📊 Business Data Integration & Economic Indicators Pipeline in R

A Reproducible Workflow for Multi-Source Firm-Level Statistics

This project demonstrates how to integrate heterogeneous business datasets into a coherent analytical framework for economic statistics. It reflects challenges commonly encountered in official statistics (e.g., Destatis, Eurostat): inconsistent identifiers, divergent variable definitions, missing values, measurement errors, and discrepancies across data sources.

The project uses fully synthetic data to illustrate transparent, reproducible methods for:

• data generation
• cleaning & validation
• harmonization across sources
• statistical integration
• computation of structural indicators
• visualization of economic patterns

All code is implemented in R using a modular pipeline suitable for production-like environments.

🚀 1. Project Overview

Modern economic statistics increasingly rely on multi-source integration: administrative business registers, structural business surveys, short-term indicators, and accounting extracts.

Such sources differ in:

• reporting frequency
• timeliness
• variable definitions
• quality and completeness
• industry and regional classification detail

This project provides a compact but realistic framework to:

1. Generate synthetic firm-level source datasets
2. Clean and validate each dataset 3 Link identifiers and harmonize classifications
3. Integrate monthly panels across sources
4. Compute economic indicators at firm, sector, and regional level
5. Produce reproducible tables and visualizations

2. Repository Structure

business-data-integration/
├── data
│   ├── clean               # cleaned intermediate data
│   ├── processed           # unified firm-level panel (analysis-ready)
│   └── raw                 # synthetic raw datasets (generated)
├── LICENSE
├── output
│   ├── figures             # visualizations
│   └── tables              # aggregated indicators
├── R
│   ├── 01_generate_synthetic_data.R
│   ├── 02_clean_and_validate_data.R
│   ├── 03_integrate_sources.R
│   ├── 04_compute_indicators.R
│   └── 05_visualize_results.R
├── README.md
├── renv
│   ├── activate.R
│   ├── library
│   ├── settings.json
│   └── staging
└── renv.lock

Reproducibility: The project uses renv for a full dependency snapshot.

🔄 Reproducibility With renv

This project uses renv to ensure that anyone who clones the repository obtains exactly the same R package environment.

Before running the pipeline for the first time, start R inside the project directory and check the environment:

renv::status()

If packages need to be restored, run:

renv::restore()

This guarantees that all scripts operate identically across machines.

🧪 3. Synthetic Data Sources

Three realistic (but fully artificial) firm-level datasets are generated:

A) Administrative Business Register

Variables:

• firm_id
• region_code
• nace_code
• legal_form
• employees
• revenue_last_year

Intentionally includes:

• missing values
• negative values
• inconsistent reporting patterns

B) Monthly Employment Survey

Panel data for Jan–Dec 2023:

• firm_id
• month
• employees
• synthetic missingness for interpolation
• regional & sector attributes copied from the register

⭐ Industry-Specific Seasonal Patterns (Added Realism)

The monthly employment dataset includes sector-specific seasonal variation, reflecting realistic trends observed in economic statistics:

• Retail (G47) — strong December activity
• Accommodation & Food (I55, I56) — summer employment peaks
• Manufacturing (C10, C29) — mild seasonal movement
• Transport (H49) — steady with slight autumn increases

Seasonality is introduced using multiplicative adjustment factors, producing more realistic monthly employment curves.

C) Monthly Turnover Survey

Variables:

• firm_id
• month
• turnover
• missing values for interpolation
• moderate log-normal variability

🔧 4. Methods & Workflow

Pipeline Diagram

                 ┌────────────────────────────┐
                 │ 01_generate_synthetic_data │
                 └──────────────┬─────────────┘
                                ▼
                 ┌────────────────────────────┐
                 │ 02_clean_and_validate_data │
                 └──────────────┬─────────────┘
                                ▼
                 ┌────────────────────────────┐
                 │   03_integrate_sources     │
                 └──────────────┬─────────────┘
                                ▼
                 ┌────────────────────────────┐
                 │   04_compute_indicators    │
                 └──────────────┬─────────────┘
                                ▼
                 ┌────────────────────────────┐
                 │   05_visualize_results     │
                 └────────────────────────────┘

Step 1 — Data Cleaning & Validation

• structural key validation
• detection of inconsistencies (negative values, missingness)
• industry/region-based imputation
• time-series interpolation (approximation)

Step 2 — Harmonization & Identifier Mapping

• standardization of variable names
• controlled join logic between datasets
• date harmonization
• preparation of unified firm-month records

Step 3 — Multi-Source Integration

For each firm × month:

• turnover precedence rules
• employment precedence rules
• consistency checks
• generation of firm-level indicators

Step 4 — Derived Indicators

Computed at firm level:

• turnover YoY growth
• monthly employment growth
• labor productivity
• simple seasonal index

Computed at sector/region level:

• total turnover
• average turnover per firm
• total employees
• productivity aggregates
• firm counts

Step 5 — Visualization

• firm size distribution
• sectoral turnover profiles
• monthly aggregate turnover trends Visual outputs stored in output/figures/.

🛠 5. Technologies Used

• R -dplyr, tidyr — reshaping, joins, aggregation
  • lubridate — date manipulation
  • ggplot2 — visualization
  • readr — data I/O
  • janitor — column cleaning
  • purrr — functional utilities
• renv for reproducibility
• Supports VS Code, RStudio, and command-line R

▶️ 6. How to Run the Pipeline

🔧 Before Running the Pipeline

Start R in the project root and ensure the correct environment is active:

renv::status()

If packages are missing:

renv::restore()

Then proceed with the pipeline steps below.

# 1. Generate synthetic data
source("R/01_generate_synthetic_data.R")

# 2. Clean & validate
source("R/02_clean_and_validate_data.R")

# 3. Integrate sources & build indicators
source("R/03_integrate_sources.R")

# 4. Compute sectoral and regional aggregates
source("R/04_compute_indicators.R")

# 5. Produce visualizations
source("R/05_visualize_results.R")

Outputs appear in:

• data/clean/
• data/processed/
• output/tables/
• output/figures/

🔭 7. Possible Extensions

Future enhancements might include:

• probabilistic record linkage
• multiple-year panels
• Monte Carlo simulations
• machine learning–based imputation
• benchmarking algorithms for multi-source coherence (e.g. Denton, Chow-Lin)
• firm-level microdata anonymization techniques

📘 8. License

MIT License

👤 9. Author

Golib Sanaev Applied Data Scientist & Analyst | ML • Data Analysis • Forecasting • Python • SQL • Econometrics

GitHub: @gsanaev
Email: gsanaev80@gmail.com
LinkedIn: golib-sanaev

📚 Citation

Sanaev, G. (2025). Business Data Integration & Economic Indicators Pipeline in R.
GitHub Repository: https://github.com/gsanaev/business-data-integration