The aim of this package is to offer more variability of graphics based on the self-organizing maps.

Functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in 'ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

Spatial data plus the power of the ggplot2 framework means easier mapping when input 
  data are already in the form of spatial objects.

Inspects 'ggplot' objects by extracting their
    full declarative specification - layers, aesthetic mappings, scales,
    facets, coordinate systems, and labels - as tidy data frames. A second
    tier of functions enables structural comparison of two 'ggplot' objects,
    supporting automated plot testing, auditing, and framework-agnostic
    grading workflows. Unlike 'ggcheck', which is designed exclusively for
    'learnr'/'gradethis' pipelines and returns ad-hoc objects, 'ggspec'
    returns rectangular, pipeable output and does not require any grading
    framework as a dependency.

Additional annotations, stats, geoms and scales for plotting 
    "light" spectra with 'ggplot2', together with specializations of ggplot()
    and autoplot() methods for spectral data and waveband definitions
    stored in objects of classes defined in package 'photobiology'. Part of the 
    'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Allows you to write queries that combine SQL (Structured Query
  Language) data retrieval with visualization specifications in a single,
  composable syntax. The 'ggsql' package binds directly with the 'ggsql' 'Rust'
  library and allows you to set up readers and writers and execute queries
  against it. The package also offers 'knitr' and 'shiny' integration allowing
  the user to use 'ggsql' in both frameworks.

Easily create overlapping grammar of graphics plots for scientific data visualization. 
  This style of plotting is particularly common in climatology and oceanography research communities.