R Programming Tutorial

Learn how to quickly find the middle value of your data using the R median() function. Discover tips for handling missing values and grouping data by categories.

Calculate the average of numeric, logical, and weighted data using R’s built-in mean functions. Understand how to handle missing values and apply the function to vectors and data frames.

From basic concepts to advanced implementation, learn to build effective Sankey diagrams using popular tools. Discover the key components, best practices, and design principles that make flow visualizations compelling and insightful.

Explore heavy-tailed distributions where traditional statistical methods don't apply. Discover how the Cauchy distribution effectively models phenomena where extreme events occur more frequently than expected.

Discover the intricacies of the negative binomial distribution and its applications. Learn how to model count data effectively. Explore practical examples and visual aids to enhance your understanding.

Explore the concept of heteroscedasticity and its implications in statistical modeling. Learn how to detect and address it. Discover techniques to improve your regression models.

Discover how Q-Q plots are a useful visual method to assess normality. Compare observed data to a theoretical distribution like the normal distribution to highlight deviations. Learn to diagnose model fit.

Explore how bootstrapping improves the estimation of confidence intervals and standard errors. Learn to distinguish between parametric and non-parametric bootstrapping techniques, and learn about bootstrapping in time series forecasting.

Eigenvectors and eigenvalues are essential for understanding linear transformations. This article covers their geometric interpretation, mathematical calculation, and importance in machine learning.

Learn how to create a contingency table and perform chi-square tests in R using the chisq.test() function. Discover practical applications and interpret results with confidence.

Learn about QR decomposition, the matrix factorization technique that decomposes matrix A into the product of an orthogonal matrix Q and an upper triangular matrix R.

We set na.rm = TRUE in common R functions to exclude missing (NA) values. This helps us compute accurate statistics and enhances the reliability of our results.