violin-plot

Chart.js Box Plot addon

R tool for automated creation of ggplots. Examines one, two, or three variables and creates, based on their characteristics, a scatter, violin, box, bar, density, hex or spine plot, or a heat map. Also automates handling of observation weights, log-scaling of axes, reordering of factor levels, and overlays of smoothing curves and median lines.

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram or map.

Create stacked violin plot

A set of community visualisations for Google Data Studio created by the Data Science Campus

Development version of vioplot R package (CRAN maintainer)

A collection of beautiful plots, and other data visualization stuff.

For easy metric logging and visualization

使用 echarts 实现的小提琴图

Python package to make Violin SuperPlots

An open-source MATLAB tool for drawing box plot and violin plot with automatic multi-way data grouping.

This is a Data Visualization App that allows the user to upload their dataset in clean format and then perform various Data Visualization like - Line Plots, Scatter Plots, Bar Plots, Box Plots, Violin Plots, Histogram Plots

Fully reproducible annotated Rmarkdown code templates to create graphs using the ggplot2 package. Designed for those with minimal experience in R who want to make publication-ready graphs.

A python package with standard data visualization functions with reasonable defaults for use in Exploratory Data Analysis and Model Diagnostics.

Plotting two different categories- box plot, barplot, histogram. Plotting single category- Pie chart, bar chart. Different Plots- Scatter Plot, Histogram, Box Plot, Violin Plot

Comprehensive dashboard that highlights commuting patterns across Canada through descriptive visualizations displaying the relationship between different means of transportation and commute times during different times of the day.

This project applies the K-Means clustering algorithm to segment customers based on their purchasing behavior. The dataset used contains transaction data for an online retail store, and the project aims to group customers using three key metrics: Recency, Frequency, and Monetary Value (RFM).

R code and example plots for all of my #TidyTuesday contributions, an initiative by the R4DS online learning community.

It is a data visualization tool built using the Unity Data Visualization Template (UDVT).