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.
2014. Five Steps to Improve Your Chart Quickly
2015. Geocode address text strings using tidygeocoder
2016. Adding context to maps made with ggplot2
2017. Useful functions for dealing with object names
2018. Data Visualisation: A Comprehensive Guide to Unlocking Your Data’s Potential
2019. How to get started with data visualization
2020. Machine Learning for Beginners – A Curriculum
2021. How to Get Good with R?
2022. Lesser-known reasons to prefer apply() over for loops
2023. The Ultimate Guide to Get Started With ggplot2
2024. Getting started with theme()
2025. Why does correlation not equal causation?
2026. Large Language Model Course
2027. Write R Code Faster with These Shortcuts
2028. How to make your own #RStats Wrapped!
2029. R date formatting
2030. Quick Stata Tips
2031. How to create separate bibliographies in a Quarto document
2032. Why is View() capitalized, anyway?
2033. 5 Example Charts with ggplot2
2034. Computational Methods for Economists using Python
2035. Creating Christmas cards with R
2036. Many Models in R: A Tutorial
2037. The case for a pipe assignment operator in R
2038. List of data visualization books
2039. Python Rgonomics
2040. 5 Powerful ggplot2 Extensions
2041. .I in data.table
2042. non-equi joins in data.table
2043. Four ways to streamline your R workflows
2044. Here’s why you should (almost) never use a pie chart for your data
2045. R data.table Joins
2046. Exploring Data Science with R and the Tidyverse: A Concise Introduction
2047. Redacting identifying information with computational methods in large text data
2048. DIY API with Make and {plumber}
2049. Awesome official statistics software
2050. 6 Common ggplot2 Mistakes
2051. Advanced tips and tricks with data.table
2052. Overview of clustering methods in R
2053. One billion row challenge using base R
2054. Six not-so-basic base R functions
2055. Let’s talk about joins
2056. Reading and Writing Data with {arrow}
2057. Modern Data Visualization with R
2058. Feature Engineering A-Z
2059. Are connected scatterplots so bad?
2060. Correlation heat maps with {ggplot2}
2061. You ‘tidyr::complete()’ me
2062. Piping data.tables
2063. VS Code for R on macOS
2064. new programming with data.table
2065. more .I in data.table
2066. Splatter: How to make a mess with ggplot2 and ambient
2067. Psychometrics in Exercises using R and RStudio
2068. Modeling Short Time Series with Prior Knowledge
2069. Everything is a Linear Model
2070. Why pandas feels clunky when coming from R
2071. How to create diverging bar plots
2072. Balanced sampling in R, Julia, and R + Julia
2073. What to consider when creating small multiple line charts
2074. Advanced Data Science Statistics and Prediction Algorithms Through Case Studies
2075. A foundation in Julia
2076. Working with data in Julia
2077. Plotting data in Julia
2078. Spring clean your R packages
2079. ggplot2 101
2080. Drawing waterlines with ggplot2 in R
2081. Romeo and Julia, where Romeo is Basic Statistics
2082. Using axis lines for good or evil
2083. Creating upset charts with ggplot2
2084. What Does a Statistical Method Assume?
2085. Reproducibility as part of code quality control
2086. 30 Python Language Features and Tricks You May Not Know About
2087. An Introduction to R
2088. Reading large spatial data
2089. Visualizing {dplyr}’s mutate(), summarize(), group_by(), and ungroup() with animations
2090. Three Ways to Include Images in Your ggplots
2091. A Rant
2092. How long until building complaints are dispositioned? A survival analysis case study
2093. The Truth About Tidy Wrappers
2094. On Indentation in R
2095. Kicking tyres
2096. Create engaging tables with R or Python using {gt}
2097. Elicit Machine Learning Reading List
2098. Correlation vs. Regression: A Key Difference That Many Analysts Miss
2099. Sketchy waffle charts in R
2100. CS388: Natural Language Processing
2101. Calculus with Julia
2102. Find Out How many Times Faster your Code is
2103. Easy data cleaning with the janitor package
2104. Why you shouldn’t use boxplots
2105. Statistical Power from Pilot Data: Simulations to Illustrate
2106. Creating R tutorial worksheets (with and without solutions) using Quarto
2107. Shiny apps for demystifying statistical models and methods
2108. Causal Inference in R
2109. I’ve Stopped Using Box Plots. Should You?
2110. Data Wrangling Recipes in R
2111. Your Journey to Fluent Python
2112. A timeline of R’s first 30 years
2113. Interactive Map Filter in Shiny
2114. What packages belong together? Learning from R code samples
2115. Ten simple rules for teaching an introduction to R
2116. Winners of the 2024 Table Contest
2117. Type safe(r) R code
2118. Introducing Positron: A New, Yet Familiar IDE for R and Python
2119. Fun with Positron
2120. Coding in R and Python with Positron
2121. Settings, Keybindings, and Extensions for Positron
2122. Choosing a Sequential Testing Framework — Comparisons and Discussions
2123. Applied Machine Learning for Tabular Data
2124. A Comparison of Packages to Generate Codebooks in R
2125. tea-tasting: statistical analysis of A/B tests
2126. Julia for Economists Bootcamp, 2022
2127. Deep Learning in Julia
2128. Statistics Minus The Math: An Introduction for the Social Sciences
2129. Positron IDE – A new IDE for data science
2130. R package development in Positron
2131. How to interpret and report nonlinear effects from Generalized Additive Models
2132. Seven basic rules for causal inference
2133. Tidy DataFrames but not Tibbles
2134. Models Demystified: A Practical Guide from t-tests to Deep Learning
2135. Deep Learning Models for Causal Inference
2136. Dev containers with R and Quarto
2137. Exploring Complex Survey Data Analysis Using R
2138. Five ways to improve your chart axes
2139. R in Production
2140. Generalized Additive Models (GAMs) for Meta-Regression using brms
2141. The Data Visualisation Catalogue
2142. Using property-based testing in R
2143. Visual Diagnostic Tools for Causal Inference
2144. Nested unit tests with testthat
2145. Comparing data.table reshape to duckdb and polars
2146. Understanding Gaussians
2147. Python for R users
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