similar to: More elegant multiplication or division of a data frame with a vector

In an earlier post, a person wanted to divide each of the rows of rawdata by the row vector sens so he did below but didn't like it and asked if there was a better solution. rawdata <- data.frame(rbind(c(1,2,2), c(4,5,6))) sens <- c(2,4,6) temp <- t(rawdata)/sens temp <- t(temp) print(temp) Gabor sent three other solutions and I understood 2 of them but not the

Hello, I am new useR, I have written some functions, which I currently use by "source"-ing them from the files. That's OK, but when I my functions start counting in the tens and hundreds I'd be glad to be able to type "help.search("my_obscure_fun")" and get a sensible reply. I also want to be able to load them as a package at startup and not have to

Hello, I want to be able to make a plot that has several series with different color and linetype. Online documentation suggest that this is possible, but I haven't found how: "We can also create redundant mappings, mapping the same variable to multiple aesthetics. This is most useful when producing a graphic for both colour and black and white display." Here's what I have to

Hello, I have a data frame with a factor column, which uniquely identifies the observations in the data frame and it looks like this: sample1_condition1_place1 sample2_condition1_place1 sample3_condition1_place1 . . . sample3_condition3_place3 I want to turn it into three separate factor columns "sample", "condition" and "place". This is what I did so far: #

Hello! I am working with signals and a plot of several signals on the same axes can get quite messy. With lines that are very fractured, distinction by only the linestyle is not very clear. If I add symbols to the plot however, there are so many symbols, that they overplot and the whole plot is unreadable once again. I am looking for advice on how to make a plot with continuous lines and symbols

Hi to the list, I am trying to find a way to painlessly move structured data back and forth between R and Matlab (also Octave). For this purpose I found the R.matlab package great help. I wish to use a Matlab -v6 MAT file as an intermediary format, because it is well read by both Matlab and Octave. It is also well read by 'readMat' function in R.matlab package, but that is where I run

Hello, I am trying to figure out how to make a bode plot (a.k.a. bode diagram) in ggplot2. An example of such a diagram can be found here: http://meweb.ecn.purdue.edu/~me475/ctm/freq/bode2.GIF The example above shows the gain and phase characteristic of a linear system. In my case, I would like to compare visually several systems on the same diagram. The data in the data frame is arranged

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

Dear all, Let say I have following data: RawData <- matrix(1:101, nr = 1); colnames(RawData) <- c("ASD", as.character(as.yearmon(seq(as.Date("2012-03-01"), length.out = 100, by = "1 month")))); rownames(RawData) <- "XYZ" CutOffDate <- as.Date("2012-09-01") NewDateSeries <- as.character(as.yearmon(seq(CutOffDate, to =

I understand that in R, for loops are not used as often as other languages, and am trying to learn how to avoid them. I am wondering if there is a more efficient way to write a certain piece of code, which right now I can only envision as a for loop. I have a data file that basically looks like: 1,55 1,23 2,12 ... that defines a matrix. Each row of the data file corresponds to a row of the

Dear R, I import data from spss into a R data.frame. On this rawdata I do some data processing (selection of observations, normalization, recoding of variables etc..). The result is stored in a new data.frame, however, in this new data.frame the value labels are lost. Example of what I do in code: # read raw data from spss rawdata <- read.spss("./data/T50937.SAV",

Hi, I am moving from MATLAB, where one can easily assign a number of output values from a function like this: [x,y] = myfun(a,b) Then variables x and y can be directly used in the caller workspace. I understand that R functions return a single argument, which could be a list. This in a way makes it possible to return multiple values with a single function call, but accessing the list variables

Hi R -users, i've a table as describe below. I'm reading the numeric value presented in this table to populate a list. #table #============ #X    A    B    C #x1    2    3    4 #x2    5    7    10 #x4    2    3    5 #============ rawData <- read.table("raw_data.txt",header=T, sep="\t") myList=list() counter=0 for (i in c(1:length(rawData$X))) {     print (i)    

Hi ! I am a new user and quite confused by R-indexing. Make a list and get the attributes lst <- list(x = 1:3, y = 4:6, z = 7:9) attributes(lst) This returns: $names [1] "x" "y" "z" I can easily do: nm <-names(lst) or nm <-attr(lst,"names") which both return the assigned names of the named list 'lst', but why then this doesn't

Hello there, Much thanks in advance for any help. I have a few questions: 1) Why do I keep getting the following error: File1 <- read.csv("../RawData/File1.csv",as.is=TRUE,row.names=1) Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file '../RawData/File1.csv': No such file or

Hi All, I have txt file like : $ cat data.txt US 10 UK 12 Ind 4 Germany 14 France 8 > rawdata <- read.table(file='data.txt',sep='\t' , header=FALSE) > rawdata V1 V2 1 US 10 2 UK 12 3 Ind 4 4 Germany 14 5 France 8 I want to draw pie chart for the above data. How to split rawdata into : con <-

Hi, More questions in my ongoing quest to convert from RapidMiner to R. One thing has become VERY CLEAR: None of the issues I'm asking about here are addressed in RapidMiner. How it handles misisng values, scaling, etc. is hidden within the "black box". Using R is forcing me to take a much deeper look at my data and how my experiments are constructed. (That's a very

I am having a problem understanding the lda package. I have a dataset here: [,1] [,2] [,3] [1,] 2.95 6.63 0 [2,] 2.53 7.79 0 [3,] 3.57 5.65 0 [4,] 3.16 5.47 0 [5,] 2.58 4.46 1 [6,] 2.16 6.22 1 [7,] 3.27 3.52 1 If I do the following; "names(d)<-c("y","x1","x2") d$x1 = d$x1 * 100 d$x2 = d$x2 * 100 g<-lda( y ~ x1 + x2, data=d) v2

Hello, I'm wondering how I can merge two featuresets into one. My dataset is two sets of microarray data and it looks like followings: > rawData $v1 TilingFeatureSet (storageMode: lockedEnvironment) assayData: 2197815 features, 59 samples element names: channel1, channel2 protocolData rowNames: LT290677RU_D1_2011-02-16 LT286300LU_D1_2010-07-24 ... LT003990RU_D1_2010-11-04 (59