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https://github.com/tidymodels/modeldb
Run models inside a database using R
https://github.com/tidymodels/modeldb
database dbplyr dplyr ggplot2 modeling rlang sql tidyeval visualization
Last synced: 2 months ago
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Run models inside a database using R
- Host: GitHub
- URL: https://github.com/tidymodels/modeldb
- Owner: tidymodels
- License: other
- Created: 2018-03-02T02:01:56.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-01-04T23:25:39.000Z (about 1 year ago)
- Last Synced: 2024-10-31T22:03:14.741Z (3 months ago)
- Topics: database, dbplyr, dplyr, ggplot2, modeling, rlang, sql, tidyeval, visualization
- Language: R
- Homepage: https://modeldb.tidymodels.org
- Size: 6.13 MB
- Stars: 79
- Watchers: 7
- Forks: 10
- Open Issues: 5
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - tidymodels/modeldb - Run models inside a database using R (R)
README
---
output: github_document
---
# modeldb 
```{r setup, include=FALSE}
library(dplyr)
library(modeldb)
```
[](https://github.com/tidymodels/modeldb/actions)
[](https://CRAN.R-project.org/package=modeldb)
[](https://app.codecov.io/gh/tidymodels/modeldb?branch=main)
[](https://CRAN.R-project.org/package=modeldb)
Fit models inside the database! **modeldb works with most database back-ends** because it leverages [dplyr](https://dplyr.tidyverse.org/) and [dbplyr](https://dbplyr.tidyverse.org/) for the final SQL translation of the algorithm. It currently supports:
- K-means clustering
- Linear regression
## Installation
Install the CRAN version with:
```{r, eval = FALSE}
install.packages("modeldb")
```
The development version is available from GitHub using remotes:
```{r, eval = FALSE}
# install.packages("remotes")
remotes::install_github("tidymodels/modeldb")
```
## Linear regression
An easy way to try out the package is by creating a temporary SQLite database, and loading `mtcars` to it.
```{r}
con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
RSQLite::initExtension(con)
dplyr::copy_to(con, mtcars)
```
```{r}
library(dplyr)
tbl(con, "mtcars") %>%
select(wt, mpg, qsec) %>%
linear_regression_db(wt)
```
The model output can be parsed by [tidypredict](https://tidypredict.tidymodels.org/) to run the predictions in the database. Please see the "Linear Regression" article to learn more about how to use `linear_regression_db()`
## K Means clustering
To use the `simple_kmeans_db()` function, simply pipe the database back end table to the function. This returns a list object that contains two items:
- A sql query table with the final center assignment
- A local table with the information about the centers
```{r}
km <- tbl(con, "mtcars") %>%
simple_kmeans_db(mpg, wt)
colnames(km)
```
The SQL statement from `tbl` can be extracted using dbplyr's `remote_query()`
```{r}
dbplyr::remote_query(km)
```
## Contributing
This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). 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://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/modeldb/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 [this helpful article on how to create reprexes](https://dbplyr.tidyverse.org/articles/reprex.html) for problems involving a database.
- 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/).