Hello Sir, I have three queries regarding your suggested code.
*1. *In my last email, I mentioned why there are missing observations in my data series. In the line, *year_mids<-seq(182,5655,by=229), * *A. what 182 indicates and what is the logic behind the consideration of 229 increments, although there are 226 observations per year?* *B. Each excel file is having different observations depending on the variation of starting dates. So, is it required to add **year_mids in the loop? I think I need to justify **year_mids object each time after importing the individual excel files. If I am wrong, kindly correct me.* 2. Further, in the command* axis(1,at=year_mids,labels=1994:2017), 1 indicates the no. of increments of year name, right?* Kindly clarify my queries Sir for which I shall be always grateful to you. Thank you very much. [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, 1:29:05 PM On Sun, Dec 16, 2018 at 12:24 PM Subhamitra Patra < subhamitra.pa...@gmail.com> wrote: > 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* > -- *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.
