Potpourri: Statistics #92

Feb 2, 2023 Updated on Oct 21, 2026

  1. Data Vis Dispatch: January 3, January 10, January 17, January 24, January 31

  2. The list of 2022 visualization lists

  3. From Documents to Data: A Framework for Total Corpus Quality

  4. Efficient Python for Data Scientists

  5. Deep R Programming

  6. Minimalist Data Wrangling with Python

  7. Everything You Wanted to Know about the Kernel Trick (But Were Too Afraid to Ask)

  8. fastStat: All of REAL Statistics

  9. Big, Open and Linked Data: Effects and Value for the Economy

  10. Introducing geom_terrorbar()

  11. A public repository of personal ML/DS blogs

  12. simple-data-analysis.js: Easy-to-use JavaScript library for most common data analysis tasks

  13. Quantile Regression as an useful Alternative for Ordinary Linear Regression

  14. Winners of the 2022 Table Contest

  15. Combining plots in ggplot2

  16. Prediction Modeling with the Cox model - all about the baseline hazard

  17. Multiple Regression using caret

  18. MLOps: The Whole Game

  19. An intro to dplyr::across

  20. Probabilistic Machine Learning: Advanced Topics

  21. Annotated History of Modern AI and Deep Learning

  22. A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, NASA CMR, and STAC Index

  23. Introduction to Graph Machine Learning

  24. A Succinct Summary of Reinforcement Learning

  25. Combining R and Python with {reticulate} and Quarto

  26. A Guide To Getting Data Visualization Right

  27. Level Up Your Python

  28. Exploratory spatial data analysis with Python

  29. 6 tips for creating charts for social media

  30. balance: a python package for balancing biased data samples

  31. A Guide to Analyzing Large N, Large T Panel Data

  32. On Moving from Statistics to Machine Learning, the Final Stage of Grief

  33. SQL and noSQL approaches to creating & querying databases (using R)

  34. The Illustrated Machine Learning website

  35. An overview of parsing algorithms

  36. Introduction to data structures and algorithms

  37. Understanding Deep Learning

  38. AI4PH: Text Analyses with R

  39. Easily re-using self-written functions: the power of gist + code snippet duo

  40. The Bitter Lesson

  41. Logistic regression is not fucked

  42. These Are Not the Effects You Are Looking For: The Fallacy of Mutual Adjustment and How to Avoid It

  43. Discovering the best Chess960 variation

  44. ggplot tricks

  45. Alone R package: Datasets from the survival TV series

  46. Geospatial distributed processing with furrr

  47. Visualising the 2022 Australian federal election with geom_sugarbag

  48. Time series resources

  49. Notes on Hainmueller, Mummolo & Xu: How Much Should we Trust Estimates from Multiplicative Interaction Models?

  50. Equivalence testing for linear regression

  51. Simpson's Paradox and Existential Terror

  52. Recreating a chart from history: a beginner’s look in the data vis world

  53. Improving the responsiveness of Shiny applications

  54. Telling Stories with Data: With applications in R

  55. ggpathway: A tutorial for pathway visualization using tidyverse, igraph, and ggraph

  56. Pandas Illustrated: The Definitive Visual Guide to Pandas

  57. Much Ado About Sampling

  58. My journey from R to Julia

  59. Never Test for Normality

  60. Foundations of Data Science

  61. Course Materials for Advanced Data Analytics in Economics

  62. 6 easy ways to map population density in R

  63. Mathematical Logic through Python

https://erikgahner.github.io/posts/feed.xml