Potpourri: Statistics #100
We made it to one hundred. I guess this will be the final post in the series. It is good to end the series on a round number. As I have explored ways to blog less in 2023, this seems like as good a time as any to end this particular series of posts. I wrote the first post more than a decade ago as an easy way to bookmark material related to statistics that I would like to share and save for future reference. In the future, I plan to update my awesome-statistics repository on GitHub with relevant material.
- Data Vis Dispatch: September 5, September 12, September 19, September 26, October 10, October 17, October 24, October 311979. Tidy evaluation in R - Simple Examples1980. Writing better R functions: part one, part two, part three, part four1981. Hugging Face, with a warm embrace, meet R️1982. Geographic data analysis in R and Python: comparing code and outputs for vector data1983. Exploring Interaction Effects and S-Learners1984. Making Large Language Models work for you1985. R for Sign Language Linguistics1986. How adding a ‘Don’t know’ response option can affect cross-national survey results1987. Engineering Production-Grade Shiny Apps1988. What This Graph of a Dinosaur Can Teach Us about Doing Better Science1989. How to add annotations in ggplot: should you use geoms or annotations?1990. The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization1991. Generative AI exists because of the transformer1992. An introduction to Python for R Users1993. Guide to understanding the intuition behind the Dirichlet distribution1994. Overview of R Modelling Packages1995. The problem with “select-all-that-apply” survey questions, graphed1996. Confidence Intervals in Election Polling: Understanding the Uncertainty of Political Forecasting1997. Connected Scatterplots Make Me Feel Dumb1998. Causality for Machine Learning1999. Creating typewriter-styled maps in {ggplot2}2000. An overview of what's out there for reproducibility with R2001. A Dataset for Violence Trends in the Ancient Middle East between 12,000 and 400 BCE2002. Geospatial Data Science with Julia2003. Remind readers of the colors in your data visualization2004. Introduction to Econometrics with R2005. Getting Started with Large Language Models: Key Things to Know2006. How to add annotations in ggplot: should you use geoms or annotations?2007. Visualizations on Statistics and Signal Processing2008. Embeddings: What they are and why they matter2009. The 6 most popular R packages for Dataviz2010. Spice up your {gt} table with {ggplot}2011. HCS 7100: Data Visualization in R2012. What’s New in tidymodels2013. Approaches to Calculating Number Needed to Treat (NNT) with Meta-Analysis
Previous posts: #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20 #21 #22 #23 #24 #25 #26 #27 #28 #29 #30 #31 #32 #33 #34 #35 #36 #37 #38 #39 #40 #41 #42 #43 #44 #45 #46 #47 #48 #49 #50 #51 #52 #53 #54 #55 #56 #57 #58 #59 #60 #61 #62 #63 #64 #65 #66 #67 #68 #69 #70 #71 #72 #73 #74 #75 #76 #77 #78 #79 #80 #81 #82 #83 #84 #85 #86 #87 #88 #89 #90 #91 #92 #93 #94 #95 #96 #97 #98 #99