similar to: Line similarity

Folks, I have the following code, that works fine on smaller data sets. For larger datasets, it runs out of memory and runs way too slow because we are essentially creating large vectors with rep() and then calling median() on it. (I learned this approach from a post on the web). Below that, I have written the corresponding SAS code. The SAS code works fast because I can just tell the proc

Gentlemen, In the documentation for reshape, in the function signature, the argument "direction" is not listed. However, it is explained in the explanation of parameters below. I am using R 2.2.1. Out of curiosity: Is the R core team still an all-male affair? I don't think I have seen a single lady's name. -- -- Vivek Satsangi Student, Rochester, NY USA

Folks, I want to make the following function more efficient, by vectorizing it: getCriterionDecisionDate <- function (quarter , year) { if (length(quarter) != length(year)) stop ("Quarter and year vectors of unequal length!"); ret <- character(0); for (i in 1:length(quarter)) { currQuarter <- quarter[i]; currYear <- year[i]; if ((currQuarter < 1) |

Folks, I was drawn to R, like many others, partly for the opportunity to draw nice, colorful graphs (occasionally ones with meaning, too :-) ). I am still quite a newbie to R. As such, I have been trying to understand the code for the graphics on the home page (the ones from the 2004 contest -- the dendrogram, the cluster plot with different coloured circles, etc.) I was wondering whether anyone

Folks, I do a series of regressions (one for each quarter in the dataset) and then go and extract the residuals from each stored lm object that is returned as follows: vResiduals <- as.vector(unlist(resid(lQuarterlyRegressions[[i]]))); Here lQuarterlyRegressions is a vector of objects returned by lm(). Next, I may go find outliers using identify() on a plot or do some other analysis which

Folks, I know that I can do the following using a loop. That's been a lot easier for me to write and understand. But I am trying to force myself to use more vectorized / matrixed code so that eventually I will become a better R programmer. I have a dataframe that has some values by Year, Quarter and Ranking. The variable of interest is the return (F3MRet), to be weighted averaged within the

Folks, 1. Is there a more appropriate list (r-devel?) for posting such suggestions? I am a newbie to R, and doubtless will have some suggestions for the documentation -- some good, others not quite so. I would actually like to help give back to the community (I was motivated by Prof. Ripley's 2001 talk in which he had commented that open source software users rarely give back anything.) --

Disclaimer/Apology: I am an R newbie I am seeing some behaviour that seems to me to be the result of some cacheing going on at some level, and perhaps this is expected behaviour. I would just like to understand the basic rules. What I have is a file with some data. I read it in and then do a summary on the resulting dataframe. I find the some values are completely outside the expected range,

Folks, Sorry if this question has been answered before or is obvious (or worse, statistically "bad"). I don't understand what was said in one of the search results that seems somewhat related. I use aggregate to get a quick summary of the data. Part of what I am looking for in the summary is, how much influence might the NA's have had, if they were included, and is excluding

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

Folks, I have a very basic question. The solution eludes me perhaps because of my own lack of creativity. I am not attaching a fully reproducible session because the issue may well be becuase of the way the data file is, and the data file is large (and I don't know whether I can legally distribute it). If people can suggest things that might be wrong in my data or the way that I am reading it,

Hello - R-experts, Is there any way with which we can specify the number after decimal point to take. Like I have a situation where the values are comming 0.160325923 but I only want 4 place to decimal say 0.1603. Is there any way for that. I am no expert in R- and this may sound simple to many.sorry Thanks for any help. With Regards Subhabrata

Dear R users, I'm having some problems with the stack() and unstack() functions, and wondered if you could help. I have a large data frame (400 rows x 2000 columns), which I need to reduce to a single column of values (and therefore 800000 rows), so that I can use it in other operations (e.g., generating predictions from a GLM object). However, the problem I'm having can be reproduced

On Thu, Mar 9, 2017 at 9:35 PM, Hal Finkel <hfinkel at anl.gov> wrote: > > On 02/25/2017 03:06 AM, vivek pandya via llvm-dev wrote: > > Note: Question is written after describing what I have coded. > > Hello LLVMDevs, > > I am trying to impliment floating point comparsion for an architecture > which > supports following type of floating point comparision if FPU

Hi Everyone, I am trying to understand the unstack() function but after struggling for two days, I have given up. More specifically, I am trying the exercises at the end of Chapter 1 of Data Analysis and Graphics Using R by Maindonald and Braun, 2nd ed. Exercise 18 (p. 41) asks to unstack the Rabbit data frame from the MASS package to get a certain data frame that is shown in the exercise.

On 03/14/2017 07:16 AM, vivek pandya wrote: > Hello Hal, > setCondCodeAction(expand) for un ordered comparison generates > semantically wrong code for me for example SETUNE gets converted to > SETOE that causes infinite loops. Can you please explain what is happening? It sounds like a bug we should fix. > > What is ideal place where I can convert unordered comparison to un

One would normally want the original order that so that one can stack a list, operate on the result and then unstack it back with the unstacked result having the same ordering as the original. LL <- list(z = 1:3, a = list()) # since we can't do s <- stack(LL,. drop = FALSE) do this instead: s <- transform(stack(LL), ind = factor(as.character(ind), levels = names(LL))) unstack(s)

Note: Question is written after describing what I have coded. Hello LLVMDevs, I am trying to impliment floating point comparsion for an architecture which supports following type of floating point comparision if FPU is available: fcmp.un --> true if one of the operand is NaN fcmp.lt --> ordered less than, if any input NaN then return false fcmp.eq --> ordered equal, if any input NaN

Hello All: I am very new to R and am trying to import some (107) text files into R while simultaneously manipulating the data into a structure that I can use. Below is a description of what I am trying to ultimately accomplish. However, I find that I am unable to even read one file into R using the read.table function. I get the following error: > test<-read.table("test4.txt")

stack() seems to drop empty levels. Perhaps there could be a drop=FALSE argument if one wanted all the original levels. In the example below, we may wish to retain level "b" in s$ind even though component LL$b has length 0. > LL <- list(a = 1:3, b = list()) > s <- stack(LL) > str(s) 'data.frame': 3 obs. of 2 variables: $ values: int 1 2 3 $ ind : Factor