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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$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=character(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$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$hippo[k]=hippo[i1] | |
| #print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i1])) | |
| } | |
| rep_data=sqldf("select * from f where hippo=(select max(hippo)from f) limit 1") | |
| repkva = as.numeric((sqldf("select KVA from rep_data"))) | |
| reptime = as.character(sqldf("select Time from rep_data")) | |
| repweekday = weekdays(rep_data$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| } |
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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$Time, format = "%H:%M:%S"), format="%H:%M:%S") | |
| tn = as.numeric(sqldf("select count(distinct Day) from t")) | |
| final=data.frame( Day=as.Date(character()), | |
| Time=character(), | |
| Rep_KVA=character(), | |
| stringsAsFactors=FALSE) | |
| for (j in 1:n) | |
| { | |
| e = fn$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$Date) | |
| final=rbind(final,data.frame(Day=repweekday,Time=reptime,Rep_KVA=repkva)) | |
| } | |
| } | |
| } |
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| d = NULL | |
| hippo=NULL | |
| for (i in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i-1,1")) | |
| for (j in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j-1,1")) | |
| if(d2>d1) | |
| { | |
| d[j]=(d2-d1)*.16 | |
| } | |
| else d[j]=0 | |
| } | |
| hippo[i]=sum(d) | |
| print(paste0("Hippo: for ", d1, d2, hippo)) | |
| } |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$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) | |
| # 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'") | |
| # Checking for which central tendency is apt (using checking of skewdness and outliners concept). | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| t = sqldf("select distinct Time from z") | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from z where Time=(select Time from t limit $j-1,1)") | |
| plot(e$KVA,xlab='Day No.', main = "Graphical representation of KVA value on 17:30:00 in 6 months.") | |
| } | |
| summary(e) | |
| n = as.numeric(sqldf("select Time(*) as m from e")) | |
| for (j in 1:as.numeric(sqldf("select count(*) as m from x"))) | |
| { | |
| r <- fn$sqldf("select * from x limit $j-1,1") | |
| for( i in 3:length(x)) | |
| { | |
| r[as.character(colnames(r)[i])] <- as.numeric(r[as.character(colnames(r)[i])]) | |
| } | |
| } | |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$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) | |
| # 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'") | |
| # Checking for which central tendency is apt (using checking of skewdness and outliners concept). | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| t = sqldf("select distinct Time from z") | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from z where Time=(select Time from t limit $j-1,1)") | |
| plot(e$KVA,xlab='Day No.', main = "Graphical representation of KVA value on 17:30:00 in 6 months.") | |
| } | |
| summary(e) | |
| n = as.numeric(sqldf("select Time(*) as m from e")) | |
| for (j in 1:as.numeric(sqldf("select count(*) as m from x"))) | |
| { | |
| r <- fn$sqldf("select * from x limit $j-1,1") | |
| for( i in 3:length(x)) | |
| { | |
| r[as.character(colnames(r)[i])] <- as.numeric(r[as.character(colnames(r)[i])]) | |
| } | |
| } | |
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| library(sqldf) | |
| x<-read.table("//home//innovator//Documents//Internship//Data//household_power_consumption.txt",header=TRUE,sep=';',dec = '.',stringsAsFactors=FALSE) | |
| x$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) | |
| # 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'") | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Checking for which central tendency is apt (using checking of skewdness and outliners concept). | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| t = sqldf("select distinct Time from z") | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from z where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$j") | |
| #plot(e$KVA,xlab='Day No.', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| qplot(f$Date,f$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$Global_reactive_power) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| # Checking for which central tendency is apt (using checking of skewdness and outliners concept). | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| t = sqldf("select distinct Time from z") | |
| e = sqldf("select * from z where Time=(select Time from t limit 1,1)") | |
| f = sqldf("select * from e where Date%7=1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| u = lines(f$Date,f$KVA) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from z where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| } | |
| } | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| if(nn%%2==1) | |
| { | |
| nn = as.integer(nn/2)+1 | |
| } | |
| b=NULL | |
| for (i in seq(nn/2,nn)) | |
| { | |
| b[i-nn/2] = nn*mean(fn$sqldf("select KVA from (select * from f order by KVA Desc) limit $i")$KVA) | |
| } | |
| sort(b,decreasing = TRUE) | |
| trapz(as.vector(f$Date),as.vector(f$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$Global_reactive_power) | |
| # Creating sample pools for six month data | |
| first<- as.Date('16-12-2008',format = '%d-%m-%Y') | |
| second <- as.Date('30-06-2009',format = '%d-%m-%Y') | |
| y = fn$sqldf("select * from x where Date>=$first and Date<=$second") | |
| # Creating sample pools of data at time 0, 15min, 30min, 45min | |
| z = fn$sqldf("select * from y where substr(Time,4,2)='00' or substr(Time,4,2)='15' or substr(Time,4,2)='30' or substr(Time,4,2)='45'") | |
| # Checking for which central tendency is apt (using checking of skewdness and outliners concept). | |
| n = as.numeric(sqldf("select count(distinct Time) from z")) | |
| t = sqldf("select distinct Time from z") | |
| e = sqldf("select * from z where Time=(select Time from t limit 1,1)") | |
| f = sqldf("select * from e where Date%7=1") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| u = lines(f$Date,f$KVA) | |
| for (j in 1:n) | |
| { | |
| e = fn$sqldf("select * from z where Time=(select Time from t limit $j-1,1)") | |
| for (i in 1:7) | |
| { | |
| f = fn$sqldf("select * from e where Date%7=$i") | |
| plot(f$Date,f$KVA,xlab='Day No.',ylab='KVA', main = "Graphical representation of KVA value in 6 months for particular time.") | |
| lines(f$Date,f$KVA) | |
| } | |
| } | |
| nn = as.numeric(sqldf("select count(*) from f")) | |
| d = NULL | |
| hippo=NULL | |
| k=0 | |
| for (i in seq(1,nn)) | |
| { | |
| d=NULL | |
| d1 = as.numeric(fn$sqldf("select KVA from f limit $i-1,1")) | |
| for (j in 1:nn) | |
| { | |
| d2 = as.numeric(fn$sqldf("select KVA from f limit $j-1,1")) | |
| d[j]=(d2-d1)*.16 | |
| } | |
| hippo[i]=sum(d) | |
| k = k+1 | |
| f$hippo[k]=hippo[i] | |
| print(paste0("Hippo: for ", d1, " and ", d2," = ", hippo[i])) | |
| } | |
| if(nn%%2==1) | |
| { | |
| nn = as.integer(nn/2)+1 | |
| } | |
| b=NULL | |
| meanfromarea =NULL | |
| for (i in seq(nn/2,nn)) | |
| { | |
| gg= fn$sqldf("select KVA from (select * from f order by KVA) limit $i") | |
| b[i+1-(nn/2)] = mean(gg$KVA) | |
| } | |
| summary(e) | |
| n = as.numeric(sqldf("select Time(*) as m from e")) | |
| for (j in 1:as.numeric(sqldf("select count(*) as m from x"))) | |
| { | |
| r <- fn$sqldf("select * from x limit $j-1,1") | |
| for( i in 3:length(x)) | |
| { | |
| r[as.character(colnames(r)[i])] <- as.numeric(r[as.character(colnames(r)[i])]) | |
| } | |
| } | |
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