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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit Readme.Rmd -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- README.md is generated from README.Rmd. Please edit README.Rmd. -->
<!-- badges: start -->
[](https://cran.r-project.org/package=PFUAggPipeline)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://www.repostatus.org/#active)
[](https://doi.org/10.5281/zenodo.6409759)
<!-- badges: end -->
# PFUAggPipeline
## Statement of need
The CL-PFU database uses many sources of input data
spanning hundreds of data files.
The [targets](https://docs.ropensci.org/targets/) pipeline
in the `PFUPipeline` package creates
**RUVY** matrices for the
[PSUT](https://www.sciencedirect.com/science/article/pii/S0306261918308298?via%3Dihub)
framework
in [matsindf](https://MatthewHeun.github.io/matsindf/) format,
containing descriptions of energy conversion chains (ECCs)
in energy and exergy terms.
Those ECCs can be used as input data
to create many aggregated versions of the ECCs,
which are amenable to presentation and efficiency calculations.
Computational tools to aggregate and calculate efficiencies
are needed.
This package (`PFUAggPipeline`) creates another
[targets](https://docs.ropensci.org/targets/) computational pipeline,
one that aggregates and calculates efficiencies
from the results of `PFUPipeline` package.
The [targets](https://docs.ropensci.org/targets/) computational pipeline
provides helpful dependency management
so what is done stays done
during debugging.
## Installation
You can install the development version of `PFUAggPipeline` from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("EnergyEconomyDecoupling/PFUAggPipeline")
```
## Quick start
At the RStudio console, type
``` r
library(targets) # to load the targets package
tar_visnetwork() # to see a directed acyclic graph of the calculations that will take place
tar_make_future(workers = 2) # to execute the calculations (or `workers = 8`, if you have enough cores)
```
## Accessing targets
`targets::tar_read(<<target>>)` pulls the value of a target out of the `targets` cache.
(`<<target>>` should be an unquoted symbol such as `Specified`.)
## Fresh start
`targets::tar_destroy()` invalidates the `targets` cache and forces reanalysis of everything.
Reanalyzing everything may take a while.
## Example
See the vignette entitled "Access PFU Database Products Via `Pins`".
## More Information
For information about the `targets` package,
see the [targets manual](https://books.ropensci.org/targets/).
For documentation on the `PFUAggPipeline` package,
see <https://EnergyEconomyDecoupling.github.io/PFUAggPipeline/>.