With the tidyverse and tidymodels at their
Novel readability & lexical dispersion
Quantile regression, property sales and anything could happen eventually
Build a serverless Shiny app with WebR to compare Wikipedia page views for chart types — directly in your browser.
Simulating stock portfolio returns inspired by bowls of porridge left by three bears
R packages & functions that make doing data science a joy based on usage across projects
Visualising the dozens of overlapping sets formed by categories of cloud services
A series of events, such as the Financial Crisis and the 2016 Brexit vote, that damped down residential property sales in London
Criminal goings-on in a random forest and predictions with tree-based and glm models
Time series forecasting using cloud services spend data
Decades-old residential property bands and inference using a sample of those recently sold
Exploring parliamentary voting patterns with hierarchical clustering
Predicting the interest rate for a fixed-rate mortgage using Bayesian regression
The grammar of tables, footnotes and occupations consigned to history
Cluster analysis and the characteristics that bind London boroughs
Animated dimension reduction and East-West historical UN voting patterns
Exploring colour palettes and small multiples using cloud services spend data
Predicting uncertain species of cetacean strandings recorded by the Natural History Museum
Quantitative textual analysis, word embeddings and analysing shifting trade-talk sentiment?
Timeseries comparison and the impact of Covid-19 on the financial markets by sector
Chunks of R and slithers of Python; in the caldron boil and bake
Visualising small multiples when crime data leave you unable to see the wood for the trees
Do we see more planning applications when house sales are depressed?
A little interactive geospatial mapping and an unexpected find
Welcome to the tidyverse with data ingestion, cleaning and tidying. And some visualisations of sales data with a little jittering.