description <- readLines("DESCRIPTION") rvers <- stringr::str_match(grep("R \\(", description, value = TRUE), "[0-9]{1,4}\\.[0-9]{1,4}\\.[0-9]{1,4}")[1,1] version <- gsub(" ", "", gsub("Version:", "", grep("Version:", description, value = TRUE)))
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
Most genomic analysis look for patterns and trends with various statistics. Bias, noise and outliers can have bounded influence on estimators and interfere with polymorphism discovery. Avoid bad data exploration and control the impact of filters on your downstream genetic analysis. Use radiator to: import, explore, manipulate, visualize, filter, impute and export your GBS/RADseq data.
radiator is designed and optimized for fast computations using Genomic Data Structure GDS file format and data science packages in tidyverse. radiator handles VCF files with millions of SNPs and files of several GB.
To try out the dev version of radiator, copy/paste the code below:
if (!require("pak")) install.packages("pak") pak::pkg_install("thierrygosselin/radiator") library(radiator)
To minimize dependencies, just the basic required packages are installed with the command above. If you want the full suits of functions and don't want to be preoccupied, run:
radiator::radiator_pkg_install() # that's it. It will update, when necessary, radiator.
Computer setup and troubleshooting vignette
To get the citation, inside R:
citation("radiator")
radiator is maturing, but in order to make the package better, changes are inevitable. Experimental functions will change, argument names will change.
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