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https://github.com/mdsumner/tacky

What the Package Does (One Line, Title Case)
https://github.com/mdsumner/tacky

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What the Package Does (One Line, Title Case)

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README

          

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```

# tacky

The goal of tacky is to illustrate workflows of satellite image processing in R
using {gdalraster}, {dplyr}, and {targets}. These workflows are comparable to similar ones
in using Opendatacube and xarray in Python, or with {gdalcubes}, {sits}, {rsi} in R.

This is experimental atm, working side-by-side with the [{geotargets}](https://github.com/njtierney/geotargets) project to convert existing alternative spatial workflows into the very efficient and scaleable check-pointing frameworks of the targetsverse in R.

## Installation

You can obtain the development version of tacky from [GitHub:mdsumner/tacky](https://github.com/mdsumner/tacky.git).

This is not an actual R package, it's just a repo to house this illustration for now.

## Example

This example loads Sentinel imagery from 'sentinel-2-c1-l2a' and plots it:

```{r example, fig.width = 10, fig.height = 10}
library(targets)
tar_make()

tar_read(figure)
```

A comparable pair of workflows in Python is illustrated here:

https://discourse.pangeo.io/t/comparing-odc-stac-load-and-stackstac-for-raster-composite-workflow/

We hope to expand the example here into a few more analogous workflows (at the very least with gdalcbues), and maybe build some helpers.

## Notes

This developed slowly from experiments:

https://gist.github.com/mdsumner/9b3b4bec40cf7f121db3337a823b413c

## Code of Conduct

Please note that the tacky project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.