similar to: Warning: condition has length > 1

Hi all .., I have a list of 7000 dataframes with similar column headers and I wanted to add a new column to each dataframe based on a certain condition which is the same for all dataframes. When I extract one dataframe and apply my code it works very well as follows :- First suppose this is my first dataframe in the list > OneDF <- Mylist[[1]] > OneDF ID Pdate

Function/file names are hypothetical. Say I have written myfunction.R, which calls myfunction.c via .C("myfunction", ...). I've compiled successfully myfunction.c via R CMD SHLIB myfunction.c in the terminal. Then, in the R console: dyn.load("myfunction.so") source("myfunction.R") test <- myfunction() # works fine So everything is in order, myfunction works

Hello there, I don't know if I'm addressing my question to the right e-mail address, I hope I do. Actually I have a little problem concerning writing a code in R. I try to briefly sum up my problem. As you can see below, I created the functions "Equation1" and "Equation2" with some conditions. Equation1 <-function(x){ if

> On Apr 15, 2018, at 4:08 AM, Allaisone 1 <Allaisone1 at hotmail.com> wrote: > > > Hi all .., > > > I have a list of 7000 dataframes with similar column headers and I wanted to add a new column to each dataframe based on a certain condition which is the same for all dataframes. > > > When I extract one dataframe and apply my code it works very well as

Hello, For verbose coding I'd like to do something like: > myfunction <- function(x){ > if (a){ > stop(paste(myfunction_name_here,"requires xyz!") > } Is that possible? Thanks for any hints, Joh

Marc and many other people ( whose names escape me ) have been very helpful in explaining the use of lapply to me. In his last response, Marc explained that if tradevectors is a list of vectors of different lengths ( excuse my terminology ) then lapply(tradevectors,function(x) G[x]*B[x] ) will go through each component of the list as if it was a vector and apply the element by element

Dear All, I hope you don't mind helping me with this small issue. I haven't been using R in years and I'm trying to fill in a matrix with the output of a function (I'm probably using the Matlab logic here and it's not working). Here is my code: for (i in 1:length(input)){ out[i,1:3] <- MyFunction(input[i,1],input[i,2], input[i,3]) out[i,4:6] <-

Dear all, I have the following problem: I have written a function genereating to plots, eg myfunction <- (data, some.parameters) { #some calculations etc . par (mfrow=c(1,2)) plot1(......) plot2(.....) } which works fine. But for analysing several variants, I tried a slope, eg: par (mfrow=c(5,5)) for ( i in 1:10) { myfunction(data, i) } Off

I have a function myFunction(beta,x) where beta is a vector of coefficients and x is a data frame (think of it as a matrix). I want to optimize the function myFunction() by ONLY changing beta, i.e. x stays constant, with 4 constraints. I have the following code (with a separate source file for the function): rm(list=ls()) source('mySourceFile')

I apologize if this issue has been raised before. I really like object oriented S3 programming. However, there's one feature of object oriented S3 programming that I don't like. Generic functions can have arguments other than dots. Lets say you have an R package with something like: print.myfunction (f, ...) { dosomething (f, ...) } Noting that I use function objects a lot. R CMD

Hi everyone, I made some tests with Rcmdr, to add a function with default parameters : For example (very simple): myfunction<-function(var="314"){ print("hello") print(var) } if I run myfunction() directly i see : > myfunction() [1] "hello" [1] "314" it's ok. But if i edit de Rcmdr-menu.txt (in

Hello R-helpers, I'm writing a long function in which I manipulate a certain number of datasets. I want the arguments of said function to allow me to adapt the way I do this. Among other things, I want my function to have an argument which I will pass on to subset() somewhere inside my function. Here is a quick and simplified example with the iris dataset. myfunction<-function(table,

I am interested in running R commands asynchronously. My first choice is in the same R session that I am currently in. Here, the goal would be to run something like RunAsynchSameSession(myfunction(), "outputname.rda") Once RunAsynchSameSession had started myfunction(), RunAsynchSameSession would complete immediately. myfunction would keep going. It is OK if execution of the

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Hello all, I would like to enhance LLVM debug info that supports setting breakpoint on labels in function. Generally, if users use GDB as their debugger, they could set breakpoints on labels in function. Following is an example. // C program static int myfunction (int arg) { int i, j, r; j = 0; /* myfunction location */ r = arg; top: ++j; /* top location */ if (j == 10) goto

Dear All I have a program that breaks at the following lines of code: bigfunction = { ... object1 = myfunction(x) object2 = strsplit(object1, ",")[[1]] ... } where myfunction is defined elsewhere outside of bigfunction. The error I get is "error in strsplit() -- object1 not found". However, when I insert browser() into my code so that the above reads, bigfunction = {

This is probably buried somewhere in the R help archives, but I've been unable to find it, so perhaps the keywords I use here will help bring the topic to the surface more easily for future users. I want to write my own modeling function (ultimately for some multidimensional windowing - but this question is on scripting basics). For purposes of figuring out my needs, lets just consider

This is probably a very noobish question, but if I want to create a function that allows an undetermined number of, say, numeric vectors to be fed to it, I would use: myfunction = function(...) { # Do something } Right? If so, how do I a) count the number of vectors "fed" to the function, and b) how do I treat those vectors as variables, e.g. for the call:

Hi, I guess that my problem has an obvious answer, but I have not been able to find it. Suppose I create a custom function, consisting of two beta-distributions: myfunction <- function(x) { dbeta(x,2,6) + dbeta(x,6,2) } How can I calculate the quantiles of myfunction? I have not seen any continous function treated in the docs, and applying the "quantile function" gives me an