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(), randomForest() and ranger() models.


    Install tidypredict from CRAN using:


    Or install the development version using devtools as follows:



    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)

    Supported models

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