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Ecology","sub_category":"Input Output Model","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# useeior \u003cimg src=\"inst/img/logo.png\" align=\"right\" width=\"240\" /\u003e\n\u003c!-- badges: start --\u003e\n[![R CI/CD test](https://github.com/USEPA/useeior/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/USEPA/useeior/actions/workflows/R-CMD-check.yaml)\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)\n[![useeior DOI](http://img.shields.io/badge/useeior%20DOI-10.5281/zenodo.7020213-blue.svg)](https://doi.org/10.5281/zenodo.7020213)\n[![useeior paper](http://img.shields.io/badge/useeior%20paper-10.3390/app12094469-blue.svg)](https://doi.org/10.3390/app12094469)\n\u003c!-- badges: end --\u003e\n\n`useeior` is an R package for building and using [USEEIO models](https://www.epa.gov/land-research/us-environmentally-extended-input-output-useeio-models).\n\nThe [model object](format_specs/Model.md) is the primary output that is built according to a given [model specification](format_specs/ModelSpecification.md) and optional hybridization specification, e.g. [disaggregation](format_specs/ModelCustomization.md).\n[Model specifications](inst/extdata/modelspecs) and associated hybridization specifications, e.g. [disaggregation](inst/extdata/disaggspecs), for EPA-validated models are included in the package.\n\n`useeior` offers various functions for validating, calculating, visualizing, and writing out models and/or their components.\n`useeior` is a core component of the [USEEIO Modeling Framework](https://github.com/USEPA/useeio) and is in a stable development state.\nUsers intending to use the package for production purposes and applications should use [Releases](https://github.com/USEPA/useeior/releases).\n`useeior` v1.0.0 was peer-reviewed internally at USEPA and published at Zenodo.\n\nAn peer-reviewed article describing `useeior` was published in the journal Applied Sciences in April 2022.\nIf you use `useeior` in a scientific publication, we would appreciate that you cite it using:\n```\n@article{li_useeior_2022,\n  title   = {useeior: {An} {Open-Source} {R} {Package} for {Building} and {Using} {US} {Environmentally-Extended} {Input-Output} {Models}},\n  journal = {Applied Sciences},\n  author  = {{Li, Mo} and {Ingwersen, Wesley} and {Young, Ben} and {Vendries, Jorge} and {Birney, Catherine}},\n  year    = {2022},\n  pages   = {4469},\n  number  = {9},\n  volume  = {12},\n  doi     = {10.3390/app12094469}\n}\n```\nor\n```\nLi, M., Ingwersen, W.W., Young, B., Vendries, J. and Birney, C., 2022. useeior: An Open-Source R Package for Building and Using US Environmentally-Extended Input–Output Models. Applied Sciences, 12(9), p.4469.\n```\n\nSee the following sections for installation and basic usage of `useeior`.\n\nSee [Wiki](https://github.com/USEPA/useeior/wiki) for advanced uses, details about built-in data and metadata and how to contribute to `useeior`.\n\n## Installation\n\n```r\n# Install development version from GitHub\ninstall.packages(\"devtools\")\ndevtools::install_github(\"USEPA/useeior\")\n```\n\n```r\n# Install a previously released version (e.g. v1.0.0) from GitHub\ndevtools::install_github(\"USEPA/useeior@v1.0.0\")\n```\n\nSee [Releases](https://github.com/USEPA/useeior/releases) for all previously released versions.\n\n## Usage\n\n### Build Model\n\nView all models with existing config files that can be built using useeior\n\n```r\nlibrary(\"useeior\")\nseeAvailableModels()\n```\n\nBuild a model that is available in useeior (e.g. the [USEEIOv2.0.1-411](inst/extdata/modelspecs/USEEIOv2.0.1-411.yml) model)\n\n```r\nmodel \u003c- buildModel('USEEIOv2.0.1-411')\n```\n\nTo build a customized model, refer to [Advanced Uses](https://github.com/USEPA/useeior/wiki/Using-useeior#advanced-uses) in Wiki.\n\nThis generates a complete USEEIO model with components described in the [Model](format_specs/Model.md#model) table.\n\n### Adjust Price Year and Type of Model Results\n\nAdjust model results (e.g. `N` matrix) to user-specified price year (e.g. `2018`) and type (producer's or purchaser's).\n\n```r\nN_adj \u003c- adjustResultMatrixPrice(\"N\", \n                                 currency_year = 2018,\n                                 purchaser_price = TRUE,\n                                 model)\n```\n\n### Calculate Model LCI and LCIA\n\nCalculate model life cycle inventory (LCI) and life cycle impact assessment (LCIA) results with a user-specified [calculation perspective](format_specs/Calculation.md#calculation-perspectives), [demand vector](format_specs/Model.md#demandvectors) (from `DemandVectors` in the model object, which includes four [default vectors](format_specs/ModelSpecification.md#demand-vector-specifications), or a user-provided vector) and a model [direct requirements matrix](format_specs/Model.md#a).\n\n```r\nresult \u003c- calculateEEIOModel(model,\n                             perspective = \"DIRECT\",\n                             demand = \"CompleteProduction\",\n                             use_domestic_requirements = FALSE)\n```\n\nThis returns a [Calculation Result](format_specs/Calculation.md#calculation-result). \n\n### Write Model to File\n\nWrite selected model matrices, demand vectors, and metadata as one `.xlsx` file to a given output folder.\n```r\nwriteModeltoXLSX(model, outputfolder)\n```\n\nWrite model matrices as `.csv` files to a given output folder.\n```r\nwriteModelMatrices(model, to_format = \"csv\", outputfolder)\n```\n\n### Validate Model\n\nComplete model validation checks can be found in [ValidateModel.Rmd](inst/doc/ValidateModel.Rmd).\nKnit [ValidateModel_render.Rmd](inst/doc/ValidateModel_render.Rmd) to perform all validation checks on selected models (specified under the [YAML header](inst/doc/ValidateModel_render.Rmd#L5)).\nThis will generate an `.html` and a `.md` file containing validation results for each model. See example output in  [inst/doc/output/](inst/doc/output). \n\n#### Examples\n\nValidate that flow totals by commodity `E_c` can be recalculated (within 1%) using the model satellite matrix `B`, market shares matrix `V_n`, total requirements matrix `L`, and demand vector `y` for US production.\n\n```r\n\u003e modelval \u003c- compareEandLCIResult(model, tolerance = 0.01)\n\u003e print(paste(\"Number of flow totals by commodity passing:\", modelval$N_Pass))\n[1] \"Number of flow totals by commodity passing: 1118742\"\n\u003e print(paste(\"Number of flow totals by commodity failing:\", modelval$N_Fail))\n[1] \"Number of flow totals by commodity failing: 0\"\n```\n\nValidate that commodity output can be recalculated (within 1%) with the model total requirements matrix `L` and demand vector `y` for US production.\n\n```r\n\u003e econval \u003c- compareOutputandLeontiefXDemand(model, tolerance = 0.01)\n\u003e print(paste(\"Number of sectors passing:\",econval$N_Pass))\n[1] \"Number of sectors passing: 409\"\n\u003e print(paste(\"Number of sectors failing:\",econval$N_Fail))\n[1] \"Number of sectors failing: 2\"\n\u003e print(paste(\"Sectors failing:\", paste(econval$Failure$rownames, collapse = \", \")))\n[1] \"Sectors failing: S00402/US, S00300/US\"\n```\nNote: `S00402/US - Used and secondhand goods` and `S00300/US - Noncomparable imports` are two commodities that are not produced by any industry in the US, therefore their commodity output naturally cannot be recalculated with the model total requirements matrix `L` and demand vector `y` for US production. Results for these sectors are not recommended for use.\n\n### Visualize Model Results\n\n#### Examples\n\nRank sectors based a composite score of selected total impacts (H_r or H_l) associated with total US demand (US production or consumption vector).\nComparing rankings may also be used as another form of model validation that incorporates the demand vectors and the indicators as well as the model result matrices.\n\n```r\n# Calculate model LCIA_d (H_r) and LCIA_f (H_l)\nresult \u003c- c(calculateEEIOModel(model, perspective = 'DIRECT', demand = \"Production\"),\n            calculateEEIOModel(model, perspective = 'FINAL', demand = \"Consumption\"))\ncolnames(result$H_r) \u003c- model$Indicators$meta[match(colnames(result$H_r),\n                                                    model$Indicators$meta$Name),\n                                                 \"Code\"]\ncolnames(result$H_l) \u003c- colnames(result$H_r)\n# Define indicators\nindicators \u003c- c(\"ACID\", \"CCDD\", \"CMSW\", \"CRHW\", \"ENRG\", \"ETOX\", \"EUTR\", \"GHG\",\n                \"HRSP\", \"HTOX\", \"LAND\", \"MNRL\", \"OZON\", \"SMOG\", \"WATR\")\n# Create figure on the left\nheatmapSectorRanking(model,\n                     matrix = result$H_r,\n                     indicators,\n                     sector_to_remove = \"\",\n                     N_sector = 20,\n                     x_title = \"H_r (DIRECT perspective) \u0026 US production demand\")\n# Create figure on the right\nheatmapSectorRanking(model,\n                     matrix = result$H_l,\n                     indicators,\n                     sector_to_remove = \"\",\n                     N_sector = 20,\n                     x_title = \"H_l (FINAL perspective) \u0026 US consumption demand\")\n```\n\n![](inst/img/ranking_direct_prod_final_cons_v2.0.1.png)\n\nMore visualization examples are available in [Example.Rmd](inst/doc/Example.Rmd).\n\n### Analyze Flow and Sector Contribution to Impact\n\n#### Examples\n\nAnalyze `flow` contribution to total (direct+indirect) `Acidification Potential` in the `Electricity` sector (`221100/US`), showing top 5 contributors below.\n\n```r\n\u003e ACID_elec \u003c- calculateFlowContributiontoImpact(model, \"221100/US\", \"Acidification Potential\")\n\u003e ACID_elec$contribution \u003c- scales::percent(ACID_elec$contribution, accuracy = 0.1)\n\u003e head(subset(ACID_elec, TRUE, select = \"contribution\"), 5)\n                                    contribution\n\"Sulfur dioxide/emission/air/kg\"           57.4%\n\"Nitrogen dioxide/emission/air/kg\"         39.2%\n\"Ammonia/emission/air/kg\"                   2.3%\n\"Sulfuric acid/emission/air/kg\"             0.7%\n\"Hydrofluoric acid/emission/air/kg\"         0.2%\n```\n\nAnalyze `sector` contribution to total (direct+indirect) `Human Health - Respiratory Effects` in the `Flours and malts` sector (`311210/US`), showing top 5 contributors below.\n\n```r\n\u003e HHRP_flour \u003c- calculateSectorContributiontoImpact(model, \"311210/US\", \"Human Health - Respiratory Effects\")\n\u003e HHRP_flour$contribution \u003c- scales::percent(HHRP_flour$contribution, accuracy = 0.1)\n\u003e head(subset(HHRP_flour, TRUE, select = \"contribution\"), 5)\n                                                                        contribution\n\"1111B0/US - Fresh wheat, corn, rice, and other grains\"                        90.7%\n\"311210/US - Flours and malts\"                                                  1.5%\n\"115000/US - Agriculture and forestry support\"                                  0.9%\n\"2123A0/US - Sand, gravel, clay, phosphate, other nonmetallic minerals\"         0.8%\n\"1111A0/US - Fresh soybeans, canola, flaxseeds, and other oilseeds\"             0.8%\n```\n\nMore analysis examples are available in [Example.Rmd](inst/doc/Example.Rmd).\n\n### Compare Model Results\n\nComparison betwen two models can be found in [CompareModel.Rmd](inst/doc/CompareModels.Rmd).\nKnit [CompareModel_render.Rmd](inst/doc/CompareModels_render.Rmd) to perform comparison on selected models (specified under the [YAML header](inst/doc/CompareModels_render.Rmd#L5)).\nThis will return an `.html` and a `.md` file containing comparison results for each model specified in the header. An example can be found [inst/doc/output/](inst/doc/output)\n\nCurrently, it only compares flow totals between two models. More comparisons will be added in the future.\n\n### Additional Information\n\nA complete list of available functions for calculating, validating, exporting and visualizing model can be found [here](https://github.com/USEPA/useeior/wiki/Using-useeior#calculate-validate-export-visualize-model) in the Wiki.\n\n## Disclaimer\n\nThe United States Environmental Protection Agency (EPA) GitHub project code is provided on an \"as is\" basis and the user assumes responsibility for its use.  EPA has relinquished control of the information and no longer has responsibility to protect the integrity , confidentiality, or availability of the information.  Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by EPA.  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