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Akeba Mitchell (AkebaMitchell)

AkebaMitchell

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Location:California

Home Page:www.linkedin.com\in\akebamitchell

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Akeba Mitchell's starred repositories

sql-server-samples

Azure Data SQL Samples - Official Microsoft GitHub Repository containing code samples for SQL Server, Azure SQL, Azure Synapse, and Azure SQL Edge

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Predicting-Health-Insurance-Cost

Predicting health insurance cost from Morality data using Machine Learning techniques

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examples

Health NLP Examples

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desktop

💻 Stencila on your desktop

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usmap

🗺 Create US maps including Alaska and Hawaii in R

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ggQC

ggQC

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ggedit

Interactively edit ggplot layer aesthetics and theme definitions

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ggforce

Accelerating ggplot2

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ggalt

:earth_americas: Extra Coordinate Systems, Geoms, Statistical Transformations & Scales for 'ggplot2'

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ggiraph

make 'ggplot' graphics interactive

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ggmuller

Create Muller Plots of Evolutionary Dynamics

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ggstance

Horizontal ggplot2 components

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ggraph

Grammar of Graph Graphics

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geomnet

Examples and data for geom_net

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ggExtra

📊 Add marginal histograms to ggplot2, and more ggplot2 enhancements

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ggfortify

Define fortify and autoplot functions to allow ggplot2 to handle some popular R packages.

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autoplotly

Automatic Generation of Interactive Visualizations for Statistical Results

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plotROC

Interactive ROC plots with ggplot + d3.js.

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ggthemes

Additional themes, scales, and geoms for ggplot2

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ggnetwork

Geoms to plot networks with ggplot2

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ggradar

radar charts with ggplot2

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ggTimeSeries

Time series visualisation

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ggseas

seasonal adjustment on the fly extension for ggplot2

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ggmosaic

Mosaicplots in the ggplot2 framework

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survminer

Survival Analysis and Visualization

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ggalluvial

ggplot2 extension for alluvial plots

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qqplotr

Extending some ggplot2 functionalities by permitting the drawing of both quantile-quantile (Q-Q) and probability-probability (P-P) points, lines, and confidence bands

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lemon

🍋 Lemon --- Freshing up your ggplots

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ggridges

Ridgeline plots in ggplot2

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ggdag

:arrow_lower_left: :arrow_lower_right: An R package for working with causal directed acyclic graphs (DAGs)

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