Package 'MineSDG'

Description

Tests beta-convergence across countries for an SDG indicator.

Usage

analyze_sdg_convergence(data, base_year = NULL, final_year = NULL)

Arguments

Value

A data.table with convergence statistics per indicator.

Description

Computes trend metrics for SDG indicator data including absolute change, percentage change, CAGR, and linear trend slope.

Usage

analyze_sdg_trend(data, group_by = c("indicator", "country"), min_points = 3)

Arguments

Details

Supports grouping by indicator, country, or combinations.

Value

A data.table containing trend statistics.

Examples

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

Description

Generic used by the report generators so that users may pass either a prepared compile_site_disclosures bundle or a raw site data.frame.

Usage

as_disclosure_bundle(x, ...)

Arguments

Value

A minesdg_disclosure_bundle.

Examples

b <- as_disclosure_bundle(demo_mine_sites, site_id = "AU-KALG",
                          years = 2023:2024)
class(b)

Description

Compares SDG indicator performance against a benchmark (global mean or user-supplied dataset).

Usage

benchmark_sdg_performance(
  data,
  benchmark_data = NULL,
  year = NULL,
  higher_is_better = TRUE
)

Arguments

Value

A data.table with benchmark comparison statistics.

Examples

dt <- fetch_sdg_country_data(goal = 15)
benchmark_sdg_performance(dt)

Description

Mapping of SEBI Business Responsibility and Sustainability Report Sections A, B and C to MineSDG outputs, including INR-lakh currency transforms for monetary lines.

Usage

brsr_crosswalk

Format

See gri_crosswalk for the common schema.

Source

Curated in data-raw/make_crosswalks.R; paraphrases referencing the SEBI BRSR format.

Description

Stable accessor used by framework mappers and report templates.

Usage

bundle_kpi(bundle, kpi_id, year = NULL)

Arguments

Value

A single numeric value, or NA if the KPI is unavailable.

Examples

b <- compile_site_disclosures(demo_mine_sites, site_id = "CU-ATAC",
                              years = 2024)
bundle_kpi(b, "trifr")

Description

Raw fields are provenance-tagged copies of validated user input; they are never recomputed by the reporting layer.

Usage

bundle_raw(bundle, field, year = NULL)

Arguments

Value

A single numeric value, or NA if the field is unavailable.

Examples

b <- compile_site_disclosures(demo_mine_sites, site_id = "CU-ATAC",
                              years = 2024)
bundle_raw(b, "ghg_scope1_t")

Description

Stable accessor for scorecard aggregates held in a disclosure bundle.

Usage

bundle_score(bundle, what = "composite", year = NULL)

Arguments

Value

A numeric score (or character grade), or NA.

Examples

b <- compile_site_disclosures(demo_mine_sites, site_id = "CU-ATAC",
                              years = 2024)
bundle_score(b)
bundle_score(b, what = "goal_8")

Description

Computes community/social investment as a percentage of revenue (GRI 203-1; ICMM Principle 9), a widely benchmarked measure of shared-value contribution in the mining sector.

Usage

calculate_community_investment(community_investment, revenue)

Arguments

Value

A named list with metric and community_investment_pct.

Examples

calculate_community_investment(24.0, 3349)

Description

Computes energy intensity per tonne of ore processed (GRI 302-3) and, optionally, the renewable share of total energy consumption (GRI 302-1 / SASB EM-MM-130a.1).

Usage

calculate_energy_intensity(energy_gj, ore_processed_kt, renewable_gj = NULL)

Arguments

Value

A named list with metric, energy_intensity (GJ/t ore) and, when renewable_gj is supplied, renewable_share_percent.

Examples

calculate_energy_intensity(10449110, 18481, renewable_gj = 550000)

Description

Computes Scope 1 + Scope 2 greenhouse-gas emissions intensity per tonne of ore processed, following the GHG Protocol Corporate Standard and GRI 305-4 / SASB EM-MM-110a.1 disclosure conventions.

Usage

calculate_ghg_intensity(scope1_t, scope2_t, ore_processed_kt)

Arguments

Details

Intensity is calculated as:

$(scope1\_t + scope2\_t) / (ore\_processed\_kt)$

expressed as tCO2e per kilotonne of ore.

Value

A named list with metric, total_emissions_t, ghg_intensity (tCO2e/kt ore), and scope1_share_percent.

Examples

calculate_ghg_intensity(636000, 342000, 18481)

Description

Computes the rehabilitation rate of disturbed mining land in alignment with SDG 15.3 (Land Degradation Neutrality). The function evaluates the percentage of disturbed land that has been restored or revegetated and reports the remaining unrestored area.

Usage

calculate_land_restoration(disturbed_area_ha, rehabilitated_area_ha)

Arguments

Details

The rehabilitation rate is calculated as:

$(rehabilitated\_area\_ha / disturbed\_area\_ha) * 100$

If disturbed_area_ha is 0, the function safely returns 0 percent restored to avoid division-by-zero errors.

Value

A named list containing:

metric

Character string. "SDG 15.3 - Land Restoration"

percent_restored

Numeric. Rehabilitation rate rounded to 2 decimal places.

unrestored_area_ha

Numeric. Remaining disturbed land not yet restored (in hectares).

Examples

calculate_land_restoration(100, 40)

calculate_land_restoration(
  disturbed_area_ha = 250,
  rehabilitated_area_ha = 180
)

calculate_land_restoration(0, 0)

Description

Computes standard mining safety frequency rates per one million hours worked, following the ICMM safety data reporting convention and GRI 403-9: Total Recordable Injury Frequency Rate (TRIFR), Lost Time Injury Frequency Rate (LTIFR), and Fatality Frequency Rate.

Usage

calculate_safety_performance(
  hours_worked,
  recordable_injuries,
  lost_time_injuries,
  fatalities = 0
)

Arguments

Details

Each rate is calculated as:

$(events / hours\_worked) \times 1{,}000{,}000$

Value

A named list with metric, trifr, ltifr, fatality_rate, and hours_worked_millions.

Examples

calculate_safety_performance(
  hours_worked = 6400000,
  recordable_injuries = 32,
  lost_time_injuries = 10
)

Description

Computes tailings-to-ore and waste-rock (strip) ratios, core circular economy and mineral-waste metrics under GRI 306 / GRI 11.8 and the Global Industry Standard on Tailings Management (GISTM) context.

Usage

calculate_waste_intensity(ore_processed_kt, tailings_kt, waste_rock_kt = NULL)

Arguments

Value

A named list with metric, tailings_ratio (t tailings / t ore) and, when supplied, waste_rock_ratio (strip ratio) and total_mineral_waste_kt.

Examples

calculate_waste_intensity(18481, 17750, waste_rock_kt = 45412)

Description

Computes water use efficiency and recycling rates in alignment with Global Reporting Initiative (GRI 303) standards and SDG Target 6.4.

Usage

calculate_water_efficiency(withdrawal_m3, discharge_m3, recycled_m3)

Arguments

Details

The function calculates the "Recycling Rate" as:

$(recycled\_m3 / (withdrawal\_m3 + recycled\_m3)) * 100$

And "Net Water Consumption" (GRI 303-5) as:

$withdrawal\_m3 - discharge\_m3$

Value

A named list containing:

metric

Character string. "SDG 6.4 - Water Efficiency"

net_consumption_m3

Numeric. Net water consumed (Withdrawal - Discharge).

recycling_rate_percent

Numeric. Percentage of total water use that is recycled, rounded to 2 decimal places.

Examples

# Standard mining operation example
calculate_water_efficiency(
  withdrawal_m3 = 50000,
  discharge_m3 = 10000,
  recycled_m3 = 20000
)

# Zero discharge example (Closed loop)
calculate_water_efficiency(1000, 0, 500)

# Edge case: No water used
calculate_water_efficiency(0, 0, 0)

Description

Computes female employment share (GRI 405-1) and local employment share (GRI 202-2 / SASB EM-MM-210b) of the site workforce.

Usage

calculate_workforce_diversity(
  workforce,
  female_employees,
  local_employees = NULL
)

Arguments

Value

A named list with metric, female_employment_pct and, when supplied, local_employment_pct.

Examples

calculate_workforce_diversity(3200, 420, local_employees = 1900)

Description

Runs the existing MineSDG scoring engine once per reporting year and assembles a framework-neutral disclosure bundle. All framework report generators consume this object, which guarantees that the KPI engine remains the single source of truth and that no calculation is duplicated in the reporting layer.

Usage

compile_site_disclosures(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list())

Arguments

Value

An object of class minesdg_disclosure_bundle: a list with elements meta, kpis, scores, goal_scores, raw, deltas, narratives and registry_used.

See Also

map_bundle_to_framework, generate_gri_report

Examples

bundle <- compile_site_disclosures(demo_mine_sites, site_id = "CU-ATAC",
                                   years = 2022:2024)
bundle
bundle_kpi(bundle, "ghg_intensity")

Description

Calculates variability and stability statistics for SDG indicator data.

Usage

compute_sdg_stability(data, group_by = c("indicator", "country"))

Arguments

Value

A data.table containing stability metrics.

Examples

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

Description

A synthetic but realistic panel dataset of six mine sites (copper, gold, iron ore, coal, zinc, bauxite) across 2019-2024 with production, emissions, energy, water, land, safety, workforce and community fields. Designed for tutorials, tests and the bundled Shiny dashboard. All values are simulated; no real operation is represented.

Usage

demo_mine_sites

Format

A data frame with 36 rows (6 sites x 6 years) and 26 variables including ore_processed_kt, ghg_scope1_t, ghg_scope2_t, energy_gj, water_withdrawal_m3, water_recycled_m3, land_disturbed_ha, land_rehabilitated_ha, hours_worked, recordable_injuries, fatalities, female_employment_pct, local_employment_pct, revenue_musd, community_investment_musd, tailings_kt, and waste_rock_kt.

Source

Simulated via data-raw/make_datasets.R (seed 20260720).

Examples

head(demo_mine_sites)

Description

A synthetic offline snapshot shaped identically to the output of fetch_sdg_country_data(): indicators 15.3.1 (land degradation), 6.4.1 (water-use efficiency) and 8.8.1 (occupational injuries) for India, Australia, Chile and South Africa, 2015-2023. Enables every analytics and risk function to run without network access.

Usage

demo_sdg_country

Format

A data.table with 108 rows and 5 variables: indicator, country, year, value, unit.

Source

Simulated via data-raw/make_datasets.R; values follow plausible national trajectories but are not official UN statistics.

Examples

analyze_sdg_trend(demo_sdg_country[demo_sdg_country$indicator == "6.4.1", ])

Description

Queries the bundled sdg_mining_ontology by SDG goal or by mining sustainability domain, returning material topics, materiality ratings and disclosure-framework references.

Usage

explore_sdg_ontology(domain = NULL, ...)

Arguments

Value

A data frame of matching ontology rows.

Examples

explore_sdg_ontology(domain = 6)
explore_sdg_ontology(domain = "biodiversity")
explore_sdg_ontology(goal = 6)

Description

Retrieves Sustainable Development Goal (SDG) indicator data from the official 'United Nations SDG API'.

Usage

fetch_sdg_country_data(
  goal = NULL,
  indicator = NULL,
  country = NULL,
  year_range = NULL,
  save = FALSE,
  save_path = "./data/sdg_downloads/",
  formats = c("csv", "rds")
)

Arguments

Details

At least one of goal or indicator must be provided.

• If indicator is supplied → fetch that specific indicator.

• If only goal is supplied → fetch all indicators under that goal.

• If both are supplied → consistency is validated.

country and year_range act as filters.

Value

A data.table containing SDG indicator data.

Description

Returns the disclosure-level mapping rules that connect reporting framework disclosures to MineSDG outputs. Mappings are shipped as package data; adding a disclosure means editing data, not code.

Usage

framework_crosswalk(framework = NULL)

Arguments

Value

A data.table with the common crosswalk schema (framework, section, disclosure_id, disclosure_title, requirement, source_type, source_id, transform, unit_out, comparative, mandatory, sdg_goal, notes).

Examples

framework_crosswalk("gri")[1:5, c("disclosure_id", "source_type",
                                  "source_id")]

Description

Returns the Section C (principle-wise essential and leadership indicator) view of the BRSR mapping. Values are read from the disclosure bundle; no calculations are duplicated.

Usage

generate_brsr_kpis(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list())

Arguments

Value

A data.table of Section C quantitative disclosure rows.

Examples

generate_brsr_kpis(demo_mine_sites, site_id = "CO-JHAR",
                   years = 2023:2024)

Description

Builds a SEBI Business Responsibility and Sustainability Report covering Section A (general disclosures), Section B (management and process disclosures) and Section C (principle-wise performance KPIs). Monetary values without a supplied exchange rate are flagged partial rather than estimated.

Usage

generate_brsr_report(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list(), output = NULL,
  format = c("html", "pdf", "docx", "xlsx"))

Arguments

Value

A minesdg_report with extras sectionA, sectionB and sectionC.

See Also

generate_brsr_sectionA, generate_brsr_kpis

Examples

rep <- generate_brsr_report(demo_mine_sites, site_id = "CO-JHAR",
                            years = 2023:2024,
                            entity_meta = list(company = "Demo Mining Ltd",
                                               fx_usd_inr = 83.2))
rep

Description

Returns the Section A view of the BRSR mapping. All three BRSR section functions are filters over one mapping computation.

Usage

generate_brsr_sectionA(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list())

Arguments

Value

A data.table of Section A disclosure rows with values and statuses.

Examples

generate_brsr_sectionA(demo_mine_sites, site_id = "CO-JHAR",
                       years = 2024,
                       entity_meta = list(company = "Demo Mining Ltd"))

Description

Returns the Section B (management and process, per NGRBC principle) view of the BRSR mapping.

Usage

generate_brsr_sectionB(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list())

Arguments

Value

A data.table of Section B disclosure rows.

Examples

generate_brsr_sectionB(demo_mine_sites, site_id = "CO-JHAR",
                       years = 2024)

Description

Builds a GRI report covering GRI 302 (Energy), 303 (Water and Effluents), 304 (Biodiversity), 305 (Emissions), 306 (Waste), 403 (Occupational Health and Safety) and 413 (Local Communities). All quantitative disclosures are resolved through the disclosure bundle; no calculation is performed in the reporting layer.

Usage

generate_gri_report(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list(),
  standards = c(302, 303, 304, 305, 306, 403, 413), output = NULL,
  format = c("html", "pdf", "docx", "xlsx"))

Arguments

Value

A minesdg_report object (invisibly when output is supplied).

See Also

render_minesdg_report, write_report_xlsx

Examples

rep <- generate_gri_report(demo_mine_sites, site_id = "CU-ATAC",
                           years = 2022:2024)
rep
gri_content_index(rep)[1:5, ]

Description

Assesses a site against the ICMM Mining Principles, producing a board scorecard, a traffic-light assessment and data-driven recommendations. Traffic lights reclassify existing MineSDG KPI scores against configurable thresholds; recommendations are evaluated from the icmm_recommendation_rules dataset.

Usage

generate_icmm_report(site_data, site_id = NULL, years = NULL,
  registry = MineSDG::mining_kpi_registry, narratives = list(),
  entity_meta = list(), params = icmm_default_params(), output = NULL,
  format = c("html", "pdf", "docx", "xlsx"))

Arguments

Value

A minesdg_report with extra elements $extras$scorecard, $extras$traffic_lights and $extras$recommendations.

Examples

rep <- generate_icmm_report(demo_mine_sites, site_id = "CU-ATAC",
                            years = 2023:2024)
rep$extras$traffic_lights[1:5, ]
rep$extras$recommendations

Description

Builds a composite Mining ESG Risk Index by aggregating multiple SDG indicators using flexible weighting schemes.

Usage

generate_mining_esg_index(
  data,
  indicators,
  weighting_method = "equal",
  custom_weights = NULL
)

Arguments

Value

A data.table containing:

country

Country ISO3

composite_score

Aggregated ESG risk score

risk_category

Overall risk classification

Description

Integrates SDG trend, stability, benchmarking, and mining-sector relevance to produce a structured ESG risk assessment.

Usage

generate_mining_risk_profile(data, indicator)

Arguments

Value

A structured list containing:

indicator

SDG indicator

domain

Mining domain

risk_score

Numeric weighted risk score

risk_category

Risk classification

executive_summary

Narrative explanation

Description

Creates an executive-ready narrative summary based on SDG trend analysis and optional benchmark comparison.

Usage

generate_sdg_executive_summary(trend_data, benchmark_data = NULL, digits = 2)

Arguments

Value

Character vector containing executive summary text.

Examples

dt <- fetch_sdg_country_data(indicator = "15.3.1", country = "IND")
trend <- analyze_sdg_trend(dt)
summary_text <- generate_sdg_executive_summary(trend)
cat(summary_text)

Description

Retrieves SDG metadata from the UN API and caches it for the current R session. Requires network access; downstream helpers such as list_sdg_indicators use this cache.

Usage

get_sdg_metadata()

Value

A data.table of SDG indicator metadata.

Description

Returns the GRI content index table (disclosure, title, status, source lineage) that assurance and reporting teams attach to published reports.

Usage

gri_content_index(report)

Arguments

Value

A data.frame with columns disclosure_id, disclosure_title, status, source and omission_reason.

Examples

rep <- generate_gri_report(demo_mine_sites, site_id = "AU-KALG",
                           years = 2024)
head(gri_content_index(rep))

Description

Disclosure-level mapping rules connecting GRI 302/303/304/305/306/403/413 disclosures to MineSDG outputs. Framework mappings are data: report generators contain no framework-specific logic.

Usage

gri_crosswalk

Format

A data.table with one row per disclosure and columns framework, section, disclosure_id, disclosure_title, requirement, source_type (kpi/raw/score/narrative/none), source_id, transform, unit_out, comparative, mandatory, sdg_goal, notes. Carries a crosswalk_version attribute.

Source

Curated in data-raw/make_crosswalks.R; requirement texts are original paraphrases referencing the GRI Standards 2021 and the GRI 11 Mining Sector Standard 2022.

Description

Mapping of ICMM Mining Principles and selected performance areas (safety, climate, water, tailings, biodiversity, social) to MineSDG outputs.

Usage

icmm_crosswalk

Format

See gri_crosswalk for the common schema.

Source

Curated in data-raw/make_crosswalks.R; paraphrases referencing the ICMM Mining Principles (2020).

Description

Builds the parameter list consumed by generate_icmm_report.

Usage

icmm_default_params(green = 70, amber = 40)

Arguments

Value

A named list of ICMM report parameters.

Examples

icmm_default_params()
icmm_default_params(green = 75, amber = 50)

Description

Data-driven recommendation rules evaluated by generate_icmm_report: when a KPI or mining-domain traffic light matches a trigger, the associated recommendation is emitted.

Usage

icmm_recommendation_rules

Format

A data.table with columns principle, trigger_type (kpi_light or domain_light), trigger_id, trigger_value, priority, recommendation.

Source

Curated in data-raw/make_crosswalks.R.

Description

Returns the bundled mining_kpi_registry, optionally filtered by SDG goal or improvement direction.

Usage

list_mining_kpis(sdg_goal = NULL, direction = NULL)

Arguments

Value

A data frame of matching KPI registry rows.

Examples

list_mining_kpis(sdg_goal = 8)
list_mining_kpis(direction = "higher_better")

Description

Retrieves the official list of SDG indicators, including indicator codes, descriptions, goal numbers, and tier classification.

Usage

list_sdg_indicators(goal = NULL)

Arguments

Details

Uses internally cached metadata via get_sdg_metadata().

Value

A data.table containing:

goal

SDG goal number

indicator

Indicator code

description

Indicator description

tier

Tier classification

Examples

list_sdg_indicators()
list_sdg_indicators(goal = 15)

Description

Joins a disclosure bundle against a framework's crosswalk dataset and resolves every disclosure to a value and a status (reported, partial, narrative_provided, narrative_required, not_in_scope). Contains no framework-specific logic: frameworks differ only in their crosswalk data.

Usage

map_bundle_to_framework(bundle, framework, include_voluntary = TRUE, ...)

Arguments

Value

An object of class minesdg_framework_mapping: a list with framework, table (one row per disclosure with value, value_prior, narrative, status), coverage and meta.

Examples

b <- compile_site_disclosures(demo_mine_sites, site_id = "CU-ATAC",
                              years = 2023:2024)
m <- map_bundle_to_framework(b, "gri")
m
summary(m)

Description

Classifies an SDG goal or indicator into a mining-sector sustainability domain. Returns structured interpretation including domain classification, relevance score (1–5), and strategic narrative explanation.

Usage

map_sdg_to_mining_domain(indicator = NULL, goal = NULL)

Arguments

Value

A structured list containing:

goal

SDG goal number

domain

Mining sustainability domain

relevance_score

Numeric score (1–5)

narrative

Mining-sector strategic interpretation

Examples

map_sdg_to_mining_domain(indicator = "15.3.1")
map_sdg_to_mining_domain(goal = 13)

Description

A reference registry of 18 site-level mining sustainability KPIs with units, SDG target alignment, improvement direction, and indicative good/poor threshold values used by score_site_sdg() for 0-100 normalisation. Thresholds are indicative sector reference points; organisations should calibrate them to their commodity and jurisdiction.

Usage

mining_kpi_registry

Format

A data frame with 18 rows and 9 variables:

kpi_id

Machine-readable KPI identifier

kpi_name

Human-readable KPI name

unit

Measurement unit

sdg_goal

Primary SDG goal number

sdg_target

Primary SDG target

direction

"lower_better" or "higher_better"

good_value

Value that scores 100

poor_value

Value that scores 0

framework_reference

GRI / ICMM / SASB / GISTM citations

Examples

list_mining_kpis(sdg_goal = 8)

Description

Rolls goal-level scores up to the ontology's mining domains and plots them as a radar chart. The rollup is an unweighted mean of goal scores per domain – a presentation-level aggregation of values already computed by the scoring engine.

Usage

plot_domain_radar(x, year = NULL)

Arguments

Value

A ggplot object.

Examples

sc <- score_site_sdg(demo_mine_sites[demo_mine_sites$site_id ==
                       "CU-ATAC" & demo_mine_sites$year == 2024, ])
plot_domain_radar(sc)

Description

Creates a ranked horizontal bar chart of composite ESG risk.

Usage

plot_mining_esg_index(index_data)

Arguments

Value

ggplot object

Description

Creates a benchmark comparison bar chart.

Usage

plot_sdg_benchmark(benchmark_data)

Arguments

Value

A ggplot object.

Description

Creates a convergence scatter plot showing initial value vs growth rate.

Usage

plot_sdg_convergence(data, base_year = NULL, final_year = NULL)

Arguments

Value

A ggplot object.

Description

Plots goal-level SDG scores as a radar (polar) chart.

Usage

plot_sdg_radar(x, year = NULL)

Arguments

Value

A ggplot object.

Examples

sc <- score_site_sdg(demo_mine_sites[demo_mine_sites$site_id ==
                       "CU-ATAC" & demo_mine_sites$year == 2024, ])
plot_sdg_radar(sc)

Description

Creates a publication-ready time-series plot for SDG indicator data.

Usage

plot_sdg_trend(data, indicator = NULL, country = NULL)

Arguments

Value

A ggplot object.

Description

Visualizes SDG indicator variability across countries.

Usage

plot_sdg_volatility(stability_data)

Arguments

Value

A ggplot object.

Description

HTML, PDF and DOCX rendering use the Quarto templates shipped in inst/quarto/ and require the Quarto CLI plus the quarto R package. XLSX output is delegated to write_report_xlsx and requires openxlsx2. Rendering happens in a temporary directory; the installed package is never written to.

Usage

render_minesdg_report(report, output_file,
  format = c("html", "pdf", "docx", "xlsx"), template = NULL,
  quiet = TRUE)

Arguments

Value

The path to the rendered file, invisibly.

Examples

rep <- generate_gri_report(demo_mine_sites, site_id = "CU-ATAC",
                           years = 2023:2024)
out <- render_minesdg_report(rep, file.path(tempdir(), "gri.html"))

Description

Starts the interactive MineSDG dashboard: portfolio SDG scorecards, site KPI deep-dives, ontology exploration, the KPI registry, and CSV upload of your own site data (schema of demo_mine_sites).

Usage

run_minesdg_dashboard(...)

Arguments

Details

Requires the shiny package (and optionally DT for enhanced tables), listed in Suggests.

Value

Invisibly returns the value of shiny::runApp() (called for its side effect of launching the app).

Examples

## Not run: 
run_minesdg_dashboard()

## End(Not run)

Description

Loops the existing score_site_sdg engine over every site-year in a portfolio and returns a tidy table of composite scores and grades, ready for portfolio dashboards and benchmarking. No new scoring logic is introduced.

Usage

score_portfolio_sdg(site_data,
  registry = MineSDG::mining_kpi_registry)

Arguments

Value

A data.table with columns site_id, site_name, commodity, country, year, composite_score, grade, ordered by year then descending score.

Examples

portfolio <- score_portfolio_sdg(demo_mine_sites)
portfolio[portfolio$year == 2024, ]

Description

Transforms one year of raw site operational data into a normalised 0-100 SDG scorecard. Each KPI is derived from the raw fields, then linearly rescaled between the poor_value (score 0) and good_value (score 100) reference thresholds in mining_kpi_registry, respecting the KPI's improvement direction. Scores are aggregated to SDG-goal level and into a materiality-weighted composite (weights from sdg_mining_ontology).

Usage

score_site_sdg(site_data, registry = MineSDG::mining_kpi_registry)

Arguments

Details

Expected columns in site_data (one row; extra columns are ignored): ore_processed_kt, ghg_scope1_t, ghg_scope2_t, energy_gj, renewable_energy_pct, water_withdrawal_m3, water_recycled_m3, land_disturbed_ha, land_rehabilitated_ha, hours_worked, recordable_injuries, lost_time_injuries, fatalities, female_employment_pct, local_employment_pct, revenue_musd, community_investment_musd, tailings_kt, waste_rock_kt. Missing fields simply drop the corresponding KPIs from the scorecard.

Value

An object of class minesdg_site_score: a list with scorecard (data.table: kpi_id, kpi_name, sdg_goal, value, unit, score), goal_scores (data.table: sdg_goal, mining_domain, weight, goal_score), composite_score (0-100), and grade (A-E).

Examples

site <- demo_mine_sites[demo_mine_sites$site_id == "CU-ATAC" &
                          demo_mine_sites$year == 2024, ]
score_site_sdg(site)

Description

A structured ontology mapping all 17 Sustainable Development Goals to mining-sector sustainability domains, material topics, sector materiality ratings, and disclosure-framework references (GRI 11 Mining Sector Standard, ICMM Mining Principles, SASB EM-MM, SEBI BRSR).

Usage

sdg_mining_ontology

Format

A data frame with 17 rows and 10 variables:

goal

SDG goal number (1-17)

goal_name

Official SDG goal name

mining_domain

Mining sustainability domain classification

materiality

Sector materiality rating (1 = peripheral, 5 = core)

material_topic

Mining-specific material topic

gri_reference

GRI Standards / GRI 11 sector standard reference

icmm_principle

ICMM Mining Principles reference

sasb_emm

SASB Metals & Mining (EM-MM) metric reference

brsr_principle

SEBI BRSR principle reference (India)

example_kpis

Semicolon-separated representative site KPIs

Source

Compiled from public framework documentation: GRI 11 (2024), ICMM Mining Principles, SASB Metals & Mining Standard, SEBI BRSR.

Examples

head(sdg_mining_ontology[, c("goal", "mining_domain", "materiality")])

Description

Generates descriptive statistics and data quality diagnostics for SDG indicator datasets retrieved via fetch_sdg_country_data().

Usage

summarize_sdg_data(data, group_by = NULL, na.rm = TRUE)

Arguments

Details

Supports optional grouping by indicator, country, year, or combinations thereof.

Value

A data.table containing summary statistics.

Examples

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

summarize_sdg_data(dt)
summarize_sdg_data(dt, group_by = "year")
summarize_sdg_data(dt,
                   group_by = c("indicator", "country"))

Description

Checks whether a provided SDG indicator code exists in the official United Nations SDG indicator database.

Usage

validate_sdg_indicator(indicator, goal = NULL)

Arguments

Details

Optionally verifies consistency between indicator and goal.

Uses internally cached metadata via get_sdg_metadata().

Value

Logical TRUE if valid. Stops with error if invalid.

Examples

validate_sdg_indicator("15.3.1")
validate_sdg_indicator("15.3.1", goal = 15)

Description

Writes a workbook with a Cover sheet (metadata and coverage), the disclosure tables, and a Data Annex containing the bundle's KPI and raw-input tables. Requires the openxlsx2 package.

Usage

write_report_xlsx(report, output_file)

Arguments

Value

The path to the written file, invisibly.

Examples

rep <- generate_gri_report(demo_mine_sites, site_id = "CU-ATAC",
                           years = 2023:2024)
out <- write_report_xlsx(rep, file.path(tempdir(), "gri.xlsx"))