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.
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...
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: GitHub link Generated plots: Scatter plot: Histogram: Conditional scatter plot (lattice): Scatter with linear trend (ggplot2): Discussion: How do the syntax and workflow differ between base, lattice, and ggplot2? Base R is very simple and step-by-step, where you first call 'plot( )', and then begin to add what you want line by line, such as points, lines, or legends. With lattice, you establish the description of the plot once with a formula 'xyplot(y ~ x | group, data=df)', with optional formatting lines, and then it renders the plot from that. It is more rigid with edits and requires you to edit the actual code rather than additional lines. Finally, ggplot2 is built together, data + aesthetics + layers 'ggplot(df, aes(x, y, color=group)) + geom_point( ) + geom_smooth( )', therefore allowing it to be easily tweaked. Which system gave you the most control or produced the most “publication‑quality” output with minimal code? I would say that ggplot2 pr...
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