Sources and further resources

This resource list was produced with the help of the following sources:

• DataScienceR – A curated list of R tutorials for Data Science, NLP and Machine Learning.
• RStartHere – A guide to some of the most useful R packages, organized by workflow.
• Paul van der Laken Resources –R resources, including free courses, books, tutorials, & cheat sheets.
• Paul van der Laken Tips – Tips and tricks for R, RStudio, Markdown, tidy data manipulation and visualization.
• Awesome R – A curated list of awesome R packages and tools.
• Awesome R Shiny – A curated list of resources for R Shiny.
• Awesome Machine Learning – A curated list of awesome Machine Learning frameworks, libraries and software.
• Awesome R Markdown – An awesome rmarkdown related package collection.
• Awesome Network Analysis – An awesome list of resources to construct, analyze and visualize network data.
• Awesome Awesomeness – A curated list of amazingly awesome awesomeness.
• lists – The definitive list of lists (of lists) curated on GitHub.
• Data Science Guide – Community-sourced data science repo which provides anyone interested in learning data science with a wealth of open source, industry-best learning materials and learning tracks.
• R Programming Blogs List – Top 40 R Programming Blogs and Websites To Follow in 2019.
• Bookdown Data Science – List of data science books written with bookdown.
• freeCodeCamp – Learn to code for free using HTML5, CSS3, JavaScript, Databases, Git & GitHub, Node.js, React.js, D3.js.