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https://github.com/tidymodels/orbital
Turn Tidymodels Workflows Into Series of Equations
https://github.com/tidymodels/orbital
Last synced: 3 days ago
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Turn Tidymodels Workflows Into Series of Equations
- Host: GitHub
- URL: https://github.com/tidymodels/orbital
- Owner: tidymodels
- License: other
- Created: 2024-06-13T19:03:09.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-10-29T16:54:37.000Z (15 days ago)
- Last Synced: 2024-10-31T22:03:17.304Z (12 days ago)
- Language: R
- Homepage: https://orbital.tidymodels.org
- Size: 1.66 MB
- Stars: 18
- Watchers: 1
- Forks: 1
- Open Issues: 10
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# orbital 
[](https://github.com/tidymodels/orbital/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=orbital)
[](https://app.codecov.io/gh/tidymodels/orbital?branch=main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
The goal of orbital is to enable running predictions of tidymodels [workflows](https://workflows.tidymodels.org/) inside databases.
## Installation
To install it, use:
``` r
install.packages("orbital")
```
You can install the development version of orbital from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("tidymodels/orbital")
```
## Example
Given a fitted workflow
```{r}
#| message: false
library(tidymodels)
rec_spec <- recipe(mpg ~ ., data = mtcars) |>
step_normalize(all_numeric_predictors())
lm_spec <- linear_reg()
wf_spec <- workflow(rec_spec, lm_spec)
wf_fit <- fit(wf_spec, mtcars)
```
You can predict with it like normal.
```{r}
predict(wf_fit, mtcars)
```
We can get the same results by first creating an orbital object
```{r}
library(orbital)
orbital_obj <- orbital(wf_fit)
orbital_obj
```
and then "predicting" with it using `predict()` to get the same results
```{r}
predict(orbital_obj, as_tibble(mtcars))
```
you can also predict in most SQL databases
```{r}
library(DBI)
library(RSQLite)
con <- dbConnect(SQLite(), path = ":memory:")
db_mtcars <- copy_to(con, mtcars)
predict(orbital_obj, db_mtcars)
```
and spark databases
```{r}
library(sparklyr)
sc <- spark_connect(master = "local")
sc_mtcars <- copy_to(sc, mtcars, overwrite = TRUE)
predict(orbital_obj, sc_mtcars)
```
# Supported models and recipes steps
Full list of supported models and recipes steps can be found here: `vignette("supported-models")`.
## contributing
This project is released with a [Contributor Code of Conduct](https://github.com/tidymodels/orbital/blob/main/.github/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on Posit Community](https://forum.posit.co/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/orbital/issues).
- Either way, learn how to create and share a [reprex](https://reprex.tidyverse.org/articles/articles/learn-reprex.html) (a minimal, reproducible example), to clearly communicate about your code.
- Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).