R Data Science Digest: April 2021

A list of the most popular posts featured on R Posts you might have missed! in March 2021. All of the most exciting R resources in statistics, visualisation, reporting, data manipulation and lots more!

Featured posts

shinysurveys • Develop and deploy surveys in Shiny/R by Jonathan Trattner and Lucy D’Agostino McGowan

  • "shinysurveys provides easy-to-use, minimalistic code for creating and deploying surveys in Shiny. Originally inspired by Dean Attali’s shinyforms, our package provides a framework for robust surveys, similar to Google Forms, in R with Shiny."

autoplotly • Automatic Generation of Interactive Visualizations for Statistical Results by Yuan Tang

  • "provides functionalities to automatically generate interactive visualizations for many popular statistical results supported by ggfortify package with plotly.js and ggplot2 style. The generated visualizations can also be easily extended using ggplot2 syntax while staying interactive."

DataCoaster Tycoon: Building 3D Rollercoaster Tours of Your Data in R by Tyler Morgan-Wall

  • "a tech demonstration showing how to create animations through 3D space with R and rayrender’s new camera animation API."

Statistics

Machine learning

Visualisation

::Graphics

::Interactive

::Themes and palettes

Publishing

Data manipulation

R learning resources

Data

Utilities

Spatial

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Python DS Digest: April 2021

A list of popular posts and resources shared on Python DS you might have missed! in March 2021. All of the most exciting python resources in machine learning, data manipulation, visualisation and lots more!

Machine learning: scikit-learn

Machine learning: deep learning

Machine learning: statistical learning

Data manipulation

Visualisation

Geospatial

Scientific python

Python development

Python in finance

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Python DS Digest: March 2021

A list of popular posts and resources shared on Python DS you might have missed! in March 2021. Topics this month include machine learning, deep learning, data manipulation, statistical modelling, IDEs and notebooks and dashboards.

Machine learning

Deep learning

Feature selection and engineering

Notebooks and IDEs

Visualisation

Statistical modelling

Dashboards and apps

Data manipulation & pandas

Tools & utilies

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R Data Science Digest: February 2021

A thematic list of popular posts shared on R posts you might have missed in January 2021. Topics this month include rmarkdown, ggplot2, machine learning and loads of cool tools & utilities!

Top picks

Markdown and publishing

ggplot2 and visualisation

  • Creating and using custom ggplot2 themes • the best way to make each plot your own, by Tom Mock.

  • ggnewscale • Multiple Fill, Color and Other Scales in {ggplot2} by Elio Campitelli.

  • ggeasy • {ggplot2} shortcuts (transformations made easy) by Jonathan Carroll.

  • ggbernie • A {ggplot2} geom for adding Bernie Sanders to {ggplot2} by R CODER.

  • ggprism • {ggplot2} extension inspired by GraphPad Prism by Charlotte Dawson.

  • basetheme • Themes for base plotting system in R by Karolis Koncevičius

  • mully • R package to create, modify and visualize graphs with multiple layers by Frank Kramer.

  • Automating exploratory plots with ggplot2 and purrr by Ariel Muldoon.

  • ggstatsplot • ggscatterstats: a publication-ready scatterplot with all statistical details included in the plot itself to show association between two continuous variables by Indrajeet Patil.

  • popcircle • Circlepacked geo polygons by Timothée Giraud.

Spatial and Mapping

Machine Learning and Statistical Modelling

R Learning Resources

Workflow and Utilities

Tools

  • officeverse • This book deals with reporting from R with the packages {officer}, {officedown}, {flextable}, {rvg} and {mschart}, by David Gohel.

  • countrycode • Convert country names and country codes. Assigns region descriptors. by Vincent Arel-Bundock.

  • disk.frame • Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data by ZJD (http://evalparse.com).

  • vroom • Read and Write Rectangular Text Data Quickly (The fastest delimited reader for R, 1.48 GB/sec.) by Jim Hester and Hadley Wickham.

  • visdat • Using {visdat} (Preliminary Exploratory Visualisation of Data) by Nicholas Tierney.

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Python DS Digest: February 2021

A list of popular posts and resources shared on Python DS you might have missed! in January 2021. Topics this month include NLP, data manipulation, machine learning, time series, Jupyter notebooks and dashboards.

NLP

  • datasets • The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools by Hugging Face.

  • Spacy Course • In the course, you’ll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches by Ines Montani & SpaCy.

  • ecco • Visualize and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2) by Jay Alammar.

Data Manipulation

Time series

  • Time series Forecasting in Python & R, Part 1 (EDA) • Time series forecasting using various forecasting methods in Python & R in one notebook by Sandeep Pawar.

  • deeptime • Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation by Moritz Hoffmann.

  • adtk • A Python toolkit for rule-based/unsupervised anomaly detection in time series by Arundo Analytics.

Machine Learning Tools

  • giotto-tda • A high-performance topological machine learning toolbox in Python by giotto.ai.

  • automlbenchmark • OpenML AutoML Benchmarking Framework by OpenML.

  • Ludwig • a toolbox that allows to train and test deep learning models without the need to write code, by Ludwig at Uber Open Source.

  • norse • Deep learning with spiking neural networks (SNNs) in PyTorch by Christian Pehle and Jens Egholm .

  • handtracking • Building a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow by Victor Dibia.

  • Python Autocomplete • Use Transformers and LSTMs to learn Python source code by LabML.

  • AutoGL • An autoML framework & toolkit for machine learning on graphs by the Media and Network Lab at Tsinghua University.

  • shapash • Shapash makes Machine Learning models transparent and understandable by everyone. Developed by MAIF (https://maif.github.io).

  • pyod • A Python Toolbox for Scalable Outlier Detection (Anomaly Detection) by Yue Zhao.

  • uncertainty-toolbox • A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization by Willie Neiswanger.

Learning Machine Learning

Jupyter

Dashboards and Web Apps

Utilities

Data Science learning resources

  • Coding for Economists Chapter 1. Intro to Mathematics with Code — In this chapter, you’ll learn about doing mathematics with code, including solving equations symbolically by Dr Arthur Turrell.

  • algorithms • Minimal examples of data structures and algorithms in Python by Keon.

  • intro-sc-python • Python Tools for Data Science, Machine Learning, and Scientific computing by Pablo Caceres.

  • Classic Computer Science Problems In Python • Source Code for the Book Classic Computer Science Problems in Python by David Kopec.

  • DS3 Practical Optim for ML • Notebooks from DS3 course on practical optimization by Alexandre Gramfort.

  • Data Science from Scratch (Chapter 8). Gradient Descent: Building gradient descent from the ground up, by Paul Apivat.

Visualisation

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