similar to: Suggestion for posting guide

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Is this not supposed to work? > dput(BOD, file = "/BOD.R") > source("/BOD.R") Error in attributes(.Data) <- c(attributes(.Data), attrib) : row names must be 'character' or 'integer', not 'double' > dput(iris, file = "/iris.R") > source("/iris.R") Error in attributes(.Data) <- c(attributes(.Data), attrib) :

I was preparing an e-mail for the help list and ran across a quandary. When asking for help it is useful to include the code/data so others can run your code and test it. I was running code on a data frame and wanted to include a small version of the data frame. The data frame was based on experimental data. What is the best way to do this? I didn't want to send an attachment so a wrote code

/src/main/attrib.c contains this comment in row_names_gets(): /* This should not happen, but if a careless user dput()s a data frame and sources the result, it will */ which svn blame says Prof Ripley placed there in r39830 with the commit message "correct the work of dput() on the row names of a data frame with compact representation." Is there a problem / better way to

It's in the Venables & Ripley MASS (ed 3) book in the section on principal components. The context is as follows > ir <- rbind(iris[,,1], iris[,,2], iris[,,3]) > ir.species <- factor(c(rep("s",50),rep("c",50),rep("v",50))) (then they use brush(ir) which I guess is not an R function) and then > princomp(log(ir[1:4]),cor=T) (there is no [1:4]

I tried it using file and it seems to work for saving: > data(iris) > con <- file("clipboard","w") > save(iris,ascii=T,file=con) > close(con) > readLines("clipboard") ... lengthy output follows which seems correct ... but not for loading: > con <- file("clipboard","r") > load(con) Error in load(con) : loading from

Dear R list, I'm trying to calculate Mahalanobis distances for 'Species' of 'iris' data as obtained below: Squared Distance to Species From Species: Setosa Versicolor Virginica Setosa 0 89.86419 179.38471 Versicolor 89.86419 0 17.20107 Virginica 179.38471 17.20107 0 These distances were obtained with proc 'CANDISC'

People read the posting guide yet they are still unable to create an acceptable post. e.g. https://stat.ethz.ch/pipermail/r-help/2008-June/164092.html I think the problem is that the guide is not clear or concise enough. I suggest we add a summary at the beginning which gets to the heart of what a poster is expected to provide: Summary To maximize your change of getting a response when posting

I didn't see anything on this in the bug reports, and a search of the archives had lots of false positives when searching on "split" to be helpful. I don't view this as particularly interesting or useful, but wanted to report it because I stumbled on it (and don't remember ever seeing "invalid permissions" as part of a segfault).? Yes, I realize this is a silly

Dear R list, I'm trying to calculate Mahalanobis distances for 'Species' of 'iris' data as obtained below: Squared Distance to Species From Species: Setosa Versicolor Virginica Setosa 0 89.86419 179.38471 Versicolor 89.86419 0 17.20107 Virginica 179.38471 17.20107 0 This distances above were obtained with proc

Hello, I am puzzled by the behavior of hist() when generating multiple plots per page on the pdf device. In the following example two pdf files are generated. The first results in 4 plots on one pdf page as expected. However, the second, which swaps one of the plot() calls for hist(), results in a 4 page pdf with one plot per page. How might I get the histogram with 3 other scatter

The following command produces red axis line in a pairs plot: pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species", pch = "+", col = c("red", "green3", "blue")[unclass(iris$Species)]) Trying to fool pairs in the following way produces the same plot as above: pairs(iris[1:4], main = "Anderson's Iris Data -- 3

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

Compiled great, got both my RSA and DSA keys' generated for Protocol 1/2, started up fine ... try to connect and get a bunch of errors: May 11 14:01:47 iris sshd[8578]: error: Couldn't wait for child '/bin/ls -alni' completion: No child processes May 11 14:01:47 iris last message repeated 3 times May 11 14:01:47 iris sshd[8578]: error: Command '/bin/ls -alni': select()

Hi, I am a newbie of R, and playing with the "ifelse" statement. I have the following codes: ## first, for(i in 1:3) { for(j in 2:4) { cor.temp <- cor(iris.allnum[,i], iris.allnum[,j]) if(i==1 & j==2) corr.iris <- cor.temp else corr.iris <- c(corr.iris, cor.temp) } } this code is working fine. I also tried to perform the same thing in another way with "ifelse":

Hi Dieter and R community: I tried both of these three versions with ylim as suggested, none work: I am getting only single (pch = 16) not overlayed (pch =3) everytime. *vs 1* require(lattice) xyplot(Sepal.Length ~ Sepal.Width | Species , data= iris, panel= function(x, y, subscripts) { panel.xyplot(x, y, pch=16, col = "green4", ylim = c(0, 10)) panel.lmline(x, y, lty=4, col =

Hi R People: Several of you pointed out that using "tapply" on a data frame will work on the iris data frame. I'm still having a problem. The iris data frame has 150 rows, 5 variables. The first 4 are numeric, while the last is a factor, which has the Species names. I can use tapply for 1 variable at a time: >tapply(iris[,1],iris[,5],mean) setosa versicolor virginica

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

Hello all, I have a large data frame and I want to look at the distribution of each variable very quickly by plotting an individual histogram for each variable. I'd like to do so using lattice. Here is a small example using the iris data set: histogram(as.formula(paste("~",paste(colnames(iris[,!sapply(iris,is.factor)]),collapse="+"))),data=iris[,!sapply(iris,is.factor)])

Hi, I tried to use naivebayes in package 'e1071'. when I use following parameter, only one predictor, there is an error. > m<- naiveBayes(iris[,1], iris[,5]) > table(predict(m, iris[,1]), iris[,5]) Error in log(sapply(attribs, function(v) { : Non-numeric argument to mathematical function However, when I use two predictors, there is not error any more. > m<-