search for: dgnn

...lue, whereupon I move to the next index and repeat. I am trying to find a Vectorized approach, and have seen similar posts where filter{stat} and rollmean{zoo} were suggested, but, I haven't been able to figure a way to use them for this problem. Best wishes to all, and thanks for the help! Dgnn -- View this message in context: http://n4.nabble.com/Extract-vector-elements-until-cumsum-x-tp998023p998023.html Sent from the R help mailing list archive at Nabble.com.

I have what may be a simple/foolish question, but I've done the due diligence and looked through pages of posts here as well as several of the PDFs on the CRAN site, but haven't been able find what I'm after. I am working with a list of say 3 histogram objects A, B & C, and each histogram is a list of 7 elements. I would like to access $name, the 6th element, of histograms A,B and

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a list

I am trying to write a function that generates one PDf containing plots from several .csv files within a directory. When I manually execute the code it seems to work, but not when it is a function. I think I need to return() something, but haven't had much luck figuring out what/how. plot.isi<-function(csv.path="~/project/csv by cell") { csv.files<-grep('.csv',

I have split my original dataframe to generate a list of dataframes each of which has 3 columns of factors and a 4th column of numeric data. I would like to use lapply to apply the fitdistr() function to only the 4th column (x$isi) of the dataframes in the list. Is there a way to do this or am I misusing lapply? As a second solution I tried splitting only the numeric data column to yield a

Hello. I am trying to remove from my dataframe, those rows in which the first 7 columns are duplicated even if subsequent columns make those rows unique. df<-data.frame(id=rep(c('amy','bob','joe') , each=5), pet1=sample(LETTERS[1:3],15, replace=T), pet2=sample(LETTERS[1:3],15, replace=T), pet3=sample(LETTERS[1:5],15, replace=T)) >df id pet1 pet2

I have a data frame containing the results of time measurements taken from several cells. Each cell was measured in conditions A and B, and there are an arbitrary number of measurements in each condition. I am trying to calculate the difference of each measurement from the mean of a given cell in a given condition without relying on loops. >my.df id cond time 1