R/04-Boxplot.R
In nasserdr/dendroSense: Analyzing dendrometers data the easy way.
# Some analyzes____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
library(lubridate)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
dendro <- read.table("dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(dendro)
str(dendro)
y <- Dendro_dataset_v4 %>%
group_by(Year)
y <- split(y, f = y$Year)
y_2017 <- y$`2017`
y_2018 <- y$`2018`
df <- dendro
df$Dendro <- as.factor(df$Dendro)
str(df)
summary(df$dendro)
#Flushes x MDS____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$MDS ~ df$Flushes , pch=20, xlab="Flushes", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$MDS, df$Flushes)
bstats <- boxplot(df$MDS ~ df$Flushes, data = df,xlab="Flushes", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="MDS") # this will plot without any outlier points
#Flushes x Daily Recovery____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$DR ~ df$Flushes , pch=20, xlab="Flushes", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$DR ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$DR, df$Flushes)
bstats <- boxplot(df$DR ~ df$Flushes, data = df,xlab="Flushes", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="DR") # this will plot without any outlier points
#Flushes x Daily Growth____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$DG ~ df$Flushes , pch=20, xlab="Flushes", ylab="DG", col="darkblue")
abline(lm(df$DG ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$DG ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$DG, df$Flushes)
bstats <- boxplot(df$DG ~ df$Flushes, data = df,xlab="Flushes", ylab="DG", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="DG") # this will plot without any outlier points
#Flushes x Acummulativ growth____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$CumGrowth ~ df$Flushes , pch=20, xlab="Flushes", ylab="CumGrowth", col="darkblue")
abline(lm(df$CumGrowth ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$CumGrowth ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$CumGrowth, df$Flushes)
bstats <- boxplot(df$CumGrowth ~ df$Flushes, data = df,xlab="Flushes", ylab="CumGrowth", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="CumGrowth") # this will plot without any outlier points
#Flushes x Abortion____________________________________________________________________________________________________________________________________________________________________
plot(df$Abortion ~ df$Flushes , pch=20, xlab="Flushes", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Abortion ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$Abortion ~ df$Flushes , data = df, xlab="Flushes", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Abortion") # this will plot without any outlier points
plot(df$Abortion ~ df$Flushes , pch=20, xlab="Flushes", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Abortion ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x Healthy fruits____________________________________________________________________________________________________________________________________________________________________
plot(df$HealthyFruits ~ df$Flushes , pch=20, xlab="Flushes", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$HealthyFruits ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$HealthyFruits ~ df$Flushes , data = df, xlab="Flushes", ylab="HealthyFruits", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="HealthyFruits") # this will plot without any outlier points
plot(df$HealthyFruits ~ df$Flushes , pch=20, xlab="Flushes", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$HealthyFruits ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x DPV____________________________________________________________________________________________________________________________________________________________________
plot(df$Acc_PLUVIO ~ df$Flushes , pch=20, xlab="Flushes", ylab="Acc_PLUVIO", col="darkblue")
abline(lm(df$Acc_PLUVIO ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Acc_PLUVIO ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$Acc_PLUVIO ~ df$Flushes , data = df, xlab="Flushes", ylab="Acc_PLUVIO", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Acc_PLUVIO") # this will plot without any outlier points
plot(df$Acc_PLUVIO ~ df$Flushes , pch=20, xlab="Flushes", ylab="Acc_PLUVIO", col="darkblue")
abline(lm(df$Acc_PLUVIO ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Acc_PLUVIO ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(y_2017$MDS ~ y_2017$Flushes, data = df,xlab="Flushes", ylab="MDS2017 ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="MDS2017 ") # this will plot without any outlier points
boxplot(y_2017$MDS ~ y_2017$Flushes)
cor(y_2017$MDS, y_2017$Flushes)
plot(y_2017$MDS ~ y_2017$Flushes, pch=20, xlab="Flushes", ylab="MDS2017 ", col="darkblue")
abline(lm(y_2017$MDS ~ y_2017$Flushes) , lwd=2, col="red")
rg <- lm(y_2017$MDS ~ y_2017$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x HR..____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Median20cm ~ df$Flowers, data = df,xlab="Flowers", ylab="Median20cm", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Median20cm") # this will plot without any outlier points
boxplot(df$Median20cm ~ df$Flowers)
cor(df$Median20cm ,df$Flowers)
plot(df$Median20cm ~ df$Flowers, pch=20, xlab="Flowers", ylab="Median20cm ", col="darkblue")
abline(lm(df$Median20cm ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$Median20cm ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$HR.. ~ df$Flowers, data = df,xlab="Flowers", ylab="HR..", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="HR..") # this will plot without any outlier points
boxplot(df$MDS ~ df$Flowers)
cor(df$MDS ,df$Flowers)
plot(df$MDS ~ df$Flowers, pch=20, xlab="Flowers", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Abortion____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$meanVPD_kPa ~ df$Flushes, data = df,xlab="Flushes", ylab="meanVPD_kPa", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="meanVPD_kPa") # this will plot without any outlier points
boxplot(df$meanVPD_kPa ~ df$Flushes)
cor(df$meanVPD_kPa ,df$Flushes)
plot(df$meanVPD_kPa ~ df$Flushes, pch=20, xlab="Flushes", ylab="meanVPD_kPa", col="darkblue")
abline(lm(df$meanVPD_kPa ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$meanVPD_kPa ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Healthy fruits____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$HealthyFruits ~ df$Flowers, data = df,xlab="Flowers", ylab="HealthyFruits", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="HealthyFruits") # this will plot without any outlier points
boxplot(df$HealthyFruits ~ df$Flowers)
cor(df$HealthyFruits ,df$Flowers)
plot(df$HealthyFruits ~ df$Flowers, pch=20, xlab="Flowers", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$HealthyFruits ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Abortion____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$Flowers, data = df,xlab="Flowers", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Abortion") # this will plot without any outlier points
boxplot(df$Abortion ~ df$Flowers)
cor(df$Abortion ,df$Flowers)
plot(df$Abortion ~ df$Flowers, pch=20, xlab="Flowers", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#VPD x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$maxVPD_kPa, data = df,xlab="maxVPD_kPa", ylab="MDS ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="maxVPD_kPa", ylab="MDS ") # this will plot without any outlier points
boxplot(df$MDS ~ df$maxVPD_kPa)
cor(df$MDS ,df$maxVPD_kPa)
plot(df$MDS ~ df$maxVPD_kPa, pch=20, xlab="maxVPD_kPa", ylab="MDS ", col="darkblue")
abline(lm(df$MDS ~ df$maxVPD_kPa) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$maxVPD_kPa)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#ETO x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$ET0_mm.j, data = df,xlab="ET0_mm.j", ylab="MDS ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="ET0_mm.j", ylab="MDS ") # this will plot without any outlier points
boxplot(df$MDS ~ df$ET0_mm.j)
cor(df$MDS ,df$ET0_mm.j)
plot(df$MDS ~ df$ET0_mm.j, pch=20, xlab="ET0_mm.j", ylab="MDS ", col="darkblue")
abline(lm(df$MDS ~ df$ET0_mm.j) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$ET0_mm.j)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x Temparature____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$MaxTemp , data = df,xlab="MaxTemp", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="MaxTemp", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$MaxTemp)
cor(df$Abortion,df$MaxTemp)
plot(df$Abortion ~ df$MaxTemp, pch=20, xlab="MaxTemp", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$MaxTemp) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$MaxTemp)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x DG____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$DG , data = df,xlab="DG", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="DG", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$DG)
cor(df$Abortion,df$DG)
plot(df$Abortion ~ df$DG, pch=20, xlab="DG", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$DG) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$DG)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x HR____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$HR.. , data = df,xlab="HR..", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="HR..", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$HR..)
cor(df$Abortion,df$HR..)
plot(df$Abortion ~ df$HR.., pch=20, xlab="HR..", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$HR..) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$HR..)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$Irrigation, data = df,xlab="Irrigation", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="Abortion") # this will plot without any outlier points
boxplot(df$Abortion ~ df$Irrigation)
cor(df$Abortion,df$Irrigation)
plot(df$Abortion ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Daily growth x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DG ~ df$Irrigation, data = df,xlab="Irrigation", ylab="DG", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="DG") # this will plot without any outlier points
boxplot(df$DG ~ df$Irrigation)
cor(df$DG ,df$Irrigation)
plot(df$DG ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="DG", col="darkblue")
abline(lm(df$DG ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$DG ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#MSD x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$Irrigation, data = df,xlab="Irrigation", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="MDS") # this will plot without any outlier points
boxplot(df$MDS ~ df$Irrigation)
cor(df$MDS ,df$Irrigation)
plot(df$MDS ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
head (df)
#Abortion x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DR ~ df$Dendro, data = df,xlab="Dendro", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Dendro", ylab="DR") # this will plot without any outlier points
boxplot(df$DR ~ df$Dendro)
cor(df$DR ,df$Dendro)
plot(df$DR ~ df$Dendro, pch=20, xlab="Dendro", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Dendro) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Abortion)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#MDS x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$Micro.climate.x, data = df,xlab="Micro.climate.x", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Micro.climate.x", ylab="MDS") # this will plot without any outlier points
boxplot(df$MDS ~ df$Micro.climate.x)
cor(df$MDS ,df$Micro.climate.x)
plot(df$MDS ~ df$Micro.climate.x, pch=20, xlab="Micro.climate.x", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Micro.climate.x)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#DR x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DR ~ df$Micro.climate.x, data = df,xlab="Micro.climate.x", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Micro.climate.x", ylab="DR") # this will plot without any outlier points
boxplot(df$DR ~ df$Micro.climate.x)
cor(df$DR ,df$Micro.climate.x)
plot(df$DR ~ df$Micro.climate.x, pch=20, xlab="Micro.climate.x", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$DR ~ df$Micro.climate.x)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#DG x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$CumGrowth ~ df$Dendro, data = df,xlab="Dendro", ylab="CumGrowth", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Dendro", ylab="CumGrowth") # this will plot without any outlier points
boxplot(df$CumGrowth ~ df$Dendro)
cor(df$CumGrowth ,df$Dendro)
plot(df$CumGrowth ~ df$Dendro, pch=20, xlab="Dendro", ylab="CumGrowth", col="darkblue")
abline(lm(df$CumGrowth ~ df$Dendro) , lwd=2, col="red")
rg <- lm(df$CumGrowth ~ df$Dendro)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x irrigation____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$RAYONNEMENTGLOBAL ~ df$Flushes , pch=20, xlab="RAYONNEMENTGLOBAL", ylab="Flushes", col="darkblue")
abline(lm(df$Flushes ~ df$RAYONNEMENTGLOBAL) , lwd=2, col="red")
rg <- lm(df$Flushes ~ df$RAYONNEMENTGLOBAL)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$df$RAYONNEMENTGLOBAL ~ df$Flowers)
bstats <- boxplot(df$RAYONNEMENTGLOBAL ~ df$Flowers, data = df,xlab="Flowers", ylab="RAYONNEMENTGLOBAL", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="RAYONNEMENTGLOBAL") # this will plot without any outlier points
#Abortion x Median20cm____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$Windmaxm.s ~ df$DG, pch=20, xlab="DG", ylab="Windmaxm.s", col="darkblue")
abline(lm(df$Windmaxm.s ~ df$DG) , lwd=2, col="red")
rg <- lm(df$Windmaxm.s ~ df$DG)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$Windmaxm.s, df$DG)
bstats <- boxplot(df$Windmaxm.s ~ df$DG, data = df,xlab="DG", ylab="Windmaxm.s", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="DG", ylab="Windmaxm.s") # this will plot without any outlier points
library(ggplot2)
require(ggplot2)
qplot(df$Median20cm, df$Flowers)
stripchart(df$Irrigation~df$Flowers,data.frame(df$Irrigation,df$Flowers),pch=1,vertical=T)
plot(cut(Irrigation, 3) ~ group, data = df)
boxplot(Irrigation~Flowers, data= df)
lapply(df$Irrigation,classe)
ggplot(df$Irrigation, df$Flowers)
ggplot(df) +
geom_point(aes(Irrigation, Flowers))
geom_point(data = df, aes(gp, mean), colour = 'red', size = 3)
head (df)
str(df)
range(df$Abortion)
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#Due to the reduction of data, these parameters are analyzed separately!!!!!!!!
# irrigation ____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
irrigation_dendro <- read.table("irrigation_dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(irrigation_dendro)
str(irrigation_dendro)
irrigation_dendro$Date <- as.Date(irrigation_dendro$Date, format = "%d.%m.%Y")
# Merge irrigation and dendro_dataset_v3____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
dendro_dataset_v4 <- merge(dendro_dataset_v3,irrigation_dendro, by = "Date", all.x = TRUE)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(dendro_dataset_v4, file = "dendro_dataset_v4.csv")
# Harvest ____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
Harvest <- read.table("harvest_d.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Harvest)
str(Harvest)
Harvest$Date <- as.Date(Harvest$Date, format = "%d.%m.%Y")
# Harvest <- Harvest %>% rename(Dendro = Label)
Harvest["Week"] <- Harvest$Date
Harvest$Week <- week(Harvest$Week)
head(Harvest)
Harvest["Year"] <- Harvest$Date
head(Harvest)
str(Harvest)
Year <- as.Date(Harvest$Date,format = "%Y")
format(Year,'%Y')
Harvest$Year <- as.numeric(format(Year,'%Y'))
head(Harvest)
# Merge Harvest and dendro_dataset_v4____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
dendro_dataset_v5 <- merge(Dendro_dataset_v4,Harvest, by = c("Dendro", "Week", "Year"), all.y= TRUE)
head(dendro_dataset_v5)
dendro_dataset_v5 <- na.omit(dendro_dataset_v5)
head(dendro_dataset_v5)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(dendro_dataset_v5, file = "Dendro_dataset_v5.csv")
dendro_dataset_v4$X <- NULL
dendro_dataset_v4$m <- NULL
dendro_dataset_v4$X.1 <- NULL
dendro_dataset_v4 <- dendro_dataset_v4[, -c(35:45)]
dendro_dataset_v4 <- dendro_dataset_v4 %>% rename(Date = Date.x)
head(dendro_dataset_v4)
str(dendro_dataset_v4)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(Dendro_dataset_v4, file = "Dendro_dataset_v4.csv")
# Harvest another option______________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
Harvest_Yield <- read.table("Harvest_Yield.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Harvest_Yield)
str(Harvest_Yield)
Harvest_Yield <- Harvest_Yield %>% rename(Dendro = Label)
harv_dendro <- Harvest_Yield %>% group_by(Dendro)%>%
summarise_at(c("Total.weight..kg..per.10.Trees", "Yield.dry.beans..grams..per.kg.of.fruit.per.10.trees", "Yield.beans.dry.per.hectare..kg."), sum, na.rm = TRUE)
## Summe of all Fruits and abortion per tree_______________________________________________________________________________________________________________________________________
d_191 <- df[df[, "Dendro"] == "191", ]
sum_dendro <- df %>% group_by(Dendro)%>%
summarise_at(c("Abortion", "HealthyFruits"), sum, na.rm = TRUE)
head(dendro_dataset_v5)
dendro_dataset_v5 <- dendro_dataset_v5[, -51]
sum_all_dendro <- merge(sum_dendro,harv_dendro, by = "Dendro", all.x = TRUE)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(sum_all_dendro, file = "sum_all_dendro.csv")
#Plots ___________________________________________________________________________________________________________________________________________
library(ggplot2)
library(tidyr)
library(dipply)
df2 <- sum_all_dendro
df2$Yield.dry.beans..grams..per.kg.of.fruit.per.10.trees <- NULL
df2$Total.weight..kg..per.10.Trees <- NULL
head(df2)
#' Dodge side by side: X = dendro, y = (value or abortion / and value healthy fruits) y1= Yield.beans.dry.per.hectare..kg.
df2%>%
gather("Variable", "Value",- Dendro) %>%
ggplot(aes(Dendro, Value, fill = Variable)) +
geom_bar(position = "dodge", stat = "identity") +
coord_cartesian(ylim = c(0, 2000))+
scale_y_continuous(name = "Abortion/HealthyFruits",
sec.axis = sec_axis(~., name = "Yield.beans.dry.per.hectare..kg.")) +
theme_bw()
library(ggplot2)
library(data.table)
length(df2$Dendro)
summary(df2$Dendro)
df2(df2[df2([1,1]:[25,1])])
df3 <- as.data.frame(df2[1:25,1])
wdf2 <- df2
df2$Abortion <- NULL
sec <- with(df2, train_sec(c(0, max(unemploy)),
c(0, max(psavert))))
df2%>% gather("Variable", "Value",- Dendro) %>%
ggplot(aes(Dendro, Value, fill = Variable)) +
geom_col(aes(y= Value), fill="blue") +
geom_col(aes(y=Value * scaleFactor), fill="red") +
scale_y_continuous(name="Healthy Fruits", sec.axis= sec_axis(~df2$rev(.), name="Yield beans dry per hectare kg")) +
scale_x_discrete( as.numeric(c(df2[1:25,1]))) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
) +
labs(title = "Production and losses", x = element_blank())
#Other possibility______________________________________________________________________________________________________________________________
df2.m <- melt(as.data.table(df2, id.vars = 1))
ggplot(df2.m, aes(as.numeric(x= c(df2.m[1:25,1])), value, fill = variable)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("Yield.beans.dry.per.hectare..kg." = "blue", "HealthyFruits" = "red")) +
facet_wrap(~variable, ncol = 1, scales = "free_y")+
coord_cartesian(ylim = c(0, 2000))+
scale_y_continuous(name = "Abortion/HealthyFruits",
sec.axis = sec_axis(~., name = "Yield.beans.dry.per.hectare..kg.")) +
theme_bw()
str(df2)
df2$Abortion <- as.numeric(df2$Abortion)
df2$HealthyFruits <- as.numeric(df2$HealthyFruits)
df2$Dendro <- as.factor(df2$Dendro)
scaleFactor <- max(df2$HealthyFruits) / max(df2$Yield.beans.dry.per.hectare..kg.)
df2.m <- melt(df2, id.vars = 1)
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#df_Flushes <- df %>%
#group_by(date.x) %>%
#summarise_at(vars(Flushes),
#list (mean(as.numeric(levels(Flushes[Flushes]))))
#df$Flushes []
#levels(df$Flushes)
#mean_Flushes <- df %>%
#mutate(date = floor_date(df$date.x)) %>%
#group_by(date, Flushes) %>%
#summarize(mean_date= mean(date.x), by = list(df$date.x))
#summarize(mean(as.factor(levels(Flushes[Flushes]))))
nasserdr/dendroSense documentation built on Dec. 21, 2021, 11:11 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# Some analyzes____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
library(lubridate)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
dendro <- read.table("dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(dendro)
str(dendro)
y <- Dendro_dataset_v4 %>%
group_by(Year)
y <- split(y, f = y$Year)
y_2017 <- y$`2017`
y_2018 <- y$`2018`
df <- dendro
df$Dendro <- as.factor(df$Dendro)
str(df)
summary(df$dendro)
#Flushes x MDS____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$MDS ~ df$Flushes , pch=20, xlab="Flushes", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$MDS, df$Flushes)
bstats <- boxplot(df$MDS ~ df$Flushes, data = df,xlab="Flushes", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="MDS") # this will plot without any outlier points
#Flushes x Daily Recovery____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$DR ~ df$Flushes , pch=20, xlab="Flushes", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$DR ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$DR, df$Flushes)
bstats <- boxplot(df$DR ~ df$Flushes, data = df,xlab="Flushes", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="DR") # this will plot without any outlier points
#Flushes x Daily Growth____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$DG ~ df$Flushes , pch=20, xlab="Flushes", ylab="DG", col="darkblue")
abline(lm(df$DG ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$DG ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$DG, df$Flushes)
bstats <- boxplot(df$DG ~ df$Flushes, data = df,xlab="Flushes", ylab="DG", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="DG") # this will plot without any outlier points
#Flushes x Acummulativ growth____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$CumGrowth ~ df$Flushes , pch=20, xlab="Flushes", ylab="CumGrowth", col="darkblue")
abline(lm(df$CumGrowth ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$CumGrowth ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$CumGrowth, df$Flushes)
bstats <- boxplot(df$CumGrowth ~ df$Flushes, data = df,xlab="Flushes", ylab="CumGrowth", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="CumGrowth") # this will plot without any outlier points
#Flushes x Abortion____________________________________________________________________________________________________________________________________________________________________
plot(df$Abortion ~ df$Flushes , pch=20, xlab="Flushes", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Abortion ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$Abortion ~ df$Flushes , data = df, xlab="Flushes", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Abortion") # this will plot without any outlier points
plot(df$Abortion ~ df$Flushes , pch=20, xlab="Flushes", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Abortion ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x Healthy fruits____________________________________________________________________________________________________________________________________________________________________
plot(df$HealthyFruits ~ df$Flushes , pch=20, xlab="Flushes", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$HealthyFruits ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$HealthyFruits ~ df$Flushes , data = df, xlab="Flushes", ylab="HealthyFruits", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="HealthyFruits") # this will plot without any outlier points
plot(df$HealthyFruits ~ df$Flushes , pch=20, xlab="Flushes", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$HealthyFruits ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x DPV____________________________________________________________________________________________________________________________________________________________________
plot(df$Acc_PLUVIO ~ df$Flushes , pch=20, xlab="Flushes", ylab="Acc_PLUVIO", col="darkblue")
abline(lm(df$Acc_PLUVIO ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Acc_PLUVIO ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
bstats <- boxplot(df$Acc_PLUVIO ~ df$Flushes , data = df, xlab="Flushes", ylab="Acc_PLUVIO", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Acc_PLUVIO") # this will plot without any outlier points
plot(df$Acc_PLUVIO ~ df$Flushes , pch=20, xlab="Flushes", ylab="Acc_PLUVIO", col="darkblue")
abline(lm(df$Acc_PLUVIO ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$df$Acc_PLUVIO ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(y_2017$MDS ~ y_2017$Flushes, data = df,xlab="Flushes", ylab="MDS2017 ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="MDS2017 ") # this will plot without any outlier points
boxplot(y_2017$MDS ~ y_2017$Flushes)
cor(y_2017$MDS, y_2017$Flushes)
plot(y_2017$MDS ~ y_2017$Flushes, pch=20, xlab="Flushes", ylab="MDS2017 ", col="darkblue")
abline(lm(y_2017$MDS ~ y_2017$Flushes) , lwd=2, col="red")
rg <- lm(y_2017$MDS ~ y_2017$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x HR..____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Median20cm ~ df$Flowers, data = df,xlab="Flowers", ylab="Median20cm", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Median20cm") # this will plot without any outlier points
boxplot(df$Median20cm ~ df$Flowers)
cor(df$Median20cm ,df$Flowers)
plot(df$Median20cm ~ df$Flowers, pch=20, xlab="Flowers", ylab="Median20cm ", col="darkblue")
abline(lm(df$Median20cm ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$Median20cm ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$HR.. ~ df$Flowers, data = df,xlab="Flowers", ylab="HR..", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="HR..") # this will plot without any outlier points
boxplot(df$MDS ~ df$Flowers)
cor(df$MDS ,df$Flowers)
plot(df$MDS ~ df$Flowers, pch=20, xlab="Flowers", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Abortion____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$meanVPD_kPa ~ df$Flushes, data = df,xlab="Flushes", ylab="meanVPD_kPa", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="meanVPD_kPa") # this will plot without any outlier points
boxplot(df$meanVPD_kPa ~ df$Flushes)
cor(df$meanVPD_kPa ,df$Flushes)
plot(df$meanVPD_kPa ~ df$Flushes, pch=20, xlab="Flushes", ylab="meanVPD_kPa", col="darkblue")
abline(lm(df$meanVPD_kPa ~ df$Flushes) , lwd=2, col="red")
rg <- lm(df$meanVPD_kPa ~ df$Flushes)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Healthy fruits____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$HealthyFruits ~ df$Flowers, data = df,xlab="Flowers", ylab="HealthyFruits", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="HealthyFruits") # this will plot without any outlier points
boxplot(df$HealthyFruits ~ df$Flowers)
cor(df$HealthyFruits ,df$Flowers)
plot(df$HealthyFruits ~ df$Flowers, pch=20, xlab="Flowers", ylab="HealthyFruits", col="darkblue")
abline(lm(df$HealthyFruits ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$HealthyFruits ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flowers x Abortion____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$Flowers, data = df,xlab="Flowers", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Abortion") # this will plot without any outlier points
boxplot(df$Abortion ~ df$Flowers)
cor(df$Abortion ,df$Flowers)
plot(df$Abortion ~ df$Flowers, pch=20, xlab="Flowers", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Flowers) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Flowers)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#VPD x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$maxVPD_kPa, data = df,xlab="maxVPD_kPa", ylab="MDS ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="maxVPD_kPa", ylab="MDS ") # this will plot without any outlier points
boxplot(df$MDS ~ df$maxVPD_kPa)
cor(df$MDS ,df$maxVPD_kPa)
plot(df$MDS ~ df$maxVPD_kPa, pch=20, xlab="maxVPD_kPa", ylab="MDS ", col="darkblue")
abline(lm(df$MDS ~ df$maxVPD_kPa) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$maxVPD_kPa)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#ETO x MDS____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$ET0_mm.j, data = df,xlab="ET0_mm.j", ylab="MDS ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="ET0_mm.j", ylab="MDS ") # this will plot without any outlier points
boxplot(df$MDS ~ df$ET0_mm.j)
cor(df$MDS ,df$ET0_mm.j)
plot(df$MDS ~ df$ET0_mm.j, pch=20, xlab="ET0_mm.j", ylab="MDS ", col="darkblue")
abline(lm(df$MDS ~ df$ET0_mm.j) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$ET0_mm.j)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x Temparature____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$MaxTemp , data = df,xlab="MaxTemp", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="MaxTemp", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$MaxTemp)
cor(df$Abortion,df$MaxTemp)
plot(df$Abortion ~ df$MaxTemp, pch=20, xlab="MaxTemp", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$MaxTemp) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$MaxTemp)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x DG____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$DG , data = df,xlab="DG", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="DG", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$DG)
cor(df$Abortion,df$DG)
plot(df$Abortion ~ df$DG, pch=20, xlab="DG", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$DG) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$DG)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x HR____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$HR.. , data = df,xlab="HR..", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="HR..", ylab="Abortion ") # this will plot without any outlier points
boxplot(df$Abortion ~ df$HR..)
cor(df$Abortion,df$HR..)
plot(df$Abortion ~ df$HR.., pch=20, xlab="HR..", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$HR..) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$HR..)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Abortion x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$Abortion ~ df$Irrigation, data = df,xlab="Irrigation", ylab="Abortion ", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="Abortion") # this will plot without any outlier points
boxplot(df$Abortion ~ df$Irrigation)
cor(df$Abortion,df$Irrigation)
plot(df$Abortion ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="Abortion", col="darkblue")
abline(lm(df$Abortion ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Daily growth x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DG ~ df$Irrigation, data = df,xlab="Irrigation", ylab="DG", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="DG") # this will plot without any outlier points
boxplot(df$DG ~ df$Irrigation)
cor(df$DG ,df$Irrigation)
plot(df$DG ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="DG", col="darkblue")
abline(lm(df$DG ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$DG ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#MSD x Irrigation____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$Irrigation, data = df,xlab="Irrigation", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Irrigation", ylab="MDS") # this will plot without any outlier points
boxplot(df$MDS ~ df$Irrigation)
cor(df$MDS ,df$Irrigation)
plot(df$MDS ~ df$Irrigation, pch=20, xlab="Irrigation", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Irrigation)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
head (df)
#Abortion x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DR ~ df$Dendro, data = df,xlab="Dendro", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Dendro", ylab="DR") # this will plot without any outlier points
boxplot(df$DR ~ df$Dendro)
cor(df$DR ,df$Dendro)
plot(df$DR ~ df$Dendro, pch=20, xlab="Dendro", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Dendro) , lwd=2, col="red")
rg <- lm(df$Abortion ~ df$Abortion)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#MDS x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$MDS ~ df$Micro.climate.x, data = df,xlab="Micro.climate.x", ylab="MDS", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Micro.climate.x", ylab="MDS") # this will plot without any outlier points
boxplot(df$MDS ~ df$Micro.climate.x)
cor(df$MDS ,df$Micro.climate.x)
plot(df$MDS ~ df$Micro.climate.x, pch=20, xlab="Micro.climate.x", ylab="MDS", col="darkblue")
abline(lm(df$MDS ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$MDS ~ df$Micro.climate.x)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#DR x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$DR ~ df$Micro.climate.x, data = df,xlab="Micro.climate.x", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Micro.climate.x", ylab="DR") # this will plot without any outlier points
boxplot(df$DR ~ df$Micro.climate.x)
cor(df$DR ,df$Micro.climate.x)
plot(df$DR ~ df$Micro.climate.x, pch=20, xlab="Micro.climate.x", ylab="DR", col="darkblue")
abline(lm(df$DR ~ df$Irrigation) , lwd=2, col="red")
rg <- lm(df$DR ~ df$Micro.climate.x)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#DG x Micro.climate.x____________________________________________________________________________________________________________________________________________________________________
head(df)
bstats <- boxplot(df$CumGrowth ~ df$Dendro, data = df,xlab="Dendro", ylab="CumGrowth", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Dendro", ylab="CumGrowth") # this will plot without any outlier points
boxplot(df$CumGrowth ~ df$Dendro)
cor(df$CumGrowth ,df$Dendro)
plot(df$CumGrowth ~ df$Dendro, pch=20, xlab="Dendro", ylab="CumGrowth", col="darkblue")
abline(lm(df$CumGrowth ~ df$Dendro) , lwd=2, col="red")
rg <- lm(df$CumGrowth ~ df$Dendro)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
#Flushes x irrigation____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$RAYONNEMENTGLOBAL ~ df$Flushes , pch=20, xlab="RAYONNEMENTGLOBAL", ylab="Flushes", col="darkblue")
abline(lm(df$Flushes ~ df$RAYONNEMENTGLOBAL) , lwd=2, col="red")
rg <- lm(df$Flushes ~ df$RAYONNEMENTGLOBAL)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$df$RAYONNEMENTGLOBAL ~ df$Flowers)
bstats <- boxplot(df$RAYONNEMENTGLOBAL ~ df$Flowers, data = df,xlab="Flowers", ylab="RAYONNEMENTGLOBAL", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="RAYONNEMENTGLOBAL") # this will plot without any outlier points
#Abortion x Median20cm____________________________________________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
plot(df$Windmaxm.s ~ df$DG, pch=20, xlab="DG", ylab="Windmaxm.s", col="darkblue")
abline(lm(df$Windmaxm.s ~ df$DG) , lwd=2, col="red")
rg <- lm(df$Windmaxm.s ~ df$DG)
summary(rg)
predict(object = rg)
summary(rg)$r.squared
cor(df$Windmaxm.s, df$DG)
bstats <- boxplot(df$Windmaxm.s ~ df$DG, data = df,xlab="DG", ylab="Windmaxm.s", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="DG", ylab="Windmaxm.s") # this will plot without any outlier points
library(ggplot2)
require(ggplot2)
qplot(df$Median20cm, df$Flowers)
stripchart(df$Irrigation~df$Flowers,data.frame(df$Irrigation,df$Flowers),pch=1,vertical=T)
plot(cut(Irrigation, 3) ~ group, data = df)
boxplot(Irrigation~Flowers, data= df)
lapply(df$Irrigation,classe)
ggplot(df$Irrigation, df$Flowers)
ggplot(df) +
geom_point(aes(Irrigation, Flowers))
geom_point(data = df, aes(gp, mean), colour = 'red', size = 3)
head (df)
str(df)
range(df$Abortion)
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#Due to the reduction of data, these parameters are analyzed separately!!!!!!!!
# irrigation ____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
irrigation_dendro <- read.table("irrigation_dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(irrigation_dendro)
str(irrigation_dendro)
irrigation_dendro$Date <- as.Date(irrigation_dendro$Date, format = "%d.%m.%Y")
# Merge irrigation and dendro_dataset_v3____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
dendro_dataset_v4 <- merge(dendro_dataset_v3,irrigation_dendro, by = "Date", all.x = TRUE)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(dendro_dataset_v4, file = "dendro_dataset_v4.csv")
# Harvest ____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
Harvest <- read.table("harvest_d.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Harvest)
str(Harvest)
Harvest$Date <- as.Date(Harvest$Date, format = "%d.%m.%Y")
# Harvest <- Harvest %>% rename(Dendro = Label)
Harvest["Week"] <- Harvest$Date
Harvest$Week <- week(Harvest$Week)
head(Harvest)
Harvest["Year"] <- Harvest$Date
head(Harvest)
str(Harvest)
Year <- as.Date(Harvest$Date,format = "%Y")
format(Year,'%Y')
Harvest$Year <- as.numeric(format(Year,'%Y'))
head(Harvest)
# Merge Harvest and dendro_dataset_v4____________________________________________________________________________________________________________________________________________________________
#____________________________________________________________________________________________________________________________________________________________________
dendro_dataset_v5 <- merge(Dendro_dataset_v4,Harvest, by = c("Dendro", "Week", "Year"), all.y= TRUE)
head(dendro_dataset_v5)
dendro_dataset_v5 <- na.omit(dendro_dataset_v5)
head(dendro_dataset_v5)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(dendro_dataset_v5, file = "Dendro_dataset_v5.csv")
dendro_dataset_v4$X <- NULL
dendro_dataset_v4$m <- NULL
dendro_dataset_v4$X.1 <- NULL
dendro_dataset_v4 <- dendro_dataset_v4[, -c(35:45)]
dendro_dataset_v4 <- dendro_dataset_v4 %>% rename(Date = Date.x)
head(dendro_dataset_v4)
str(dendro_dataset_v4)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(Dendro_dataset_v4, file = "Dendro_dataset_v4.csv")
# Harvest another option______________________________________________________________________________________________________________________________________
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
Harvest_Yield <- read.table("Harvest_Yield.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Harvest_Yield)
str(Harvest_Yield)
Harvest_Yield <- Harvest_Yield %>% rename(Dendro = Label)
harv_dendro <- Harvest_Yield %>% group_by(Dendro)%>%
summarise_at(c("Total.weight..kg..per.10.Trees", "Yield.dry.beans..grams..per.kg.of.fruit.per.10.trees", "Yield.beans.dry.per.hectare..kg."), sum, na.rm = TRUE)
## Summe of all Fruits and abortion per tree_______________________________________________________________________________________________________________________________________
d_191 <- df[df[, "Dendro"] == "191", ]
sum_dendro <- df %>% group_by(Dendro)%>%
summarise_at(c("Abortion", "HealthyFruits"), sum, na.rm = TRUE)
head(dendro_dataset_v5)
dendro_dataset_v5 <- dendro_dataset_v5[, -51]
sum_all_dendro <- merge(sum_dendro,harv_dendro, by = "Dendro", all.x = TRUE)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R")
write.csv(sum_all_dendro, file = "sum_all_dendro.csv")
#Plots ___________________________________________________________________________________________________________________________________________
library(ggplot2)
library(tidyr)
library(dipply)
df2 <- sum_all_dendro
df2$Yield.dry.beans..grams..per.kg.of.fruit.per.10.trees <- NULL
df2$Total.weight..kg..per.10.Trees <- NULL
head(df2)
#' Dodge side by side: X = dendro, y = (value or abortion / and value healthy fruits) y1= Yield.beans.dry.per.hectare..kg.
df2%>%
gather("Variable", "Value",- Dendro) %>%
ggplot(aes(Dendro, Value, fill = Variable)) +
geom_bar(position = "dodge", stat = "identity") +
coord_cartesian(ylim = c(0, 2000))+
scale_y_continuous(name = "Abortion/HealthyFruits",
sec.axis = sec_axis(~., name = "Yield.beans.dry.per.hectare..kg.")) +
theme_bw()
library(ggplot2)
library(data.table)
length(df2$Dendro)
summary(df2$Dendro)
df2(df2[df2([1,1]:[25,1])])
df3 <- as.data.frame(df2[1:25,1])
wdf2 <- df2
df2$Abortion <- NULL
sec <- with(df2, train_sec(c(0, max(unemploy)),
c(0, max(psavert))))
df2%>% gather("Variable", "Value",- Dendro) %>%
ggplot(aes(Dendro, Value, fill = Variable)) +
geom_col(aes(y= Value), fill="blue") +
geom_col(aes(y=Value * scaleFactor), fill="red") +
scale_y_continuous(name="Healthy Fruits", sec.axis= sec_axis(~df2$rev(.), name="Yield beans dry per hectare kg")) +
scale_x_discrete( as.numeric(c(df2[1:25,1]))) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
) +
labs(title = "Production and losses", x = element_blank())
#Other possibility______________________________________________________________________________________________________________________________
df2.m <- melt(as.data.table(df2, id.vars = 1))
ggplot(df2.m, aes(as.numeric(x= c(df2.m[1:25,1])), value, fill = variable)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("Yield.beans.dry.per.hectare..kg." = "blue", "HealthyFruits" = "red")) +
facet_wrap(~variable, ncol = 1, scales = "free_y")+
coord_cartesian(ylim = c(0, 2000))+
scale_y_continuous(name = "Abortion/HealthyFruits",
sec.axis = sec_axis(~., name = "Yield.beans.dry.per.hectare..kg.")) +
theme_bw()
str(df2)
df2$Abortion <- as.numeric(df2$Abortion)
df2$HealthyFruits <- as.numeric(df2$HealthyFruits)
df2$Dendro <- as.factor(df2$Dendro)
scaleFactor <- max(df2$HealthyFruits) / max(df2$Yield.beans.dry.per.hectare..kg.)
df2.m <- melt(df2, id.vars = 1)
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#df_Flushes <- df %>%
#group_by(date.x) %>%
#summarise_at(vars(Flushes),
#list (mean(as.numeric(levels(Flushes[Flushes]))))
#df$Flushes []
#levels(df$Flushes)
#mean_Flushes <- df %>%
#mutate(date = floor_date(df$date.x)) %>%
#group_by(date, Flushes) %>%
#summarize(mean_date= mean(date.x), by = list(df$date.x))
#summarize(mean(as.factor(levels(Flushes[Flushes]))))
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