The truly important reason to try the #positron R/Python editor is that it can work in parallel with another R session open in #RStudio for a different project...
Also amazed by the #Pluto notebook for #Julia scientific work. One of its nice features is that the associated file is in text format and the code can be easily extracted. Not yet ready to switch from #ggplot to #VegaLite though.
https://positron.posit.co/start.html
https://plutojl.org/
#rstat
R-devel NEWS says:
> "The deprecated and seemingly never-used S-compatibility macros ‘F77_COM’ and ‘F77_COMDECL’ have been removed from header ‘R_ext/RS.h’."
does this mean...."because of compatibility with S" is no longer a valid argument?
10 years of writing #RStat code, and I still get bitten by using [ ] instead of [[ ]]
The new #rstat #dispRity pkg v1.9 is now out live!
Major internal overhaul: the functions are now more efficient (and faster), specially when dealing with distance matrices!
You can find the details on the CRAN https://cran.r-project.org/web/packages/dispRity
GitHub: https://github.com/TGuillerme/dispRity
or the old paper in @MethodsEcolEvol https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13022
Apply functions with purrr :: Cheatsheet #rstat ditch your for loop with purrr https://rstudio.github.io/cheatsheets/purrr.pdf
![# R code for reversing elements within blocks within a vector
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' Flip the endianness of elements in a vector
#'
#' This will create a new vector with the values reversed within the given
#' block size. This can be used for changing the endianness of a set of values
#'
#' @param x vector. Usually a raw vector, but can be any type
#' @param size block size. Usually a multiple of 2
#'
#' @return vector of the same type as the initial vector with the values
#' within each block reversed.
#' @examples
#' vec <- c(1, 2, 3, 4)
#' flip_endian(vec, 2) # should give: c(2, 1, 4, 3)
#' @export
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
flip_endian <- function(x, size) {
if (size <= 1) return(x)
if (length(x) %% size != 0) {
stop("Input vector length must be a multiple of the 'size'")
}
dim(x) <- c(size, length(x)/size)
x[] <- x[nrow(x):1,]
as.vector(x)
}
vec <- c(1, 2, 3, 4, 5, 6, 7, 8)
all.equal(flip_endian(vec, 1), vec)
all.equal(flip_endian(vec, 2), c(2, 1, 4, 3, 6, 5, 8, 7))
all.equal(flip_endian(vec, 4), c(4, 3, 2, 1, 8, 7, 6, 5))](https://cdn.fosstodon.org/media_attachments/files/113/263/233/215/484/466/original/d8546c05548cd17a.png "# R code for reversing elements within blocks within a vector
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' Flip the endianness of elements in a vector
#'
#' This will create a new vector with the values reversed within the given
#' block size. This can be used for changing the endianness of a set of values
#'
#' @param x vector. Usually a raw vector, but can be any type
#' @param size block size. Usually a multiple of 2
#'
#' @return vector of the same type as the initial vector with the values
#' within each block reversed.
#' @examples
#' vec <- c(1, 2, 3, 4)
#' flip_endian(vec, 2) # should give: c(2, 1, 4, 3)
#' @export
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
flip_endian <- function(x, size) {
if (size <= 1) return(x)
if (length(x) %% size != 0) {
stop(\"Input vector length must be a multiple of the 'size'\")
}
dim(x) <- c(size, length(x)/size)
x[] <- x[nrow(x):1,]
as.vector(x)
}
vec <- c(1, 2, 3, 4, 5, 6, 7, 8)
all.equal(flip_endian(vec, 1), vec)
all.equal(flip_endian(vec, 2), c(2, 1, 4, 3, 6, 5, 8, 7))
all.equal(flip_endian(vec, 4), c(4, 3, 2, 1, 8, 7, 6, 5))")
Nous commençons la saison 2024-2025 en douceur en vous partageant une petite astuce en R pour convertir les identifiants en symboles de gènes 
https://bioinfo-fr.net/r-convertir-des-ensembl-ids-en-symboles-de-genes
#bioinfofr #Rstat #bioconductor
We're back online!
Many thanks to the CRAN maintainer known as 'k'
https://cran.r-project.org/web/packages/arcpbf/index.html
#rstat
Recently decided to do some analysis of the FBI’s National Instant Criminal Background System compiled by the Data Liberation Project. Used Facebooks Prophet time series analysis on Monthly Permits so got a lot of noise in the forecasting but can see a broad leveling off of permits in the next couple of years:
1/
I’ve been developing some genotype allele frequency (AF) #imputation #rstat scripts. While my algorithm returns a low raw bias (RB) of 0.05 to 0.1, simply imputing with the meanAF gives slightly worse but largely similar RB values. I’m wanting to quantifying accuracy better because despite the similar RB values, I find meanAF imputation inferior.
#genomics
@archaeoriddle Please, you still have a few days do fill our survey! if everyone have heard (even slightly remotely) about the project could answer that would be amazing and super helpful! don't hesitate to share the link. #archaeoriddle #archaeodon #histodon #rstat #modelling #simulation #floss #ArchaeoFOSS
We are super lucky to have an amazing line up of speakers, from both Japan & GB. Organic #residue #analysis, #computer #modelling, #Bayesian #statistics, #genetics, and much more topics will be covered, so don't miss it! #rstat #archaeofloss
#TidyTuesday week 22 - Lisa's vegetable garden. Made an interactive table using {reactablefmtr}. #rstat #dataviz
So even after symlinking #rstat's BLAS and LAPACK to the Intel 's MKL/oneApi ,one must source the environmental script to make the darned thing work, every single time R is used. Best done by sourcing it in their .bashrc script.
This took a couple restarts to figure out :P
Is anyone here familiar (or got in touch) with Julia? Since DataCamp calls it the fastest growing programming language and it reads like something I'd like to learn. But actually I'm busy with learning R at the moment and wanted to learn Python second. Should I learn Julia, too?
