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R|Statistical Consulting

Expert R Analysis &
Research Methodology Consulting

We help researchers, academics, and data science teams build reproducible, publication-ready analyses — from R script development to full methodology design and statistical review.

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library(tidyverse)
library(lme4)

# Reproducible. Auditable. Publication-ready.
fit <- lmer(outcome ~ treatment + (1|subject))
Services

What We Do

End-to-end support for research projects that demand rigor, transparency, and reproducibility.

R Script Development & Audit

We write clean, well-documented R and RStudio scripts for your analysis pipelines — and audit existing code for correctness, efficiency, and reproducibility.

tidyverse, ggplot2, lme4, survival
Annotated, version-controlled scripts
Code review & audit reports

Research Methodology Design

From study design to sampling strategy and pre-registration, we help you build a methodology that holds up to peer review and institutional scrutiny.

Study design & power analysis
Pre-registration frameworks
IRB-ready documentation

Statistical Review & Evaluation

We review your statistical analyses, validate model assumptions, and provide written evaluation reports suitable for journal submission or internal audit.

Model diagnostics & assumption checks
Written evaluation reports
Effect size & interpretation guidance
Who We Help

Built for researchers
who demand precision

Whether you're submitting to a peer-reviewed journal, defending a dissertation, or presenting to a board — Quantara ensures your analysis is airtight, transparent, and defensible.

🎓
PhD Researchers
Methodology support, statistical models, reproducible analysis pipelines
🏛️
Academic Teams
R script development, code auditing, collaborative analysis workflows
📊
Data Science Teams
Statistical review, model evaluation, publication-quality outputs
🔬
Market Researchers
Survey analysis, segmentation models, executive-ready reporting
Why Quantara

The standard your work deserves

Reproducible Code

Every script we deliver follows best practices for reproducibility — fully commented, version-controlled, and runnable from a fresh environment.

Rigorous Methods

We apply the correct statistical methods for your design — no shortcuts, no overfitting, no p-hacking. Your analysis will survive peer review.

Fast Turnaround

Typical engagements complete within 5–10 business days. Rush timelines available for conference deadlines or journal revisions.

Audit-Ready Outputs

All deliverables include a written methodology summary and annotated code — ready for internal audit, committee review, or journal appendix.

Pricing

Transparent, fixed-price packages

No hourly surprises. Choose the service that fits your project and get started immediately.

R Script Audit

$149one-time

Expert review of your existing R scripts for correctness, efficiency, and reproducibility.

Delivered as a written feedback report with line-level comments and recommendations.

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Research Methodology Review

$249one-time

Comprehensive evaluation of your research design, sampling strategy, and statistical approach.

Covers variable operationalization, validity threats, and delivered as a structured written report.

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R Script Development

$349one-time

Custom R script development for your data analysis project — from cleaning to visualization.

Delivered as clean, commented, reproducible code covering modeling and statistical analysis.

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Best Value

Full Research Support Package

$799one-time

End-to-end support: methodology design, R script development, statistical analysis, and final audit.

Ideal for thesis, dissertation, or publication-track projects requiring comprehensive coverage.

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All packages include a written deliverable. Need something custom? Contact us for a tailored quote.

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Ready to strengthen your research?

Tell us about your project and we'll respond within 24 hours with a scope outline and initial recommendations.

0/500

Free Resource

R Analysis Starter Checklist

The essential pre-submission checklist every researcher should run through before sharing or publishing an R-based analysis. Based on best practices from reproducible research and peer-reviewed methodology.

Compiled by the Quantara team · Free to use and share

Data Cleaning & Validation

Check for missing values (NA/NaN) and document imputation strategy
Verify variable types match expected (numeric, factor, date)
Detect and document outliers using IQR or z-score rules
Confirm sample size matches expected N after merges/filters
Validate ranges and logical constraints (e.g., age > 0, proportions ∈ [0,1])
Check for duplicate rows and resolve or document intentional duplicates

Reproducibility

Set a random seed before any stochastic operations (set.seed())
Use renv or packrat to lock package versions and record lockfile
Include sessionInfo() or renv::snapshot() output in the deliverable
Use relative paths and a project root — no hardcoded absolute paths
Script runs cleanly from a fresh R session with no side effects
Data files referenced in code are documented and versioned

Assumption Checking

Normality: Shapiro-Wilk test + Q-Q plot; note sample size limitations
Homoscedasticity: Levene's or Breusch-Pagan test for residual variance
Multicollinearity: Variance Inflation Factor (VIF < 5 threshold)
Independence: ACF plot or Durbin-Watson if time structure is present
Linearity: residuals vs. fitted plot for regression models
Document any violated assumptions and justify chosen corrections

Visualization Standards

All axes labeled with units; title is informative, not generic
Color palette is accessible (colorblind-safe; test with colorblindr)
Figures exported at ≥ 300 DPI for print (use ggsave() with dpi arg)
Legend is present wherever color/shape encodes a variable
Error bars or CIs included for group comparisons
Avoid pie charts for comparisons; prefer bar or dot plots

Script Documentation & Commenting

Header block: author, date, purpose, input/output files, R version
Section dividers (── Load Data ─────) for multi-step scripts
Non-obvious logic has inline comments explaining the 'why', not just 'what'
Variable names are descriptive (avoid x1, tmp, df2 in final scripts)
Remove dead code, commented-out experiments, and scratch blocks
README or Rmd vignette summarizes the full pipeline for reviewers

This checklist reflects the same standards we apply to every Quantara engagement. Want us to run through it on your project?

Want us to apply this to your project? Book a free consult