README.md
In aurelius: Generates PFA Documents from R Code and Optionally Runs Them

aurelius

aurelius is a toolkit for translating models and analytics from the R programming language into the Portal Format for Analytics (PFA). There are functions for importing, exporting and converting common R classes of models into PFA. There are also functions for converting variable assignment, control structures, and other elements of the R syntax into PFA.

devtools::install_github('opendatagroup/hadrian', subdir='aurelius')
library("aurelius")

The main purpose of the package is to create PFA documents based on logic created in R. This example shows how to build a simple linear regression model and save as PFA. PFA is a plain-text JSON format.

# build a model
lm_model <- lm(mpg ~ hp, data = mtcars)

# convert the lm object to a list of lists PFA representation
lm_model_as_pfa <- pfa(lm_model)

The model can be saved as PFA JSON and used in other systems.

# save as plain-text JSON
write_pfa(lm_model_as_pfa, file = "my-model.pfa")

Just as models can be written as a PFA file, they can be read.

my_model <- read_pfa("my-model.pfa")

The pfa() function in this package supports direct conversion to PFA for objects created by the following functions:

| Model | Function | Prediction | Libraries | |:-------------------------------------------------|:-------------------------------------|:-------------------------------------|:--------------------| | Autoregressive Integrated Moving Average (ARIMA) | arima(), Arima(), auto.arima() | Time Series | stats, forecast | | Classification and Regression Trees (CART) | rpart() | Classification, Regression, Survival | rpart | | Exponential Smoothing State Space | ets(), ses(), hw(), holt() | Time Series | forecast | | Generalized Boosted Regression Models | gbm() | Classification, Regression, Survival | gbm | | Generalized Linear Model | glm() | Classification, Regression | stats | | Holt-Winters Filtering | HoltWinters() | Time Series | stats, forecast | | K-Centroids Clustering | kcca() | Clustering | flexclust | | K-Means Clustering | kmeans() | Clustering | stats | | k-Nearest Neighbour | knn3(), knnreg(), ipredknn() | Classification, Regression | caret, ipred | | Linear Discriminant Analysis | lda() | Classification | MASS | | Linear Model | lm() | Regression | stats | | Naive Bayes Classifier | naiveBayes() | Classification | e1071 | | Random Forest | randomForest() | Classification, Regression | randomForest | | Regularized Generalized Linear Models | glmnet(), cv.glmnet() | Classification, Regression, Survival | glmnet |