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
It works with several databases back-ends because it leverages
dbplyr for the final SQL translation of the algorithm. It
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:
## <SQL> 39.6862614802529 + (`wt` * -3.19097213898374) + (`cyl` * -1.5077949682598)
The following models are supported:
New parsed models are now list objects as opposed to data frames.
tidypredict_to_column() no longer supports
randomForest because of the multiple queries generated by multiple trees.
All functions that read the parsed models and create the tidy eval formula now use the list object.
Most of the code that depends on dplyr programming has been removed.
Removes dependencies on: tidyr, tibble
x ~.in a randomForest() formula fails (#18 @washcycle).