NDEXR.COM

I'm starting a not for profit site to create high quality R programming courses. I am starting with an R in Production course, where I will be teaching everything I wish I knew 7 years ago when I first started.

If you like, you can also sign up and create courses as well.

Feel free to check it out - the #rip course is a work in process.

It was requested in the past to but together some resources for new R users. Below is a collection of everything I could find to help others out.

Data Science Learning Resources

Sections

• Programming

• Machine Learning

• Leadership & Strategy

Programming

General

• The Pragmatic Programmer (Book)

• Clean Code (Book)

• Architecture Playbook (Online guide)

Python

• A Whirlwind Tour of Python (Book)

• Python Data Science Handbook

• Python Tricks (Book)

• Learning Python (Book)

• Effective Python (Book)

R

• R for Data Science (Book)

• Advanced R (Book)

• R Markdown: The Definitive Guide (Book)

• bookdown: Authoring Books and Technical Documents with R Markdown (Book)

• Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Book)

• Automated Data Collection with R (Book)

• Introduction to Data Science (Book)

Spark

• Spark: The Definitive Guide: Big Data Processing Made Simple (Book)

• Learning Spark: Lightning-Fast Big Data Analysis (Book)

• Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling (Book)

Command Line

• The Missing Semester of Your CS Education (Online course)

• Learning the bash Shell (Book)

• The Art of the Command Line (GitHub resources)

• explainshell.com (Online help)

Containers

• Docker tips & tricks or just useful commands (Online article)

• Rocker: R configurations for Docker (GitHub resources)

• Docker and Python: making them play nicely and securely for Data Science and ML (PyCon Talk)

Functional Programming

• An Introduction to the Basic Principles of Functional Programming (Online article)

• R for Data Science, Ch. 21 (Book)

• Advanced R, Ch. 9 (Book)

• Jenny Bryan's purrr tutorials (Online tutorial)

• Foundations of Functional Programming with purrr (DataCamp)

• Intermediate Functional Programming with purrr (DataCamp)

Version Control

• Excuse me, do you have a moment to talk about version control? (Paper)

• Happy Git and GitHub for the useR (Book)

• Learn Git (Online tutorial)

• Git Commit Message Style Guide (Online guide)

Code Packaging

• Python Packaging Authority

• Python Packaging User Guide

• R Packages

Style Guide, Readability, Best Practices

• The Art of Readable Code (Book)

• The Tidyverse Style Guide (Online book)

• PEP 8 -- Style Guide for Python Code (Online guide)

• Guidelines for code reviews (README)

• Code Review Best Practices (Blog post)

Testing

• Testing R Code (Book)

• Python Testing with pytest (Book)

• Multiply your Testing Effectiveness with Parameterized Testing (PyCon Talk)

• Test-Driven Development (Book)

Machine Learning

General

• Introduction to Statistical Learning (Book)

• Applied Predictive Modeling (Book)

• Elements of Statistical Learning (Book)

• Computer Age of Statistical Inference (Book)

• Statistical Modeling: The Two Cultures (Paper)

• Deep Learning (Book)

• Hands-On Machine Learning with Scikit-Learn & TensorFlow (Book | GitHub)

• Hands-On Machine Learning with R (Book)

• Google's Machine Learning Crash Course (MOOC)

Unsupervised Modeling

• ISLR: Ch. 10.3 Clustering Methods (Book chapter)

• A K-Means Clustering Algorithm (Paper)

• Generalized Low Rank Models (Paper)

• Deep Learning Ch. 15 Autoencoders (Book chapter)

• Hands-On Mach. Learning with Scikit-Learn Ch. 15 Autoencoders (Book chapter | GitHub resource)

• Sparse autoencoder (Andrew Ng CS294A lecture notes)

A/B Testing

• Lessons from Running Thoursands of A/B Tests (Online presentation with many references)

• Online Controlled Experiments at Large Scale (Paper)

• Peaking at A/B Tests (Paper)

• Multi-armed Bandit (Online tutorial)

• A Modern Bayesian Look at the Multi-armed Bandit (Paper behind above online tutorial)

• Predicting Search Satisfaction Metrics with Interleaved Comparisons (Paper)

• Evaluating Retrieval Performance using Clickthrough Data (Paper)

Multivariate Adaptive Regression Splines

• Multivariate Adaptive Regression Splines (Friedman's original paper)

• APM: Ch. 7.2 Multivariate Adaptive Regression Splines (Book chapter)

• ESL: Ch. 9.4 Multivariate Adaptive Regression Splines (Book chapter)

• Notes on the earth package (Paper)

K-Nearest Neighbor

• k-Nearest neighbour classifiers (Paper)

• APM: Ch. 7.4 & 13.5 K-Nearest Neighbors (Book chapter)

• ESL: Ch. 13.3 k-Nearest-Neighbor Classifiers (Book chapter)

Random Forests

• An Introduction to Recursive Partitioning Using the RPART Routines (Paper)

• Random Forests - Leo Breiman's original research paper (Paper)

Gradient Boosting Machines

• How to explain gradient boosting (Online tutorial)

• Trevor Hastie - Gradient Boosting & Random Forests at H2O World 2014 (YouTube)

• Trevor Hastie - Data Science of GBM (2013) (slides)

• Mark Landry - Gradient Boosting Method and Random Forest at H2O World 2015 (YouTube)

• Peter Prettenhofer - Gradient Boosted Regression Trees in scikit-learn at PyData London 2014 (YouTube)

• Alexey Natekin1 and Alois Knoll - Gradient boosting machines, a tutorial (Paper)

Deep Learning

• Deep Learning with R (Book)

• Deep Learning with Python (Book)

• Deep Learning Specialization (MOOC)

• keras.rstudio.com (Online articles & tutorials)

• blogs.rstudio.com/tensorflow (Online articles & tutorials)

• Illustrated Guide to Recurrent Neural Networks (Blog)

• Illustrated Guide on Vanishing Gradients (Blog)

• Illustrated Guide to LSTMs and GRUs (Blog)

• Understanding LSTMs (Blog)

• Rohan & Lenny: Recurrent Neural Networks & LSTMs (Blog)

• The Unreasonable Effectiveness of Recurrent Neural Networks (Blog)

• Revisiting Small Batch Training for Deep Neural Networks (Paper)

• On Loss Functions for Deep Neural Networks in Classification (Paper)

• Practical Recommendations for Gradient-Based Training of Deep Architectures (Paper)

• Efficient BackProp (Paper)

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Paper)

• Cyclical Learning Rates for Training Neural Networks (Paper)

• A Disciplined Approach to Neural Network Hyperparameters: Part 1 – Learning Rate, Batch Size, Momentum, and Weight Decay (Paper)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (Paper)

Ensembles / Model Stacking / Super Learners

• Ensemble Methods in Machine Learning (Paper)

• Stacked Regressions (Paper)

• Super Learner (Paper)

Natural Language Processing / Text Mining

• Text Mining with R (Book)

• Probabilistic Topic Models (Paper)

• The Illustrated Word2vec (Online tutorial)

• Sebastian Ruder's series on Word Embeddings (Online articles & tutorials)

• Neural Models for Information Retrieval (Paper)

• Why do we use word embeddings in NLP? (Blog)

Tuning

• Hyperparameters and Tuning Strategies for Random Forest (Paper)

• Tunability: Importance of Hyperparameters of Machine Learning Algorithms (Paper)

• Machine Learning Benchmarks and Random Forest Regression (Paper)

• Random Search for Hyperparameter Optimization (Paper)

Feature Engineering

• Feature Engineering for Machine Learning (Book)

• Feature Engineering and Selection: A Practical Approach for Predictive Models (Book)

Feature Selection

• Feature Selection with the Boruta Package (Paper)

• APM: Ch. 19 An Introduction to Feature Selection (Book chapter)

Machine Learning Interpretability

• Scott Lundberg's presentation on SHAP

• H2O.ai Machine Learning Interpretability Resources (GitHub resources)

• Patrick Hall's Awesome Machine Learning Interpretability Resources (GitHub resources)

• Interpretable Machine Learning (Book)

• Visualizing the Feature Importance for Black Box Models (Paper)

• A Simple and Effective Model-Based Variable Importance Measure (Paper)

• Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation (Paper)

• pdp: An R Package for Constructing Partial Dependence Plots (Paper)

• "Why Should I Trust You?": Explaining the Predictions of Any Classifier (Paper)

• A Unified Approach to Interpreting Model Predictions (Paper)

• Consistent Individualized Feature Attribution for Tree Ensembles (Paper)

• On the Art and Science of Machine Learning Explanations (Paper)

• Explanation in artificial intelligence: Insights from the social sciences (Paper)

• Please Stop Permuting Features: An Explanation and Alternatives (Paper)

• A Stratification Approach to Partial Dependence for Codependent Variables (Paper)

• Explaining Machine Learning Classifiers through Diverse Counterfactual Examples (Paper)

Auto ML

• A Review of Automatic Selection Methods for Machine Learning Algorithms and Hyperparameter Values (Paper)

• Learning Multiple Defaults for Machine Learning Algorithms (Paper)

Benchmarking

• The Design and Analysis of Benchmark Experiments (Paper)

• Szilard Pafka's ML Benchmarking Research (GitHub resources)

• Data-driven advice for applying machine learning to bioinformatics problems (Paper)

Resampling Procedures

• Futility Analysis in the Cross-Validation of Machine Learning Models (Paper)

• Estimating Classification Error Rate: Repeated Cross-validation, Repeated Hold-out, and Bootstrap (Paper)

Productionalization

• 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com (Paper)

• Hidden Technical Debt in Machine Learning Systems (Paper)

• Deep Learning in Production (Github resources)

Leadership & Strategy

• Platform Revolution (Book)

• No Rules Rules: Netflix and the Culture of Reinvention (Book)

• The Influential Product Manager: How to Lead and Launch Successful Technology Products (Book)

• Mastering Product Management: A Step-by-Step Guide (Book)

There is a certain user who will, for now, remain unnamed who regularly posts links to their low quality R tutorials in order to acquire many inbound links, this boosting their ranking in Google search results for anything R related.

If the content were high quality I would not take that much umbrage with the behaviour, but they seem less adept at using R than most off my undergraduate students after their first R course.

I feel this person is actively harming the R community by attempting to forcefully impose themselves as a prominent R user, particularly give the exceptionally low quality of their "tutorials."

We should not continue to allow this person or any others who may follow in the future, to take advantage of this subreddit for their personal gain.

Obviously, a person working in finance would have different needs than someone in biostatistics. But it'd be cool to know what packages you use with a brief description of what you use it for.

Hello, I am a long time R user, about 15 years. I am a base R user, although I admit, I do use ggplot2 and shiny. I teach doctoral R programming courses and hold frequent workshops.

A trend I have noticed is that more and more of my students entering my course are unable to follow my syntax. They increasingly ask me where are the pipes?!

I must admit I really dislike the pipe. It’s only purpose is to change the reading and writing style of the human interaction with syntax. This is leading to a sharp fracture in the community.

I have students who cant follow programming from the inside out, they want to read left to right like it’s some form of English.

I guess… I respect Hadly and the tidy mission to make the language more accessible. That said, I think it’s having the opposite effect. A fractured community with sub languages so different that both sides can’t understand one another.

I love R, I always start my semester by jokingly telling students R is more of a lifestyle than a language.

I just wanted to hear some thoughts. When I repeat this to colleagues I get mixed responses, almost always culminating in “but really who cares”…. I care!

I have been working as a Data Scientist for about 9 years and have an M.S. in stats. Currently a Lead Data Scientist. I am good at programming in both R and python, but strongly prefer R over python.

Broadly, has anyone made a living with R in Data Science? If so, how? What industry are you in? Is your official title Data Scientist?

R seems to be making ground on SAS in clinical trials. Besides working in this industry, I don't see a path forward to making a living with R.

Edit: I have had only one job that used R and we transitioned to python going forward. I ended up learning python out of necessity, not desire.

Good day, I spent my summer break learning R and making my own package, and I successfully created one. Of course the next reasonable thing to do was to upload it to CRAN, so I did. And today, after several submissions, I finally got an email stating that they will manually check it.

So, I'm in 8th Grade and I was wondering if there was a possibility of me becoming the youngest one to do this. Thanks!

I've just published the 700th (!) tutorial on my R programming and statistics learning platform https://statisticsglobe.com/. Please check it out in case you want to learn R for free. :)

I had to use R at my new job and was frustrated, as I didn't find it intuitive at all (I liked pandas way more). However, that was before I started using the tidyverse library.

If there are other struggling R newbies out there, here is an article about what tidyverse is all about and how it is linked to SQL tidyverse intro 🙂

I've just finished a programming challenge where I created 100 R tutorials on my homepage https://statisticsglobe.com/. It took me exactly 15 days, 80 hours, 23 minutes, and 06 seconds to finish, so I hope you like the results! :D Please find a list of all tutorials below:

https://statisticsglobe.com/correlation-matrix-in-r

https://statisticsglobe.com/display-large-numbers-separated-with-comma-in-r

https://statisticsglobe.com/zoom-into-ggplot2-plot-without-removing-data-in-r

https://statisticsglobe.com/read-files-directory-apply-function-to-each-data-frame-in-r

https://statisticsglobe.com/subset-of-data-frame-rows-containing-na-in-r

https://statisticsglobe.com/remove-all-special-characters-from-string-in-r

https://statisticsglobe.com/remove-first-row-of-data-frame-in-r

https://statisticsglobe.com/convert-character-string-to-date-object-in-r

https://statisticsglobe.com/fit-smooth-curve-to-plot-of-data-in-r

https://statisticsglobe.com/convert-unix-timestamp-to-date-object-in-r

https://statisticsglobe.com/select-rows-with-partial-string-match-in-r

https://statisticsglobe.com/unique-combinations-of-all-elements-from-two-vectors-in-r

https://statisticsglobe.com/export-nicely-formatted-data-frame-in-r

https://statisticsglobe.com/disable-messages-when-loading-a-package-in-r

https://statisticsglobe.com/randomly-reorder-data-frame-by-row-and-column-in-r

https://statisticsglobe.com/ggplot2-title-subtitle-with-different-size-and-color-in-r

https://statisticsglobe.com/suppress-output-of-command-in-r

https://statisticsglobe.com/paste-multiple-columns-together-in-r

https://statisticsglobe.com/compute-eulers-number-in-r

https://statisticsglobe.com/change-spacing-of-axis-tick-marks-in-base-r-plot

https://statisticsglobe.com/round-up-to-nearest-10-or-100-in-r

https://statisticsglobe.com/find-position-of-character-in-string-in-r

https://statisticsglobe.com/print-time-with-milliseconds-in-r

https://statisticsglobe.com/find-values-contained-in-first-vector-but-not-another-in-r

https://statisticsglobe.com/overlay-density-plots-in-base-r

https://statisticsglobe.com/repeat-rows-of-data-frame-n-times-in-r

https://statisticsglobe.com/quit-restart-clean-r-session-from-within-r

https://statisticsglobe.com/skip-loop-to-next-iteration-in-r

https://statisticsglobe.com/left-align-text-in-ggplot2-plot-in-r

https://statisticsglobe.com/convert-vector-to-list-in-r

https://statisticsglobe.com/report-missing-values-in-data-frame-in-r

https://statisticsglobe.com/set-ggplot2-axis-limit-only-on-one-side-in-r

https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r

https://statisticsglobe.com/remove-multiple-data-objects-using-rm-function-in-r

https://statisticsglobe.com/convert-characters-in-data-frame-to-uppercase-in-r

https://statisticsglobe.com/add-count-labels-on-top-of-ggplot2-barchart-in-r

https://statisticsglobe.com/extract-numbers-from-character-string-vector-in-r

https://statisticsglobe.com/apply-function-to-data-table-in-each-specified-column-in-r

https://statisticsglobe.com/specify-column-names-for-x-y-when-joining-with-dplyr-package-in-r

https://statisticsglobe.com/convert-data-frame-to-data-table-in-r

https://statisticsglobe.com/remove-multiple-columns-from-data-table-in-r

https://statisticsglobe.com/get-column-index-in-data-frame-by-variable-name-in-r

https://statisticsglobe.com/fix-aspect-ratio-in-ggplot2-plot-in-r

https://statisticsglobe.com/plot-all-columns-of-data-frame-in-r

https://statisticsglobe.com/reset-par-to-default-values-in-r

https://statisticsglobe.com/control-size-of-ggplot2-legend-items-in-r

https://statisticsglobe.com/r-save-all-console-input-output-to-file

https://statisticsglobe.com/r-split-data-frame-into-list-of-data-frames-based-on-id-column

https://statisticsglobe.com/extract-data-table-column-as-vector-using-index-position-in-r

https://statisticsglobe.com/set-area-margins-of-ggplot2-plot-in-r

https://statisticsglobe.com/change-y-axis-to-percentages-in-ggplot2-barplot-in-r

https://statisticsglobe.com/count-number-of-words-in-character-string-in-r

https://statisticsglobe.com/insert-new-column-between-two-data-frame-variables-in-r

https://statisticsglobe.com/sort-variables-of-data-frame-by-column-names-in-r

https://statisticsglobe.com/remove-empty-rows-of-data-frame-in-r

https://statisticsglobe.com/create-comma-separated-vector-in-r

https://statisticsglobe.com/randomize-vector-in-r

https://statisticsglobe.com/split-data-frame-in-r

https://statisticsglobe.com/indexing-up-to-the-end-of-vector-data-frame-in-r

https://statisticsglobe.com/replace-multiple-letters-with-accents-in-r

https://statisticsglobe.com/select-first-row-of-each-group-in-data-frame-in-r

https://statisticsglobe.com/select-first-element-of-nested-list-in-r

https://statisticsglobe.com/arrange-list-of-ggplot2-plots-in-r

https://statisticsglobe.com/multiply-rows-of-matrix-by-vector-in-r

https://statisticsglobe.com/replace-values-in-data-frame-conditionally-in-r

https://statisticsglobe.com/create-data-frame-where-a-column-is-a-list-in-r

https://statisticsglobe.com/sum-across-multiple-rows-columns-using-dplyr-package-in-r

https://statisticsglobe.com/extract-just-number-from-named-numeric-vector-in-r

https://statisticsglobe.com/ggplot2-plot-in-script-is-not-displayed-in-r

https://statisticsglobe.com/change-display-order-of-ggplot2-plot-legend-in-r

https://statisticsglobe.com/save-plot-in-data-object-in-base-r

https://statisticsglobe.com/unique-rows-of-data-frame-based-on-selected-columns-in-r

https://statisticsglobe.com/draw-multiple-lattice-plots-in-one-window-in-r

https://statisticsglobe.com/convert-named-vector-to-data-frame-in-r

https://statisticsglobe.com/use-apply-function-only-for-specific-data-frame-columns-in-r

https://statisticsglobe.com/check-existence-of-local-file-in-r

https://statisticsglobe.com/remove-gridlines-in-ggplot2-plot-in-r

https://statisticsglobe.com/coerce-list-object-to-type-double-in-r

https://statisticsglobe.com/create-sequence-of-repeated-values-in-r

https://statisticsglobe.com/select-multiple-elements-from-list-in-r

https://statisticsglobe.com/undefined-columns-selected-when-subsetting-data-frame-in-r

https://statisticsglobe.com/dplyr-mutate-function-with-logical-condition-in-r

https://statisticsglobe.com/change-colors-in-ggplot2-line-plot-in-r

https://statisticsglobe.com/keep-unused-factor-levels-in-ggplot2-barplot-in-r

https://statisticsglobe.com/number-of-months-between-two-dates-in-r

https://statisticsglobe.com/drop-multiple-columns-from-data-frame-using-dplyr-package-in-r

https://statisticsglobe.com/combine-two-ggplot2-plots-from-different-data-frames-in-r

https://statisticsglobe.com/determine-path-of-current-script-in-r

https://statisticsglobe.com/extract-regression-coefficients-of-linear-model-in-r

https://statisticsglobe.com/draw-time-series-plot-with-events-using-ggplot2-in-r

https://statisticsglobe.com/count-number-of-rows-by-group-using-dplyr-package-in-r

https://statisticsglobe.com/reorder-facets-in-ggplot2-plot-in-r

https://statisticsglobe.com/combine-multiple-ggplot2-legends-in-r

https://statisticsglobe.com/display-only-integer-values-on-ggplot2-axis-in-r

https://statisticsglobe.com/get-second-sub-element-of-every-list-element-in-r

https://statisticsglobe.com/merge-unequal-data-frames-replace-na-with-0-in-r

https://statisticsglobe.com/annotate-text-outside-of-ggplot2-plot-in-r

https://statisticsglobe.com/control-point-border-thickness-of-ggplot2-plot-in-r

https://statisticsglobe.com/two-functions-with-same-name-in-different-packages-in-r

https://statisticsglobe.com/convert-data-frame-with-date-column-to-time-series-object-in-r