I was able to read the data using the following code: jd1 <- read.table('Practicedata.dat',header=T,sep="\t",nrow=6240) jd2 <- read.table('Practicedata.dat',header=T,sep="\t",skip=6241) colnames(jd1) <- c("Date","Mod") colnames(jd2) <- c("Date", "Obs") p <- ggplot(jd1,aes(x=Date,y=Mod))+geom_line() p <- p + geom_line(data=jd2,aes(x=Date,y=Obs),color="red") p
Now, I want to make a scatter plot between jd1$Mod and jd2$Obs. But I cannot create one since both of them have different number of rows. Since I have less number of rows for Mod I am planning to use the date of Mod and then find the corresponding values of Obs for those time periods. How can I find the corresponding values of Obs for the give date in jd1 ? Or is there any way to create a scatter plot and put the regression equation and correlation coefficient. Thank you so much. Best Regards, Janesh On Mon, Jan 21, 2013 at 1:19 AM, Jd Devkota <janesh.devk...@gmail.com>wrote: > Hello All, > > I have a data file in a text format and there are two data sets. The data > set are continuous. > For each data set there is a header which has the number of data rows and > the name of data series. > For example first data set has "6240 Terry Cove-Model". Then the data for > that series follows upto 6240 rows. Then another data would start and it > will have the header such as "5200 Terry-Observed" > > The sample data would look like: > > 6240 Terry Cove-Model > 300 .300110459327698 > 300.041656494141 .289277672767639 > 300.083343505859 .276237487792969 > 300.125 .258902788162231 > 300.166656494141 .236579895019531 > 300.208343505859 .221315026283264 > 300.25 .214318037033081 > 300.291656494141 .190926909446716 > 300.333343505859 .158144593238831 > 300.375 .113302707672119 > 300.416656494141 .103684902191162 > 300.458343505859 9.72903966903687E-02 > 300.5 8.76948833465576E-02 > 300.541656494141 8.42459201812744E-02 > 300.583343505859 .078397274017334 > 300.625 8.44632387161255E-02 > 300.666656494141 9.32939052581787E-02 > 300.708343505859 .113663911819458 > 300.75 .123064398765564 > 300.791656494141 .157548069953918 > 300.833343505859 .148393034934998 > 300.875 .135645747184753 > 300.916656494141 .137590646743774 > 300.958343505859 .133154153823853 > 301 .131152510643005 > 301.041656494141 .114152908325195 > 301.083343505859 8.04083347320557E-02 > 301.125 5.53587675094604E-02 > 301.166656494141 3.17397117614746E-02 > 301.208343505859 4.07266616821289E-03 > 301.25 -2.15455293655396E-02 > 301.291656494141 -4.07489538192749E-02 > 301.333343505859 -5.85414171218872E-02 > 301.375 -7.53517150878906E-02 > 301.416656494141 -8.49723815917969E-02 > 301.458343505859 -7.91778564453125E-02 > 301.5 -7.02846050262451E-02 > 301.541656494141 -7.24701881408691E-02 > 301.583343505859 -7.76907205581665E-02 > 301.625 -6.82642459869385E-02 > 62401 Terry Cove-Data > 300 .216407993 > 300.0042 .204216005 > 300.0083 .210311999 > 300.0125 .195071996 > 300.0167 .192023999 > 300.0208 .179831992 > 300.025 .188976001 > 300.0292 .185928004 > 300.0333 .195071996 > 300.0375 .219456009 > 300.0417 .210311999 > 300.0458 .204216005 > 300.05 .195071996 > 300.0542 .188976001 > 300.0583 .195071996 > 300.0625 .195071996 > 300.0667 .185928004 > 300.0708 .173735998 > 300.075 .170688001 > 300.0792 .167640004 > 300.0833 .167640004 > 300.0875 .167640004 > 300.0917 .167640004 > 300.0958 .161543991 > 300.1 .1524 > 300.1042 .158495994 > 300.1083 .149352003 > 300.1125 .158495994 > 300.1167 .1524 > 300.1208 .1524 > 300.125 .149352003 > 300.1292 .143256 > 300.1333 .146303997 > 300.1375 .149352003 > 300.1417 .146303997 > 300.1458 .137159996 > 300.15 .131064002 > 300.1542 .124967999 > 300.1583 .128015996 > 300.1625 .124967999 > 300.1667 .131064002 > 300.1708 .124967999 > 300.175 .124967999 > 300.1792 .134111999 > 300.1833 .118871996 > 300.1875 .128015996 > 300.1917 .131064002 > 300.1958 .128015996 > 300.2 .131064002 > 300.2042 .128015996 > 300.2083 .121920002 > 300.2125 .115823999 > 300.2167 .112776001 > 300.2208 .103632001 > 300.225 .097535998 > 300.2292 .103632001 > 300.2333 .094488001 > 300.2375 .082296003 > 300.2417 .0762 > 300.2458 .079247997 > 300.25 .067056 > 300.2542 .064007998 > 300.2583 .045720002 > 300.2625 .033528 > 300.2667 .036575999 > 300.2708 .036575999 > 300.275 .036575999 > 300.2792 .027432001 > 300.2833 .027432001 > 300.2875 .021336 > 300.2917 .012192 > 300.2958 .009144 > 300.3 .009144 > 300.3042 .003048 > 300.3083 0 > 300.3125 -.003048 > 300.3167 -.006096 > 300.3208 0 > 300.325 .006096 > 300.3292 -.003048 > 300.3333 .006096 > > The full data set can be downloaded from > https://www.dropbox.com/s/chhw3vz6ru1godk/Practicedata.Dat > > I want to make a comparison graph between modeled and observed. Once I am > able to read two data sets as two sets of data or combined in one I would > be able to create the time series graph. > > Another thing I need to do is create another sub data set where both the > series have common data. One data might have more intervals than another. > After I find two data sets of same interval then I want to plot a > correlation graph. > > I hope I made it clear what I want to do. > > Thank you so much. > > Best Regards, > Janesh > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.