Visualizing Categorical Data

Visualization techniques, data sets, summary and inference procedures aimed particularly at categorical data. Special emphasis is given to highly extensible grid graphics. The package was package was originally inspired by the book "Visualizing Categorical Data" by Michael Friendly and is now the main support package for a new book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer (2015).

API Wrapper for Nasdaq Data Link

Functions for interacting directly with the Nasdaq Data Link API to offer data in a number of formats usable in R, downloading a zip with all data from a Nasdaq Data Link database, and the ability to search. This R package uses the Nasdaq Data Link API. For more information go to < https://docs.data.nasdaq.com/>. For more help on the package itself go to < https://data.nasdaq.com/tools/r>.

Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files

Provides a high-level R interface to data files written using Unidata's netCDF library (version 4 or earlier), which are binary data files that are portable across platforms and include metadata information in addition to the data sets. Using this package, netCDF files (either version 4 or "classic" version 3) can be opened and data sets read in easily. It is also easy to create new netCDF dimensions, variables, and files, in either version 3 or 4 format, and manipulate existing netCDF files. This package replaces the former ncdf package, which only worked with netcdf version 3 files. For various reasons the names of the functions have had to be changed from the names in the ncdf package. The old ncdf package is still available at the URL given below, if you need to have backward compatibility. It should be possible to have both the ncdf and ncdf4 packages installed simultaneously without a problem. However, the ncdf package does not provide an interface for netcdf version 4 files.

Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...

Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.

Automatically Position Non-Overlapping Text Labels with 'ggplot2'

Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels. Labels repel away from each other and away from the data points.

World Map Data from Natural Earth

Facilitates mapping by making natural earth map data from < https://www.naturalearthdata.com/> more easily available to R users.

Manipulating Labelled Data

Work with labelled data imported from 'SPSS' or 'Stata' with 'haven' or 'foreign'. This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package.

Tidy Temporal Data Frames and Tools

Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.