Testing and documenting code that communicates with remote
  databases can be painful. Although the interaction with R is usually relatively 
  simple (e.g. data(frames) passed to and from a database), because they rely on 
  a separate service and the data there, testing them can be difficult to set up,
  unsustainable in a continuous integration environment, or impossible without 
  replicating an entire production cluster. This package addresses that by 
  allowing you to make recordings from your database interactions and then play 
  them back while testing (or in other contexts) all without needing to spin up 
  or have access to the database your code would typically connect to.

A comprehensive visualization toolkit built with coders of all
    skill levels and color-vision impaired audiences in mind. It allows creation
    of finely-tuned, publication-quality figures from single function calls.
    Visualizations include scatter plots, compositional bar plots, violin, box,
    and ridge plots, and more. Customization ranges from size and title
    adjustments to discrete-group circling and labeling, hidden data overlay
    upon cursor hovering via ggplotly() conversion, and many more, all with
    simple, discrete inputs. Color blindness friendliness is powered by legend
    adjustments (enlarged keys), and by allowing the use of shapes or
    letter-overlay in addition to the carefully selected dittoColors().

The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to 
 support storm data management, monitoring,  and early warning applications in Sri Lanka. 
 The publicly available data were converted to tidy data format for easy analysis.
 The package processes weather data, flood data and situation report data (families affected, etc.).
 The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.