[machine learning, classification, unsupervised, resampling, model, linear, tree, vector, r]
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Web Scraping & APIsAvoid Getting Blocked While Scraping Extract Data From APIs Read & Write Data From APIs Scrape Dynamic Websites Scrape Static Websites
Workflows for Online Data CollectionAssess what Data is Available Calculate Sample Sizes for Web Scrapers Monitor and Safeguard Your Data Quality Online Data Collection and Management Safeguard Legal Compliance When Scraping
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Store DataChoose a Data Repository for Long-Term Storage Configure Environment Variables Download Data Programmatically Use AWS S3 Buckets
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Data PreparationImport Large Datasets Into R Manipulate Data Clean Data Scraped From Social Media Data Preparation and Workflow Management
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RegressionsRun a Regression Analysis Impact evaluation with Difference-in-Differences and Regression Discontinuity
Machine LearningIn-depth Introduction to Machine Learning and R
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Automate your WorkflowUse Makefiles to Re-Run Your Code Use this Checklist to Improve Your Project's Structure Make Tables and Figures Reproducible with Autofilling Values Automatically Install R Packages Used in a Project Integrating Stata in Automated Workflows Schedule Recurring Tasks (e.g., by day, hour) Share Large Temporary Files Use Software Environments to Ensure Replicability
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Version and Share Code with GithubGet Started with Using Git and GitHub The Most Important Git Commands You Should Know Contribute to Open Source Projects The GitHub Workflow Exclude Files from Versioning Make Your Code Citable with DOI Remove Sensitive or Large Files From Your Repository Working With Large Files on GitHub Archiving Repositories when They're Done Export Repositories with Github Releases
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Professionalize Project ManagementManage your Projects using the GitHub Project Board How to Prepare for (and Follow-up On) Scrum Meetings Write GitHub Issues That Get the Job Done Bring Open Science Practices to your Research Group
Publish on the WebBuild Interactive Dashboards With R Shiny


In-depth Introduction to Machine Learning and R


Get started with machine learning and R with more than 15 hours of videos at dataschool.io.

You will find 10 chapters ranging from linear regressions to unsupervised learning.


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