NOTE: the notebook named "Anaconda and R Base experiments" contains instructions for creating an Anaconda environment using R 4.1 with Jupyter Notebooks. As of March 2023, Anaconda makes working with R 4.0+ very difficult.
Anaconda Navigator 2.3.1 (the GUI for the Anaconda distribution) had an issue with the default channel not having a version of R newer than 3.6.1. Updating Anaconda Navigator to 2.4.0 and deleting the default channel and adding a new channel for conda-forge fixed the problem and allowed Anaconda to create a new environment using newer versions of both Python and R (R 4.1.3).
Display conda-forge packages in Anaconda Navigator
https://conda-forge.org/docs/user/introduction.html#display-conda-forge-packages-in-anaconda-navigator
======================
This repository will start with a notebook titled "R Programming for SPSS Users" and will include basic statistical analysis tasks written in R. Other important resources will include the links to tutorials and code repositories in this README.md file.
Copilot for code generation
NOTE: ChatGPT is useful for generating code as well
GitHub Copilot: Fly With Python at the Speed of Thought
https://realpython.com/github-copilot-python/
NOTE: officially, GitHub Copilot did not support R as of late Feb 2023
How to set up VSCode for R
https://www.infoworld.com/video/110179/how-to-set-up-vscode-for-r
Introduction to Statistical Learning with Applications in Python, 1st Ed (2023), G. James et al
https://www.statlearning.com/
https://www.statlearning.com/resources-python
https://www.statlearning.com/online-course
Python Data Science Handbook (2016) J. VanderPlas
https://jakevdp.github.io/PythonDataScienceHandbook/
Chris Albon - Python Basics & Data Wrangling
https://chrisalbon.com/#python
Web site and YouTube videos by Sentdex (very good)
https://pythonprogramming.net/
https://www.youtube.com/@sentdex
Python for Data Analysis 2nd, Ed (2017) W. McKinney
https://github.com/wesm/pydata-book
Pandas 1.x Cookbook, 2nd Ed (2020) M. Harrison, T. Petrou
https://github.com/PacktPublishing/Pandas-Cookbook-Second-Edition
** NOTE: There is a third edition of this book along with a code repository **
Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow, 3rd Ed (2023), A. Géron
https://github.com/ageron/handson-ml3
Python 3 code snippets for data science (by Chris Albon)
https://github.com/chrisalbon/code_py
R for SAS and SPSS Users, 2nd Ed (2011), R. Muenchen
https://r4stats.com/books/free-version/
https://r4stats.com/books/downloads/
R Cookbook, 2nd Ed (2019), Long & Teetor (OReilly)
https://rc2e.com/
R Graphics Cookbook (2019), 2nd Ed, Winston Chang
https://r-graphics.org/
install.packages("gcookbook")
An Introduction to Statistical Learning with Applications in R, 2nd Ed (2021), G. James et al
https://www.statlearning.com/
https://www.statlearning.com/resources-second-edition
https://www.statlearning.com/online-course
Applied Predictive Modeling (2013), M. Kuhn & K. Johnson
Uses the Caret and AppliedPredictiveModeling packages
https://appliedpredictivemodeling.com/
https://github.com/cran/AppliedPredictiveModeling
NOTE: the R code is available from github, but the book does not appear to be freely available for download
R for Data Science (2017) - Hadley Wickham (O’Reilly)
https://r4ds.had.co.nz/
R for Data Science, 2nd Ed (2023) - Hadley Wickham (O’Reilly)
https://r4ds.hadley.nz/
Collection of R Scripts for Reuse (by Chris Albon)
https://github.com/chrisalbon/code_r
Statistics for Data Scientists resources
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Ed (2020)
https://github.com/gedeck/practical-statistics-for-data-scientists