DAL Toolbox is an R framework for data analytics workflows. It
organizes preprocessing, modeling, evaluation, tuning, visualization,
and extensibility resources around a consistent Experiment Line style
API, helping users build reproducible end-to-end analytical
pipelines.
The package supports data preprocessing, classification, regression,
clustering, pattern mining, graphics, and time series prediction. It is
designed for teaching, experimentation, and applied data science
projects where the same workflow needs to be reused, compared, and
extended across methods.
Current package version in this repository: 1.3.767.
Installation
The stable version is available on CRAN:
https://CRAN.R-project.org/package=daltoolbox
install.packages("daltoolbox")
The development version is available on GitHub:
https://github.com/cefet-rj-dal/daltoolbox
library(devtools)
devtools::install_github("cefet-rj-dal/daltoolbox", force = TRUE, dependencies = FALSE, upgrade = "never")
Documentation
Documentation and examples are available in the package site and in
the repository:
The documentation is organized around two complementary entry
points:
- a guided tutorial track for readers who want to learn the workflow
step by step
- thematic example collections for readers who want to inspect a
specific family of methods
If you are new to daltoolbox, start with the tutorials.
If you already know the package structure, the thematic collections
provide focused examples by method family.
Guided Tutorial Track
The tutorials are part of the examples
collection. They form a 13-part learning sequence covering first
experiment, sampling, data quality, preprocessing, baselines, metrics,
model comparison, tuning, end-to-end pipelines, regression, clustering,
visual analysis, and custom extensions.
The sequence is cumulative. Each tutorial introduces one main
decision in a data mining study, explains why that step matters, and
keeps the code close to that learning objective.
Thematic Example Collections
The examples
collection includes thematic subcollections:
- Transformations - sampling, balancing, cleaning, scaling, encoding,
smoothing, feature selection, dimensionality reduction, and
curvature-based heuristics.
- Classification - baseline models, decision trees, instance-based
methods, probabilistic models, linear models, ensembles, neural models,
support vector machines, boosting, and tuning.
- Regression - interpretable models, instance-based learners, random
forests, support vector machines, neural models, and tuning for numeric
prediction.
- Clustering - partitional, medoid-based, density-based, fuzzy,
model-based, hierarchical, graph-based methods, and clustering model
selection.
- Pattern Mining - association rules, frequent itemsets, and sequence
mining.
- Graphics - comparison, distribution, relationship, time-oriented,
and export-focused visualizations.
- Custom Extensions - examples showing how to integrate new
transformations, classifiers, regressors, clusterers, autoencoders, and
pattern miners into the Experiment Line workflow.
Main Capabilities
- Unified abstractions for learners, transformations, predictors, and
tuners.
- Reusable preprocessing workflows for sampling, balancing, cleaning,
normalization, encoding, smoothing, feature selection, and
dimensionality reduction.
- Classification, regression, clustering, and pattern mining examples
with consistent fit, predict, evaluate, and tune stages.
- Time series support for preprocessing, augmentation, normalization,
filtering, and prediction.
- Visualization helpers for comparisons, distributions, relationships,
time series, and report-oriented graphics.
- Extensible interfaces for custom analytical components.
Playlist
DAL
Toolbox videos
Bugs and Feature Requests
Please report bugs, questions, and feature requests at:
https://github.com/cefet-rj-dal/daltoolbox/issues
DAL Toolbox