similar to: Function to Define a Function

Here is a problem I ran across in RStudio, which uses masks a number of standard R functions with versions that do a bit more than the original does. The RStudio people have figured out a way to prevent default values of arguments from being found when an argument is missing from a call! junk1 <- function(...) { fun <- function(pkgs, lib = NULL) { list(missing=missing(lib),

All, I'm re-running some analysis that has been augmented with additional data. When I use the exact same code for the augmented data, the behavior of the aggregate function is very strange, viz., one of the resulting variables is now coded as a factor while it was coded as numeric for the original data. Unfortunately, I cannot provide a reproducible code example since it only seems to occur

Often, when exploring a dataset, I'd like to plot several very different Y variables against the same X variable, in panels stacked one over the other. Is there an easy way to do this? I'd like to achieve an elegant look similar to the look achieved by lattice in conditioned plots--for instance no space between panels. But unlike in straightforward conditioned plot, each panel may be on a

Hello Everyone, I've been playing with an agi script for paging sip phones. page.agi will take all available sip extensions and assign them to the global variable PAGE_GROUP. Allowing the phones to be paged from the dialplan with the new Page cmd. Extensions to be excluded are presented as arguments to the agi. Each time a page is made this agi refreshes the global variable. This works with

I''ve been using btrfs as a spool space on our back-up server to get familiar with it and things work fine until the volume fills up. Our backup software (bacula) usually spools until the volume is full, then despools and respools, etc. With btrfs, it fills up and bacula thinks there is still space so it keeps trying and then finally errors out. I thought this was a problem because I was

Hi All: I have been looking for an elegant way to do the following, but haven't found it, I have never had a good understanding of any of the "apply" functions. A simplified idea is I have an array, say: junk(5, 10, 3) where (5, 10, 3) give the dimension sizes, and I want to reverse the second dimension, so I could do: junk1 <- junk[, rev(seq_len(10), ] but what I am

Classification: UNCLASSIFIED Caveats: NONE Dear R users, How can I retrieve an evaluated gradient value from "deriv" function provided below? I want to retrieve b0 value. Appreciate your help. Kyong junk1<-deriv(~(1/b1)*k-b0/b1,"b0",c("k","b0","b1"),formal=T) junk1(k=0,b0=-14.0236,b1=2.44031) [1] 5.746647 attr(, "gradient"):

Thanks to all for responses/. There was a question of exactly what was wanted. It is the generalization of the obvious example I gave, >>> junk1 <- junk[, rev(seq_len(10), ] so that junk[1,1,1 ] = junk1[1,10,1] junk[1,2,1] = junk1[1,9,1] etc. The genesis of this is the program is downloading data from a variety of sources on (time, altitude, lat, lon) coordinates, but all

How about this: f <- function(a,wh){ ## a is the array; wh is the index to be reversed l<- lapply(dim(a),seq_len) l[[wh]]<- rev(l[[wh]]) do.call(`[`,c(list(a),l)) } ## test z <- array(1:120,dim=2:5) ## I omit the printouts f(z,2) f(z,3) Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into

On the off chance that anyone is still interested, here is the corrected function using aperm(): z <- array(1:120,dim=2:5) f2 <- function(a, wh) { idx <- seq_len(length(dim(a))) dims <- setdiff(idx, wh) idx <- append(idx[-1], idx[1], wh-1) aperm(apply(a, dims, rev), idx) } all.equal(f(z, 1), f2(z, 1)) # [1] TRUE all.equal(f(z, 2), f2(z, 2)) # [1] TRUE

> On 1 Jun 2017, at 22:42, Roy Mendelssohn - NOAA Federal <roy.mendelssohn at noaa.gov> wrote: > > Thanks to all for responses/. There was a question of exactly what was wanted. It is the generalization of the obvious example I gave, > >>>> junk1 <- junk[, rev(seq_len(10), ] > > > so that > > junk[1,1,1 ] = junk1[1,10,1] > junk[1,2,1] =

Thanks again. I am going to try the different versions. But I probably won't be able to get to it till next week. This is probably at the point where anything further should be sent to me privately. -Roy > On Jun 1, 2017, at 1:56 PM, David L Carlson <dcarlson at tamu.edu> wrote: > > On the off chance that anyone is still interested, here is the corrected function using

My error. Clearly I did not do enough testing. z <- array(1:24,dim=2:4) > all.equal(f(z,1),f2(z,1)) [1] TRUE > all.equal(f(z,2),f2(z,2)) [1] TRUE > all.equal(f(z,3),f2(z,3)) [1] "Attributes: < Component ?dim?: Mean relative difference: 0.4444444 >" [2] "Mean relative difference: 0.6109091" # Your earlier example > z <- array(1:120, dim=2:5) >

Here is an alternative approach using apply(). Note that with apply() you are reversing rows or columns not indices of rows or columns so apply(junk, 2, rev) reverses the values in each column not the column indices. We actually need to use rev() on everything but the index we are interested in reversing: f2 <- function(a, wh) { dims <- seq_len(length(dim(a))) dims <-

?? > z <- array(1:24,dim=2:4) > all.equal(f(z,3),f2(z,3)) [1] "Attributes: < Component ?dim?: Mean relative difference: 0.4444444 >" [2] "Mean relative difference: 0.6109091" In fact, > dim(f(z,3)) [1] 2 3 4 > dim(f2(z,3)) [1] 3 4 2 Have I made some sort of stupid error here? Or have I misunderstood what was wanted? Cheers, Bert Bert Gunter

I'm having trouble making contour lines for this attached, sparse dataset (low data:NA ratio!). Is it the high number of NA's, or funny layout of the densities, or something else that's causing this? This is a subsample of the dataset, and I get the same problem when using the full data. The only reason I get any contours is because I'm forcing specific levels. If no levels are

I have : raw <- read.table("monthly.text", skip=3, sep="|", col.names=c("junk", "junk2", "wpi", "g.wpi", "wpi.primary", "g.wpi.primary", "wpi.fuel", "g.wpi.fuel", "wpi.manuf", "g.wpi.manuf",

Below is an example of a problem I encounter repeatedly when I write functions. A call works at the command line, but it does not work inside a function, even when I have made sure that all required variables are available within the function. The only way I know to solve it is to make the required variable global, which of course is dangerous. What is the elegant or appropriate way to solve

Hello, I have a file that looks like this: Date,Hour,DA_DMD,DMD,DA_RTP,RTP,, 1/1/2006,1,3393.9,3412,76.65,105.04,, 1/1/2006,2,3173.3,3202,69.20,67.67,, 1/1/2006,3,3040.0,3051,69.20,77.67,, 1/1/2006,4,2998.2,2979,67.32,69.10,, 1/1/2006,5,3005.8,2958,65.20,68.34,, where the ',' is the separator and I tried to read it into R, but... > y <- read.csv("Data/Data_tmp.csv",

Hi all, I have a following problem: - Remus network buffering doesn''t work. It seems to be because of no vif is reported by the function server.xend.domain on line 29 of /usr/local/lib/python2.7/dist-packages/xen/remus/vm.py (seen using pdb) : 27 if domid: 28 try: 29 self.dominfo = server.xend.domain(domid, ''all'') >