bar-charts

📊 Data visualization library for React. Maintained by @goodcodeus.

Personalized graphs featuring customizable options for React Native app 📈

Unix terminal histograms, sequencing depth and bar charts (hissyfit). Small data/file munge, morph, find and count scripts/functions in various languages, mostly bash and perl. For apparix see the apparix repository that is a sibling to this one.

Matlab implementation of Neural Networks Results for Alarm-Warning Control System of mobile robot with five Ultrasonic sensors

Marimekko and bar mekko graphics in R

Predictive Data Mining for Health Insurance data

A data visualization case study portraying plastic waste in the United States.

This is a Repository made for Coursera Assignments, and Tutorials which includes many interesting plots such as waffle charts, folium charts, chloropeth charts etc.

Analogue bar charts using terminal box-drawing characters

d3 charts https://kre1z0.github.io/d3-charts/

Interactive geovisualizations of NY cancer data using Plotly

A powerful 🚀 Android chart view / graph view library, supporting line- bar- pie- radar- bubble- and candlestick charts as well as scaling, dragging and animations.

Simplify time-consuming coding for the data scientist. Create beautiful charts, pandas transformers, and find the best model with the best parameters for your data.

Package for Creating PSRC consistent data visualizations with R

Data Visualization (DSC 530/602-02) Assignment 2

Bar chart race visualization for MATLAB (动态条形直方图)

Discover how to build a layered column chart using the .NET MAUI Toolkit to visualize real-world survey data from the European accommodation sector. This project demonstrates how to represent various operational challenges across countries using multiple layers in a column chart with rich customization features.

This is an Android app that takes the data about student groups and shows bar charts depicting the student data distribution.

This project demonstrates how to create, customize, and visualize bar charts using the Python Matplotlib library. It covers the fundamentals of data visualization, including chart styling, labeling, colors, axes customization, and presenting data effectively through graphical representation.

Toolkit for publication-ready interactive Plotly figures: OLS confidence and prediction bands, LOWESS, bootstrap CIs, z-score anomaly flags, colour-vision-safe palettes, LTTB downsampling for million-point series and provenance-tagged exports, plus notebooks, a CLI and a Streamlit app, all typed and tested.