MineSDG - Strategic Analytics & Sector Intelligence • MineSDG

1. Introduction

MineSDG provides tools to explore and retrieve official Sustainable Development Goal (SDG) indicator data from the United Nations SDG API.

The package includes:

Metadata exploration
Indicator validation
Country-level data retrieval
Optional data persistence
Session-level metadata caching

2. SDG Analytics Layer

Listing SDG Indicators

To view all indicators:

head(list_sdg_indicators())

To list indicators under a specific goal:

list_sdg_indicators(goal = 15)

Validating Indicators

validate_sdg_indicator("15.3.1")

Goal consistency validation:

validate_sdg_indicator("15.3.1", goal = 15)

Fetching Data for a Single Indicator

dt <- fetch_sdg_country_data(
  indicator = "15.3.1",
  country = "IND",
  year_range = c(2015, 2020)
)

head(dt)

Fetching All Indicators Under a Goal

dt_goal <- fetch_sdg_country_data(
  goal = 15,
  country = "IND",
  year_range = c(2015, 2020)
)

head(dt_goal)

Saving Data

fetch_sdg_country_data(
  indicator = "15.3.1",
  country = "IND",
  year_range = c(2015, 2020),
  save = TRUE
)

Files are saved under:

./data/sdg_downloads/

Advanced Analytics

MineSDG 0.2.0 expands the package beyond data retrieval into a complete SDG analytics engine.

The following analytical layers are now available:

Trend analysis
Stability and volatility diagnostics
Benchmark comparison
Convergence testing
Executive-ready narrative generation
Visualization utilities

Trend Analysis

trend <- analyze_sdg_trend(dt)
trend

This function computes:

Absolute change
Percent change
CAGR (Compound Annual Growth Rate)
Linear trend slope
Trend direction (Increasing / Decreasing / Stable)

Stability & Volatility Diagnostics

stability <- compute_sdg_stability(dt)
stability

Includes:

Standard deviation
Coefficient of variation
Volatility index
Stability classification

These metrics are especially useful for ESG risk evaluation and operational performance diagnostics.

Benchmarking Performance

benchmark <- benchmark_sdg_performance(dt)
benchmark

Outputs:

Deviation from benchmark
Percent gap
Z-score
Ranking
Performance category (Strong / Moderate / Weak)

Convergence Analysis

convergence <- analyze_sdg_convergence(dt)
convergence

This tests beta-convergence across countries, helping assess whether lagging countries are catching up in SDG performance.

Executive Summary Generation

summary_text <- generate_sdg_executive_summary(trend, benchmark)
cat(summary_text)

Produces narrative, board-ready interpretation suitable for:

ESG reports
Policy briefs
Academic summaries
Strategic reviews

Visualization Layer

MineSDG includes publication-ready visualization tools:

plot_sdg_trend(dt)
plot_sdg_benchmark(benchmark)
plot_sdg_volatility(stability)
plot_sdg_convergence(dt)

All plots use ggplot2 (optional dependency) and follow minimal, publication-ready styling.

3. Mining-Sector Interpretation Layer

MineSDG now includes a sector-specific interpretation layer tailored for mining sustainability analytics.

This enables translation of SDG indicators into mining-relevant sustainability domains, relevance scoring, and strategic narrative interpretation.

Mapping SDG to Mining Domains

map_sdg_to_mining_domain(indicator = "15.3.1")

Example output:

Goal: 15
Domain: Biodiversity & Land
Relevance Score: 5
Narrative: Land degradation, rehabilitation, and biodiversity restoration are core mining sustainability metrics.

Domain Categories

The following mining sustainability domains are currently defined:

Climate & Energy
Water & Resource Efficiency
Biodiversity & Land
Community & Social Impact
Governance & Institutions
Economic Development
Health & Safety
Education & Workforce

This layer enables sector-aware ESG analytics beyond generic SDG evaluation.

Mining Risk Engine

dt <- fetch_sdg_country_data(indicator = "15.3.1", country = "IND")
generate_mining_risk_profile(dt, indicator = "15.3.1")

Mining Risk Profiling

Indicator-level risk scoring
Trend + volatility + benchmark integration
Domain relevance scoring

Example:

risk_profile <- generate_mining_risk_profile(
  data = dt,
  indicator = "15.3.1"
)

risk_profile

Composite Mining ESG Index

index <- generate_mining_esg_index(
  data = dt_multi,
  indicators = c("6.4.1", "13.2.2", "15.3.1"),
  weighting_method = "domain_weighted"
)

index

Plot Mining ESG Index

plot_mining_esg_index(index)

Summary

MineSDG now supports a full analytical workflow:

Data Retrieval → Trend Diagnostics → Stability Analysis → Benchmarking → Convergence Testing → Executive Reporting → Visualization

This positions MineSDG as a research-grade SDG analytics framework suitable for:

ESG reporting
Policy evaluation
Academic research
Mining-sector sustainability assessment