Scilit with the Integrated Impact Indicator Assessment (Code)

🚀 Quick start (run from project root) All scripts assume the working directory is the project root (the folder that contains data/, scripts/, and outputs/). Option A: Command line (recommended) From Code/: Copy Rscript scripts/s4.2.1_plot_result.R Rscript scripts/s4.2.2_plot.R Rscript scripts/s4.2.3_plot.R Rscript scripts/s4.2.3_result.R Rscript scripts/s4.2.4_plot.R Rscript scripts/s4.3_plot.R Option B: RStudio Open the Code/ folder as a project (or set it as the working directory). Run scripts in scripts/ in the desired order. All outputs will be written to outputs/. 🗺️ Script guide (what each script does & what it produces) Below is a reader-friendly map from script → purpose → key outputs.(Exact filenames are defined inside each script, typically in ggsave(), write.xlsx(), or other save calls.) 1) Section 4.2.1 — Cross-year and cross-metric comparisons (2023 vs 2024) Script: scripts/s4.2.1_plot_result.R What it does Scatterplots (2024 vs 2023) for: I3/N Journal IF (JIF) CiteScoreAnnotated with: R² from linear regression Spearman correlation Lin’s Concordance Correlation Coefficient (CCC) Within-year pairwise comparisons among metrics (2023 and 2024): pairwise Spearman + CCC exports a table ECDF plots (with optional compressed/piecewise x-axis) and ECDF difference curves: ECDF(2024) - ECDF(2023) relative/standardized ECDF difference scaled to [-1, 1] Required input data/task3_all_years_IF_Citescore_I3.xlsx Key outputs outputs/section_4_2_1_scatter_2024_vs_2023.png outputs/section_4_2_1_pairwise_stats.xlsx outputs/section_4_2_1_ecdf_and_diff.png outputs/section_4_2_1_ecdf_diff_relative.png 2) Section 4.2.2 — Plot for Section 4.2.2 Script: scripts/s4.2.2_plot.R What it does Generates the visualization(s) used in Section 4.2.2 (see in-script comments and parameters). Inputs/outputs Inputs are read from data/ as specified inside the script. Outputs are saved to outputs/ (filenames are defined inside the script). 3) Section 4.2.3 — Cross-database subject mapping network (visualization) Script: scripts/s4.2.3_plot.R What it does Builds and visualizes a cross-database subject mapping network across: Scopus Web of Science (WoS) Scilit Inputs/outputs Inputs are read from data/ as specified inside the script. Outputs are saved to outputs/ (filenames are defined inside the script). 4) Section 4.2.3 — Tri-database mapping + paired tests (results) Script: scripts/s4.2.3_result.R What it does Performs tri-database subject mapping (Scopus / WoS / Scilit). Runs paired tests on overlapping journals comparing key metrics (2023 vs 2024). Inputs/outputs Inputs are read from data/ as specified inside the script. Outputs are saved to outputs/ (filenames are defined inside the script). 5) Section 4.2.4 — Subjects associated with extreme rank differences (I3/N vs CiteScore, 2024) Script: scripts/s4.2.4_plot.R What it does Identifies subject categories associated with extreme differences between percentile ranks of: Scilit I3/N CiteScore (2024) Visualizes top subjects for the defined extreme groups (e.g., Group 1 and Group 4). Inputs/outputs Inputs are read from data/ as specified inside the script. Outputs are saved to outputs/ (filenames are defined inside the script). 6) Section 4.3 — Publisher-level comparison of Scilit metrics (2023 vs 2024) Script: scripts/s4.3_plot.R What it does Produces publisher-level distribution comparisons of Scilit metrics (2023 vs 2024). Includes support for multiple plotting modes (e.g., violin/box/beeswarm/jitter; see script options). Inputs/outputs Inputs are read from data/ as specified inside the script (often an .RData object such as raw). Outputs are saved to outputs/ (filenames are defined inside the script). 🧪 Reproducibility notes Relative paths only: scripts expect to be run from the project root. Outputs may be overwritten: re-running scripts may overwrite files in outputs/. Determinism: most plots are deterministic. If any random jitter/placement is used, set a seed in the script if you need exact pixel-level reproducibility.