Run predictions inside the database. `tidypredict`

parses a fitted R model object, and returns a formula in ‘Tidy Eval’ code that calculates the predictions.

**It works with several databases back-ends** because it leverages `dplyr`

and `dbplyr`

for the final SQL translation of the algorithm. It currently supports `lm()`

, `glm()`

and `randomForest()`

models.

Install `tidypredict`

from CRAN using:

`install.packages("tidypredict")`

Or install the development version using `devtools`

as follows:

`devtools::install_github("edgararuiz/tidypredict")`

`tidypredict`

is able to parse an R model object, such as:

`model <- lm(mpg ~ wt + cyl, data = mtcars)`

And then creates the SQL statement needed to calculate the fitted prediction:

`tidypredict_sql(model, dbplyr::simulate_mssql())`

`## <SQL> ((39.6862614802529) + ((`wt`) * (-3.19097213898374))) + ((`cyl`) * (-1.5077949682598))`

The following R models are currently supported. For more info please review the corresponding vignette:

- Linear Regression -
`lm()`

- Generalized Linear model -
`glm()`

- Random Forest -
`randomForest()`