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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=(d2-d1)*.16 | |
| } | |
| f$hippo[i1]=sum(d) | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h2 = fn$sqldf("select * from finaldata where Day=(select Day from yyy limit $h1-1,1)") | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=(d2-d1)*.16 | |
| } | |
| f$hippo[i1]=sum(d) | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h2 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| # Labelling Data properly | |
| axis(1, at=h4$Row_no, labels=h4$Label,las=2) |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=(d2-d1)*.16 | |
| } | |
| f$hippo[i1]=sum(d) | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h2 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| plot(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| lines(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| # Analysing this weekday for apt pricing plan | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| #Creating price sheet | |
| pricesheet=NULL | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=5)) | |
| pricesheet$Price=pricesheet$Hour*pricesheet$Size*.10*4 | |
| #UserChoice | |
| choice = NULL | |
| print(paste0("Enter number of blocks to be taken of your choice. ")) | |
| block= as.integer(readline()) | |
| for (g in 1:block) | |
| { | |
| print(paste0("Enter hour plan to be taken for block choice ",g)) | |
| choice$Hour[g] = as.numeric(readline()) | |
| print(paste0("Enter set plan to be taken for block choice ",g)) | |
| choice$Set[g] = as.numeric(readline()) | |
| if(choice$Hour[g]!=24) | |
| { | |
| print(paste0("Enter time plan to be taken for block choice ",g)) | |
| choice$Time[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Time[g] = NULL | |
| } | |
| if (choice$Set[g]==5) | |
| { | |
| print(paste0("Enter Start Day plan to be taken for block choice ",g)) | |
| choice$Day[g] = as.character(readline()) | |
| } | |
| } | |
| #Updating price column | |
| choice$Price = choice$Hour*choice$Set*.10*4 | |
| #Integrator for Area calculation | |
| #A = dataframe for the dataset [example using final from above] | |
| #kva = value above which data points are to be considered | |
| AreaIntegrator = function(A,kva) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA>$kva") | |
| B$Row_no = seq(1,length(B$Day)) | |
| B$ModifiedRep = (B$Rep_KVA-kva)*.16 | |
| return(sum(B$ModifiedRep)) | |
| } | |
| # Labelling Data properly | |
| # axis(1, at=h4$Row_no, labels=h4$Label,las=2) |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1 and Global_active_power!='NA'") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=ifelse(d2-d1>0,(d2-d1)*.16,0) | |
| } | |
| f$hippo[i1]=sum(d) + d1*(nn)*0.1 | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h2 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| plot(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| lines(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| # Analysing this weekday for apt pricing plan | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| #Creating price sheet | |
| pricesheet=NULL | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=5)) | |
| pricesheet$Price=pricesheet$Hour*pricesheet$Size*.10*4 | |
| #UserChoice | |
| choice = NULL | |
| print(paste0("Enter number of blocks to be taken of your choice. ")) | |
| block= as.integer(readline()) | |
| for (g in 1:block) | |
| { | |
| print(paste0("Enter hour plan to be taken for block choice ",g)) | |
| choice$Hour[g] = as.numeric(readline()) | |
| print(paste0("Enter set plan to be taken for block choice ",g)) | |
| choice$Set[g] = as.numeric(readline()) | |
| if(choice$Hour[g]!=24) | |
| { | |
| print(paste0("Enter time plan to be taken for block choice ",g)) | |
| choice$Time[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Time[g] = NULL | |
| } | |
| if (choice$Set[g]==5) | |
| { | |
| print(paste0("Enter Start Day plan to be taken for block choice ",g)) | |
| choice$Day[g] = as.character(readline()) | |
| } | |
| } | |
| #Updating price column | |
| choice$Price = choice$Hour*choice$Set*.10*4 | |
| #Integrator for Area calculation | |
| #A = dataframe for the dataset [example using final from above] | |
| #kva = value above which data points are to be considered | |
| AreaIntegrator = function(A,kva) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA>$kva") | |
| if(length(B$Day)!=0) | |
| { | |
| B$Row_no = seq(1,length(B$Day)) | |
| B$ModifiedRep = ifelse((B$Rep_KVA-kva>0),(B$Rep_KVA-kva)*.16,0) | |
| return(sum(B$ModifiedRep)) | |
| } | |
| else | |
| { | |
| return(0.0) | |
| } | |
| } | |
| # Value of Integrator for just Indexing System | |
| Total_Price_on_indexing = AreaIntegrator(final,kva = as.numeric(sqldf("select min(Rep_KVA) from final")))*.16 | |
| #Finding value of Rep_KVA such that area under that is equal to min block price plan area | |
| optim = NULL | |
| for (u in 1:length(final$Day)) | |
| { | |
| final$optim[u]=Total_Price_on_indexing-AreaIntegrator(final,as.numeric(final$Rep_KVA[u])) | |
| } | |
| #Find ceiling and floor and possible KVA values height of block [which will be output] | |
| # Labelling Data properly | |
| # axis(1, at=h4$Row_no, labels=h4$Label,las=2) |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1 and Global_active_power!='NA'") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=ifelse(d2-d1>0,(d2-d1)*.16*.4,0) | |
| } | |
| f$hippo[i1]=sum(d)+d1*(nn)*0.1*.4 | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select min(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h2 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| plot(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| lines(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| # Analysing this weekday for apt pricing plan | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| #Creating price sheet | |
| pricesheet=NULL | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=5)) | |
| pricesheet$Price=pricesheet$Hour*pricesheet$Size*.10*0.4 | |
| #UserChoice | |
| choice = NULL | |
| print(paste0("Enter number of blocks to be taken of your choice. ")) | |
| block= as.integer(readline()) | |
| for (g in 1:block) | |
| { | |
| print(paste0("Enter hour plan to be taken for block choice ",g)) | |
| choice$Hour[g] = as.numeric(readline()) | |
| print(paste0("Enter set plan to be taken for block choice ",g)) | |
| choice$Set[g] = as.numeric(readline()) | |
| if(choice$Hour[g]!=24) | |
| { | |
| print(paste0("Enter time plan to be taken for block choice ",g)) | |
| choice$Time[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Time[g] = NA | |
| } | |
| if (choice$Set[g]==5) | |
| { | |
| print(paste0("Enter Start Day plan to be taken for block choice ",g)) | |
| choice$Day[g] = as.character(readline()) | |
| } | |
| } | |
| choice=data.frame(choice) | |
| #Updating price column | |
| choice$Price = choice$Hour*choice$Set*.10*0.4 | |
| #Integrator for Area calculation | |
| #A = dataframe for the dataset [example using final from above] | |
| #kva = value above which data points are to be considered | |
| AreaIntegrator = function(A,kva,gorl=1) | |
| { | |
| if(gorl==1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA>$kva") | |
| } | |
| else if(gorl==-1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA<$kva") | |
| } | |
| if(length(B$Day)!=0) | |
| { | |
| B$Row_no = seq(1,length(B$Day)) | |
| B$ModifiedRep = ifelse((B$Rep_KVA-kva>0),(gorl*(B$Rep_KVA-kva)),0) | |
| return(sum(B$ModifiedRep)) | |
| } | |
| else | |
| { | |
| return(0.0) | |
| } | |
| } | |
| # Value of Integrator for just Indexing System | |
| Total_Price_on_indexing = AreaIntegrator(final,kva = 0.0)*.16*0.4 | |
| #Finding value of Rep_KVA such that area under that is equal to min block price plan area | |
| no=length(final$Day) | |
| for (u in 1:no) | |
| { | |
| final$optim[u]=(AreaIntegrator(final,as.numeric(final$Rep_KVA[u]))*.16*.4+final$Rep_KVA[u]*no*.4*.1) | |
| final$Unusedmoney[u]=final$optim[u]-Total_Price_on_indexing | |
| } | |
| #Find ceiling and floor and possible KVA values height of block [which will be output] | |
| # Labelling Data properly | |
| # axis(1, at=h4$Row_no, labels=h4$Label,las=2) |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1 and Global_active_power!='NA'") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=ifelse(d2-d1>0,(d2-d1)*.16*.4,0) | |
| } | |
| f$hippo[i1]=sum(d)+d1*(nn)*0.1*.4 | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select min(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| yyy =sqldf("select distinct Day from final") | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h0 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| h2 = fn$sqldf("select * from h0 order by Time") | |
| plot(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| lines(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| # Analysing this weekday for apt pricing plan | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| #Creating price sheet | |
| pricesheet=NULL | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=5)) | |
| pricesheet$Price=pricesheet$Hour*pricesheet$Size*.10*0.4 | |
| #UserChoice | |
| choice = NULL | |
| print(paste0("Enter number of blocks to be taken of your choice. ")) | |
| block= as.integer(readline()) | |
| for (g in 1:block) | |
| { | |
| print(paste0("Enter hour plan to be taken for block choice ",g)) | |
| choice$Hour[g] = as.numeric(readline()) | |
| print(paste0("Enter set plan to be taken for block choice ",g)) | |
| choice$Set[g] = as.numeric(readline()) | |
| if(choice$Hour[g]!=24) | |
| { | |
| print(paste0("Enter time plan to be taken for block choice ",g)) | |
| choice$Time[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Time[g] = NA | |
| } | |
| if (choice$Set[g]==5) | |
| { | |
| print(paste0("Enter Start Day plan to be taken for block choice ",g)) | |
| choice$Day[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Day[g] = NA | |
| } | |
| } | |
| choice=data.frame(choice) | |
| #Updating price column | |
| choice$Price = choice$Hour*choice$Set*.10*0.4 | |
| #Integrator for Area calculation | |
| #A = dataframe for the dataset [example using final from above] | |
| #kva = value above which data points are to be considered | |
| AreaIntegrator = function(A,kva,gorl=1) | |
| { | |
| if(gorl==1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA>$kva") | |
| } | |
| else if(gorl==-1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA<$kva") | |
| } | |
| if(length(B$Day)!=0) | |
| { | |
| B$Row_no = seq(1,length(B$Day)) | |
| B$ModifiedRep = ifelse((B$Rep_KVA-kva>0),(gorl*(B$Rep_KVA-kva)),0) | |
| return(sum(B$ModifiedRep)) | |
| } | |
| else | |
| { | |
| return(0.0) | |
| } | |
| } | |
| # Value of Integrator for just Indexing System | |
| Total_Price_on_indexing = AreaIntegrator(h3,kva = 0.0)*.16*0.4 | |
| # Finding optimum for 24 * 7 plan | |
| no=length(h3$Day) | |
| for (u in 1:no) | |
| { | |
| h3$optim[u]=(AreaIntegrator(h3,as.numeric(h3$Rep_KVA[u]))*.16*.4+h3$Rep_KVA[u]*no*.4*.1) | |
| h3$Unusedmoney[u]=h3$Rep_KVA[u]*no*.4*.1-(AreaIntegrator(h3,as.numeric(h3$Rep_KVA[u]),-1)*.1*.4) | |
| } | |
| PriceCalculation = function(tib,kvaheight,Hour,Set,Weekday,Time) | |
| { | |
| #Creating a equivalent kvaheight column to for applicable plan in that duration | |
| if(Hour==24) | |
| { | |
| PlanLabel = Just Weekdays dependent # Create label set from given to Set+given | |
| if(Set == 7) | |
| tib$KVAheight=ifelse(tib$Rep_KVA-kvaheight>0,tib$Rep_KVA-kvaheight,0) | |
| } | |
| else{ | |
| if(Set==7) | |
| PlanLabel = Just Time dependent # Create label set from given Time to Hour+given | |
| } | |
| PlanLabel = Label repetition based on Set amount | |
| if(Set ==5) | |
| { | |
| weekdaylist=c("Sunday", "Monday", "Tuesday","Wednesday","Thurday","Friday","Saturday") | |
| for(ooo in 1:length(weekdaylist)) | |
| { | |
| if(Weekday==weekdaylist[ooo]) | |
| { | |
| index = (ooo) | |
| } | |
| } | |
| labelweek = weekdaylist[index:index+Set] | |
| Weekday=capitalize(tolower(Weekday[1][1])) | |
| } | |
| planlabel=paste0(Weekday," - ",Time) | |
| BlockP=(kvaheight*0.4*Hour*Set*4*0.1) | |
| IndexP=Are | |
| } | |
| # Generalising optimising procedure for any plan set | |
| # Task FindLabel | |
| time=as.numeric(strsplit(g,':')[[1]][1]) | |
| if(length(grep('p', g, ignore.case = TRUE))) | |
| { | |
| time = as.numeric(time +12) | |
| }else { | |
| time = paste0("0",time) | |
| } | |
| Weekday=capitalize(tolower(Weekday[1][1])) | |
| PlanLabel=paste0(Weekday," - ",time,":00:00") | |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1 and Global_active_power!='NA'") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=ifelse(d2-d1>0,(d2-d1)*.16*.4,0) | |
| } | |
| f$hippo[i1]=sum(d)+d1*(nn)*0.1*.4 | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select min(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } | |
| # Rearranging the distinct Days in our sequence | |
| weekdaylist= c( "Monday","Tuesday", "Wednesday","Thursday","Friday","Saturday","Sunday" ) | |
| yyy =sqldf("select distinct Day from final") | |
| tutu=NULL | |
| for (iii in 1:length(yyy$Day)) | |
| { | |
| day = weekdaylist[iii] | |
| if(grep(day, yyy$Day)!=0) | |
| { | |
| tut=data.frame(Day=day) | |
| } | |
| else | |
| tut=NULL | |
| tutu= rbind(tutu,tut) | |
| } | |
| yyy=tutu | |
| # Created Dataset based on Daywise grouping | |
| h3=NULL | |
| for (h1 in 1:length(yyy$Day)) | |
| { | |
| h0 = fn$sqldf("select * from final where Day=(select Day from yyy limit $h1-1,1)") | |
| h2 = fn$sqldf("select * from h0 order by Time") | |
| plot(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| lines(h2$Time,h2$Rep_KVA,xlab="Time on Day",ylab="Rep_KVA",las=2) | |
| # Analysing this weekday for apt pricing plan | |
| h3 = rbind(h3,h2) | |
| } | |
| # One week representation of Data | |
| h3=transform(Label=paste0(h3$Day," - ",h3$Time),h3) | |
| Row_no =seq(1,length(h3$Day)) | |
| h3 = cbind(Row_no,h3) | |
| plot(h3$Row_no,h3$Rep_KVA) | |
| lines(h3$Row_no,h3$Rep_KVA) | |
| #Creating price sheet | |
| pricesheet=NULL | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=24,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=16,Size=5)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=7)) | |
| pricesheet = rbind(pricesheet,data.frame(Hour=8,Size=5)) | |
| pricesheet$Price=pricesheet$Hour*pricesheet$Size*.10*0.4 | |
| #UserChoice | |
| choice = NULL | |
| print(paste0("Enter number of blocks to be taken of your choice. ")) | |
| block= as.integer(readline()) | |
| for (g in 1:block) | |
| { | |
| print(paste0("Enter hour plan to be taken for block choice ",g)) | |
| choice$Hour[g] = as.numeric(readline()) | |
| print(paste0("Enter set plan to be taken for block choice ",g)) | |
| choice$Set[g] = as.numeric(readline()) | |
| if(choice$Hour[g]!=24) | |
| { | |
| print(paste0("Enter time plan to be taken for block choice ",g)) | |
| choice$Time[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Time[g] = NA | |
| } | |
| if (choice$Set[g]==5) | |
| { | |
| print(paste0("Enter Start Day plan to be taken for block choice ",g)) | |
| choice$Day[g] = as.character(readline()) | |
| } | |
| else | |
| { | |
| choice$Day[g] = NA | |
| } | |
| } | |
| choice=data.frame(choice) | |
| #Updating price column | |
| choice$Price = choice$Hour*choice$Set*.10*0.4 | |
| #Integrator for Area calculation | |
| #A = dataframe for the dataset [example using final from above] | |
| #kva = value above which data points are to be considered | |
| AreaIntegrator = function(A,kva,gorl=1) | |
| { | |
| if(gorl==1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA>$kva") | |
| } | |
| else if(gorl==-1) | |
| { | |
| B = fn$sqldf("select * from A where Rep_KVA<$kva") | |
| } | |
| if(length(B$Day)!=0) | |
| { | |
| B$Row_no = seq(1,length(B$Day)) | |
| B$ModifiedRep = ifelse((B$Rep_KVA-kva>0),(gorl*(B$Rep_KVA-kva)),0) | |
| return(sum(B$ModifiedRep)) | |
| } | |
| else | |
| { | |
| return(0.0) | |
| } | |
| } | |
| # Value of Integrator for just Indexing System | |
| Total_Price_on_indexing = AreaIntegrator(h3,kva = 0.0)*.16*0.4 | |
| # Label creator | |
| time=as.numeric(strsplit(g,':')[[1]][1]) | |
| if(length(grep('p', g, ignore.case = TRUE))) | |
| { | |
| time = as.numeric(time +12) | |
| }else { | |
| time = paste0("0",time) | |
| } | |
| Weekday=capitalize(tolower(Weekday[1][1])) | |
| PlanLabel=paste0(Weekday," - ",time,":00:00") |
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| # Finding KVA min | |
| PriceCalculation = function(tib,Hour,Set,Weekday,Time) | |
| { | |
| library(Hmisc) | |
| weekdaylist=c("Sunday", "Monday", "Tuesday","Wednesday","Thurday","Friday","Saturday") | |
| # Daywise working | |
| TotalPrice=0 | |
| if(is.na(Weekday)) | |
| {jo=weekdaylist[1]} else | |
| {jo=index=match(Weekday,weekdaylist)} | |
| while( jo>=index || (index+Set)%%7>jo) | |
| { | |
| day1= weekdaylist[[jo]] | |
| fuf=sqldf(paste0("select * from tib where Day='",day1,"'")) | |
| fuf$Row_no=seq(1:length(fuf$Row_no)) | |
| if (is.na(Weekday)) | |
| {Weekday=day1} | |
| if(is.na(Time)) | |
| {Time='00:00 am'} else | |
| { | |
| time1=as.numeric(strsplit(Time,':')[[1]][1]) | |
| time2=(substring(strsplit(Time,':')[[1]][2],1,2)) | |
| if(length(grep('p', g, ignore.case = TRUE))) | |
| {time1 = as.numeric(time1 +12)} | |
| else { if(!(time1==10 || time1==11 || time1==12)) | |
| time1 = paste0("0",time1)} | |
| } | |
| PlanLabel=as.character(paste0(day1," - ",time1,":",time2,":00")) | |
| divtocover=Hour*4 | |
| Point1=as.integer(fn$sqldf(paste0("select Row_no from fuf where Label='",PlanLabel,"'"))) | |
| # Block Table | |
| Table1=fn$sqldf(paste0("select * from fuf where Row_no>=$Point1 and Row_no<($Point1+$divtocover)")) | |
| # Index Table | |
| Table2=fn$sqldf(paste0("select * from fuf where Row_no<$Point1 or Row_no>=($Point1+$divtocover)")) | |
| BlockP=Hour*0.4*0.1*4 | |
| if(length(Table1$Row_no)!=0) | |
| { | |
| mkva=MinKvaCalculation(Table1) | |
| p=mkva[[1]] | |
| ond=0 | |
| while(p>=mkva[[1]] && p<=(mkva[[2]])+1 ) | |
| { | |
| if(p*divtocover*.10<=AreaIntegrator(Table1,kva=p)*.16) | |
| { | |
| ond[p]=AreaIntegrator(Table1,kva=p,gorl=-1) | |
| p=p+1 | |
| } | |
| else{ | |
| ond[p]=0 | |
| break} | |
| } | |
| usekva=mkva[1]+(match(min(ond),ond))-1 | |
| IndexP1=AreaIntegrator(Table1,kva=usekva)*.16*.4 | |
| } | |
| else | |
| IndexP1=0 | |
| if(length(Table2$Row_no)!=0) | |
| { | |
| IndexP2=AreaIntegrator(Table2,kva=0)*.16*.4 | |
| } | |
| else IndexP2=0 | |
| TotalPrice[jo]=BlockP+IndexP1+IndexP2 | |
| if(j==7) | |
| j=1 | |
| else jo=jo+1 | |
| print(TotalPrice) | |
| } | |
| if(Set==5) | |
| { | |
| Table3=fn$sqldf(paste0("select * from tib where Day=$(weekdaylist[jo])")) | |
| TablePrice[jo]=AreaIntegrator(Table3,kva=0)*.16*.4 | |
| Table4=fn$sqldf(paste0("select * from tib where Day=$(weekdaylist[jo+1])")) | |
| TablePrice[jo+1]=AreaIntegrator(Table4,kva=0)*.16*.4 | |
| } | |
| print(TotalPrice) | |
| return(sum(TotalPrice)) | |
| } | |
| MinKvaCalculation = function(tob) | |
| { | |
| nom=as.numeric(sqldf("select count(*) from tob")) | |
| hr=nom/4 | |
| mink=as.numeric(sqldf("select Min(Rep_KVA) from tob")) | |
| maxk=as.numeric(sqldf("select Max(Rep_KVA) from tob")) | |
| return(c(floor(mink),ceiling(maxk))) | |
| } | |
| for (iu in 0:) | |
| { | |
| BlockP=Hour*Set*0.4*0.1*4 | |
| # Making 3 tables and getting values from area integator for finding price. | |
| tab1=fn$sqldf("select * from h3 where Row_no<$Point1") | |
| tab2=fn$sqldf("select * from h3 where Row_no>$Point1 and Row_no<$Point2") | |
| tab3=fn$sqldf("select * from h3 where Row_no>$Point2") | |
| Index1=AreaIntegrator() | |
| } | |
| Optimize | |
| # Not needed | |
| for(ooo in 1:length(weekdaylist)) | |
| { | |
| if(Weekday==weekdaylist[ooo]) | |
| { | |
| index = (ooo) | |
| } | |
| } | |
| weekdaylist= c( "Monday","Tuesday", "Wednesday","Thurday","Friday","Saturday","Sunday" ) | |
| tutu=NULL | |
| for (iii in 1:length(yyy$Day)) | |
| { | |
| day = weekdaylist[iii] | |
| if(length(grep(day, yyy$Day))) | |
| { | |
| tut=data.frame(Day=day) | |
| } | |
| else | |
| tut=NULL | |
| tutu= rbind(tutu,tut) | |
| } | |
| yyy=tutu | |
| Weekday=capitalize(tolower(Weekday[1][1])) | |
| # Creating a equivalent kvaheight column to for applicable plan in that duration | |
| if(Hour==24) | |
| { | |
| PlanLabel = Just Weekdays dependent # Create label set from given to Set+given | |
| if(Set == 7) | |
| tib$KVAheight=ifelse(tib$Rep_KVA-kvaheight>0,tib$Rep_KVA-kvaheight,0) | |
| } | |
| else | |
| { | |
| if(Set==7) | |
| PlanLabel = Just Time dependent # Create label set from given Time to Hour+given | |
| } | |
| PlanLabel = Label repetition based on Set amount | |
| if(Set ==5) | |
| { | |
| labelweek = weekdaylist[index:(index+Set-1)] | |
| } | |
| BlockP=(kvaheight*0.4*Hour*Set*4*0.1) | |
| IndexP=Are | |
| labelweek=as.character(paste0(Weekday," - ",time1,":",time2,":00")) | |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$Date<- as.Date(x$Date,"%d/%m/%Y") | |
| x$Time = format(as.POSIXlt(x$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| x$Global_active_power = as.numeric(x$Global_active_power) | |
| x$Global_reactive_power = as.numeric(x$Global_reactive_power) | |
| x$KVA = sqrt(x$Global_active_power*x$Global_active_power+x$Global_reactive_power*x$Global_reactive_power) | |
| x$Day = weekdays(x$Date) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| rep_data = sqldf("select count(distinct Time) from z") | |
| # Checking for which central tendency is apt | |
| t = z | |
| #t$Time = format(as.POSIXlt(t$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=numeric(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from t where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i-1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| # Finding the best optimized central tendency for the dataset | |
| #(forparticular weekday and particular time) | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| hippo =d = NULL | |
| #k=0 | |
| for (i1 in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i1-1,1")) | |
| for (j1 in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j1-1,1")) | |
| d[j1]=(d2-d1)*.16 | |
| } | |
| f$hippo[i1]=sum(d) | |
| #k = k+1 | |
| #f$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| if(!is.na(f$hippo[1])) | |
| { | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| else | |
| { | |
| final=rbind(final,data.frame(Day=f$Day[1],Time=f$Time[1],Rep_KVA=0)) | |
| } | |
| } | |
| } |
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