It is a data visualization tool built using the Unity Data Visualization Template (UDVT).
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Updated
Jul 29, 2023 - C#
It is a data visualization tool built using the Unity Data Visualization Template (UDVT).
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
Plotting two different categories- box plot, barplot, histogram. Plotting single category- Pie chart, bar chart. Different Plots- Scatter Plot, Histogram, Box Plot, Violin Plot
A demo application showcasing LightningChart JS Box and Violin charts
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.
Exploratory Data Analysis and Visualization of Google Play Store Dataset
Python package to make Violin SuperPlots
A python package with standard data visualization functions with reasonable defaults for use in Exploratory Data Analysis and Model Diagnostics.
Docs for Cloudy Mountain Plot
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
Youtube API project for data analytics
An open-source MATLAB tool for drawing box plot and violin plot with automatic multi-way data grouping.
R code and example plots for all of my #TidyTuesday contributions, an initiative by the R4DS online learning community.
A collection of beautiful plots, and other data visualization stuff.
For easy metric logging and visualization
Development version of vioplot R package (CRAN maintainer)
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.
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