Coding SOP

Coding SOP — Research Experiments & Data Analysis

1. Experiment Execution SOP

1.1 Hypothesis → Code → Execute → Verify

1. Hypothesize: state what you expect and why
2. Design: define variables, controls, sample size / iteration count
3. Code: workspace_save to outputs/scripts/; include docstring (hypothesis, expected outcome, dependencies)
4. Execute: exec in workspace (safe commands — see §5); capture stdout + stderr
5. Verify: compare against hypothesis; check for NaN/Inf/warnings; save to outputs/reports/
6. Iterate: if contradicted, revise (do NOT cherry-pick); if confirmed, document

1.2 Script Template

Every script must include: shebang, docstring (experiment title, hypothesis, dependencies, date), seed setting (see §6), and four sections: loading, processing, analysis, output.

2. Data Processing SOP

2.1 Pipeline: Clean → Transform → Analyze

1. Inspect: df.info(), df.describe(), df.head() — check dtypes, nulls, duplicates
2. Clean: handle missing values (drop/impute/flag — document choice), fix dtypes, remove duplicates, detect outliers (IQR, z-score, domain rules)
3. Transform: normalize/standardize, encode categoricals, feature engineering, reshape (pivot, melt, merge)
4. Validate: assert expected shape, sanity-check stats, save cleaned data via workspace_save("sources/data/<name>_clean.csv")

2.2 Common Libraries

Python: pandas/polars (DataFrames), numpy (numerics), dask (large files), spaCy (text). R: dplyr/tidyr (wrangling), data.table/arrow (large files), stringr/tidytext (text).

Browse analysis/wrangling/ for 10 deep-dive skills (pandas, missing data, survey, text mining).

3. Statistical Analysis Guide

3.1 Test Selection Tree

What is your research question?
│
├── Comparing groups?
│   ├── 2 groups
│   │   ├── Continuous DV, normal → Independent t-test
│   │   ├── Continuous DV, non-normal → Mann-Whitney U
│   │   ├── Paired/matched → Paired t-test / Wilcoxon signed-rank
│   │   └── Categorical DV → Chi-square / Fisher's exact
│   ├── 3+ groups
│   │   ├── 1 factor, normal → One-way ANOVA → post-hoc (Tukey/Bonferroni)
│   │   ├── 1 factor, non-normal → Kruskal-Wallis → post-hoc (Dunn)
│   │   ├── 2+ factors → Two-way / N-way ANOVA (check interactions)
│   │   └── Repeated measures → Repeated-measures ANOVA / Friedman
│   └── Pre/post with control → Mixed ANOVA / DiD
│
├── Predicting an outcome?
│   ├── Continuous outcome → Linear regression (OLS)
│   │   ├── Multiple predictors → Multiple regression
│   │   ├── Non-linear → Polynomial / GAM / splines
│   │   └── Endogeneity → IV / 2SLS (see econometrics skills)
│   ├── Binary outcome → Logistic regression
│   ├── Count/ordinal → Poisson / Ordinal logistic
│   ├── Time-to-event → Cox proportional hazards
│   └── Panel data → Fixed/random effects (see econometrics skills)
│
├── Exploring relationships?
│   ├── 2 continuous vars → Pearson r (normal) / Spearman rho (non-normal)
│   ├── 2 categorical vars → Chi-square test of independence
│   ├── Latent constructs → Factor analysis / SEM
│   └── Dimensionality → PCA / t-SNE
│
└── Estimating causal effects?
    ├── Randomized experiment → t-test / ANOVA with random assignment
    ├── Natural experiment → DiD, RDD, IV
    └── Observational → Propensity score matching, synthetic control

3.2 Reporting Checklist

Every test must report: test name, statistic value (t/F/chi-sq/U/z), df, exact p-value, effect size (Cohen's d / eta-sq / Cramer's V / OR), 95% CI, assumptions checked (normality, homoscedasticity, independence), sample size per group.

3.3 Common Pitfalls

• Multiple comparisons: Bonferroni, Holm, or FDR correction
• p-hacking: pre-register hypotheses; never fish for p < 0.05
• Small samples: exact tests or bootstrap over asymptotic tests
• Normality: Shapiro-Wilk (n < 50) or Q-Q plot + KS test
• Confounders: include as covariates or stratify

Deep-dive skills: browse analysis/statistics/ (10 skills: Bayesian, meta-analysis, SEM, survival, power analysis, nonparametric) and analysis/econometrics/ (12 skills: causal inference, panel data, IV, time series).

4. Visualization SOP

4.1 Chart Type Selection

4.2 Publication-Quality Standards

Set plt.rcParams for journal figures: figure.dpi: 300, font.family: serif, font.size: 10, savefig.bbox: tight. Key rules:

1. Resolution: 300 DPI minimum (600 DPI for line art)
2. Format: PDF/SVG for vector; PNG/TIFF for raster (avoid JPEG)
3. Color: colorblind-safe palettes (viridis, cividis, Set2)
4. Labels: every axis labeled with units; legend outside if crowded
5. Font: 8-12pt, match journal spec (serif or sans-serif)
6. Size: single column ~3.5in, double column ~7in width
7. Save: workspace_save("outputs/figures/fig-<desc>.pdf", content)

Deep-dive skills: browse analysis/dataviz/ (14 skills: matplotlib, plotly, D3, publication figures, color accessibility, geospatial, network viz).

5. exec Safety & Patterns

Safe (no approval): python3, Rscript, xelatex, pandoc, jq, wc, grep, find. Requires approval_card: pip install, brew install, curl, wget, anything outside workspace. Full safety rules in Workspace SOP.

Common patterns:

• exec("python3 outputs/scripts/experiment.py") — run analysis
• exec("Rscript outputs/scripts/analysis.R") — R script
• exec("python3 -c \"import pandas; ...\"") — quick inspection

Output paths: All script outputs (figures, data, reports) MUST use workspace-relative paths. Set the working directory to workspace root before execution, and use paths like outputs/figures/, outputs/reports/. Template for Python:

import os
os.chdir(os.environ.get('WORKSPACE_ROOT', '.'))

6. Reproducibility Protocol

6.1 Environment Recording

At analysis start, capture Python version, platform, and key package versions (numpy, pandas, scipy, matplotlib, sklearn). Save via workspace_save("outputs/reports/env-snapshot-<date>.json").

6.2 Reproducibility Checklist

• Random seeds: Set at script top for numpy, random, torch, tensorflow
• Version locking: Record exact versions (pip freeze / conda list)
• Data provenance: Source URL, download date, SHA-256 for raw data
• Execution order: Scripts must run independently (no notebook-state dependency)
• Relative paths: Use workspace-relative paths, not absolute
• For ML: log hyperparams, track metrics per step, save checkpoints, record hardware

7. Coding Complexity Delegation

Before writing code, assess complexity:

Simple task (single file, stdlib only, no iteration)
  → RC handles directly via exec
Complex task (multi-file, dependencies, iterative debugging)
  → Check MEMORY.md Environment for installed CLIs (codex, claude, opencode)
    → CLI found → inform user, suggest delegating via exec
      → User agrees → exec the CLI (read claude-code / codex-cli / opencode-cli skill)
      → User wants RC → proceed with RC's own capabilities
    → No CLI → recommend installation, wait for user decision
      → User insists → RC proceeds via repeated workspace_save + exec (slower)

Boundary: "complex coding" = multi-file projects, dependency management, iterative debugging, beamer/multi-chapter LaTeX, interactive visualizations. For these, the Claude Code, Codex CLI, and OpenCode CLI skills provide delegation guidance.

8. RC Local Tools Reference

• workspace_save: persist code to outputs/scripts/ (or outputs/notebooks/), figures to outputs/figures/, processed data to sources/data/. Commit message prefix: Add: / Update:.
• workspace_append: add results to an existing report or data file without overwriting. Preferred over read + save for incremental updates.
• workspace_download: save binary outputs (plots, exports) from URLs.
• exec: run scripts from workspace root. Default timeout 120s (increase for long-running). Always inspect both stdout and stderr. On failure: fix code, workspace_save again, re-run.

Related Research-Plugins Skills

For detailed methodology beyond this SOP, browse these RP skill indexes: