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
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.
Exploratory Data Analysis and Visualization of Google Play Store Dataset
Python package to make Violin SuperPlots
Exploring the implementation of plots in matplotlib
Docs for Cloudy Mountain Plot
A python package with standard data visualization functions with reasonable defaults for use in Exploratory Data Analysis and Model Diagnostics.
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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