FSelectorRcpp_1

# install.packages(c('magrittr', 'FSelectorRcpp'))
library(magrittr)
library(FSelectorRcpp)
information_gain(               # Calculate the score for each attribute
    formula = Species ~ .,      # that is on the right side of the formula.
    data = iris,                # Attributes must exist in the passed data.
    type  = "infogain",         # Choose the type of a score to be calculated.
    threads = 2                 # Set number of threads in a parallel backend.
  ) %>%                          
  cut_attrs(                    # Then take attributes with the highest rank.
    k = 2                       # For example: 2 attrs with the higehst rank.
  ) %>%                         
  to_formula(                   # Create a new formula object with 
    attrs = .,                  # the most influencial attrs.
    class = "Species"           
  ) %>%
  glm(
    formula = .,                # Use that formula in any classification algorithm.
    data = iris,                
    family = "binomial"         
  )