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deepr

Deep R Programming (Open-Access Textbook)

https://github.com/gagolews/deepr

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 4 DOI reference(s) in README
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    Scientific vocabulary similarity
    Low similarity (17.8%) to scientific vocabulary

Keywords

cran data-frame data-science evaluation functional-programming graphics matrix-calculations numerical-methods numerical-simulations r scientific-computing scientific-visualization statistics statistics-for-data-science statistics-for-engineering tensor vector vectorization
Last synced: 4 months ago · JSON representation ·

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Deep R Programming (Open-Access Textbook)

Basic Info
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  • Stars: 109
  • Watchers: 6
  • Forks: 4
  • Open Issues: 1
  • Releases: 6
Topics
cran data-frame data-science evaluation functional-programming graphics matrix-calculations numerical-methods numerical-simulations r scientific-computing scientific-visualization statistics statistics-for-data-science statistics-for-engineering tensor vector vectorization
Created almost 5 years ago · Last pushed 11 months ago
Metadata Files
Readme License Code of conduct Citation

README.md

Deep R Programming

Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning.

For many students around the world, educational resources are hardly affordable. Therefore, I have has decided that this book should remain an independent, non-profit, open-access project. You can read it at:

You can also order a paper copy.

Whilst, for some people, the presence of a "designer tag" from a major publisher might still be a proxy for quality, it is my hope that this publication will prove useful to those who seek knowledge for knowledge's sake.

Please spread the news about this project.

Consider citing this book as: Gagolewski M. (2025), Deep R Programming, Melbourne, DOI: 10.5281/zenodo.7490464, ISBN: 978-0-6455719-2-9, URL: https://deepr.gagolewski.com/.

Any remarks and bug fixes are appreciated. Please submit them via this repository's Issues tracker. Thank you.

About the Author

Marek Gagolewski is currently an Associate Professor in Data Science at the Faculty of Mathematics and Information Science, Warsaw University of Technology.

His research interests are related to data science, in particular: modelling complex phenomena, developing usable, general-purpose algorithms, studying their analytical properties, and finding out how people use, misuse, understand, and misunderstand methods of data analysis in scientific, business, and decision-making settings.

He is an author of ~100 publications, including journal papers in outlets such as Proceedings of the National Academy of Sciences (PNAS), Journal of Statistical Software, The R Journal, Journal of Classification, Information Fusion, International Journal of Forecasting, Statistical Modelling, Physica A: Statistical Mechanics and its Applications, Information Sciences, Knowledge-Based Systems, IEEE Transactions on Fuzzy Systems, and Journal of Informetrics.

In his "spare" time, he writes books for his students (check out Minimalist Data Wrangling with Python) and develops open-source software for data analysis, such as stringi (one of the most often downloaded R packages) and genieclust (a fast and robust hierarchical clustering algorithm in both Python and R).

Copyright (C) 2022–2025, Marek Gagolewski. Some rights reserved.

This material is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

Owner

  • Name: Marek Gagolewski
  • Login: gagolews
  • Kind: user
  • Location: Melbourne, VIC, Australia
  • Company: Deakin University

Free universities!

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "Please cite this book as follows."
title: "Deep R Programming"
doi: 10.5281/zenodo.7490464
url: "https://deepr.gagolewski.com/"
repository-code: "https://github.com/gagolews/deepr"
abstract: >
    Deep R Programming is a comprehensive and in-depth introductory course on
    one of the most popular languages for data science. It equips ambitious
    students, professionals, and researchers with the knowledge and skills to
    become independent users of this potent environment so that they can tackle
    any problem related to data wrangling and analytics, numerical computing,
    statistics, and machine learning. This textbook is a non-profit project.
    Its online and PDF versions are freely available at
    https://deepr.gagolewski.com/.
keywords:
  - R
  - S
  - programming
  - data wrangling
  - statistics
  - data science
  - machine learning
  - data frames
  - matrices
  - vectors
  - tensors
  - data cleansing
  - text processing
  - graphics
authors:
  - family-names: Gagolewski
    given-names: Marek
    orcid: "https://orcid.org/0000-0003-0637-6028"
    website: "https://www.gagolewski.com/"
preferred-citation:
    type: book
    year: 2025
    title: "Deep R Programming"
    url: "https://deepr.gagolewski.com/"
    doi: 10.5281/zenodo.7490464
    isbn: "978-0-6455719-2-9"
    publisher:
      city: Melbourne
    authors:
      - family-names: Gagolewski
        given-names: Marek
        orcid: "https://orcid.org/0000-0003-0637-6028"
        website: "https://www.gagolewski.com/"

GitHub Events

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  • Issues event: 2
  • Watch event: 8
  • Push event: 5
  • Fork event: 1
Last Year
  • Issues event: 2
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  • Avg Commits per committer: 133.0
  • Development Distribution Score (DDS): 0.0
Past Year
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  • Committers: 1
  • Avg Commits per committer: 16.0
  • Development Distribution Score (DDS): 0.0
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gagolews m****i@g****m 133

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  • Average comments per issue: 1.32
  • Average comments per pull request: 0
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Past Year
  • Issues: 11
  • Pull requests: 0
  • Average time to close issues: 1 day
  • Average time to close pull requests: N/A
  • Issue authors: 4
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