Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic

Check out this R Shiny App by Ben Rottman designed to explore causality and multiple regression: pitt.shinyapps.io/Rottman_Causal… This tool helps you understand possible relationships between variables, including noise, confusion, alternative effects, mediation, and

This animated map offers a fascinating look at train travel accessibility from Paris, showing how far you can go in just 1 to 12 hours. It’s a great example of how visual tools can make data both engaging and informative. Thank you Simon Kuestenmacher for sharing this resource.

Spearman's rank correlation coefficient is a non-parametric measure that assesses how well the relationship between two variables can be described using a monotonic function. Unlike Pearson’s correlation, which assumes a linear relationship, Spearman's method is more flexible and

Understanding the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can be challenging. These terms are often used interchangeably, but they represent distinct concepts. AI is the broadest category, encompassing any system designed to

Activation functions are essential in neural networks, enabling them to model complex, non-linear relationships. They determine how inputs are transformed into outputs at each neuron, introducing the non-linearity needed for the network to learn patterns beyond simple linear

When drawing conclusions from your data, it's important to understand the variance of your estimators, especially when using complex sampling designs such as clustering or stratification. The design effect measures how much these designs increase the variance of your estimates

Combining Principal Component Analysis (PCA) with k-means Clustering in Python can significantly improve your data analysis by simplifying dimensionality and enhancing clustering outcomes. Here’s a practical guide to implementing these techniques in Python: 1️⃣ Apply PCA: ✔️

Structural Equation Modeling (SEM) is a powerful statistical technique used to analyze complex relationships between variables. It allows researchers to examine both direct and indirect effects, making it especially useful for fields like psychology, economics, and social

Bring your visualizations to life with see, a dynamic R package from the easystats ecosystem that extends ggplot2 to create modern and intuitive graphics. Whether you're visualizing statistical models or exploring data, see simplifies the process and enhances the presentation of

Quantile regression is a valuable tool for analyzing the relationship between variables, especially when data is not evenly distributed or has outliers. Unlike traditional linear regression, which focuses only on the mean, quantile regression allows us to predict different

I’ve launched a series of tutorial posts on Statistical Methods across my social media channels. These posts are designed to deepen your understanding of statistical concepts and how to apply them practically using R and Python. Here are some of the posts: - The Four Moments in

Principal Component Analysis (PCA) is a powerful technique for reducing the dimensionality of your data while maintaining its essential structure. A key advantage of PCA is its ability to transform high-dimensional data into a lower-dimensional space, enabling new ways to

In my opinion, R should be your go-to programming language! Here are 5 reasons why you should learn R: 1️⃣ Comprehensive Data Analysis: R offers powerful tools for manipulating, calculating, and displaying data. Its extensive libraries handle large data sets with ease. 2️⃣

Efficient data manipulation is essential when working with large data sets, and dplyr in R makes row and column operations simple and intuitive by offering a consistent set of functions that abstract away complexity, enabling users to focus on data analysis rather than technical

Discover how the Code Context plugin can significantly enhance the capabilities of Claude Code and other AI coding tools. With full codebase indexing and semantic search, it delivers deeper context and improved code outputs — all while being completely open-source. Thanks for

Merging data in R is a crucial skill for any data analyst or scientist. I've created an extensive playlist with 19 video tutorials to help you understand and master different types of joins and how to merge data using both base R and dplyr: youtube.com/watch?v=rlvWJd… Here are some

Sampling can be done in many different ways, and choosing the right sampling method is very important for ensuring accurate and reliable results. Stratified sampling is one such method that ensures specific subgroups within a population are adequately represented in a data set.

Time series analysis is a method used to analyze data points collected or recorded at specific time intervals. It helps identify trends, patterns, and fluctuations, making it invaluable for forecasting and decision-making in various fields like finance, healthcare, and marketing.

Accessing reliable sub-national population data can be a challenge in many countries. Fortunately, there are several valuable geospatial datasets that can help fill the gap for researchers, planners, and analysts. Thank you for sharing this resource, Yohan.