R/Others_dataset.R
In nasserdr/dendroSense: Analyzing dendrometers data the easy way.
library(lubridate)
require(lubridate)
# install.packages("dplyr")
library("dplyr")
require("dplyr")
# install.packages("tapply")
library('tidyr')
library(ggplot2)
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")
Tree_data_dendro <- read.table("Tree_data_dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Tree_data_dendro)
str(Tree_data_dendro)
Tree_data_dendro$Date <- as.Date(Tree_data_dendro$Date, format = "%d.%m.%Y")
Tree_data_dendro <- Tree_data_dendro %>% rename(Dendro = Label)
# Trying a better way to do the graphs about the abortion size_________________________________________________________________________________
pcsize <- pivot_longer(Tree_data_dendro, cols=c(5:8), names_to = "PCSize", values_to = "Value",names_repair = "unique")
pcsize<- as.data.frame(pcsize)
Tree_data_dendro <- pcsize
Tree_data_dendro$PCSize <- as.factor(Tree_data_dendro$PCSize)
str(Tree_data_dendro)
head(Tree_data_dendro)
p1 <- ggplot(Tree_data_dendro,aes(x=Label, y=Value, fill=PCSize)) +
geom_bar(stat="identity", position = "dodge")
p1 + ylim(0, 20)
#geom_text(aes(label = Value...51, vjust = -0.2))
# Add "year" and "weeks" column____________________________________________________________________________________________________________________________________________________________
Tree_data_dendro["Week"] <- Tree_data_dendro$Date
Tree_data_dendro$Week <- week(Tree_data_dendro$Week)
head(Tree_data_dendro)
Tree_data_dendro["Year"] <- Tree_data_dendro$Date
head(Tree_data_dendro)
str(Tree_data_dendro)
Year <- as.Date(Tree_data_dendro$Date,format = "%Y")
format(Year,'%Y')
Tree_data_dendro$Year <- as.numeric(format(Year,'%Y'))
head(Tree_data_dendro)
df <- Tree_data_dendro
#Define factors
df$Date <- as.Date (df$Date, format ="%d.%m.%Y")
df$Flushes <- as.factor (df$Flushes)
df$Flowers <- as.factor (df$Flowers)
df$Dendro <- as.factor (df$Dendro)
df$Linie <- as.factor (df$Linie)
df$Plant <- as.factor (df$Plant)
df$Week <- as.integer (df$Week)
df$Year <- as.integer (df$Year)
df$PC2.5cm <- as.numeric (df$PC2.5cm)
df$PC5cm <- as.numeric (df$PC5cm)
df$PC7.5cm <- as.numeric (df$PC7.5cm)
df$PC10cm <- as.numeric (df$PC10cm)
df$Fruit <- as.numeric (df$Fruit)
df$TotalPC <- as.numeric (df$TotalPC)
summary(df)
head(df)
str(df)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R/data_v3")
dendro <- read.table("Dendro_dataset_v4.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(dendro)
head(df)
dendro$Date <- as.Date(dendro$Date , format = "%d.%m.%Y")
str(dendro)
df
str(dendro)
colnames(df)[colnames(df) == 'Label'] <- 'Dendro'
dendro$Date.x <- NULL
#df<- rename(df, Date= Date.x)
df2 <- merge(dendro, df, by= c("Dendro","Week", "Year"), all =TRUE)
head(df2)
str(df2)
write.csv(df2, file = "df2_v2.csv")
# Data format
pcsize <- pivot_longer(df2, cols=c(46:49), names_to = "PCSize", values_to = "Value",names_repair = "unique")
pcsize<- as.data.frame(pcsize)
df2 <- pcsize
df2$PCSize <- as.factor (df2$PCSize)
str(df2)
head(df2)
# Other data from HR
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
clima_day <- read.table("clima_day.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(clima_day)
str(clima_day)
clima_day$Date <- as.Date(clima_day$Date , format = "%d.%m.%Y")
head(dendro)
df2$Date.x <- as.Date(df2$Date.x , format = "%d.%m.%Y")
colnames(df2)[colnames(df2) == 'Date.x'] <- 'Date'
str(df2)
head(df2)
df3 <- merge(df2,clima_day, by= "Date", all.x= TRUE)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R/data_v3")
write.csv(df3, file = "df3_v3.csv")
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R/data_v3")
df3 <- read.table("df3_v3.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(df3)
head(df3)
df3$Date <- as.Date(df3$Date , format = "%d.%m.%Y")
head(df3)
str(df3)
#colnames(df3)[colnames(df3) == 'Date.x'] <- 'Date'
#Data <- Data[,-2]
#df4 <- merge(clima_day, df3, by.y = "Date", all.y =TRUE)
#Define factors
df$Date <- as.Date(df$Date, format ="%d.%m.%Y")
df3$Flushes.x <- as.factor(df3$Flushes.x)
df$Median20cm <- as.numeric(df$Median20cm)
df$Media40cm <- as.numeric (df$Media40cm)
df3$Flowers.x <- as.factor (df3$Flowers.x)
df$ET0_mm.j <- as.numeric (df$ET0_mm.j)
df$Acc_PLUVIO <- as.numeric (df$Acc_PLUVIO)
df$Dendro <- as.factor (df$Dendro)
df$DG <- as.numeric (df$DG)
df$MDS <- as.numeric (df$MDS)
df$max <- as.numeric (df$max)
df$min <- as.numeric (df$min)
df$Type <- as.factor (df$Type)
df$RADIATIONSOLAIRE <- as.numeric (df$RADIATIONSOLAIRE)
df$Windmaxm.s <- as.numeric (df$Windmaxm.s)
df$MaxTemp <- as.numeric (df$MaxTemp)
df$Micro.climate.x <- as.factor (df$Micro.climate.x)
df$Irrigation <- as.factor (df$Irrigation)
df$minVPD_kPa <- as.numeric (df$minVPD_kPa)
df$maxVPD_kPa <- as.numeric (df$maxVPD_kPa)
# Some graphs_____________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
bstats <- boxplot(df3$maxVPD_kPa ~ df3$Flushes.x, data = df3, xlab="Flushes.x", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Vapour-pressure deficit") # this will plot without any outlier points
bstats <- boxplot(df$HR...y ~ df$Flowers.x , data = df, xlab="Flowers.x", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Cumulative growth") # this will plot without any outlier points
bstats <- boxplot(df$ETP ~ df$Flushes.x , data = df, xlab="Flushes.x", ylab="TotalPC", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes.x", ylab="MDS") # this will plot without any outlier points
# Another way_________________________________________________________________________________________________________________________________
library(ggplot2)
library(viridis)
library(hrbrthemes)
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_viridis(discrete = T, option = "E") +
ggtitle("Abortion size...") +
facet_wrap(~Flushes.x) +
theme_ipsum() +
theme(legend.position="none") +
xlab("")
# Another way____________________________________________________________________________________________________________________________________
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
# Small multiple
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x), na.rm= FALSE) +
geom_bar(position="stack", stat="identity") +
scale_fill_viridis(discrete = T) +
ggtitle("Abortion size...") +
theme_ipsum() +
xlab("")
# Stacked
#attach(df3$Value...51)
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_bar(stat="identity")+
xlab("Flush intensity")+
ylab("Unit")
#geom_text(aes(label = Value...51), position= position_stack(vjust = 1.5), colour = "white")
# Another way____________________________________________________________________________________________________________________________________
ggplot(data = df3, aes(Flushes.x, Value.y, group = PCSize)) +
geom_col(aes(fill = PCSize)) +
geom_text(aes(label = Value.y), position = position_stack(vjust = 0.5))
# Another way____________________________________________________________________________________________________________________________________
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_col(position="dodge")+
geom_text(aes(label = df3$Value.y), vjust = 1.5, colour = "white")
#ploting a bar plot for the different Flushes.x where the count is Value..51 and the fill is PCSize
# Another way____________________________________________________________________________________________________________________________________
table(complete.cases(df3))
df3 <- df3[complete.cases(df3),]
library(ggplot2)
library(reshape2)
df4 <- melt(df3 , id.vars = 'time', variable.name = 'series')
# plot on same grid, each series colored differently --
# good if the series have same scale
ggplot(df4, aes(time,value)) + geom_line(aes(colour = series))
# or plot on different plots
ggplot(df4, aes(time,value)) + geom_line() + facet_grid(series ~ .)
# Another way____________________________________________________________________________________________________________________________________
plot(df3$Flushes.x ~ df3$PCSize, data=df3,
pch= as.integer(Value...51),
col= Value...51)
# Another way____________________________________________________________________________________________________________________________________
p1 <- ggplot(df3,aes(x=Flushes.x, y=Value...51, fill=PCSize),na.rm=FALSE) +
geom_bar(stat="identity", position = "dodge")
p1 + ylim(0, 20)
#geom_text(aes(label = Value...51, vjust = -0.2))
# Another way____________________________________________________________________________________________________________________________________
plot(x=df3$Flushes.x, y= df3$Value.y)
+
nasserdr/dendroSense documentation built on Dec. 21, 2021, 11:11 p.m.
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library(lubridate)
require(lubridate)
# install.packages("dplyr")
library("dplyr")
require("dplyr")
# install.packages("tapply")
library('tidyr')
library(ggplot2)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/02-original_dendro_data/Paper_1")
Tree_data_dendro <- read.table("Tree_data_dendro.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(Tree_data_dendro)
str(Tree_data_dendro)
Tree_data_dendro$Date <- as.Date(Tree_data_dendro$Date, format = "%d.%m.%Y")
Tree_data_dendro <- Tree_data_dendro %>% rename(Dendro = Label)
# Trying a better way to do the graphs about the abortion size_________________________________________________________________________________
pcsize <- pivot_longer(Tree_data_dendro, cols=c(5:8), names_to = "PCSize", values_to = "Value",names_repair = "unique")
pcsize<- as.data.frame(pcsize)
Tree_data_dendro <- pcsize
Tree_data_dendro$PCSize <- as.factor(Tree_data_dendro$PCSize)
str(Tree_data_dendro)
head(Tree_data_dendro)
p1 <- ggplot(Tree_data_dendro,aes(x=Label, y=Value, fill=PCSize)) +
geom_bar(stat="identity", position = "dodge")
p1 + ylim(0, 20)
#geom_text(aes(label = Value...51, vjust = -0.2))
# Add "year" and "weeks" column____________________________________________________________________________________________________________________________________________________________
Tree_data_dendro["Week"] <- Tree_data_dendro$Date
Tree_data_dendro$Week <- week(Tree_data_dendro$Week)
head(Tree_data_dendro)
Tree_data_dendro["Year"] <- Tree_data_dendro$Date
head(Tree_data_dendro)
str(Tree_data_dendro)
Year <- as.Date(Tree_data_dendro$Date,format = "%Y")
format(Year,'%Y')
Tree_data_dendro$Year <- as.numeric(format(Year,'%Y'))
head(Tree_data_dendro)
df <- Tree_data_dendro
#Define factors
df$Date <- as.Date (df$Date, format ="%d.%m.%Y")
df$Flushes <- as.factor (df$Flushes)
df$Flowers <- as.factor (df$Flowers)
df$Dendro <- as.factor (df$Dendro)
df$Linie <- as.factor (df$Linie)
df$Plant <- as.factor (df$Plant)
df$Week <- as.integer (df$Week)
df$Year <- as.integer (df$Year)
df$PC2.5cm <- as.numeric (df$PC2.5cm)
df$PC5cm <- as.numeric (df$PC5cm)
df$PC7.5cm <- as.numeric (df$PC7.5cm)
df$PC10cm <- as.numeric (df$PC10cm)
df$Fruit <- as.numeric (df$Fruit)
df$TotalPC <- as.numeric (df$TotalPC)
summary(df)
head(df)
str(df)
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R/data_v3")
dendro <- read.table("Dendro_dataset_v4.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(dendro)
head(df)
dendro$Date <- as.Date(dendro$Date , format = "%d.%m.%Y")
str(dendro)
df
str(dendro)
colnames(df)[colnames(df) == 'Label'] <- 'Dendro'
dendro$Date.x <- NULL
#df<- rename(df, Date= Date.x)
df2 <- merge(dendro, df, by= c("Dendro","Week", "Year"), all =TRUE)
head(df2)
str(df2)
write.csv(df2, file = "df2_v2.csv")
# Data format
pcsize <- pivot_longer(df2, cols=c(46:49), names_to = "PCSize", values_to = "Value",names_repair = "unique")
pcsize<- as.data.frame(pcsize)
df2 <- pcsize
df2$PCSize <- as.factor (df2$PCSize)
str(df2)
head(df2)
# Other data from HR
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")
clima_day <- read.table("clima_day.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(clima_day)
str(clima_day)
clima_day$Date <- as.Date(clima_day$Date , format = "%d.%m.%Y")
head(dendro)
df2$Date.x <- as.Date(df2$Date.x , format = "%d.%m.%Y")
colnames(df2)[colnames(df2) == 'Date.x'] <- 'Date'
str(df2)
head(df2)
df3 <- merge(df2,clima_day, 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/data_v3")
write.csv(df3, file = "df3_v3.csv")
setwd("~/mnt/Data-Work-RE/26_Agricultural_Engineering-RE/263_DP/03_Persoenliche_Unterlagen/Wata/08-R_dendro_files/06-Paper_1_R/data_v3")
df3 <- read.table("df3_v3.csv", header = TRUE, sep = ";", stringsAsFactors = FALSE, encoding = "latin1")
head(df3)
head(df3)
df3$Date <- as.Date(df3$Date , format = "%d.%m.%Y")
head(df3)
str(df3)
#colnames(df3)[colnames(df3) == 'Date.x'] <- 'Date'
#Data <- Data[,-2]
#df4 <- merge(clima_day, df3, by.y = "Date", all.y =TRUE)
#Define factors
df$Date <- as.Date(df$Date, format ="%d.%m.%Y")
df3$Flushes.x <- as.factor(df3$Flushes.x)
df$Median20cm <- as.numeric(df$Median20cm)
df$Media40cm <- as.numeric (df$Media40cm)
df3$Flowers.x <- as.factor (df3$Flowers.x)
df$ET0_mm.j <- as.numeric (df$ET0_mm.j)
df$Acc_PLUVIO <- as.numeric (df$Acc_PLUVIO)
df$Dendro <- as.factor (df$Dendro)
df$DG <- as.numeric (df$DG)
df$MDS <- as.numeric (df$MDS)
df$max <- as.numeric (df$max)
df$min <- as.numeric (df$min)
df$Type <- as.factor (df$Type)
df$RADIATIONSOLAIRE <- as.numeric (df$RADIATIONSOLAIRE)
df$Windmaxm.s <- as.numeric (df$Windmaxm.s)
df$MaxTemp <- as.numeric (df$MaxTemp)
df$Micro.climate.x <- as.factor (df$Micro.climate.x)
df$Irrigation <- as.factor (df$Irrigation)
df$minVPD_kPa <- as.numeric (df$minVPD_kPa)
df$maxVPD_kPa <- as.numeric (df$maxVPD_kPa)
# Some graphs_____________________________________________________________________________________________________________________________________
par (mfrow= c (1, 1))
bstats <- boxplot(df3$maxVPD_kPa ~ df3$Flushes.x, data = df3, xlab="Flushes.x", ylab="DR", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes", ylab="Vapour-pressure deficit") # this will plot without any outlier points
bstats <- boxplot(df$HR...y ~ df$Flowers.x , data = df, xlab="Flowers.x", ylab="Abortion", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flowers", ylab="Cumulative growth") # this will plot without any outlier points
bstats <- boxplot(df$ETP ~ df$Flushes.x , data = df, xlab="Flushes.x", ylab="TotalPC", col = "lightgray")
#need to "waste" this plot
bstats$out <- NULL
bstats$group <- NULL
bxp(bstats,xlab="Flushes.x", ylab="MDS") # this will plot without any outlier points
# Another way_________________________________________________________________________________________________________________________________
library(ggplot2)
library(viridis)
library(hrbrthemes)
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_bar(position="dodge", stat="identity") +
scale_fill_viridis(discrete = T, option = "E") +
ggtitle("Abortion size...") +
facet_wrap(~Flushes.x) +
theme_ipsum() +
theme(legend.position="none") +
xlab("")
# Another way____________________________________________________________________________________________________________________________________
# library
library(ggplot2)
library(viridis)
library(hrbrthemes)
# Small multiple
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x), na.rm= FALSE) +
geom_bar(position="stack", stat="identity") +
scale_fill_viridis(discrete = T) +
ggtitle("Abortion size...") +
theme_ipsum() +
xlab("")
# Stacked
#attach(df3$Value...51)
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_bar(stat="identity")+
xlab("Flush intensity")+
ylab("Unit")
#geom_text(aes(label = Value...51), position= position_stack(vjust = 1.5), colour = "white")
# Another way____________________________________________________________________________________________________________________________________
ggplot(data = df3, aes(Flushes.x, Value.y, group = PCSize)) +
geom_col(aes(fill = PCSize)) +
geom_text(aes(label = Value.y), position = position_stack(vjust = 0.5))
# Another way____________________________________________________________________________________________________________________________________
ggplot(df3, aes(fill=PCSize, y=Value.y, x=Flushes.x)) +
geom_col(position="dodge")+
geom_text(aes(label = df3$Value.y), vjust = 1.5, colour = "white")
#ploting a bar plot for the different Flushes.x where the count is Value..51 and the fill is PCSize
# Another way____________________________________________________________________________________________________________________________________
table(complete.cases(df3))
df3 <- df3[complete.cases(df3),]
library(ggplot2)
library(reshape2)
df4 <- melt(df3 , id.vars = 'time', variable.name = 'series')
# plot on same grid, each series colored differently --
# good if the series have same scale
ggplot(df4, aes(time,value)) + geom_line(aes(colour = series))
# or plot on different plots
ggplot(df4, aes(time,value)) + geom_line() + facet_grid(series ~ .)
# Another way____________________________________________________________________________________________________________________________________
plot(df3$Flushes.x ~ df3$PCSize, data=df3,
pch= as.integer(Value...51),
col= Value...51)
# Another way____________________________________________________________________________________________________________________________________
p1 <- ggplot(df3,aes(x=Flushes.x, y=Value...51, fill=PCSize),na.rm=FALSE) +
geom_bar(stat="identity", position = "dodge")
p1 + ylim(0, 20)
#geom_text(aes(label = Value...51, vjust = -0.2))
# Another way____________________________________________________________________________________________________________________________________
plot(x=df3$Flushes.x, y= df3$Value.y)
+
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