Potpourri: Statistics #101

Nov 1, 2024

A year ago I decided to make a "final" post with links to interesting statistics material. I use quotation marks because, lo and behold, here is another post. Who cares? I do not. We are back.

As always, you can access previous (and future) links on GitHub (there is a CSV-file with all links in the repository). Enjoy.

  1. Five Steps to Improve Your Chart Quickly
  2. Geocode address text strings using tidygeocoder
  3. Adding context to maps made with ggplot2
  4. Useful functions for dealing with object names
  5. Data Visualisation: A Comprehensive Guide to Unlocking Your Data’s Potential
  6. How to get started with data visualization
  7. Machine Learning for Beginners - A Curriculum
  8. How to Get Good with R?
  9. Lesser-known reasons to prefer apply() over for loops
  10. The Ultimate Guide to Get Started With ggplot2
  11. Getting started with theme()
  12. Why does correlation not equal causation?
  13. Large Language Model Course
  14. Write R Code Faster with These Shortcuts
  15. How to make your own #RStats Wrapped!
  16. R date formatting
  17. Quick Stata Tips
  18. How to create separate bibliographies in a Quarto document
  19. Why is View() capitalized, anyway?
  20. 5 Example Charts with ggplot2
  21. Computational Methods for Economists using Python
  22. Creating Christmas cards with R
  23. Many Models in R: A Tutorial
  24. The case for a pipe assignment operator in R
  25. List of data visualization books
  26. Python Rgonomics
  27. 5 Powerful ggplot2 Extensions
  28. .I in data.table
  29. non-equi joins in data.table
  30. Four ways to streamline your R workflows
  31. Here’s why you should (almost) never use a pie chart for your data
  32. R data.table Joins
  33. Exploring Data Science with R and the Tidyverse: A Concise Introduction
  34. Redacting identifying information with computational methods in large text data
  35. DIY API with Make and {plumber}
  36. Awesome official statistics software
  37. 6 Common ggplot2 Mistakes
  38. Advanced tips and tricks with data.table
  39. Overview of clustering methods in R
  40. One billion row challenge using base R
  41. Six not-so-basic base R functions
  42. Let's talk about joins
  43. Reading and Writing Data with {arrow}
  44. Modern Data Visualization with R
  45. Feature Engineering A-Z
  46. Are connected scatterplots so bad?
  47. Correlation heat maps with {ggplot2}
  48. You ‘tidyr::complete()’ me
  49. Piping data.tables
  50. VS Code for R on macOS
  51. new programming with data.table
  52. more .I in data.table
  53. Splatter: How to make a mess with ggplot2 and ambient
  54. Psychometrics in Exercises using R and RStudio
  55. Modeling Short Time Series with Prior Knowledge
  56. Everything is a Linear Model
  57. Why pandas feels clunky when coming from R
  58. How to create diverging bar plots
  59. Balanced sampling in R, Julia, and R + Julia
  60. What to consider when creating small multiple line charts
  61. Advanced Data Science Statistics and Prediction Algorithms Through Case Studies
  62. A foundation in Julia
  63. Working with data in Julia
  64. Plotting data in Julia
  65. Spring clean your R packages
  66. ggplot2 101
  67. Drawing waterlines with ggplot2 in R
  68. Romeo and Julia, where Romeo is Basic Statistics
  69. Using axis lines for good or evil
  70. Creating upset charts with ggplot2
  71. What Does a Statistical Method Assume?
  72. Reproducibility as part of code quality control
  73. 30 Python Language Features and Tricks You May Not Know About
  74. An Introduction to R
  75. Reading large spatial data
  76. Visualizing {dplyr}’s mutate(), summarize(), group_by(), and ungroup() with animations
  77. Three Ways to Include Images in Your ggplots
  78. A Rant
  79. How long until building complaints are dispositioned? A survival analysis case study
  80. The Truth About Tidy Wrappers
  81. On Indentation in R
  82. Kicking tyres
  83. Create engaging tables with R or Python using {gt}
  84. Elicit Machine Learning Reading List
  85. Correlation vs. Regression: A Key Difference That Many Analysts Miss
  86. Sketchy waffle charts in R
  87. CS388: Natural Language Processing
  88. Calculus with Julia
  89. Find Out How many Times Faster your Code is
  90. Easy data cleaning with the janitor package
  91. Why you shouldn’t use boxplots
  92. Statistical Power from Pilot Data: Simulations to Illustrate
  93. Creating R tutorial worksheets (with and without solutions) using Quarto
  94. Shiny apps for demystifying statistical models and methods
  95. Causal Inference in R
  96. I’ve Stopped Using Box Plots. Should You?
  97. Data Wrangling Recipes in R
  98. Your Journey to Fluent Python
  99. A timeline of R's first 30 years
  100. Interactive Map Filter in Shiny
  101. What packages belong together? Learning from R code samples
  102. Ten simple rules for teaching an introduction to R
  103. Winners of the 2024 Table Contest
  104. Type safe(r) R code
  105. Introducing Positron: A New, Yet Familiar IDE for R and Python
  106. Fun with Positron
  107. Coding in R and Python with Positron
  108. Settings, Keybindings, and Extensions for Positron
  109. Choosing a Sequential Testing Framework — Comparisons and Discussions
  110. Applied Machine Learning for Tabular Data
  111. A Comparison of Packages to Generate Codebooks in R
  112. tea-tasting: statistical analysis of A/B tests
  113. Julia for Economists Bootcamp, 2022
  114. Deep Learning in Julia
  115. Statistics Minus The Math: An Introduction for the Social Sciences
  116. Positron IDE - A new IDE for data science
  117. R package development in Positron
  118. How to interpret and report nonlinear effects from Generalized Additive Models
  119. Seven basic rules for causal inference
  120. Tidy DataFrames but not Tibbles
  121. Models Demystified: A Practical Guide from t-tests to Deep Learning
  122. Deep Learning Models for Causal Inference
  123. Dev containers with R and Quarto
  124. Exploring Complex Survey Data Analysis Using R
  125. Five ways to improve your chart axes
  126. R in Production
  127. Generalized Additive Models (GAMs) for Meta-Regression using brms
  128. The Data Visualisation Catalogue
  129. Using property-based testing in R
  130. Visual Diagnostic Tools for Causal Inference
  131. Nested unit tests with testthat
  132. Comparing data.table reshape to duckdb and polars
  133. Understanding Gaussians
  134. Python for R users

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 #100

Erik Gahner Larsen
RSS
https://erikgahner.github.io/posts/feed.xml