I suspect that this does look a bit easier to interpret. It certainly beats 68 lines on one graph. Still it's a lot of individual subplots.
What I was suggesting was grouping all NASS, HASS, etc. together and graphing each so that you get several lines per subplot. I don't know if this makes sense in your context but it should reduce the size of the overall plot. Ugly simple-minded example of what I meant below. library(ggplot2) names <- c('hass', "ham", 'nacs', 'nass') line <- c('alpha', "beta") xval <- 1:4 dat1 <- data.frame(group= rep(names, each = 3, 4), tt = rep(line, each =12 , 2), xx = rep(xval, 12 ), yy = rnorm(48)) p <- ggplot(dat1, aes(xx, yy, colour = tt)) + geom_line() + facet_grid(. ~ group) p John Kane Kingston ON Canada > -----Original Message----- > From: mir.sa...@uef.fi > Sent: Fri, 13 Mar 2015 16:27:06 +0000 > To: jrkrid...@inbox.com, petr.pi...@precheza.cz, r-help@r-project.org > Subject: RE: [R] graphs > > > __Dear all, > > You can see the plot specif curves in the enclosed document. Inclusion of > all plot specif curves in one plot may not be look good and finally it > will be unreadable. I am agree with Petrr and John. This graph is more > readable. > > > Best regards > Salam > > > > > > > > > ______________________________________ > From: John Kane <jrkrid...@inbox.com> > Sent: Friday, March 13, 2015 4:12 PM > To: PIKAL Petr; Mir Salam; r-help@r-project.org > Subject: Re: [R] graphs > > @Petr > > I agree. I think Mir would get a totally unreadable graph. I occasionally > look at some spagetti graphs from climate research, and I find 8 - 12 > lines are incomprehensible (I'm not a subject matter expert)' > > @Mir > > Is there any logical way to break down the data ? I think Petr is correct > in that ggplot2 should be able to do it but I'd suggest thinking about, > perhaps, facetting the data to produce some reasonable number of panels. > > Here is a simple three-panel plot to illustrate what I mean but I have > easily produced a 9 or 10 panel plot which reduces visual clutter > immensely. > > library(plyr) library(ggplot2) > > df1 = data.frame(basel_asset_class = c("bank","bank","bank","bank","bank","bank","bank","corporate","corporate","corporate","corporate","corporate","corporate","corporate","sovereign","sovereign","sovereign","sovereign","sovereign","sovereign","sovereign"), > ratings = c("AAA","AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC","AAA","AA","A","BBB","BB","B","CCC"), > default_probability = > c(0.0027,0.0029,0.0031,0.0034,0.0037,0.004,0.0043,0.0025,0.0024,0.0024,0.0023,0.0022,0.0021,0.0021,0.003,0.0031,0.0032,0.0033,0.0034,0.0035,0.0036)) > > names(df1) <- c("class", "rate", "default") > > p <- ggplot(df1, aes(rate, default,colour=class)) + geom_point() + > facet_grid(class ~.) + theme(legend.position="none") > p > > John Kane > Kingston ON Canada > > >> -----Original Message----- >> From: petr.pi...@precheza.cz >> Sent: Fri, 13 Mar 2015 11:34:42 +0000 >> To: mir.sa...@uef.fi, r-help@r-project.org >> Subject: Re: [R] graphs >> >> Hi >> >> Your example is not reproducible, but I presume you could use ggplot >> together with predict. However, I wonder how do you want to distinguish >> 68 curves in one picture. >> I would >> >> Cheers >> Petr >> >> >>> -----Original Message----- >>> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Mir >>> Salam >>> Sent: Sunday, March 08, 2015 10:57 PM >>> To: r-help@r-project.org >>> Subject: [R] graphs >>> >>> Dear all, >>> >>> I need help to get different 68 plots specifc fitted curves in one >>> plot with respective field data observations (age vs dominant height). >>> >>> >>> >>> aspdomH2<-groupedData(domH2~age|plotno,data=aspdomH2) >>> >>> >>> >>> names(aspdomH2) >>> >>> plotno, age, origin, soilcharacter, domH2, >>> >>> >>> >>> plotno-different plot no. I have 68 plots >>> >>> age- every plot have from age 5 to 30 years >>> >>> origin- two, native aspen and hybrid aspen >>> >>> domH2<-dominant height >>> >>> soilcharacter-3, clay, silt and mold. both origin have different soil >>> charcter >>> >>> >>> >>> #### then I fit model >>> >>> fm2cham.nlme<-nlme(domH2~cham(age,b0,b1,b2), >>> data=aspdomH2, >>> fixed = list(b0~1+origin+soilcharacter,b1~ 1,b2 ~ >>> 1+origin+soilcharacter), >>> random = b0+b2~1|plotno, >>> start=c(b0=26.3387,0,0,0,b1=0.1065,b2=1.9453,0,0,0), >>> weights=varPower(form = ~age, 0.5), >>> correlation=corAR1()) >>> >>> >>> >>> #### parameter values >>> >>> Fixed effects: list(b0 ~ 1 + origin + soil character, b1 ~ 1, b2 ~ 1 + >>> origin + soil character) >>> >>> Value >>> b0.(Intercept) 21.081124 >>> b0.origin1 7.735064 >>> b0.soilcharactermold 10.689051 >>> b0.soilcharactersilt 3.906585 >>> b1 0.079035 >>> b2.(Intercept) 1.616360 >>> b2.origin1 -0.384421 >>> b2.soilcharactermold 0.612285 >>> b2.soilcharactersilt 0.527462 >>> >>> >>> >>> ##### I can easily get the augmented plot. I got different 68 plots >>> specific curves. >>> >>> ####### >>> >>> Any body can help me how can I will get all 68 plots specific fitted >>> curves in one plot with respective plot specific age and dominant >>> height obervations? (x axis will represent age, y axis will represent >>> dominant height and fitted curves of all 68 plots) >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> [[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. >> >> ________________________________ >> Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a jsou >> určeny pouze jeho adresátům. >> Jestliže jste obdržel(a) tento e-mail omylem, informujte laskavě >> neprodleně jeho odesílatele. 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