Hi
r-help-boun...@r-project.org napsal dne 31.10.2010 09:59:00: > Hi Dennis, > > > > Thank you for your extensive explanations. Yes, I guess I did not explain what > I would like to do. > > Basically I would like to conduct a linear regression for each of 15 classes. In that case you can also use a nlme package. However I would strongly recommend a Pinheiro-Bates book which accompanies this package for complete utilisation the power of nlme package. Or you can convert your column with values 1-15 to factor and call simple linear model fit<-lm(y~x+factor.column, data=razred) Regards Petr > Your answers gave me new > > Perspective on how R works. > > > > Thanks again for the help, > > m > > > > From: Dennis Murphy [mailto:djmu...@gmail.com] > Sent: Sunday, October 31, 2010 4:40 AM > To: MatevĹľ PavliÄŤ > Cc: r-help@r-project.org > Subject: Re: [R] for loop > > > > Hi: > > If your objective is to make 15 plots, one for each level of razred, then you > don't need to make 15 individual data frames first. The lattice and ggplot2 > packages allow conditioning plots. You haven't mentioned what types of plots > you're interested in getting, but if it's something simple like a scatterplot > of y vs. x for each level of razred, it's not that hard to do. Let's fake some data: > > d <- data.frame(razred = rep(LETTERS[1:15], each = 10), > x = rep(1:10, 15), > y = rep(2 + 0.5 * 1:10, 15) + rnorm(150)) > > d has 15 levels of razred with 10 observations at each level. razred is a > factor, the other variables are either integer or numeric. > > Produce scatterplots of y vs. x for each level of razred, using both the > lattice and ggplot2 packages: > > library(lattice) > # each plot adds a new feature - run one plot at a time. > xyplot(y ~ x | razred, data = d, type = c('p', 'r')) > xyplot(y ~ x | razred, data = d, type = c('p', 'r'), layout = c(3, 5)) > xyplot(y ~ x | razred, data = d, type = c('p', 'r'), layout = c(3, 5), as.table = TRUE) > > library(ggplot2) > ggplot(d, aes(x, y)) + geom_point() + geom_smooth(method = 'lm') + > facet_wrap( ~ razred, ncol = 3) > ggplot(d, aes(x, y)) + geom_point() + geom_smooth(method = 'lm', se = FALSE) + > facet_wrap( ~ razred, ncol = 3) > > If instead you want something like a scatterplot matrix for each data subset > defined by level of razred, then maybe something like this (?): > > # add a new variable to the data frame > # splom is the scatterplot matrix function in lattice > d$z1 <- rnorm(150) > splom(~ d[, -1] | razred, data = d, layout = c(2, 2, 4)) > > Just guessing here since you didn't make your objective explicit. > > It's entirely possible that you can conduct a significant part of your data > analysis without having to split the data into subsets. Several summary > functions, for example, can compute a number of summary functions by group > with a one-line call. Here are a couple of examples, one using aggregate() > from the base package and another using function ddply() from the plyr package: > > aggregate(y ~ razred, data = d, FUN = mean) > razred y > 1 A 4.816841 > 2 B 4.520804 > 3 C 5.196329 > 4 D 4.615575 > 5 E 3.982240 > 6 F 4.466559 > 7 G 4.938669 > 8 H 4.539541 > 9 I 4.354991 > 10 J 4.573654 > 11 K 4.450624 > 12 L 5.138087 > 13 M 4.931111 > 14 N 4.879493 > 15 O 5.087452 > > library(plyr) > ddply(d, 'razred', summarise, mx = mean(x), my = mean(y), mz1 = mean(z1)) > razred mx my mz1 > 1 A 5.5 4.816841 -0.01745305 > 2 B 5.5 4.520804 0.24724069 > 3 C 5.5 5.196329 0.18717750 > 4 D 5.5 4.615575 0.18885590 > 5 E 5.5 3.982240 -0.91284339 > 6 F 5.5 4.466559 0.36479266 > 7 G 5.5 4.938669 -0.36359562 > 8 H 5.5 4.539541 0.06061162 > 9 I 5.5 4.354991 0.05138409 > 10 J 5.5 4.573654 0.31160018 > 11 K 5.5 4.450624 0.17458712 > 12 L 5.5 5.138087 -0.26482357 > 13 M 5.5 4.931111 -0.39194953 > 14 N 5.5 4.879493 0.33154075 > 15 O 5.5 5.087452 0.32816931 > > There are a number of functions and packages that will do this sort of thing > quite well - I'll mention doBy, data.table, Hmisc and sqldf as excellent > options, noting that there are other packages and functions in the apply > family that can perform groupwise processing seamlessly. The point of > mentioning this is so that you don't automatically think you have to split the > data in myriad ways before you can process a function. The good folks that > designed this language, and the many people who have contributed code to the R > project, are pretty smart, and have devised fairly simple ways to process > data, even if it's large. > > Of course, it's always possible that splitting is necessary; if you're willing > to be a little more forthcoming about your analysis goals, you might get a > better targeted response.. > > HTH, > Dennis > > > > On Sat, Oct 30, 2010 at 12:00 PM, MatevĹľ PavliÄŤ <matevz.pav...@gi-zrmk.si> wrote: > > Just one more thing... > I get a list with 15 data.frames : > > List of 15 > $ 1:'data.frame': 7 obs. of 9 variables: > ..$ vrtina : Factor w/ 6 levels "T1A-1","T1A-2",..: 1 1 2 2 5 5 5 > ..$ globina.meritve: num [1:7] 7.6 8.5 10.4 17.4 12.5 15.5 16.5 > ..$ E0 : num [1:7] 4109 2533 491 810 2374 ... > ..$ Eur1 : num [1:7] 6194 4713 605 1473 NA ... > ..$ Eur2 : num [1:7] 3665 7216 266 4794 7387 ... > ..$ Eur3 : num [1:7] 3221 3545 920 3347 6768 ... > ..$ H : num [1:7] 8 5.9 5.9 6.9 9.3 10.9 10 > ..$ Mpl : num [1:7] 61.9 136.7 19.9 96.4 178.5 ... > ..$ class : int [1:7] 1 1 1 1 1 1 1 > . > . > . > > But how would I acces them (i.e. to draw a plot for each data.frame for each > data.frame in a list)? > > Thanks,m > > > -----Original Message----- > From: David Winsemius [mailto:dwinsem...@comcast.net] > Sent: Saturday, October 30, 2010 8:24 PM > To: MatevĹľ PavliÄŤ > Cc: r-help@r-project.org > Subject: Re: [R] for loop > > > On Oct 30, 2010, at 2:07 PM, MatevĹľ PavliÄŤ wrote: > > > Hi, > > > > I know this is probalby a very trivial thing to do for most of the R > > users, but since I just strated using it I have some problems.... > > > > I have a data.frame with a field called "razred". This field has > > values from 1 up to 15. > > > > Is it possible to create a for loop that would create a new data frame > > for each of the "razred" values. > > The R-way would be to use the split function and leave the result in a list to > which the same operation could be also repeatedly performed using lapply. > > ?split > > And take a look at the fourth example applying split to the builtin airqualitydataframe. > > The plyr package also provides functions on dataframes. > > -- > > David Winsemius, MD > West Hartford, CT > > ______________________________________________ > 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. > > > > > [[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. ______________________________________________ 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.
