DOI: 10.5281/zenodo.18013019
title: Scilit with the Integrated Impact Indicator Assessment (Code)

description: 🚀 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.