Run Predictions Inside the Database

It 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(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.

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(), ranger() ane earth() models.

Installation

Install tidypredict from CRAN using:

install.packages("tidypredict")

Or install the development version using devtools as follows:

devtools::install_github("edgararuiz/tidypredict")

Intro

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 models are supported:

• Linear Regression - lm()
• Generalized Linear model - glm()
• Random Forest models - randomForest::randomForest()
• Random Forest models, via ranger - ranger::ranger()
• MARS models - earth::earth()