Javier Luraschi

R interface to Apache Spark, a fast and general engine for big data processing, see <http://spark.apache.org>. This package supports connecting to local and remote Apache Spark clusters, provides a 'dplyr' compatible back-end, and provides an interface to Spark's built-in machine learning algorithms.

A collection of 'HTML', 'JavaScript', and 'CSS' assets that dynamically generate beautiful documentation from a 'Swagger' compliant API: <https://swagger.io/specification/>.

Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine learning models.

Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This allows to read files from the Common Crawl project <http://commoncrawl.org/>.

A tool for drawing sassy 'UML' diagrams based on a simple syntax, see <http://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams in the PNG format.

Low-level socket-based interface to calling the Spark API via the RBackend server included in Spark.

Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.

Convert R Markdown documents into a variety of formats.

Interactive visualizations for profiling R code.

R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.

Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing 'cloudml' compatible services.

This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is a framework based on 'scala' and 'spark' to analyze genome datasets, see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples each one containing 80 million features in either unsupervised clustering approaches and supervised applications, like classification and regression. The genome datasets are usually writing in VCF, a specific text file format used in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great tool for genome research, because it is able to read VCF files, run analyses and return the output in a 'spark' data frame.