Interactive R environment in the browser

Python implementation of R package gridmappr for automated gridmap layout generation. Pygridmappr automates the generation of small multiple gridmap layouts. Given a set of geographic point locations, it creates a grid with specified row and column dimensions, placing each point in a grid cell such that the distance between points in geographic space and grid space is minimized.

This implementation maintains full feature parity with the original R package and replicates the mathematical logic as faithfully as possible.

This GitHub repository contains the course materials and code for the 2024 edition of the “Statistical Rethinking” course taught by Richard McElreath, focusing on Bayesian data analysis using R and Stan.

By the end of this chapter you should gain the following knowledge and practical skills. 5.1 Introduction

ROUGH NOTES: [let me know if you spot any errors– there might be a couple!] Often, in randomized control trial where individuals are randomly assigned to treatment and control conditions, covariates are included to improve precision by reducing error and improving statistical power. However, when binary outcomes are used (e.g., patient recovers or not), there are several additional concerns that have gone unnoticed by many applied researchers.Take a simulated example where our true model data generating process is (to keep things simple, the intercept is zero and the parameters are both set to 1):

It has been a minute since I wrote anything new, but thankfully I have found time for another side project and, by extension, another blog post. This one is going to be a little different though. Like everything on this blog it is in fact a data science post. I’m going to talk about art, object-oriented programming in R, and the grid graphics system. It being the time of the Sydney Gay and Lesbian Mardi Gras – or “Gay Christmas” as it is affectionately known – I’ll do it with a rainbow palette. Nevertheless, pretty palettes notwithstanding this won’t be a particularly upbeat pride-flag-waving kind of post. There will be art, and there will be code. But there will also be little slivers of darker stories, and in a moment I’ll explain why I’ve made the decision to include them. But let’s start with the art.

Computer-generated artwork has been around for quite a while. The idea of using R for this purpose, however, is a little more recent. Designed originally as a programming language for academic statistical computing, R is now a mainstream language for data science and analytics. Can it also work as an artistic medium? Is there an overlap between our familiar data science workflows and the artistic process? Perhaps we can become better data scientists through art, and vice versa.

The focus of this document is on data science tools and techniques in R, including basic programming knowledge, visualization practices, modeling, and more, along with exercises to practice further. In addition, the demonstrations of most content in Python is available via Jupyter notebooks.