similar to: new syntax: bash-like pipe operator

Dear all, I would like to log the calls to my functions. I am trying to do this using the function match.call(): fTest<-function(x) { theCall<-match.call() print(theCall) return(x) } > fTest(2) fTest(x = 2) [1] 2 I can see "theCall" printed into the console, but I don't manage to convert it into a character to write it into a log file

Dear useRs, Some time ago I queried the list as to an efficient way of building a function which acts as ls() but with a different default for all.names: http://tolstoy.newcastle.edu.au/R/e6/help/09/03/7588.html I have struck upon a solution which so far has performed admirably. In particular, it uses ls() and not its explicit source code, so only has a dependency on its name and the name of

I have a vector of values -1, 0, 1, say a <- c(0, 1, 0, 1, 1, -1, -1, -1, 0, -1, -1) I want to create a vector of the cumulative sum of this, but I need to set a maximum and minimum value for the cumsum, for example: max_value <- 2 min_value <- -2 the expected result would be (0, 1, 1, 2, 2, 1, 0, -1, -1, -2, -2) The only way I managed to do It, was res <-

Hello everybody, Data is myd <- data.frame(id1=rep(c("a","b","c"),each=3),id2=rep(1:3,3),val=rnorm(9)) I want to get a cumulative sum over each of id1. trying aggregate does not work myd$pcum <- aggregate(myd[,c("val")],list(orig=myd$id1),cumsum) Please suggest a solution. In real the dataframe is huge so looping with for and subsetting is not a

Hello Group, I could use an advice on how SEXP handles work. My aim is to implement a system where I initially set a few global variables that are used for communication between C and R code. Then I do some work with R code and periodically call a function of my own that will update the system state. Such a design is useful for many purposes (for GUIs to name one). I am not entirely sure that R

I sent a post to find a clever way to compute a Rolling Average of columns in a matrix and I was given the solution below which I am very pleased with. RollingAverage <- function(x, RollingObs) { cx <- cumsum(x); N <- length(x); Temp <- (cx[RollingObs:N] - c(0, cx[1:(N-RollingObs)]))/RollingObs Output <- array(NA, N) Output[RollingObs:N] <- Temp; Output } The only

Hi, I have a data.frame of the following type: F = data.frame(read.table(textConnection(" A B 1 1 4 2 1 3 3 1 1 4 1 4 5 1 2 6 1 2 7 1 2 8 2 1 9 2 1 10 2 1 11 2 1 12 3 2 13 3 4 14 3 1 15 3 1 16 3 1"),head=TRUE,stringsAsFactors=FALSE)) F A B 1 1 4 2 1 3 3 1 1 4 1 4 5 1 2 6 1 2 7 1 2 8 2 1 9 2 1 10 2 1 11 2 1 12 3 2 13 3 4 14 3 1 15 3 1 16 3 1

If you object to names(x)[2]<- ... then use replace: z |> list(x = _) |> within(replace(names(x), 2, "foo")) |> _$x On Sun, Jul 21, 2024 at 11:10?AM Bert Gunter <bgunter.4567 at gmail.com> wrote: > > hmmm... > But note that you still used the nested assignment, names()[2] <- > "foo", to circumvent R's pipe limitations, which is exactly

hmmm... But note that you still used the nested assignment, names()[2] <- "foo", to circumvent R's pipe limitations, which is exactly what Iris's solution avoids. So I think I was overawed by your cleverness ;-) Best, Bert On Sun, Jul 21, 2024 at 8:01?AM Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Wow! > Yes, this is very clever -- way too clever for

Apologies for the empty email earlier! I have species abundance data sampled at a weekly frequency or sometimes monthly depending on the year. The goal is to identify the dates in an annual cycle in which the cumulative abundance of a species reaches some threshold. Here's an example of the data for 1 species over an annual period: "mc_pheno" is the object created from this data:

This - is non-destructive (does not change z) - passes the renamed z onto further pipe legs - does not use \(x)... It works by boxing z, operating on the boxed version and then unboxing it. z <- data.frame(a = 1:3, b = letters[1:3]) z |> list(x = _) |> within(names(x)[2] <- "foo") |> _$x ## a foo ## 1 1 a ## 2 2 b ## 3 3 c On Sat, Jul 20, 2024 at

Wow! Yes, this is very clever -- way too clever for me -- and meets my criteria for a solution. I think it's also another piece of evidence of why piping in base R is not suited for complex/nested assignments, as discussed in Deepayan's response. Maybe someone could offer a better Tidydata piping solution just for completeness? Best, Bert On Sun, Jul 21, 2024 at 7:48?AM Gabor

This post is likely pretty useless; it is motivated by a recent post from "Val" that was elegantly answered using Tidyverse constructs, but I wondered how to do it using base R only. Along the way, I ran into the following question to which I think my answer (below) is pretty awful. I would be interested in more elegant base R approaches. So... z <- data.frame(a = 1:3, b =

Yes it is. I'm remote at the moment so I can't send you the code but google for mobile remote receiver and you'll find what you are looking for. Lots of people do it so they don't have calls to cell phones picked up by voicemail. Cheers dean -----Original Message----- From: Pete Kay <petedao at gmail.com> Sent: Monday, March 31, 2008 2:27 PM To: Asterisk Users Mailing

Hi, How can I discern which elements in x (see below) are in 'order', but more specifically.. only the 1st 'ordered run'? I would like for it to return elements 1:8... there may be ordered values after 1:8, but those are not of interest. x <- c(1, 2, 3, 4, 5, 6, 7, 8, 20, 21, 22, 45) Thanks for any suggestions. -- View this message in context:

Hi, I have the following sequence: in <- c(0, 0, 0, 2, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 0, 2, 0, 0, 2) >From this sequence I would like to get to the following sequence: out <- c(0, 0, 0, 3, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 0, 2, 0, 2, 0, 0, 2) Basically,

Please consider following string: MyString <- "ABCFR34564IJVEOJC3434" Here you see that, there are 4 groups in above string. 1st and 3rd groups are for english letters and 2nd and 4th for numeric. Given a string, how can I separate out those 4 groups? Thanks for your time [[alternative HTML version deleted]]

#Is there a way to break the below zoo object into non-NA data frames algorithmically #this is a small example of a much larger problem. #It is really no even necessary to have the continuous chunks #end up as zoo objects but it is important to have them end #up with the index column. #thanks for all of your help in advance, and #if you need anything else please let me know library(zoo) ind.

>>>>> Tomas Kalibera >>>>> on Tue, 25 Aug 2020 09:29:05 +0200 writes: > On 8/23/20 5:02 PM, Rory Winston wrote: >> Hi >> >> I noticed a small inconsistency when using sum() vs cumsum() >> >> I have a char-based series >> >> > tryjpy$long >> >> [1]

Hi, I need to keep a running count of events that have happened in my data.frame. I found a document called usingR that had an example of doing this for random coin flips and I tried to modify it. It seems to sort of work in the beginning, but then it stops and I don't understand why. I'm trying to duplicate essentially the Excel capability of =SUM($A$1:$A(Row number)) The example