/explore
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www.civilytics.com | ||
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timogrossenbacher.ch
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| | | | | This post guides you through creating a beautiful, bivariate thematic map using solely two R packages, ggplot2 and sf. | |
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jaredknowles.com
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| | | | | [AI summary] This tutorial covers advanced R programming techniques including coding style, for loops, functions, mixed effect models, data mining with caret, and performance optimization. | |
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www.cedricscherer.com
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| | | | | Discover how to effortlessly generate custom and even complex graphics for subsets of your data by seamlessly integrating {ggplot2}'s versatile plotting functionalities with {purrr}'s powerful functional programing capabilities. This is especially helpful for data featuring many categories or step-by-step graphical storytelling | |
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www.rdatagen.net
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| | | Inspired by a free online course titled Complier Average Causal Effects (CACE) Analysis and taught by Booil Jo and Elizabeth Stuart (through Johns Hopkins University), I've decided to explore the topic a little bit. My goal here isn't to explain CACE analysis in extensive detail (you should definitely go take the course for that), but to describe the problem generally and then (of course) simulate some data. A plot of the simulated data gives a sense of what we are estimating and assuming. | ||
