daltoolboxdp
Python-Based Extensions for Data Analytics Workflows
Python-Based Extensions for Data Analytics Workflows provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. It includes tools for:
- Data sampling and transformation
- Feature selection
- Balancing strategies (e.g., SMOTE)
- Model construction and tuning
These capabilities leverage Python libraries via the reticulate
interface, enabling seamless integration with the broader Python machine
learning ecosystem. The package supports instance selection and hybrid
workflows that combine R and Python functionalities for flexible and
reproducible analytical pipelines.
The architecture is inspired by the Experiment Lines approach, which
promotes modularity, extensibility, and interoperability across tools.
More information on Experiment Lines is available in Ogasawara et
al. (2009).
Installation
You can install the latest stable version from CRAN:
install.packages("daltoolboxdp")
To install the development version from GitHub:
# install.packages("devtools")
library(devtools)
devtools::install_github("cefet-rj-dal/daltoolboxdp", force = TRUE, dependencies = FALSE, upgrade = "never")
Examples
Example scripts are available at:
library(daltoolboxdp)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#> Registered S3 methods overwritten by 'forecast':
#> method from
#> head.ts stats
#> tail.ts stats
# Example usage (replace with actual function when available)
# e.g., data <- my_sampler_function(data, method = "undersample")
Bug reports and feature requests
Please report issues or suggest new features via: