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()