Thank you very much sir. Actually, I excluded all the non-trading days. Therefore, Each year will have 226 observations and total 6154 observations for each column. The data which I plotted is not rough data. I obtained the rolling observations of window 500 from my original data. So, the no. of observations for each resulted column is (6154-500)+1=5655. So, It is not accurate as per the days of calculations of each year.
Ok, Sir, I will go through your suggestion, obtain the results for each column of my data and would like to discuss the results with you. After solving of this problem, I would like to discuss another 2 queries. Thank you very much Sir for educating a new R learner. [image: Mailtrack] <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=signaturevirality5&;> Sender notified by Mailtrack <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=signaturevirality5&;> 12/16/18, 12:20:17 PM On Sun, Dec 16, 2018 at 8:10 AM Jim Lemon <drjimle...@gmail.com> wrote: > Hi Subhamitra, > Thanks. Now I can provide some assistance instead of just complaining. > Your first problem is the temporal extent of the data. There are 8613 days > and 6512 weekdays between the two dates you list, but only 5655 > observations in your data. Therefore it is unlikely that you have a > complete data series, or perhaps you have the wrong dates. For the moment > I'll assume that there are missing observations. What I am going to do is > to match the 24 years (1994-2017) to their approximate positions in the > time series. This will give you the x-axis labels that you want, close > enough for this illustration. I doubt that you will need anything more > accurate. You have a span of 24.58 years, which means that if your missing > observations are uniformly distributed, you will have almost exactly 226 > observations per year. When i tried this, I got too many intervals, so I > increased the increment to 229 and that worked. To get the positions for > the middle of each year in the indices of the data: > > year_mids<-seq(182,5655,by=229) > > Now I suppress the x-axis by adding xaxt="n" to each call to plot. Then I > add a command to display the years at the positions I have calculated: > > axis(1,at=year_mids,labels=1994:2017) > > Also note that I have added braces to the "for" loop. Putting it all > together: > > year_mids<-seq(182,5655,by=229) > pdf("EMs.pdf",width=20,height=20) > par(mfrow=c(5,4)) > # import your first sheet here (16 columns) > EMs1.1<-read.csv("EMs1.1.csv") > ncolumns<-ncol(EMs1.1) > for(i in 1:ncolumns) { > plot(EMs1.1[,i],type="l",col = "Red", xlab="Time", > ylab="APEn", main=names(EMs1.1)[i],xaxt="n") > axis(1,at=year_mids,labels=1994:2017) > } > #import your second sheet here, (1 column) > EMs2.1<-read.csv("EMs2.1.csv") > ncolumns<-ncol(EMs2.1) > for(i in 1:ncolumns) { > plot(EMs2.1[,i],type="l",col = "Red", xlab="Time", > ylab="APEn", main=names(EMs2.1)[i],xaxt="n") > axis(1,at=year_mids,labels=1994:2017) > } > # import your Third sheet here, (1 column) > EMs3.1<-read.csv("EMs3.1.csv") > ncolumns<-ncol(EMs3.1) > for(i in 1:ncolumns) { > plot(EMs3.1[,i],type="l",col = "Red", xlab="Time", > ylab="APEn", main=names(EMs3.1)[i],xaxt="n") > axis(1,at=year_mids,labels=1994:2017) > } > # import your fourth sheet here, (1 column) > EMs4.1<-read.csv("EMs4.1.csv") > ncolumns<-ncol(EMs4.1) > for(i in 1:ncolumns) { > plot(EMs4.1[,i],type="l",col = "Red", xlab="Time", > ylab="APEn", main=names(EMs4.1)[i],xaxt="n") > axis(1,at=year_mids,labels=1994:2017) > } > # finish plotting > dev.off() > > With any luck, you are now okay. Remember, this is a hack to deal with > data that are not what you think they are. > > Jim > > -- *Best Regards,* *Subhamitra Patra* *Phd. Research Scholar* *Department of Humanities and Social Sciences* *Indian Institute of Technology, Kharagpur* *INDIA* [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
