---
title: "Introduction"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Introduction}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
collapse = TRUE,
comment = "#>",
dev = "ragg_png"
)
```
```{r setup, echo = FALSE}
library(paleta)
```
A **data visualisation design system** is a framework or set of guidelines that provides standards, components, and best practices for creating effective and consistent data visualisations within an organization or project. It's essentially a systematic approach to designing and implementing data visualisations to ensure they are both visually appealing and communicate insights effectively.
Key components of a data visualisation design system include:
* **Style Guidelines:** Establishing consistent visual styles such as colour palettes, typography, iconography, and layout principles to maintain coherence across all visualisations.
* **Data visualisation Principles:** Defining principles for effective data visualisation design, such as emphasizing clarity, simplicity, accuracy, and relevance.
* **Component Library:** Providing a library of pre-designed visualisation components (e.g., charts, graphs, maps) that adhere to the established style guidelines. These components can be reused across different projects to ensure consistency.
* **Interaction Patterns:** Defining guidelines for interactive elements within visualisations, including hover effects, click interactions, tooltips, and filtering mechanisms, to enhance user engagement and exploration.
* **Accessibility Standards:** Ensuring that visualisations are accessible to all users, including those with disabilities, by following accessibility standards for colour contrast, text size, alternative text, and keyboard navigation.
* **Responsive Design:** Guidelines for designing visualisations that adapt gracefully to different screen sizes and devices, ensuring a consistent user experience across desktop, tablet, and mobile devices.
* **Documentation and Training:** Providing documentation and training materials to help designers and developers understand and implement the design system effectively, including tutorials, code examples, and design principles.
* **Feedback Mechanisms:** Establishing channels for collecting feedback from users and stakeholders to continuously improve the design system and address any usability or accessibility issues.
Overall, a data visualisation design system serves as a valuable resource for designers, developers, and analysts to create high-quality and consistent data visualisations that effectively communicate insights and drive decision-making. It promotes efficiency, scalability, and collaboration within an organization by providing a common language and framework for visualizing data.
With more and more organisations developing their style guidelines and making them available publicly, the `paleta` package aims to consolidate these guidelines and express them within the R language for statistical computing's graphics and publishing ecosystem.
`paleta` provides or will be providing utilities for applying the visual identity and/or branding guidelines of various non-governmental organisations, government agencies, and United Nations units.
Currently, `paleta` has colours, palettes, and `ggplot2` themes based on the following organisations' branding/visual identity guidelines:
* [Africa CDC](https://africacdc.org/) [visual identity guidelines](https://africacdc.org/download/visual-identity-africa-cdc-branding-guide/).
* [UNICEF](https://www.unicef.org/) [brand manual](https://www.unicef.org/jordan/media/7166/file/ANNEX_G_-_Brand_book_V3.1.pdf)
* [World Bank](https://www.worldbank.org) [visual identity guidelines](https://thedocs.worldbank.org/en/doc/723361567518322252-0060022019/original/WBGBrandingandVisualIdentityGuidelinesFebruary2016.pdf).
* [NHS](https://www.nhs.uk/) [identity guidelines](https://www.england.nhs.uk/nhsidentity/identity-guidelines/)