Using useeior

Reviewing an example or reading the Help pages embedded in the package are the best ways to learn how to use useeior. An example script covering a basic use for useeior can be found in examples. As an R package, useeior provides standard Help pages for all functions.

Basic usage of useeior is demonstrated in README. Additional functions that calculate, validate, export, visualize model are available for assisting further applications. More advanced uses include creating a new model by customizing a model configuration file or disaggregating the model.

Calculate, Validate, Export, Visualize Model

Once a model is built, useeior provides a number of functions that take the model object. The following table lists these functions. Read more about any of these functions by searching their names using the help menu inside your R editor.

Function Name	General Purpose
adjustResultMatrixPrice	Result Calculation and Analysis
calculateEEIOModel	Result Calculation and Analysis
calculateFlowContributiontoImpact	Result Calculation and Analysis
calculateSectorContributiontoImpact	Result Calculation and Analysis
calculateSectorPurchasedbySectorSourcedImpact	Result Calculation and Analysis
calculateMarginSectorImpacts	Result Calculation and Analysis
aggregateResultMatrix	Result Calculation and Analysis
aggregateResultMatrixbyRow	Result Calculation and Analysis
normalizeResultMatrixByTotalImpacts	Result Calculation and Analysis
compareOutputandLeontiefXDemand	Validation
compareEandLCIResult	Validation
compareCommodityOutputandDomesticUseplusProductionDemand	Validation
compareCommodityOutputXMarketShareandIndustryOutputwithCPITransformation	Validation
generateModelIdentifier	Export
writeModelforAPI	Export
writeModelMatrices	Export
writeModeltoXLSX	Export
heatmapSatelliteTableCoverage	Visualization
barplotIndicatorScoresbySector	Visualization
barplotFloworImpactFractionbyRegion	Visualization
heatmapSectorRanking	Visualization
plotMatrixCoefficient	Visualization
compareFlowTotals	Comparison

Advanced Uses

Changing Model Parameters

Model specifications can be modified by adjusting parameters within the appropriate model config file (.yml) located in inst/extdata/modelspecs. See the details on all fields in the ModelSpecifications.md. Static source files for satellite tables and indicators must be stored locally or can be downloaded from a remote source like the EPA Data Commons. See Data Storage for more details.

Disaggregation

USEEIO is designed to enable a reproducible, automated approach to disaggregating sectors in EEIO models. Disaggregation specifications are included within the package for selected sectors. When a disaggregation is applied, a single sector is divided into two or more sectors enabling a greater level of detail in the model. Current disaggregation specs include:

• Disaggregation of Waste Management and remediation services (Detailed model, 562000)

Further details on disaggregation methods can be found in Disaggregation of Sectors

Building Customized Models

To build a customized model, a model config file (.yml) and related aggregation/disaggregation config file (.yml) as well as data files must be prepared following the format of config files (.yml) and data files (.csv) located in inst/extdata/modelspecs, inst/extdata/aggspecs, and inst/extdata/disaggspecs.

Once all the files are properly created and stored in the same folder, build the customized model use:

model <- buildModel(modelname, configpaths)

where modelname is the name of the model config .yml file, and configpaths is a string vector of config file paths, for example:

configpaths <- file.path(user-specified-dir, # replace this with your desired directory
                         c("USEEIOv2.1.yml",
                           "ElectricityDisaggregationDetail.yml",
                           "ElectricityAggregationDetail.yml",
                           "WasteDisaggregationDetail.yml"))

Note that user-specified-dir should be replaced by actual folder directory.

Custom Demand Vectors

Custom demand vectors can be used for calculating results beyond those included by default in the DemandVectorFunctionRegistry. These vectors can be created in code:

y <- setNames(c(245000, 140000), c("1111A0/US", "211000/US"))
y <- formatDemandVector(y, model$L)

Or by pointing to a csv file:

file_path <- 'path-to-file/demand.csv'
y <- extractAndFormatDemandVector(file_path, "MyDemand", model)

where the csv file takes the following format:

Code	MyDemand
1111A0/US	245000
211000/US	140000

The demand vector y can now be used in other calculation functions:

impact_result <- calculateSectorPurchasedbySectorSourcedImpact(y, model, "Greenhouse Gases")
result <- calculateEEIOModel(model, perspective = "DIRECT", demand = y)