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README.Rmd
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README.Rmd
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---
output:
github_document:
html_preview: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# {fio}
<div style="text-align: justify">
Friendly & Fast Input-Output Analysis <img src="man/figures/leontief.jpg" align="right" width="240px" style="margin-left: 20px;" />
<!-- badges: start -->
[](https://CRAN.R-project.org/package=fio)
[](https://albersonmiranda.r-universe.dev/fio)
[](https://github.com/albersonmiranda/fio/actions/workflows/R-CMD-check.yaml)
[](https://app.codecov.io/gh/albersonmiranda/fio?branch=main)
[](https://CRAN.R-project.org/package=fio)
<!-- badges: end -->
`{fio}` (*Friendly Input-Output*) is a R package designed for input-output analysis, emphasizing usability for Excel users and performance. It includes an [RStudio Addin](https://rstudio.github.io/rstudioaddins/) and a suite of functions for straightforward import of input-output tables from Excel, either programmatically or directly from the clipboard.
The package is optimized for speed and efficiency. It leverages the [R6 class](https://r6.r-lib.org/) for clean, memory-efficient object-oriented programming. Furthermore, all linear algebra computations are implemented in [Rust](https://www.rust-lang.org/) to achieve highly optimized performance.
## Installation
### CRAN Release
You can install the latest stable release of {fio} from CRAN with:
```r
install.packages("fio")
```
### Latest Tested version
install the latest tested but unreleased version from the [main branch](https://github.com/albersonmiranda/fio/tree/main), use the precompiled binaries available on [R-universe](https://albersonmiranda.r-universe.dev/fio):
```r
install.packages("fio", repos = c("https://albersonmiranda.r-universe.dev", "https://cloud.r-project.org"))
```
### Development version
For the cutting-edge development version from the [dev branch](https://github.com/albersonmiranda/fio/tree/dev), you'll need to compile it from source. This requires [Rust](https://www.rust-lang.org/) to be installed on your system. You can install Rust using the following commands:
- Debian/Ubuntu: `apt-get install cargo`
- Fedora/CentOS: `dnf install cargo`
- macOS: `brew install rustc`
- Windows: https://www.rust-lang.org/tools/install
## Getting Started
If you are just getting started with `{fio}`, we recommend you to read the [vignettes](https://albersonmiranda.github.io/fio/articles/index.html) for a comprehensive overview of the package.
## Examples
Calculate Leontief's inverse from brazilian 2020 input-output matrix:
```{r example}
# load included dataset
iom_br <- fio::br_2020
# calculate technical coefficients matrix
iom_br$compute_tech_coeff()
# calculate Leontief's inverse
iom_br$compute_leontief_inverse()
```
And pronto! `r emoji::emoji("tada")`, you're all good to carry on with your analysis. You can evoke the Data Viewer to inspect the results with `iom_br$technical_coefficients_matrix |> View()` and `iom_br$leontief_inverse_matrix |> View()`.
*<small>Leontief's inverse from brazilian 2020 input-output matrix</small>*
## Related tools
Other great tools for input-output analysis in R include:
- [{leontief}](https://pachamaltese.github.io/leontief/)
- [{ioanalysis}](https://cran.r-project.org/package=ioanalysis)
</div>