No issues were encountered while installing R and RStudio. My OS version is MacOS Sequoia 15.6, the R version is 4.3.3, and the RStudio version is 2025.05.1+513.
disordR: a minimal IDP toolkit in R Project Goal I built disordR to provide a small toolkit for analyzing intrinsically disordered proteins (IDPs). The package includes functions for amino-acid properties, classic Uversky charge-hydropathy metrics/plots, and a simple consensus combiner for per-residue disorder scores. A small, bundled dataset makes testing the functions simple and reproducible. Design Reasoning I chose four functions to cover common IDP tasks: aa_props() → mean Kyte–Doolittle hydropathy, net charge, and fraction charged residues (FCR). uversky_metrics() → scaled hydropathy (0–1) + mean net charge per residue with a simple IDP vs Ordered call uversky_plot() → classic Uversky scatter with the boundary line for visual interpretation. consensus_disorder() → mean consensus or predictor scores I limited dependencies to tibble and ggplot2 to make installs reliable and simple. A f uture direction of this package could include lig...
R code for creating A and B, and for computing invA , detA , invB , and detB : Outputs for each operation: Why solve(A) and det(A) behave this way: Since Matrix A is 10x10 and therefore a square, it could technically have an inverse. However, since the determinant of A is 0, it is considered a singular matrix, meaning it does not have full rank and therefore cannot be inverted. Due to this, solve(A) returns an error even though the matrix is a square, yet it is still able to run and confirm the singularity. Why operations on B fail (non‑square matrix): Since Matrix B is 10x100, it is not square. Inverses and determinants can only be defined for square matrices, meaning both solve(B) and det(B) return errors. Though it is expected, it highlights how important it is to check matrix dimensions before attempting further operations. Notes on numeric stability or performance: There are important considerations w...
This week, our objectives were: - Learn the structure of an R package and the role of the DESCRIPTION file - Practice writing machine-readable metadata (authors, dependencies, versioning) - Draft a coherent package proposal for your final project - Publish your package scaffold proposal online (GitHub and blog) My R package: disordR Purpose and scope of the project The disordR package is a small toolkit designed to be used for students or researchers studying intrinsically disordered proteins (IDPs). It has a simple pipeline that can be used in class projects or advanced research. First, it will calculate charge-hydropathy metrics (based on the Uversky plot). It will then create a concise output of disorder predictors, and finally, it will map AlphaFold pLDDT to assumed intrinsically disordered regions (IDRs). Key functions I plan to implement aa_props() - mean hydropathy, net charge, and fraction of charged residues uversky_metrics() - hydropathy and charge per residue; classi...
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