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Hello, I have trouble converting a character string to a R object. Let me describe this by an example; > dim(a) [1] 270 14 > dim("a") NULL > names(a) [1] "Var1" "Var2" "Var3" "Var4" "Var5" "Var6" "Var7" "Var8" "Var9" [10] "Var10" "Var11" "Var12"

Hi all, I've been trying for hours, but I do not find a Solution. I want to plot 12 variables over time in separate diagrams in one plot/window using lattice. Two columns, six rows. I used print with the split command, but the graphics are getting really small. Can someone please help me. Following data example: dta = data.frame( day=c(1,2,3,4,5,6,7),

Hello colleagues, I have tried to use the package bigmemory, biganalytics and biglm. I want to specify a multivariate regression with a weight. I have imported a large dataset with the library(bigmemory). I load the library (biglm) and specified a regression with a weight. But I get everytime an error message like "object not found" or "`weights' must be a

Hello, I have a large data set that has more columns than rows (sample data below). I am trying to perform a partitioning cluster analysis and then plot that using pca. I have tried using CLUSPLOT(), but that only allows for 'princomp' where I need 'prcomp' as I do not want to reduce my columns. Is there a way to edit the CLUSPLOT() code to use 'prcomp', please? #

Dear R helpers   Following is a part of R code.   data_lab <- expand.grid(c("R11", "R12", "R13"), c("R21", "R22", "R23"), c("R31", "R32", "R33"), c("R41", "R42", "R43"), c("R51", "R52", "R53"), c("R61", "R62", "R63"),

Dear R help list, I have a training dataset that looks like Table1. I have an unknown dataset that looks like Table2. I want to have a program that should search the training dataset and identify that the unknown sample belongs to which category (type1, type2 or type3) and also if the unknown does not belong to any of the categories, it should let me know. The real dataset has 600 variables and

Dear R help list, I have a training dataset that looks like Table1. I have an unknown dataset that looks like Table2. I want to have a program that should search the training dataset and identify that the unknown sample belongs to which category (type1, type2 or type3) and also if the unknown does not belong to any of the categories, it should let me know. The real dataset has 600 variables and

Dear all, I have a dataset that looks like this: x <- read.table(textConnection("col1 col2 3 1 2 2 4 7 8 6 5 10"), header=TRUE) I want to rewrite it as below: var1 var2 var3 var4 var5 var6 var7 var8 var9 var10 1 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0

var1 var2 var3 var4 var5 var6 var7 var8 var9 var10 gold 2 3 1 2 4 0 1 4 4 3 2 2 4 2 4 3 4 2 4 4 4 2 3 3 0 0 4 1 0 2 4 4 2 1 4 0 3 2 0 0 2 4 4 2 3 4 0 2 2 0 0 0 3 4 2 2 2 3 2 2 0 0 0 2 4 2 2 4 1 1 2 0 0 3 3 3 2 3 4 1 4 0 0 0 0 3 4 2 3 1 0 2 2 1 0 2 3 3 2 0 3 1 1 1 1 2 1 2 3 2 1

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not

Hi, I am trying to merge data similar to the example data below > dat0 id var1 var2 var3 2 1 0 1 3 1 0 1 4 0 1 1 5 0 1 1 > dat1 id var4 var5 var6 2 1 0 1 3 1 0 1 6 0 1 1 7 0 1 1 > dat2 id

Dear list members, This is a question is about building an R package under windows and cygwin. Please bear with me. I have a package in R that compiles well on my stationary computer (WINDOWS NT, R 1.8.1 and prior), and the resulting (package_version).zip file works well there. It contains an R script file and a C file. After installing tools to build the package on my laptop, the package

I am trying to Sweave the output of calculating correlations between one variable and several others. I wanted to print a table where the odd-numbered rows contain the variable names and the even-numbered rows contain the correlations. So if VarA is correlated with all the variables in mydata.df, then it would look like var1 var2 var3 corr1 corr2 corr3 var4 var5

Hola.. yo lo haría de la siguiente manera ... En excel:genero una columna con la serie de fechas continuas ... con la función buscarv, agrego las variables que tienen dato a esta serie.. en base a la fecha muy largo y mecánico para mi gusto.... En R ..de igual manera genero el vector de fechas require(chron)# crear el vector continuo de fechasfch01 <- data.frame(''fch'' =

Hello all, I upgraded from R 1.8.1 to 1.9.0 (Windows XP), and spotted an odd thing. The last three letters in the Swedish alphabet are ??, ?? and ??. (In case they don't show correctly: they are a with a ring, a with two dots, and o with two dots (HTML: &aring; &auml; &ouml;). When I use these as variable names in a data.frame, odd things happen: In R 1.8.1, ?? (&aring;)

Hi, Is it possible to create new vars in a loop For instance: for (i in 1:11) var'i' <- assign something output = var1 var2 ... var11 Is there a way to combine/join the value of 'i' to the string/char var in de loop. -- View this message in context: http://www.nabble.com/join-chars-in-loops-tp16895854p16895854.html Sent from the R help mailing list archive at

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

In "An Introduction to R" (See R help menu), there is an example of a function 'open.account' that makes use of the lexical scope in R. I have a set of functions that can be used to output R tables and graphics into a single report document. (I am aware that several tools can do this already). While reorganizing my code, I realize that I can collect my functions in a list, in

Dear all, I am trying (!!!) to generate pdfs that have 8 plots on one page: df = data.frame( day = c(1,2,3,4), var1 = c(1,2,3,4), var2 = c(100,200,300,4000), var3 = c(10,20,300,40000), var4 = c(100000,20000,30000,4000), var5 = c(10,20,30,40), var6 = c(0.001,0.002,0.003,0.004), var7 = c(123,223,123,412), var8 = c(213,123,234,435), all = as.factor(c(1,1,1,1)))

Dear all, I have a dataset that looks like id var1 var2 var4 var7 var8 1 0.0 0.1 0.3 0.9 0.0 2 0.4 0.6 0.0 0.0 0.2 3 0.0 0.0 0.0 0.8 0.7 Some columns are missed, for example, here the fourth (var3), sixth(var5) and seventh (var6) columns. I want to first determine which columns are missed in a huge dataset and then add the missed