similar to: Applying a function to categorized data?

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

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.

I've been reading the R mail archives and I've found a lot of messages with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function

Folks, I have the following dataframe: > x <- structure(list(name = c("EU B", "EU B", "EU B", "EU B", "EU B", "EU B", "AU A", "AU A", "AU A", "AU A", "AU A", "AU A"), date = c("2010-10-11", "2010-10-12", "2010-10-13",

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)

Hi, I'm trying to analyse some time series data on dissolved organic nitrogen. Because it has gaps in it, I try to interpolate (linear) with the regul method from the pastecs package. I have a number of stations with measurement series in a matrix constructed from a data frame with the unstack method (temp) I also used the unstack method to make a similar matrix for the time points

Hello All, I was wondering if it's possible to reshape data from long to wide in R without using a "timevar". I've pasted some sample data below along with some code. The data are sorted by Subject and Drug. I want to transpose the Drug variable into multiple columns in alphabetical order. My data have a variable called "RowNo" that functions almost like a

Hi folks, I am new to the list and relatively new to R. I am trying to unstack data "arraywise" and could not find a convenient solution yet. I tried to find a solution for the problem on help archives. I also tried to use the reshape command in R and the reshape package but could not get result. I will illustrate the case below, but the real dataset is quite large so that I would

Hi all, Thanks a lot for anyone's help in advance. I am trying to find a way to compute the day-to-day return (log return) from a n x r matrix containing, n different stocks and price quotes over r days. The time series of prices are already split by using unstack function. For the result, I would like to see a n x (r-1) matrix, where by each entry is the day-over-day return of

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Dear R-helpers, my data.frame is of the form x <- data.frame( f=gl(4,3), X=rep(0:2,4), p=c(.1,.2,.3)) x f X p 1 1 0 0.1 2 1 1 0.2 3 1 2 0.3 4 2 0 0.1 5 2 1 0.2 6 2 2 0.3 7 3 0 0.1 8 3 1 0.2 9 3 2 0.3 10 4 0 0.1 11 4 1 0.2 12 4 2 0.3 which tabulates some values p(X) for several factors f. Now I want to put it in "wide"

We need to look at how to support this on ia64, so how about making this an architecture dependent feature for now? Not sure what powerpc needs here, so I''m just lumping it into the old way bucket. Thanks, Alex Signed-off-by: Alex Williamson <alex.williamson@hp.com> --- diff -r 3ac19fda0bc2 tools/python/xen/xend/XendDomainInfo.py --- a/tools/python/xen/xend/XendDomainInfo.py

Hi all, I'm having a problem restructuring my data the way I'd like it. I have data that look like this: Candidate.ID Specialty Office Score 110002 C London 47 110002 C East 48 110003 RM West 45 110003 RM

Hi and thanks in advance, I am fairly new with R so I hope this problem isn't too amateur. I have a vector of count data which correspond to vectors of date (%m/%d/%Y) and time of day (%H:%M:%S). I am trying to compute various statistics (e.g. daily max) by lumping the data together by day. I have been able to utilize tapply() and group the counts together, but with the method I use I end

The following tinkers with the strip labels, where the different panels are for different levelf of a conditioning factor. tau <- (0:5)/2.5; m <- length(tau); n <- 200; SD <- 2 x0 <- rnorm(n, mean=12.5, sd=SD) matdf <- data.frame( x = as.vector(sapply((0:5)/2.5, function(s)x0+rnorm(n, sd=2*s))), y <- rep(15+2.5*x0, m), taugp = factor(rep(tau, rep(n,m)))) names(matdf)

In the implementation of the AliasSetTracker, I see LoadInst, StoreInst, and VAArgInst are treated specially. Why aren't AtomicCmpXchgInst and AtomicRMWInst treated with similar care? Lumping together with other unknown instructions seems unnecessarily expensive in compile time. Preston -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi there, I am not getting anywhere near a solution, so here is my question. I have searched the archives and didn't find a solution, but maybe my search didn't use the right words. So here it is: I have a dataframe with the following structure factor1 factor2 value In the (rather large) dataset I have to work on, there are some factor combinations missing, e.g. factor1 <-

On 03/01/2018 03:06 AM, hw wrote: > >> It is illogical to lump all network access together into a single >> category. > ... >> If your device can communicate with a switch, even for the purpose of >> authenticating, then it has network access. > > The device has access to the switch which, depending on what answer to an > authentication request it gets from

I have this frame in the long format which I want to convert to wide format. There are three columns in the frame, the id for the rows in the wide format, the name of the column where the final value (the third column) is to be placed. The complicating factor is that the long format is not complete in at least two ways, (a) there may be more than one reference to one cell in the wide

Sorry about the odd terminology, but I suspect that my intent might be completely missed had I used "aggregate" or "classify" (each of which appears to have some rather special meanings in statistical analysis and modeling). I have some data about software builds; one of the characteristics of each is the name of the branch. A colleague has generated some fairly interesting

Hello R users, My problem is that the data I've got is in the minimum number of columns with each ward (geographic area) appearing multiple times. The first 30 terms look like this > HHum02 CASW Btype Yr CO2Group NumVeh 170597 00CCFA CARS 2002 C 2 170598 00CCFA CARS 2002 D 2 170599 00CCFA CARS 2002 E 22 170600 00CCFA CARS 2002