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tesisfunctions.R
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#This is function to pre-process my dataset and leave them in the same format directly from the CSV given by DeXCell.
setupdataset <- function(n,...){
myworkingd <- getwd()
setwd("C:/Users/Alvaro/Dropbox/TESIS/R tesis/Datos/Consumo/CSV/")
thisfile <- paste(n)
dataframe <- read.csv(file = thisfile, sep = ";", header = FALSE)[-c(1:15),c(1,2,12,18)]
names(dataframe) = c("Day", "Hour", "Consumption", "Planta4")
dataframe$Day<- as.POSIXct(as.character(dataframe$Day), format = "%d/%m/%Y")
dataframe$Hour <- as.character.POSIXt(dataframe$Hour) #Por defecto, la columna Hour tiene 34 levels de factors. Lo reduzco a 24.
dataframe$Hour <- as.factor(dataframe$Hour)
dataframe$Consumption <- as.numeric(gsub(",", "." ,dataframe$Consumption))
dataframe$Planta4 <-as.numeric(gsub(",", "." ,dataframe$Planta4))
row.names(dataframe) <- c(1:nrow(dataframe))
dataframe$Date <- paste(dataframe$Day, dataframe$Hour)
dataframe <- dataframe[,c(1,2,5,3,4)]
dataframe$Date <- as.POSIXct(as.character(dataframe$Date), format = "%Y-%m-%d %H:%M")
setwd("C:/Users/Alvaro/Dropbox/TESIS/R tesis/")
return(dataframe)
}
#This is function to pre-process my dataset and leave them in the same format.
setupmyforecast <- function(n,...){
myfile <- paste(n)
dataframe <- read.csv(file = myfile, sep = ",", header = FALSE)[-c(1),c(1,2,3)]
names(dataframe) = c("Date", "Consumption", "Forecast")
dataframe$Date <- as.POSIXct(as.character(dataframe$Date), format = "%Y-%m-%d %H:%M")
dataframe$Consumption <- as.numeric(as.character(dataframe$Consumption))
dataframe$Forecast <- as.numeric(as.character(dataframe$Forecast))
return(dataframe)
}
#Graphics original plots
#In order to plot the data of interest, in this case the energy consumptio versus the date, I create a function to set-up the design of the plot:
#same with dataframes list. The funcion argument is a number that it is related with the list mydataframes. To check properly the dataframe you
#want to plot you have to first know which position of the list are u interested in.
graphmydataframe <- function(x, onelist, ...){
dates <- c(date(onelist[[x]]$Date[1]), date(onelist[[x]]$Date[nrow(onelist[[x]])]))
selection <- onelist[[x]]$Date[which(hour(onelist[[x]]$Date) == 00)]
ggplot(data = onelist[[x]], aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "General"), size = 0.8) +
geom_line(aes(y = Planta4, colour = "Planta 4"), size = 0.8) +
geom_vline(xintercept = as.numeric(selection), linetype=1, colour="gray", na.rm = TRUE, size = 0.1) +
ggtitle(names(onelist)[x]) +
labs(x="", y= "Energy in kWh") +
scale_colour_manual(name = "Consumption", values=c("red", "darkolivegreen4")) +
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
coord_cartesian(ylim=c(0, 150))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 10),
axis.text.x = element_text(size = 9, angle = 270, vjust = -1.2),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1),
legend.background = element_rect(fill = "white", colour = NA),
legend.text = element_text(size = rel(0.8), color="black" ),
legend.text.align = NULL,
legend.title = element_text(size = rel(0.8), face = "bold", hjust = 0, color="black"),
legend.title.align = NULL)
}
graphmyforecast <- function(x, onelist, ...){
dates <- c(date(onelist[[x]]$Date[1]), date(onelist[[x]]$Date[nrow(onelist[[x]])]))
selection <- onelist[[x]]$Date[which(hour(onelist[[x]]$Date) == 00)]
ggplot(data = onelist[[x]], aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "Actual"), size = 0.8) +
geom_line(aes(y = Forecast, colour = "Forecast"), size = 0.8) +
geom_vline(xintercept = as.numeric(selection), linetype=1, colour="gray", na.rm = TRUE, size = 0.1) +
ggtitle(names(onelist)[x]) +
labs(x="", y= "Energy in kWh") +
scale_colour_manual(name = "Consumption", values=c("red", "dodgerblue4"))+
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
#coord_cartesian(ylim=c(0, 150))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 20),
axis.text.x = element_text(size = 15, angle = 270, vjust = -1.2),
axis.title = element_text(size=14,face="bold"),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1),
legend.background = element_rect(fill = "white", colour = NA),
legend.text = element_text(size = 14, color="black" ),
legend.text.align = NULL,
legend.title = element_text(size = 18, face = "bold", hjust = 0, color="black"),
legend.title.align = NULL)
}
graphmytemp <- function(x, onelist, ...){
dates <- c(date(onelist[[x]]$Date[1]), date(onelist[[x]]$Date[nrow(onelist[[x]])]))
selection <- onelist[[x]]$Date[which(hour(onelist[[x]]$Date) == 00)]
ggplot(data = onelist[[x]], aes(x = Date)) +
geom_line(aes(y = MyTemp, colour = "Temperat"), size = 0.8) +
geom_vline(xintercept = as.numeric(selection), linetype=1, colour="gray", na.rm = TRUE, size = 0.1) +
ggtitle(names(onelist)[x]) +
labs(x="", y= "Temperature in (ºC)") +
scale_colour_manual(name = "Tª", values=c("darkgreen"))+
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
coord_cartesian(ylim=c(0, 40))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 10),
axis.text.x = element_text(size = 9, angle = 270, vjust = -1.2),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1))
}
graphmyconsumption <- function(x, onelist, ...){
dates <- c(date(onelist[[x]]$Date[1]), date(onelist[[x]]$Date[nrow(onelist[[x]])]))
selection <- onelist[[x]]$Date[which(hour(onelist[[x]]$Date) == 00)]
ggplot(data = onelist[[x]], aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "General"), size = 0.8) +
geom_vline(xintercept = as.numeric(selection), linetype=1, colour="gray", na.rm = TRUE, size = 0.1) +
ggtitle(names(onelist)[x]) +
labs(x="", y= "Energy in kWh") +
scale_colour_manual(name = "Consumption", values=c("red")) +
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
coord_cartesian(ylim=c(0, 150))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 10),
axis.text.x = element_text(size = 9, angle = 270, vjust = -1.2),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1),
legend.background = element_rect(fill = "white", colour = NA),
legend.text = element_text(size = rel(0.8), color="black" ),
legend.text.align = NULL,
legend.title = element_text(size = rel(0.8), face = "bold", hjust = 0, color="black"),
legend.title.align = NULL)
}
graphmysinglelonggraph <- function(x, onelist, ...){
dates <- c(date(onelist[[x]]$Date[1]), date(onelist[[x]]$Date[nrow(onelist[[x]])]))
ggplot(data = onelist[[x]], aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "General"), size = 0.8) +
ggtitle(names(onelist)[x]) +
labs(x="", y= "Energy in kWh") +
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
coord_cartesian(ylim=c(0, 150))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 10),
axis.text.x = element_text(size = 9, angle = 270, vjust = -1.2),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1))
}
graphmygraph <- function(dataframe, ...){
dates <- c(date(dataframe$Date[1]), date(dataframe$Date[nrow(dataframe)]))
selection <- dataframe$Date[which(hour(dataframe$Date) == 00)]
ggplot(data = dataframe, aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "Actual"), size = 0.8) +
geom_line(aes(y = Forecast, colour = "Forecast"), size = 0.8) +
geom_vline(xintercept = as.numeric(selection), linetype=1, colour="gray", na.rm = TRUE, size = 0.1) +
ggtitle("Data") +
labs(x="", y= "Energy in kWh") +
scale_colour_manual(name = "Consumption", values=c("red", "dodgerblue4"))+
scale_x_datetime(labels = date_format("%b-%d"), breaks=pretty_breaks(n = 32)(as_datetime(dates))) +
coord_cartesian(ylim=c(0, 150))+
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(colour = "gray", size = 0.1),
panel.grid.major.x = element_blank(),
panel.background = element_blank(),
axis.text.y = element_text(colour="black", size = 10),
axis.text.x = element_text(size = 9, angle = 270, vjust = -1.2),
axis.ticks.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 20, vjust = 1),
panel.border = element_rect(colour = "gray", fill= NA, size=0.1),
legend.background = element_rect(fill = "white", colour = NA),
legend.text = element_text(size = rel(0.8), color="black" ),
legend.text.align = NULL,
legend.title = element_text(size = rel(0.8), face = "bold", hjust = 0, color="black"),
legend.title.align = NULL)
}
#Functions to obtain error metrics
#We calculate the key performance indicators : MAE, RelativeError, R2 and RMSE. For that purpose the function "myerrorcalculations" is written with the dataframe with the data as an argument. Also
#function cleanMYdataset carries the process over a copy of the Comparison Dataframe to which it applies the cleaning process step by step and stores the error metrics in a table.
myerrorcalculations <- function(dataframe,...){
#Update the dataframe to obtain the error metrics
dataframe$Consumption[which(is.na(dataframe$Forecast))] <- NA #Both Vectors need to have the same NAs in order for the R squared to be calculated
dataframe$Dif <- dataframe$Consumption - dataframe$Forecast
dataframe$RelativeError <- round(dataframe$Dif/dataframe$Consumption*100, digits = 2)
dataframe$SSE <- round(( dataframe$Consumption - dataframe$Forecast)^2, digits = 2)
dataframe$SST <- round(( dataframe$Consumption - mean(dataframe$Consumption, na.rm = TRUE))^2, digits = 2)
dataframe$SSR <- round(( dataframe$Forecast - mean( dataframe$Consumption, na.rm = TRUE))^2, digits = 2)
#Mean absolute error (MAE). Is the same as Mean() o Aritmetic average.
errorMAE <- sum(abs(dataframe$Dif), na.rm = TRUE)/(length(which(!is.na(dataframe$Dif))))
#Mean Relative Error. Old RelativeError (Percentage RelativeError from real consumption)
AVGRelativeError <- mean(abs(dataframe$RelativeError), na.rm = TRUE)
#Root Mean Square (RMSE)
RMSe <- sqrt(sum((dataframe$Dif)^2, na.rm = TRUE)/length(which(!is.na(dataframe$Dif))))
#R squared
coeffdet <- cor(dataframe$Consumption, dataframe$Forecast, use = "pairwise.complete.obs" ) ^ 2 # R squared is correlation of the two vectores squared
#Std deviation
stdeviation <- sd(dataframe$Dif, na.rm = TRUE)
#R2 General
rsquaredGEN <- (1 - sum(dataframe$SSE, na.rm = TRUE)/sum(dataframe$SST, na.rm = TRUE))
# Gathering together all the errors into a single error vector
NAsForTOT <- length(which(is.na(dataframe$Forecast)))/length(dataframe$Forecast)*100
errorsvector <- c(stdeviation, errorMAE, AVGRelativeError, RMSe, rsquaredGEN, coeffdet, NAsForTOT)
names(errorsvector) <- c("Std Dev", "MAE (kWh)", "MRE (%)", "RMSE", "R2 General", "Coeff. Det.", "Missed Values (%)" )
return(errorsvector)
}
myerrorcalculationsBIS <- function(dataframe,n,...){
#Update the dataframe to obtain the error metrics
dataframe$Consumption[which(is.na(dataframe$Forecast))] <- NA #Both Vectors need to have the same NAs in order for the R squared to be calculated
dataframe$Dif <- dataframe$Consumption - dataframe$Forecast
dataframe$BPE <- round(dataframe$Dif/dataframe$Consumption*100, digits = 2)
dataframe$APE <- round(abs(dataframe$Dif)/abs(dataframe$Consumption)*100, digits = 2)
dataframe$SSE <- round(( dataframe$Consumption - dataframe$Forecast)^2, digits = 2)
dataframe$SSR <- round(( dataframe$Forecast - mean( dataframe$Forecast, na.rm = TRUE))^2, digits = 2)
dataframe$SST <- dataframe$SSE + dataframe$SSR
#Mean absolute error (MAPE). Is the same as Mean() o Aritmetic average.
errorMAE <- round( sum(abs(dataframe$Dif), na.rm = TRUE)/(length(which(!is.na(dataframe$Dif)))) , digits = 2)
#Mean absolute error (MAPE). Is the same as Mean() o Aritmetic average.
errorMAPE <- round( sum(abs(dataframe$Dif)/dataframe$Consumption*100, na.rm = TRUE)/(length(which(!is.na(dataframe$Dif)))) , digits = 2)
#Mean Biased Error
errorMBPE <- round ( mean(dataframe$BPE, na.rm = TRUE) , digits = 2)
#Root Mean Square (RMSE)
RMSe <- round( sqrt(sum((dataframe$Dif)^2, na.rm = TRUE)/length(which(!is.na(dataframe$Dif)))) , digits = 2)
#R2
rsquaredGEN <- round( (1 - sum(dataframe$SSE, na.rm = TRUE)/sum(dataframe$SST, na.rm = TRUE)) , digits = 2)
# Gathering together all the errors into a single error vector
NAsForTOT <- length(which(is.na(dataframe$Forecast)))/length(dataframe$Forecast)*100
errorsvectorBIS <- round( c(errorMAE,errorMAPE, errorMBPE, RMSe, rsquaredGEN, NAsForTOT) , digits = 2)
names(errorsvectorBIS) <- c("MAE (kWh)", "MAPE (%)", "MBPE (%)", "RMSE (kWh)", "R2", "NAs(%)" )
setwd("C:/Users/Alvaro/Dropbox/TESIS/R tesis/TablesResults/")
write.csv(errorsvectorBIS, file = paste(n,"TableResults.csv", sep = "-"))
return(errorsvectorBIS)
}
cleanMYdataset <- function(dataframe,...) {
#------------- RAW
metricasRAW <- myerrorcalculations(dataframe) # Obtain errors in RAW dataset
#------------- OUTLIERS
differlimit <- 0.9*max(dataframe$Consumption)
toEraseOutlier <- which(abs(dataframe$Dif) > differlimit)
print(toEraseOutlier)
dataframe$Forecast[toEraseOutlier] <- NA
metricasOUTLIER <- myerrorcalculations(dataframe) # Obtain errors after first modification (1-. Set Outliers to NAs)
metricasOUTLIER
#------------- ZERO VALUES
toEraseZero <- which(dataframe$Forecast == 0)
dataframe$Forecast[toEraseZero] <- NA
metricasZERO <- myerrorcalculations(dataframe) # Obtain errors after second modification (2-. Set Zero Values to NAs)
#------------- BELOW BASE LOAD
weekends <- dataframe %>% filter(DayType == "Saturday" | DayType == "DOMINGO")
myrange <- 0.6
toEraseUnderBL <- which(dataframe$Forecast > 0 & dataframe$Forecast < (myrange*min(dataframe$Consumption)))
dataframe$Forecast[toEraseUnderBL] <- NA
metricasBASELOAD <- myerrorcalculations(dataframe) # Obtain errors after third modification (3-. Set UnderBase Values to NAs)
#------------- HOLIDAYS
detach("package:plyr")
detectHOLIDAY <- dataframe %>%
filter(hours(dataframe$Date) >= 07 & hours(dataframe$Date) < 19) #Filter by workingHours
detectHOLIDAY <- detectHOLIDAY %>%
group_by(Day) %>%
summarise(avgcon = mean(Consumption, na.rm = TRUE),
avgfor = mean(Forecast, na.rm =TRUE))
detectHOLIDAY$avgDIF <- detectHOLIDAY$avgfor - detectHOLIDAY$avgcon
forecastlimit <- 40
overforecastlimit <- which(detectHOLIDAY$avgDIF > forecastlimit) #Posicion del vector que nos dirá los días con la AVG > forecastlimit
myfailHolidaysdates <- detectHOLIDAY[overforecastlimit,]$Day #Días detectados como Holidays en los que se ha predecido consumo
toEraseHoliday <- which(dataframe$Day %in% myfailHolidaysdates) #Filas del Dataset que ocupan todos estos días.
library(plyr)
dataframe$Forecast[toEraseHoliday] <- NA
metricasHOLIDAYS <- myerrorcalculations(dataframe) # Obtain errors after third modification (4-. Set HOLIDAYS to NAs)
#------------- STRANGE
strangeforecast <- as.POSIXct(c("2016-12-16","2016-11-04","2016-11-25", "2016-10-17"))
toEraseSTRANGE <- which((dataframe$Day %in% strangeforecast & !is.na(dataframe$Forecast ))) #!is.na is needed in order to obtain the proper
#length of the elements to be erased
dataframe$Forecast[toEraseSTRANGE] <- NA
metricasSTRANGE <- myerrorcalculations(dataframe) # Obtain errors after third modification (5-. Set STRANGE to NAs)
#------------- ALLTOGTHER
metricasRAW <- as.data.frame(metricasRAW)
metricasOUTLIER <- as.data.frame(metricasOUTLIER)
metricasZERO <- as.data.frame(metricasZERO)
metricasBASELOAD <- as.data.frame(metricasBASELOAD)
metricasHOLIDAYS <- as.data.frame(metricasHOLIDAYS)
metricasSTRANGE <- as.data.frame(metricasSTRANGE)
metricasFINAL <- metricasSTRANGE
metricasERROR <- cbind(metricasRAW, metricasOUTLIER,metricasZERO, metricasBASELOAD, metricasHOLIDAYS, metricasFINAL)
names(metricasERROR) <- c("Raw Data", "wo/ Outliers", "wo/ZEROs", "wo/Below BS", "wo/HOLIDAYS", "wo/Strange B")
metricasERROR <- round(t(metricasERROR), digits = 3)
return(metricasERROR)
}
cleanMYdatasetBIS <- function(dataframe,...) {
#------------- RAW
metricasRAW <- myerrorcalculationsBIS(dataframe) # Obtain errors in RAW dataset
#------------- OUTLIERS
differlimit <- 0.9*max(dataframe$Consumption)
toEraseOutlier <- which(abs(dataframe$Dif) > differlimit)
print(toEraseOutlier)
dataframe$Forecast[toEraseOutlier] <- NA
metricasOUTLIER <- myerrorcalculationsBIS(dataframe) # Obtain errors after first modification (1-. Set Outliers to NAs)
metricasOUTLIER
#------------- ZERO VALUES
toEraseZero <- which(dataframe$Forecast == 0)
dataframe$Forecast[toEraseZero] <- NA
metricasZERO <- myerrorcalculationsBIS(dataframe) # Obtain errors after second modification (2-. Set Zero Values to NAs)
#------------- BELOW BASE LOAD
weekends <- dataframe %>% filter(DayType == "Saturday" | DayType == "DOMINGO")
myrange <- 0.6
toEraseUnderBL <- which(dataframe$Forecast > 0 & dataframe$Forecast < (myrange*min(dataframe$Consumption)))
dataframe$Forecast[toEraseUnderBL] <- NA
metricasBASELOAD <- myerrorcalculationsBIS(dataframe) # Obtain errors after third modification (3-. Set UnderBase Values to NAs)
#------------- HOLIDAYS
detach("package:plyr")
detectHOLIDAY <- dataframe %>%
filter(hours(dataframe$Date) >= 07 & hours(dataframe$Date) < 19) #Filter by workingHours
detectHOLIDAY <- detectHOLIDAY %>%
group_by(Day) %>%
summarise(avgcon = mean(Consumption, na.rm = TRUE),
avgfor = mean(Forecast, na.rm =TRUE))
detectHOLIDAY$avgDIF <- detectHOLIDAY$avgfor - detectHOLIDAY$avgcon
forecastlimit <- 40
overforecastlimit <- which(detectHOLIDAY$avgDIF > forecastlimit) #Posicion del vector que nos dirá los días con la AVG > forecastlimit
myfailHolidaysdates <- detectHOLIDAY[overforecastlimit,]$Day #Días detectados como Holidays en los que se ha predecido consumo
toEraseHoliday <- which(dataframe$Day %in% myfailHolidaysdates) #Filas del Dataset que ocupan todos estos días.
library(plyr)
dataframe$Forecast[toEraseHoliday] <- NA
metricasHOLIDAYS <- myerrorcalculationsBIS(dataframe) # Obtain errors after third modification (4-. Set HOLIDAYS to NAs)
#------------- STRANGE
strangeforecast <- as.POSIXct(c("2016-12-16","2016-11-04","2016-11-25", "2016-10-17"))
toEraseSTRANGE <- which((dataframe$Day %in% strangeforecast & !is.na(dataframe$Forecast ))) #!is.na is needed in order to obtain the proper
#length of the elements to be erased
dataframe$Forecast[toEraseSTRANGE] <- NA
metricasSTRANGE <- myerrorcalculationsBIS(dataframe) # Obtain errors after third modification (5-. Set STRANGE to NAs)
#------------- ALLTOGTHER
metricasRAW <- as.data.frame(metricasRAW)
metricasOUTLIER <- as.data.frame(metricasOUTLIER)
metricasZERO <- as.data.frame(metricasZERO)
metricasBASELOAD <- as.data.frame(metricasBASELOAD)
metricasHOLIDAYS <- as.data.frame(metricasHOLIDAYS)
metricasSTRANGE <- as.data.frame(metricasSTRANGE)
metricasFINAL <- metricasSTRANGE
metricasERRORbis <- cbind(metricasRAW, metricasOUTLIER,metricasZERO, metricasBASELOAD, metricasHOLIDAYS, metricasFINAL)
names(metricasERRORbis) <- c("Raw Data", "wo/Outliers", "wo/ZVs", "wo/BelowBBL", "wo/HOLIDAYS", "wo/StrangeB")
metricasERRORbis <- round(t(metricasERRORbis), digits = 2)
return(metricasERRORbis)
}
# FUNCTIONS to prepare the RESULTS and graph BOXPLOTs.
# Script to create a function to compare results of different models.
#This funnction will have as inputa a dtaset result of carrying out a prediction model and the three columns expected are:
# Date / Consumption / Forecast
resultsevaluation <- function(dataframe, model,...){
dataframe$Forecast <- model$pred
dataframe$Dif <- dataframe$Consumption - dataframe$Forecast
dataframe$BPE <- round(dataframe$Dif/dataframe$Consumption*100, digits = 2)
dataframe$APE <- round(abs(dataframe$Dif)/abs(dataframe$Consumption)*100, digits = 2)
# dataframe$Seasson <- 0
# for(i in 1:nrow(dataframe)){
# if( dataframe$Date[i] <= "2016-06-20 23:00"){
# dataframe$Seasson[i] <- 1
# } else if( dataframe$Date[i] > "2016-06-20 23:00" & dataframe$Date[i] <= "2016-09-22 00:00"){
# dataframe$Seasson[i] <- 2
# } else if( dataframe$Date[i] > "2016-09-22 00:00" & dataframe$Date[i] <= "2016-12-21 00:00"){
# dataframe$Seasson[i] <- 3
# } else if( dataframe$Date[i] > "2016-12-21 00:00" & dataframe$Date[i] <= "2017-03-20 15:00"){
# dataframe$Seasson[i] <- 4
# } else if( dataframe$Date[i] > "2017-03-20 15:00" & dataframe$Date[i] <= "2017-06-21 15:00"){
# dataframe$Seasson[i] <- 1
# } else if( dataframe$Date[i] > "2017-06-21 15:00" & dataframe$Date[i] <= "2017-09-22 15:00"){
# dataframe$Seasson[i] <- 2
# }
# }
#
# dataframe$Seasson <- as.factor(dataframe$Seasson)
# #dataframe$Seasson <- factor(dataframe$Seasson,
# labels = c("Spring", "Summer", "Autumn", "Winter"))
return(dataframe)
}
resultsevaluationANN <- function(dataframe,...){
dataframe$Dif <- dataframe$Consumption - dataframe$Forecast
dataframe$BPE <- round(dataframe$Dif/dataframe$Consumption*100, digits = 2)
dataframe$APE <- round(abs(dataframe$Dif)/abs(dataframe$Consumption)*100, digits = 2)
# dataframe <- dataframe[,c(1:5,14,6:13)]
return(dataframe)
}
graphmyresults <- function(dataframe,n,...){
fill <- "#4271AE"
line <- "#1F3552"
meanAPEcategory <- aggregate(APE ~ DayCategory, dataframe, mean)
# meanAPEseasson <- aggregate(APE ~ Seasson, dataframe, mean)
meanAPEhour <- aggregate(APE ~ Hour, dataframe, mean)
meanAPEmonth <- aggregate(APE ~ Month, dataframe, mean)
meanBPEcategory <- aggregate(BPE ~ DayCategory, dataframe, mean)
# meanBPEseasson <- aggregate(BPE ~ Seasson, dataframe, mean)
meanBPEhour <- aggregate(BPE ~ Hour, dataframe, mean)
meanBPEmonth <- aggregate(BPE ~ Month, dataframe, mean)
# BY DAY CATEGORY
APEcategory <- ggplot(dataframe, aes(x = DayCategory, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by Day Category") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEcategory, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEcategory
partname <- paste(substr(n,1,3),"/", sep ="")
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APEcategory.png", sep = "-"), width = 709, height = 800)
APEcategory
print(APEcategory)
dev.off()
BPEcategory <- ggplot(dataframe, aes(x = DayCategory, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),
limits=c(-200, 150)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("BPE by Day Category") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
geom_text(data = meanBPEcategory, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
BPEcategory
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPEcategory.png", sep = "-"), width = 709, height = 800)
BPEcategory
print(BPEcategory)
dev.off()
#
# # BY SEASSON
#
# APEseasson <- ggplot(dataframe, aes(x = Seasson, y = APE)) +
# geom_boxplot(fill = fill, colour = line, alpha = 0.7,
# outlier.colour = "#1F3552", outlier.shape = 20) +
# scale_y_continuous(name = "Percentage",
# breaks = seq(0, 175, 25),
# limits=c(0, 175)) +
# #scale_x_discrete(name = "Day of the Week") +
# ggtitle("APE by Season") +
# stat_summary(fun.y=mean, colour="darkred", geom="point",
# shape=18, size=3,show_guide = FALSE) +
# scale_colour_manual(name = "Consumption") +
# geom_text(data = meanAPEseasson, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
# theme(axis.text.y = element_text(colour="black", size = 12),
# axis.text.x = element_text(size = 12, face = "bold"),
# axis.title.y = element_text(size=14,face="bold"),
# axis.title.x=element_blank(),
# plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
# APEseasson
#
# setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
# png(paste(n,"APE-Seasson.png", sep = "-"), width = 709, height = 800)
# APEseasson
# print(APEseasson)
# dev.off()
#
# BPEseasson <- ggplot(dataframe, aes(x = Seasson, y = BPE)) +
# geom_boxplot(fill = fill, colour = line, alpha = 0.7,
# outlier.colour = "#1F3552", outlier.shape = 20) +
# scale_y_continuous(name = "Percentage",
# breaks = seq(-200, 150, 25),
# limits=c(-200, 150)) +
# #scale_x_discrete(name = "Day of the Week") +
# ggtitle("BPE by Season") +
# stat_summary(fun.y=mean, colour="darkred", geom="point",
# shape=18, size=3,show_guide = FALSE) +
# geom_text(data = meanBPEseasson, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
# theme(axis.text.y = element_text(colour="black", size = 12),
# axis.text.x = element_text(size = 12, face = "bold"),
# axis.title.y = element_text(size=14,face="bold"),
# axis.title.x=element_blank(),
# plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
# BPEseasson
#
# setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
# png(paste(n,"BPE-seasson.png", sep = "-"), width = 709, height = 800)
# BPEseasson
# print(BPEseasson)
# dev.off()
# BY MONTH
APEmonth <- ggplot(dataframe, aes(x = Month, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by Month") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEmonth, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEmonth
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APE-month.png", sep = "-"), width = 709, height = 800)
APEmonth
print(APEmonth)
dev.off()
BPEmonth <- ggplot(dataframe, aes(x = Month, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),
limits=c(-200, 150)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("BPE by month") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
geom_text(data = meanBPEmonth, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
BPEmonth
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPE-month.png", sep = "-"), width = 709, height = 800)
BPEmonth
print(BPEmonth)
dev.off()
# BY HOUR
APEhour <- ggplot(dataframe, aes(x = Hour, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by hour") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEhour, aes(label = round( APE, digits = 1)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, angle = 90,face = "bold", vjust = 0),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEhour
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APE-hour.png", sep = "-"), width = 709, height = 800)
APEhour
print(APEhour)
dev.off()
BPEhour <- ggplot(dataframe, aes(x = Hour, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),
limits=c(-200, 150)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("BPE by hour") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
geom_text(data = meanBPEhour, aes(label = round( BPE, digits = 1)), color = "darkred", vjust = 1.2)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, angle = 90, face = "bold", vjust = 0),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
BPEhour
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPE-hour.png", sep = "-"), width = 709, height = 800)
BPEhour
print(BPEhour)
dev.off()
# Error Graphs
distributionBPE <- ggplot(dataframe, aes(BPE)) +
geom_histogram(binwidth = 1, color = line, fill = fill)+
ggtitle("BPE Distribution") +
scale_x_continuous(name = "Percentage") +
scale_y_continuous(name = "Number of instances")+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x= element_text(size=14),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
distributionBPE
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPE-distribution.png", sep = "-"), width = 709, height = 432)
distributionBPE
print(distributionBPE)
dev.off()
distributionMAPE <- ggplot(dataframe, aes(x = Date)) +
geom_point(aes(y = APE), color = line, fill = fill) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 300, 25),
limits=c(0, 300)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE Evolution") +
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
distributionMAPE
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"MAPE-Evolution.png", sep = "-"), width = 709, height = 432)
distributionMAPE
print(distributionMAPE)
dev.off()
wholeperiod <- ggplot(data = dataframe %>% filter(Date > Date2 & Date <= Date2comparison), aes(x = Date)) +
geom_line(aes(y = Consumption, colour = "Actual"), size = 0.8) +
geom_line(aes(y = Forecast, colour = "Forecast"), size = 0.8)
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"Whole-Period.png", sep = "-"), width = 709, height = 432)
wholeperiod
print(wholeperiod)
dev.off()
# finalperiod <- ggplot(data = dataframe %>% filter(Date > Date2comparison & Date <= Datevalidation), aes(x = Date)) +
# geom_line(aes(y = Consumption, colour = "Actual"), size = 0.8) +
# geom_line(aes(y = Forecast, colour = "Forecast"), size = 0.8)
#
# setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
# png(paste(n,"Final-Period.png", sep = "-"), width = 709, height = 432)
# finalperiod
# print(finalperiod)
# dev.off()
#
mygraphs <- list(APEcategory, BPEcategory, APEmonth, BPEmonth, APEhour, BPEhour, distributionBPE, distributionMAPE, wholeperiod)
return(mygraphs)
}
graphmyresultsFIVE <- function(dataframe,n,...){
fill <- "#4271AE"
line <- "#1F3552"
meanAPEcategory <- aggregate(APE ~ DayCategory, dataframe, mean)
# meanAPEseasson <- aggregate(APE ~ Seasson, dataframe, mean)
meanAPEhour <- aggregate(APE ~ Hour, dataframe, mean)
meanAPEmonth <- aggregate(APE ~ Month, dataframe, mean)
meanBPEcategory <- aggregate(BPE ~ DayCategory, dataframe, mean)
# meanBPEseasson <- aggregate(BPE ~ Seasson, dataframe, mean)
meanBPEhour <- aggregate(BPE ~ Hour, dataframe, mean)
meanBPEmonth <- aggregate(BPE ~ Month, dataframe, mean)
# BY DAY CATEGORY
APEcategory <- ggplot(dataframe, aes(x = DayCategory, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by Day Category") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEcategory, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEcategory
partname <- paste(substr(n,1,3),"/", sep ="")
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APEcategory.png", sep = "-"), width = 709, height = 800)
APEcategory
print(APEcategory)
dev.off()
BPEcategory <- ggplot(dataframe, aes(x = DayCategory, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),
limits=c(-200, 150)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("BPE by Day Category") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
geom_text(data = meanBPEcategory, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
BPEcategory
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPEcategory.png", sep = "-"), width = 709, height = 800)
BPEcategory
print(BPEcategory)
dev.off()
#
# # BY SEASSON
#
# APEseasson <- ggplot(dataframe, aes(x = Seasson, y = APE)) +
# geom_boxplot(fill = fill, colour = line, alpha = 0.7,
# outlier.colour = "#1F3552", outlier.shape = 20) +
# scale_y_continuous(name = "Percentage",
# breaks = seq(0, 175, 25),
# limits=c(0, 175)) +
# #scale_x_discrete(name = "Day of the Week") +
# ggtitle("APE by Season") +
# stat_summary(fun.y=mean, colour="darkred", geom="point",
# shape=18, size=3,show_guide = FALSE) +
# scale_colour_manual(name = "Consumption") +
# geom_text(data = meanAPEseasson, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
# theme(axis.text.y = element_text(colour="black", size = 12),
# axis.text.x = element_text(size = 12, face = "bold"),
# axis.title.y = element_text(size=14,face="bold"),
# axis.title.x=element_blank(),
# plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
# APEseasson
#
# setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
# png(paste(n,"APE-Seasson.png", sep = "-"), width = 709, height = 800)
# APEseasson
# print(APEseasson)
# dev.off()
#
# BPEseasson <- ggplot(dataframe, aes(x = Seasson, y = BPE)) +
# geom_boxplot(fill = fill, colour = line, alpha = 0.7,
# outlier.colour = "#1F3552", outlier.shape = 20) +
# scale_y_continuous(name = "Percentage",
# breaks = seq(-200, 150, 25),
# limits=c(-200, 150)) +
# #scale_x_discrete(name = "Day of the Week") +
# ggtitle("BPE by Season") +
# stat_summary(fun.y=mean, colour="darkred", geom="point",
# shape=18, size=3,show_guide = FALSE) +
# geom_text(data = meanBPEseasson, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
# theme(axis.text.y = element_text(colour="black", size = 12),
# axis.text.x = element_text(size = 12, face = "bold"),
# axis.title.y = element_text(size=14,face="bold"),
# axis.title.x=element_blank(),
# plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
# BPEseasson
#
# setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
# png(paste(n,"BPE-seasson.png", sep = "-"), width = 709, height = 800)
# BPEseasson
# print(BPEseasson)
# dev.off()
# BY MONTH
APEmonth <- ggplot(dataframe, aes(x = Month, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by Month") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEmonth, aes(label = round( APE, digits = 2)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEmonth
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APE-month.png", sep = "-"), width = 709, height = 800)
APEmonth
print(APEmonth)
dev.off()
BPEmonth <- ggplot(dataframe, aes(x = Month, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),
limits=c(-200, 150)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("BPE by month") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
geom_text(data = meanBPEmonth, aes(label = round( BPE, digits = 2)), color = "darkred", vjust = 1.2)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, face = "bold"),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
BPEmonth
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"BPE-month.png", sep = "-"), width = 709, height = 800)
BPEmonth
print(BPEmonth)
dev.off()
# BY HOUR
APEhour <- ggplot(dataframe, aes(x = Hour, y = APE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(0, 175, 25),
limits=c(0, 175)) +
#scale_x_discrete(name = "Day of the Week") +
ggtitle("APE by hour") +
stat_summary(fun.y=mean, colour="darkred", geom="point",
shape=18, size=3,show_guide = FALSE) +
scale_colour_manual(name = "Consumption") +
geom_text(data = meanAPEhour, aes(label = round( APE, digits = 1)), color = "darkred", vjust = -0.5)+
theme(axis.text.y = element_text(colour="black", size = 12),
axis.text.x = element_text(size = 12, angle = 90,face = "bold", vjust = 0),
axis.title.y = element_text(size=14,face="bold"),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, size = 18, face="bold"))
APEhour
setwd(paste0("C:/Users/Alvaro/Dropbox/TESIS/R tesis/graficas/BOXPLOT/", partname, sep=""))
png(paste(n,"APE-hour.png", sep = "-"), width = 709, height = 800)
APEhour
print(APEhour)
dev.off()
BPEhour <- ggplot(dataframe, aes(x = Hour, y = BPE)) +
geom_boxplot(fill = fill, colour = line, alpha = 0.7,
outlier.colour = "#1F3552", outlier.shape = 20) +
scale_y_continuous(name = "Percentage",
breaks = seq(-200, 150, 25),