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Copy file name to clipboardExpand all lines: README.Rmd
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[](https://codecov.io/gh/tidymodels/modeldb?branch=master)
Fit models inside the database.**`modeldb` works with most databases back-ends** because it leverages `dplyr` and `dbplyr` for the final SQL translation of the algorithm. It currently supports:
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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:
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- K-means clustering
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## Installation
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Install the CRAN version with:
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```{r, eval = FALSE}
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# install.packages("modeldb")
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install.packages("modeldb")
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```
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The development version is available using `devtools` as follows:
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The development version is available from GitHub using remotes:
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```{r, eval = FALSE}
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# install.packages("remotes")
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# remotes::install_github("tidymodels/modeldb")
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remotes::install_github("tidymodels/modeldb")
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```
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## Linear regression
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An easy way to try out the package is by creating a temporary SQLite database, and loading `mtcars` to it
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An easy way to try out the package is by creating a temporary SQLite database, and loading `mtcars` to it.
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```{r}
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con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")
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linear_regression_db(wt)
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```
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The model output can be parsed by `tidypredict` to run the predictions in the database. Please see the `Linear Regression` article to learn more about how to use `linear_regression_db()`
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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()`
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## K Means clustering
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colnames(km)
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```
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The SQL statement from `tbl` can be extracted using `dbplyr`'s `remote_query()`
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The SQL statement from `tbl` can be extracted using dbplyr's `remote_query()`
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```{r}
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dbplyr::remote_query(km)
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```
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## Contributing
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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.
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- For questions and discussions about tidymodels packages, modeling, and machine learning, [join us on RStudio Community](https://rstd.io/tidymodels-community).
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- If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/modeldb/issues).
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- Either way, learn how to create and share a [reprex](https://rstd.io/reprex) (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.
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- 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/).
The model output can be parsed by `tidypredict` to run the predictions
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in the database. Please see the `Linear Regression` article to learn
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more about how to use `linear_regression_db()`
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The model output can be parsed by
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[tidypredict](https://tidypredict.tidymodels.org/) to run the
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predictions in the database. Please see the “Linear Regression” article
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to learn more about how to use `linear_regression_db()`
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## K Means clustering
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## [7] "hp" "drat" "wt" "qsec" "vs" "am"
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## [13] "gear" "carb"
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The SQL statement from `tbl` can be extracted using `dbplyr`’s
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The SQL statement from `tbl` can be extracted using dbplyr’s
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`remote_query()`
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```r
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dbplyr::remote_query(km)
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```
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## <SQL> SELECT `RHS`.`center` AS `k_center`, `LHS`.`k_mpg` AS `k_mpg`, `LHS`.`k_wt` AS `k_wt`, `RHS`.`mpg` AS `mpg`, `RHS`.`cyl` AS `cyl`, `RHS`.`disp` AS `disp`, `RHS`.`hp` AS `hp`, `RHS`.`drat` AS `drat`, `RHS`.`wt` AS `wt`, `RHS`.`qsec` AS `qsec`, `RHS`.`vs` AS `vs`, `RHS`.`am` AS `am`, `RHS`.`gear` AS `gear`, `RHS`.`carb` AS `carb`
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## FROM (SELECT `center` AS `k_center`, `mpg` AS `k_mpg`, `wt` AS `k_wt`
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## FROM (SELECT `center`, AVG(`mpg`) AS `mpg`, AVG(`wt`) AS `wt`
## FROM (SELECT `LHS`.`mpg` AS `mpg`, `LHS`.`cyl` AS `cyl`, `LHS`.`disp` AS `disp`, `LHS`.`hp` AS `hp`, `LHS`.`drat` AS `drat`, `LHS`.`wt` AS `wt`, `LHS`.`qsec` AS `qsec`, `LHS`.`vs` AS `vs`, `LHS`.`am` AS `am`, `LHS`.`gear` AS `gear`, `LHS`.`carb` AS `carb`, `LHS`.`k_center` AS `k_center`, `RHS`.`k_mpg` AS `k_mpg`, `RHS`.`k_wt` AS `k_wt`
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## FROM (SELECT `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, `center` AS `k_center`
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## FROM (SELECT `mpg`, `cyl`, `disp`, `hp`, `drat`, `wt`, `qsec`, `vs`, `am`, `gear`, `carb`, `center_1`, `center_2`, `center_3`, CASE
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## WHEN (`center_1` >= `center_1` AND `center_1` < `center_2` AND `center_1` < `center_3`) THEN ('center_1')
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## WHEN (`center_2` < `center_1` AND `center_2` >= `center_2` AND `center_2` < `center_3`) THEN ('center_2')
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## WHEN (`center_3` < `center_1` AND `center_3` < `center_2` AND `center_3` >= `center_3`) THEN ('center_3')
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