Python-Based Extensions for Data Analytics Workflows.

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:

library(devtools)
devtools::install_github("cefet-rj-dal/daltoolboxdp", force = TRUE, dependencies = FALSE, upgrade = "never")

Examples

Example scripts are available at:

• Examples
• Autoenconders
• Time Series

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:

• GitHub Issues.