dplyr backend for Revolution Analytics xdf files (dplyrXdf)
The dplyr package is a popular toolkit for data transformation and manipulation. Over the last year and a half, dplyr has become a hot topic in the R community, for the way in which it streamlines and simplifies many common data manipulation tasks. Out of the box, dplyr supports data frames, data tables (from the data.table package), and the following SQL databases: MySQL/MariaDB, SQLite, and PostgreSQL. However, a feature of dplyr is that it’s extensible: by writing a specific backend, you can make it work with many other kinds of data sources. For example the development version of the RSQLServer package implements a dplyr backend for Microsoft SQL Server. The dplyrXdf package implements such a backend for the xdf file format, a technology supplied as part of Revolution R Enterprise. All of the data transformation and modelling functions provided with Revolution R Enterprise support xdf files, which allow you to break R’s memory barrier: by storing the data on disk, rather than in memory, they make it possible to work with multi-gigabyte or terabyte-sized datasets. dplyrXdf brings the benefits of dplyr to xdf files, including support for pipeline notation, all major verbs, and the ability to incorporate xdfs into dplyr pipelines.
Simulating Homogenous & Non-Homogenous Poisson Processes (poisson)
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.
R package to provide mclapply() syntax for Windows machines (parallelsugar)
An R package to provide mclapply() syntax for Windows machines.
Barnard’s Unconditional Test (Barnard)
Barnard’s unconditional test for 2×2 contingency tables.
Data Visualization Tools for Statistical Analysis Results (ggfortify)
Unified plotting tools for statistics commonly used, such as GLM, time series, PCA families, clustering and survival analysis. The package offers a single plotting interface for these analysis results and plots in a unified style using ‘ggplot2’.
R Packages worth a look
24 Saturday Oct 2015
Posted R Packages
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