R
- Data Science for Psychologists – ”this book provides an introduction to data science that is tailored to the needs of psychologists”
- The Composer of Plots • patchwork The goal of patchwork is to make it ridiculously simple to combine separate ggplots into the same graphic.
- A curated list of awesome ggplot2 tutorials, packages etc.
- Package R in Docker Containers • containerit
- Apple Health Export Part I – Can I Blog Too
- Load Apple Health Kit export.xml file in R then analyze and visualize Steps Data using R. See the full post here
- Analyze and visualize your iPhone’s Health app data in R | Taras Kaduk
- GitHub – deepankardatta/AppleHealthAnalysis: A R package to analyse exported Apple Health XML data
- Web Scraping Javascript Content – Can I Blog Too
- An Implementation of Narcissism in R – Can I Blog Too
- Working with New Haven Area Census Data Using R – Can I Blog Too
- Guilford Demographics – Can I Blog Too
- Materials for Introduction to Data Science in the Tidyverse, a two-day workshop @ rstudio::conf 2020
- R for Excel Users
- Two-table verbs • dplyr
- R Studio Conf 2020 Slides for rstudio::conf 2020
- TEACUPS, GIRAFFES, & STATISTICS
- Chapter 15 Join two tables | STAT 545 More of the above, but part of an introductory STATS course
- Happy Git and GitHub for the useR
- Statistical Rethinking 2nd edition – 15 March 2020
- Australian football (AFL) statistics
- GitHub – jimmyday12/fitzRoy: Easily scrape AFL data
- Getting started with AFL Men’s data • fitzRoy
- Easily Scrape and Process AFL Data • fitzRoy
- ELO Ratings Example • fitzRoy
- Geelong and the curse of the bye | What You’re Doing Is Rather Desperate
- How long since your team scored 100+ points? This blog’s first foray into the fitzRoy R package | What You’re Doing Is Rather Desperate
- Web Scraping Product Data in R with rvest and purrr
- Data journalism training materials
- maps_Spain· aaumaitre/maps_Spain · GitHub
- https://timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf/
- Beautiful thematic maps with ggplot2 (only) | Timo Grossenbacher – OLD method
- GitHub – r-lib/conflicted: An alternative conflict resolution strategy for R
- Data journalism training materials
- conveRt to R: the short coursej
- R for the Rest of Us on Twitter: “Great example of the generosity of the #rstats community, sharing code to help others learn from.… “
While we’re at it, here are three blocks of #rstats code to:
1) Scrape data on number of Coronavirus cases by day
2) Do the same for cases by day for SARS in 2003
3) Plot them both and export the image
https://gist.github.com/johnburnmurdoch/bb2342ea81d3b1ce598084cfc626f390
Python
- fastpages | An easy to use blogging platform with support for Jupyter Notebooks.
- Python for Beginners – YouTube 44 videos 405,440 views Last updated on 19 Sep 2019
- Microsoft series for learning Python*
Ship better Python software, faster blog about putting python into production for data science - Python | NickZeng|曾广宇
- Switching Between Tidyverse and Pandas for Tabular Data
- Tidying Up Pandas – Towards Data Science
- Blog | Steven Morse – Army officer who teaches stats
- GitHub – markwk/python4selftrackers: Presentation on Quantified Self and Self-Tracking and How Python Can Help with Data Aggregation and Data Analysis
- GitHub – mjhea0/awesome-flask: A curated list of awesome things related to Flask
- gazpacho – gazpacho is a web scraping library. It replaces requests and BeautifulSoup for most projects.
- Max Humber – How to Win Fantasy Hockey
- GitHub – maxhumber/quote: 📚 quote is a wrapper for the Goodreads Quote API powered by [gazpacho](https://github.com/maxhumber/gazpacho)
- newspaper3k · PyPI. *“Newspaper is an amazing python library for extracting & curating articles.” – Kenneth Reitz, Author of * tweeted by requests – Newspaper3k: Article scraping & curation — newspaper 0.0.2 documentation
- Automate the Boring Stuff with Python 2nd edition of the book free online.
- Polynote | The polyglot Scala notebook
Visual Studio Code
- Python in Visual Studio Code – October 2019 Release | Python
- Announcing Support for Native Editing of Jupyter Notebooks in VS Code | Python
- Working with Jupyter Notebooks in Visual Studio Code
- Get Started Tutorial for Python in Visual Studio Code
- Python in Visual Studio Code
- Setting up SSH to Manage Connections to Multiple Hosts
- Shortcuts Archive – MacStories Requires subscription
Raspberry Pi
Compile/install Python 3.8 on Raspberry Pi | Michael Hirsch, Ph.D.
Raspberry Pi 4 Bootloader Firmware Updating / Recovery Guide
Hass.io – Home Assistant
Turns your Raspberry Pi (or another device) into the ultimate home automation hub powered by Home Assistant
Dual Fan Aluminium Heatsink Case for Raspberry Pi 4 Black Australia
Raspberry Pi 4 USB Boot Config Guide for SSD / Flash Drives
GitHub – log2ram: ramlog like for systemd (Put log into a ram folder)
Log2Ram: Extending SD Card Lifetime for Raspberry Pi
Cycling
JaYoe World Tour Homepage | Follow Matt Cycling Around The World!
Purchased “The Art of Statistics” from Amazon for $30
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever.
In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial.
How many trees are there on the planet? Do busier hospitals have higher survival rates? Why do old men have big ears? Spiegelhalter reveals the answers to these and many other questions – questions that can only be addressed using statistical science.
https://dspiegel29.github.io/ArtofStatistics/
The Art of Statistics: Code, Data, Errata and Additions | ArtofStatistics