1786. Data Vis Dispatch: April 4, April 11, April 18, April 25

1787. Mastering the Many Models Approach

1788. A Survey of Large Language Models

1789. Balancing Classes in Classification Problems

1790. Plot Prediction Interval in R using ggplot2

1791. Julia’s latency: Past, present and future

1792. A User’s Guide to Statistical Inference and Regression

1793. Why the Cross-Lagged Panel Model Is Almost Never the Right Choice

1794. Deep Learning and Scientific Computing with R torch

1795. Hello Deep Learning

1796. Using fixed and random effects models for panel data in Python

1797. What we learned from creating a custom graphics package in R using ggplot2

1798. Nonresponse rates on open-ended survey questions vary by demographic group, other factors

1799. How we review code at Pew Research Center

1800. Bayesian Regression: Theory & Practice

1801. An Introduction to Data Analysis

1802. Perfect Bar Charts in 150 Seconds

1803. What are people commenting about their loaded packages?

1804. Introducing rtlr – an R Package for RTL Languages

1805. How to Modify Variables the Right Way in R

1806. {surveydown}: An open source, markdown-based survey framework (that doesn’t exist yet)

1807. A data analyst workflow, part 1: SQL & tidyverse

1808. On Efficient Training of Large-Scale Deep Learning Models: A Literature Review

1809. Dependently Typing R Vectors, Arrays, and Matrices

1810. Tidyteam code review principles

1811. The tidymodels is getting a whole lot faster

1812. Making maps with R

1813. Preventing common misconceptions about Bayes Factors

1814. A Course in Machine Learning

1815. Unleash the Power of Functional Programming in R with the purrr Package

1816. Deep Learning Is Better Than Linear Regression

1817. Dev containers with R and Quarto

1818. Styling Tables for Excel with {styledTables}

1819. Deep Learning

1820. Writing performant code with tidy tools

1821. Unlocking the Power of Machine Learning: A Beginner’s Guide to Understanding Algorithms and Models

1822. Charting Our Adventures: How I Created a Personalized Map with R, JavaScript, and more

1823. Differences between the base R and magrittr pipes

1824. Can you have confidence in a confidence interval?

1825. Detecting heart murmurs from time series data in R

1826. What are the differences between R’s new native pipe `|>` and the magrittr pipe `%>%`?

1827. Julia for biologists

1828. The Statistics That Come Out of Nowhere

1829. The Practical Guides for Large Language Models

1830. The Little Book of Deep Learning

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