEST ENVIRONMENTAL CONDITIONS - plots
## ***************************************************
{
# p.temp = ggplot()+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, t_mean), color='red')+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, t_max), color='red', alpha=0.3)+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, t_min), color='red', alpha=0.3)+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, t_mean), color='blue')+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, t_max), color='blue', alpha=0.3)+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, t_min), color='blue', alpha=0.3)+
# geom_vline(xintercept = dafb_1harvest2018, color='red')+
# geom_vline(xintercept = dafb_2harvest2018, color='red')+
# geom_vline(xintercept = dafb_3harvest2018, color='red')+
# geom_vline(xintercept = dafb_1harvest2019, color='blue')+
# geom_vline(xintercept = dafb_2harvest2019, color='blue')+
# theme_pubr()
# p.temp
#
# p.rh = ggplot()+
# ylim(c(30,100))+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, RH), color='red')+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, RH), color='blue')+
# geom_vline(xintercept = dafb_1harvest2018, color='red')+
# geom_vline(xintercept = dafb_2harvest2018, color='red')+
# geom_vline(xintercept = dafb_3harvest2018, color='red')+
# geom_vline(xintercept = dafb_1harvest2019, color='blue')+
# geom_vline(xintercept = dafb_2harvest2019, color='blue')+
# theme_pubr()
# p.rh
#
# p.rain = ggplot()+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, rain_mm), color='red')+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, rain_mm), color='blue')+
# geom_vline(xintercept = dafb_1harvest2018, color='red')+
# geom_vline(xintercept = dafb_2harvest2018, color='red')+
# geom_vline(xintercept = dafb_3harvest2018, color='red')+
# geom_vline(xintercept = dafb_1harvest2019, color='blue')+
# geom_vline(xintercept = dafb_2harvest2019, color='blue')+
# theme_pubr()
# p.rain
#
#
# p.wd = ggplot()+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, wetness_duration_h), color='red')+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, wetness_duration_h), color='blue')+
# geom_vline(xintercept = dafb_1harvest2018, color='red')+
# geom_vline(xintercept = dafb_2harvest2018, color='red')+
# geom_vline(xintercept = dafb_3harvest2018, color='red')+
# geom_vline(xintercept = dafb_1harvest2019, color='blue')+
# geom_vline(xintercept = dafb_2harvest2019, color='blue')+
# theme_pubr()
# p.wd
#
# ggplot()+
# geom_point(environconditions_pre_2018, mapping = aes(RH, wetness_duration_h), color='red')+
# geom_point(environconditions_pre_2019, mapping = aes(RH, wetness_duration_h), color='blue')+
# theme_pubr()
#
#
# p.cdd = ggplot()+
# geom_line(environconditions_pre_2018, mapping = aes(date_dafb, cum_deg_day), color='red')+
# geom_line(environconditions_pre_2019, mapping = aes(date_dafb, cum_deg_day), color='blue')+
# geom_vline(xintercept = dafb_1harvest2018, color='red')+
# geom_vline(xintercept = dafb_2harvest2018, color='red')+
# geom_vline(xintercept = dafb_3harvest2018, color='red')+
# geom_vline(xintercept = dafb_1harvest2019, color='blue')+
# geom_vline(xintercept = dafb_2harvest2019, color='blue')+
# theme_pubr()
# p.cdd
}
## ****************************************************************
## DATAFRAMES HARVEST - Ecopêche 18-19 Avignon (Daniel Plenet)
## ***************************************************************
{
## ********** 2018 ##
harvest_caliber18 <- read.table("./data/Harvest_caliber_2018.csv", dec=".", sep=";", header=TRUE) %>%
mutate( Harvest=as.factor(Harvest), Tree=as.factor(Tree),
perc_rot=round( (N_rot_cal/N_tot_cal)*100, 1 ),
Syst=as.factor(substr(Mod,1,2)), Exp=as.factor(substr(Mod,3,4)),
Irr= as.factor(ifelse( Exp=="M1" | Exp=="M2", '100%',
ifelse(Exp=="M3" | Exp=='M4', '50%', NA) ) ),
FL= as.factor(ifelse( Exp=="M1" | Exp=="M3", '400',
ifelse(Exp=="M2" | Exp=='M4', '200', NA) ) )
) %>%
group_by(Harvest, Syst, Mod, Tree) %>%
mutate( perc_cal = round( N_tot_cal/sum(N_tot_cal)*100, 1 ),
perc_rot_rel = round((perc_rot/100)*(perc_cal/100),2) # (percentual of rotten fruit for caliber i) X (number of fruits from caliber i over harvested total)
)%>%
ungroup()
levels(harvest_caliber18$Cal) <- c("C","B","A","2A","3A")
harvest_caliber18$perc_rot_rel[is.nan(harvest_caliber18$perc_rot_rel)]=NA
harvest_caliber18$perc_rot[is.nan(harvest_caliber18$perc_rot)]=NA
harvest_rot18 = harvest_caliber18 %>% group_by(Syst, Harvest) %>%
dplyr::summarize( n_tot = sum(N_sain_cal,na.rm = T), n_rot = sum(N_rot_cal , na.rm = T) ) %>% mutate( perc_rot = n_rot/n_tot*100 ) %>% droplevels()
## ********** 2019 ##
harvest_caliber19 <- read.table("./data/Harvest_caliber_2019.csv", dec=".", sep=";", header=TRUE)
harvest_rot19 = harvest_caliber19 %>% group_by(Syst, Harvest) %>%
dplyr::summarize( n_tot = sum(N_sain_cal,na.rm = T), n_rot = sum(N_rot_cal , na.rm = T) ) %>% mutate( perc_rot = n_rot/n_tot*100 ) %>% droplevels()
}
## ***************************************************
## LOAD BROWN ROT DATA
## ***************************************************
{
## ************************************************************** ##
## 2018
## ************************************************************** ##
{
monilinia18 = read.table("data/nectarlove_monilinia_post_2018.csv", dec=".", sep=";", header=TRUE) %>%
mutate(
box = as.factor(box), n_fruit=as.factor(n_fruit), dah = as.factor(dah), harvest_n=as.factor(harvest_n),
harvest_date = as.character(harvest_date),
id.fruit = paste0(harvest_n, ".", mod, ".", box, ".", n_fruit),
Syst = as.factor(substr(mod,1,2)),
Exp = as.factor(substr(mod,3,4)),
Irr= as.factor(ifelse( Exp=="M1" | Exp=="M2", '100%',
ifelse(Exp=="M3" | Exp=='M4', '50%', NA) ) ),
FL= as.factor(ifelse( Exp=="M1" | Exp=="M3", '400',
ifelse(Exp=="M2" | Exp=='M4', '200', NA) ) )
)
# length( unique(monilinia18[monilinia18$harvest_n=="1" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="2" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="3" & monilinia18$Syst=="S2",]$id.fruit ) )
monilinia18 <- monilinia18[monilinia18$infection!="Absent ",]
monilinia18 <- monilinia18[monilinia18$infection!="Absent",]
# length( unique(monilinia18[monilinia18$harvest_n=="1" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="2" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="3" & monilinia18$Syst=="S2",]$id.fruit ) )
## *****************************
## TABLE FRUIT SIZE
poidsmon = read.table("./data/PH_PoidsMon_2018.csv", dec=".", sep=";", header=TRUE) %>%
mutate(
Box=as.factor(Box), Tree=as.factor(Tree), Fruit=as.factor(Fruit), Harvest=as.factor(Harvest),
# Weight=as.numeric(as.character(Weight)),
id.fruit = paste0(Harvest, ".", Mod, ".", Box, ".", Fruit),
# Syst=substr(Mod,1,2),
H_Date=NULL)
colnames(poidsmon)[colnames(poidsmon)=="X0" ] <- 0
colnames(poidsmon)[colnames(poidsmon)=="X4" ] <- 4
colnames(poidsmon)[colnames(poidsmon)=="X6" ] <- 6
colnames(poidsmon)[colnames(poidsmon)=="X12"] <- 12
## *****************************
## TABLE MERGE BROWN ROT - FRUIT SIZE
monilinia18 <- merge(monilinia18, poidsmon, by.x ="id.fruit", by.y ="id.fruit" ) %>%
mutate(
weight_g = as.numeric(Weight), Weight = NULL, H_Date=NULL,
dah.int = as.numeric(as.character(dah)),
temp = 20,
caliber = ifelse( weight_g >= 65 & weight_g < 85, "D",
ifelse( weight_g >= 85 & weight_g < 105, "C",
ifelse( weight_g >= 105 & weight_g < 135, "B",
ifelse( weight_g >= 135 & weight_g < 180, "A",
ifelse( weight_g >= 180 & weight_g < 220, "2A",
ifelse( weight_g >= 220 & weight_g < 300, "3A",
ifelse( weight_g >= 300 , "4A",NA ))))))),
caliber = as.factor(caliber),
# caliber = car::recode(caliber, "c('B','C','D') = 'D+C+B' ; c('2A','3A','4A') = '2A+3A+4A' "),
caliber = car::recode(caliber, "c('C','D') = 'D+C' ; c('3A','4A') = '3A+4A' ")
) %>%
droplevels()
monilinia18 = monilinia18 %>% filter( !is.na(caliber) ) %>% droplevels()
# monilinia18$caliber = factor(monilinia18$caliber, levels=c("D+C+B","A", "2A+3A+4A") )
monilinia18$caliber = factor(monilinia18$caliber, levels=c("D+C","B","A", "2A", "3A+4A") )
rm('poidsmon')
monilinia18 =
merge(monilinia18, environconditions_pre_2018, by.x = "harvest_date", by.y="date") %>% droplevels() %>%
merge( harvest_rot18, by.x = c("harvest_n","Syst"), by.y = c("Harvest","Syst") ) %>% droplevels() %>%
# filter( dah.int >= storage.start ) %>% mutate( dah.int = dah.int - storage.start )
filter( dah.int >= storage.start & Syst == "S2") %>%
mutate( dah.int = dah.int - storage.start )
# length( unique(monilinia18[monilinia18$harvest_n=="1" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="2" & monilinia18$Syst=="S2",]$id.fruit ) )
# length( unique(monilinia18[monilinia18$harvest_n=="3" & monilinia18$Syst=="S2",]$id.fruit ) )
## *****************************
## TABLE SURVIVAL ANALYSIS
un_id = unique(monilinia18$id.fruit)
mon18 = monilinia18 %>% dplyr::select(c('id.fruit','harvest_n', 'temp', 'weight_g', 'year',
'cum_deg_day', 'RH', "t_mean", 'date_dafb', 'harvest_date', 'perc_rot', 'Irr', 'FL',
'rain_mm', 'rain1', 'rain2', 'rain3', 'rain4', 'rain5', 'rain6', 'rain7',
't1', 't2','t3','t4', 't5', 't6', 't7',
'rh1', 'rh2', 'rh3', 'rh4', 'rh5', 'rh6', 'rh7',
'tmin1', 'tmin2', 'tmin3', 'tmin4', 'tmin5', 'tmin6', 'tmin7',
'tmax1', 'tmax2', 'tmax3', 'tmax4', 'tmax5', 'tmax6', 'tmax7',
'wetness_duration_h', 'wd1', 'wd2', 'wd3', 'wd4', 'wd5', 'wd6', 'wd7')) %>% distinct()
time_status = c()
for ( i in 1:length(unique(monilinia18$id.fruit)) ){
m = monilinia18[ monilinia18$id.fruit == un_id[i], ]
m = m[order(m$dah.int),]
mm = min(which(m$infection==1))
if (mm<Inf){
tim = m[mm,'dah.int']
status = 1
} else {
tim = max(m$dah.int)
status = 0
}
time_status = rbind(time_status, c('id.fruit' = un_id[i],
'time' = tim,
'status' = status))
}
mon18 = merge(mon18, time_status, by='id.fruit')
mon18$time = as.numeric(mon18$time)
mon18$status = as.numeric(mon18$status)
mon18$id.fruit = as.factor(mon18$id.fruit)
mon.survival18 = mon18
length( unique(mon.survival18[mon.survival18$harvest_n=="1",]$id.fruit ) )
length( unique(mon.survival18[mon.survival18$harvest_n=="2",]$id.fruit ) )
length( unique(mon.survival18[mon.survival18$harvest_n=="3",]$id.fruit ) )
length( unique(mon.survival18$id.fruit ) )
rm("mon18", "m")
## *****************************
## PH model calibration DB
monilinia_stat.caliber18 = monilinia18 %>%
group_by(harvest_n, dah, caliber, temp, year, harvest_date) %>%
dplyr::summarize( mean_w = mean( weight_g, na.rm = T ),
# incidence.test = sum( infection=="1" | infection=="2", na.rm = T ),
n_tot = length(infection),
incidence = sum( infection=="1" | infection=="2", na.rm = T ) /( length(infection)),
n_I = sum( infection=="1" | infection=="2", na.rm = T ),
wd4 = mean(wd4,na.rm=T),
temp4 = mean(t4) ,
rot = mean(perc_rot),
cum_deg_day = mean(cum_deg_day, na.rm=T)
) %>%
ungroup() %>%
mutate ( dah_int = as.numeric(as.character(dah)),
temp = as.factor(temp) ) %>% droplevels()
# ggplot(monilinia_stat.caliber18)+
# scale_colour_brewer(palette = "Set1")+
# # scale_colour_brewer(palette = wes_palette(name="Zissou1", type = "continuous"))+
# geom_line(mapping = aes(dah_int, n_tot - n_I), size=1.5) +
# geom_line(mapping = aes(dah_int, n_tot), size = 1.2)+
# ylab('Fruit number')+
# xlab('Days after harvest')+
# facet_grid(as.factor(cum_deg_day)~caliber)+
# theme_bw(base_size = 18)+
# theme(legend.position="bottom")+
# guides(shape = guide_legend(title = "Storage temperature"),
# color = guide_legend(title = "Storage temperature") )
}
## ************************************************************** ##
## 2019
## ************************************************************** ##
{
monilinia19 = read.table("./data/nectarlove_monilinia_post_2019.csv", dec=".", sep=";", header=TRUE) %>%
mutate(temp=as.factor(temp), irr=as.factor(irr), fruit_n=as.factor(fruit_n),
dah.int = dah, dah = as.factor(dah),
harvest_n = as.factor(harvest_n),
weight_g = as.numeric(as.character(weight)), weight = NULL,
mod = as.factor(paste0(temp,"_",irr)),
# fruit unique id
id.fruit = paste0(harvest_n, ".", temp, ".", irr, ".", box, ".", fruit_n),
caliber = ifelse( weight_g >= 65 & weight_g < 85, "D",
ifelse( weight_g >= 85 & weight_g < 105, "C",
ifelse( weight_g >= 105 & weight_g < 135, "B",
ifelse( weight_g >= 135 & weight_g < 180, "A",
ifelse( weight_g >= 180 & weight_g < 220, "2A",
ifelse( weight_g >= 220 & weight_g < 300, "3A",
ifelse( weight_g >= 300 , "4A",NA ))))))),
caliber = as.factor(caliber),
# caliber = car::recode(caliber, "c('B','C','D') = 'D+C+B' ; c('2A','3A','4A') = '2A+3A+4A' "),
caliber = car::recode(caliber, "c('C','D') = 'D+C' ; c('3A','4A') = '3A+4A' ")
)
monilinia19 = monilinia19 %>% filter( !is.na(caliber) ) %>% droplevels()
# monilinia19$caliber = factor(monilinia19$caliber, levels=c("D+C+B","A", "2A+3A+4A") )
monilinia19$caliber = factor(monilinia19$caliber, levels=c("D+C","B","A", "2A", "3A+4A") )
length( unique(monilinia19[monilinia19$harvest_n=="1",]$id.fruit ) )
length( unique(monilinia19[monilinia19$harvest_n=="2",]$id.fruit ) )
length( unique(monilinia19$id.fruit ) )
monilinia19 = merge(monilinia19, environconditions_pre_2019, by.x = "harvest_date", by.y="date") %>%
merge( harvest_rot19, by.x = "harvest_n", by.y = "Harvest" ) %>%
filter( dah.int >= storage.start & dah.int <= storage.length2019 ) %>% droplevels() %>%
mutate( dah.int = dah.int - storage.start )
## *****************************
## dataframe for survival analysis
un_id = unique(monilinia19$id.fruit)
mon19 = monilinia19 %>% dplyr::select(c('id.fruit', 'harvest_n', 'temp','weight_g', 'year',
'cum_deg_day', 'RH', "t_mean", 'date_dafb', 'harvest_date', 'perc_rot', 'irr',
'rain_mm', 'rain1', 'rain2', 'rain3', 'rain4', 'rain5', 'rain6', 'rain7',
't_mean', 't1', 't2','t3','t4', 't5', 't6', 't7',
'rh1', 'rh2', 'rh3', 'rh4', 'rh5', 'rh6', 'rh7',
'tmin1', 'tmin2', 'tmin3', 'tmin4', 'tmin5', 'tmin6', 'tmin7',
'tmax1', 'tmax2', 'tmax3', 'tmax4', 'tmax5', 'tmax6', 'tmax7',
'wetness_duration_h', 'wd1', 'wd2', 'wd3', 'wd4', 'wd5', 'wd6', 'wd7')) %>% distinct()
time_status = c()
for ( i in 1:length(unique(monilinia19$id.fruit)) ){
m = monilinia19[ monilinia19$id.fruit == un_id[i], ]
m = m[order(m$dah.int),]
mm = min(which(m$infection==1))
if (mm<Inf){
tim = m[mm,'dah.int']
status = 1
} else {
tim = max(m$dah.int)
status = 0
}
time_status = rbind(time_status, c('id.fruit' = un_id[i],
'time' = tim,
'status' = status))
}
mon19 = merge(mon19, time_status, by='id.fruit')
mon19$time = as.numeric(mon19$time)
mon19$status = as.numeric(mon19$status)
mon19$id.fruit = as.factor(mon19$id.fruit)
mon.survival19 = mon19%>%
mutate( temp = as.numeric(as.character(substr(temp,2,3))) )
rm("mon19","m")
length( unique(mon.survival19[mon.survival19$harvest_n=="1",]$id.fruit ) )
length( unique(mon.survival19[mon.survival19$harvest_n=="2",]$id.fruit ) )
## *****************************
## PH model calibration DB
monilinia_stat.caliber19 = monilinia19 %>%
group_by(harvest_n, dah, caliber, temp, year, harvest_date) %>%
dplyr::summarize(
mean_w = mean( weight_g, na.rm = T ),
# incidence.test = sum( infection=="1" | infection=="2", na.rm = T ),
n_tot = length(infection),
incidence = sum( infection=="1" | infection=="2", na.rm = T ) /( length(infection)),
n_I = sum( infection=="1" | infection=="2", na.rm = T ),
wd4 = mean(wd4,na.rm=T),
temp4 = mean(t4) ,
rot = mean(perc_rot),
cum_deg_day = mean(cum_deg_day, na.rm=T)
) %>%
ungroup() %>%
mutate ( dah_int = as.numeric(as.character(dah)),
temp = as.factor(substr(temp,2,3)) ) %>% droplevels()
# ggplot(monilinia_stat.caliber19)+
# scale_colour_brewer(palette = "Set1")+
# # scale_colour_brewer(palette = wes_palette(name="Zissou1", type = "continuous"))+
# geom_line(mapping = aes(dah_int, n_tot - n_I, colour=temp), size=1.5) +
# geom_line(mapping = aes(dah_int, n_tot, colour=temp), size = 1.2)+
# ylab('Fruit number')+
# xlab('Days after harvest')+
# facet_grid(as.factor(cum_deg_day)~caliber)+
# theme_bw(base_size = 18)+
# theme(legend.position="bottom")+
# guides(shape = guide_legend(title = "Storage temperature"),
# color = guide_legend(title = "Storage temperature") )
## *****************************
## PH model calibration DB
# monilinia_stat.caliber = rbind(monilinia_stat.caliber19, monilinia_stat.caliber18) %>%
# mutate ( dah_int = as.numeric(as.character(dah)) )
# # mutate(temp = as.numeric(as.character(temp)) )
}
}
## ************************************************************** ##
## MONILINIA SURVIVAL DATAFRAME //// 2018 + 2019 ////
## ************************************************************** ##
{
enviropost19 = environconditions_post_2019 %>%
group_by(temp)%>%dplyr::summarize(Ha = mean(Ha_mean)/100)%>%ungroup() %>%
mutate(temp = as.numeric(as.character(temp)))
mon.survival19 = merge(mon.survival19, enviropost19)
mon.survival18$Ha=NA
# mon.survival = rbind(mon.survival18, mon.survival19) %>%
mon.survival = rbind(mon.survival18%>%dplyr::select(-Irr,-FL),
mon.survival19%>%dplyr::select(-irr) ) %>%
mutate(
caliber = ifelse( weight_g >= 65 & weight_g < 85, "D",
ifelse( weight_g >= 85 & weight_g < 105, "C",
ifelse( weight_g >= 105 & weight_g < 135, "B",
ifelse( weight_g >= 135 & weight_g < 180, "A",
ifelse( weight_g >= 180 & weight_g < 220, "2A",
ifelse( weight_g >= 220 & weight_g < 300, "3A",
ifelse( weight_g >= 300 , "4A",NA ))))))),
caliber = as.factor(caliber),
# caliber = car::recode(caliber, "c('B','C','D') = 'D+C+B' ; c('2A','3A','4A') = '2A+3A+4A' "),
caliber = car::recode(caliber, "c('C','D') = 'D+C' ; c('3A','4A') = '3A+4A' ") ,
## interaction term 'mean temperature before harvest' x 'cumulative wetness duration' on the # days before harvest
wd_x_t = wetness_duration_h*t_mean,
wd_x_t2 = wd2*t2,
wd_x_t3 = wd3*t3,
wd_x_t4 = wd4*t4,
wd_x_t7 = wd7*t7,
temp.factor = as.factor(temp),
cum_deg_day.factor = as.factor(cum_deg_day)
) %>% droplevels()
# filter( temp != "2" ) %>% droplevels()
# mon.survival$caliber = factor(mon.survival$caliber, levels=c("D+C+B","A", "2A+3A+4A") )
mon.survival$caliber = factor(mon.survival$caliber, levels=c("D+C","B","A", "2A", "3A+4A") )
mon.survival$rep = mon.survival$time-1
mon.survival$wd7=round(mon.survival$wd7,2)
mon.survival$tempK = mon.survival$temp + 273.15
mon.survival$tempK_1 = (1/mon.survival$tempK)
mon.survival$tempK_1_vh = (1/293.15) - (1/mon.survival$tempK)
mon.survival$weight_g.scale = scale(mon.survival$weight_g)
mon.survival$rh7.scale = scale(mon.survival$rh7)
mon.survival$t7.scale = scale(mon.survival$t7)
mon.survival$tmin7.scale = scale(mon.survival$tmin7)
mon.survival$tmax7.scale = scale(mon.survival$tmax7)
mon.survival$wd7.scale = scale(mon.survival$wd7)
wg.mu.scale = attributes(mon.survival$weight_g.scale)[[2]]
wg.si.scale = attributes(mon.survival$weight_g.scale)[[3]]
mon.survival$weight_g.scale = round(as.numeric( mon.survival$weight_g.scale ),2)
mon.survival$cum_deg_day.scale = scale(mon.survival$cum_deg_day)
GDD.mu.scale = attributes(mon.survival$cum_deg_day.scale)[[2]]
GDD.si.scale = attributes(mon.survival$cum_deg_day.scale)[[3]]
mon.survival$cum_deg_day.scale = round(as.numeric( mon.survival$cum_deg_day.scale ),2)
}
# ## verify unique values ##
# unique(mon.survival$cum_deg_day.factor)
# unique(mon.survival$wd1)
# unique(mon.survival$wd2)
# unique(mon.survival$wd3)
# unique(mon.survival$wd4)
# unique(mon.survival$wd7)
# unique(mon.survival$perc_rot)
mon.survival18$weight_g.scale = (mon.survival18$weight_g-wg.mu.scale) / wg.si.scale
mon.survival18$cum_deg_day.scale = (mon.survival18$cum_deg_day-GDD.mu.scale) / GDD.si.scale
mon.survival19$weight_g.scale = (mon.survival19$weight_g-wg.mu.scale) / wg.si.scale
mon.survival19$cum_deg_day.scale = (mon.survival19$cum_deg_day-GDD.mu.scale) / GDD.si.scale
rm("enviropost19")
## *****************************************
## Nectarine weight - ANOVA and Tukey test
## *****************************************
generate_label_df <- function(TUKEY, variable){
# TUKEY = TukeyWeight18
# variable = "id.hort"
# Extract labels and factor levels from Tukey post-hoc
Tukey.levels <- TUKEY[[variable]][,4]
Tukey.labels <- data.frame(multcompLetters(Tukey.levels)['Letters'])
#I need to put the labels in the same order as in the boxplot :
Tukey.labels$treatment=rownames(Tukey.labels)
Tukey.labels=Tukey.labels[order(Tukey.labels$treatment) , ]
return(Tukey.labels)
}
mon18Weight = monilinia18 %>% group_by(harvest_n, box, tree, Irr, FL, n_fruit) %>%
dplyr::summarise( weight_g = mean(weight_g, na.rm=T) ) %>% mutate( id.hort = as.factor(paste0(harvest_n,"_", Irr,"_", FL) ) ) %>% ungroup()
summarySE(mon18Weight, measurevar = "weight_g", groupvars = "id.hort")
aovWeight18 = aov(weight_g ~ id.hort, data = mon18Weight)
TukeyWeight18 = TukeyHSD(aovWeight18)
labelTukeyWeight18 <- generate_label_df(TukeyWeight18 , "id.hort"); labelTukeyWeight18
mon19Weight = monilinia19 %>% group_by(harvest_n, box, fruit_n, irr, temp) %>%
dplyr::summarise( weight_g = mean(weight_g, na.rm=T) ) %>% mutate( id.hort = as.factor(paste0(harvest_n,"_", irr,"_", temp) ) ) %>% ungroup()
summarySE(mon19Weight, measurevar = "weight_g", groupvars = "id.hort")
aovWeight19 = aov(weight_g ~ id.hort, data = mon19Weight)
TukeyWeight19 = TukeyHSD(aovWeight19)
labelTukeyWeight19 <- generate_label_df(TukeyWeight19 , "id.hort"); labelTukeyWeight19
## *****************************************************************************************************************
## Cumulative prevalence at the end of storage - Kruskal-Wallis and Conover-Iman test (ad-hoc multiple comparison)
## *****************************************************************************************************************
library(FSA)
mon18.prevalence = monilinia18 %>%
filter(Syst == "S2" & harvest_date != "3") %>%
group_by(harvest_n, Irr, FL, box, dah, Exp) %>%
dplyr::summarize( incidence = round(sum( infection=="1" )*100 /( length(infection)),2) ) %>% ungroup() %>%
mutate( exp_harvest = paste0(Exp,"_",harvest_n) )
mon19.prevalence = monilinia19 %>%
group_by(harvest_n, irr, temp, box, dah, mod) %>%
dplyr::summarize( incidence = sum( infection=="1" | infection=="2", na.rm = T )*100 /( length(infection)),
weight_mean = mean(weight_g, na.rm=T), weight_sd = sd(weight_g, na.rm=T) ) # cumulated incidence
## 2018 - 1 harvest
mon18.prevalence.1h = mon18.prevalence %>% filter( dah=="14" & harvest_n=="1")
kw_18.1h = kruskal.test( incidence ~ Exp, data = mon18.prevalence.1h)
conover.test_18.1h = conover.test::conover.test(mon18.prevalence.1h$incidence, g=mon18.prevalence.1h$Exp, method='bh', kw=F, label = T)
rcompanion::cldList(P.adjusted ~ comparison,
data = conover.test_18.1h,
threshold = 0.05)
## 2018 - 2 harvest
mon18.prevalence.2h = mon18.prevalence %>% filter( dah=="14" & harvest_n=="2")
kw_18.2h = kruskal.test( incidence ~ Exp, data = mon18.prevalence.2h)
conover.test_18.2h = conover.test::conover.test(mon18.prevalence.2h$incidence, g=mon18.prevalence.2h$Exp, method='bh', kw=F, label = T)
rcompanion::cldList(P.adjusted ~ comparison,
data = conover.test_18.2h,
threshold = 0.05)
## 2019 - 1 harvest
mon19.prevalence.1h = mon19.prevalence %>% filter( dah=="17" & harvest_n=="1")
kw_19.1h = kruskal.test( incidence ~ mod, data = mon19.prevalence.1h)
conover.test_19.1h = conover.test::conover.test(mon19.prevalence.1h$incidence, g=mon19.prevalence.1h$mod, method='bh', kw=F, label = T)
rcompanion::cldList(P.adjusted ~ comparison,