Handledningar i R-programmering

A complete guide to coding best practices including DRY/SOLID principles, automated tooling, Git workflows, and responsible use of AI coding assistants.

Learn how to use ggplot2 in R to create compelling visualizations of climate change data. This step-by-step tutorial teaches you to find, analyze, and visualize historical weather data.

Uncover the intricacies of non-linear models in comparison to linear models. Learn about their applications, limitations, and how to fit them using real-world data sets.

Delve into K-Nearest Neighbors (KNN) classification with R. Learn how to use 'class' and 'caret' R packages, tune hyperparameters, and evaluate model performance.

Determine if there is a significant difference between the means of the two groups using t.test() in R.

A complete overview to understanding multiple linear regressions in R through examples.

Explore regular expressions in R, why they're important, the tools and functions to work with them, common regex patterns, and how to use them.

A complete guide to getting started with RStudio: installation, interface, and key features for data science

Discover how to execute Python and R in SQL and unlock powerful new machine learning possibilities for your databases.

The data.table cheat sheet helps you master the syntax of this R package, and helps you to do data manipulations.

In this tutorial, learn about plot character (PCH) in R.

Learn about box plots in R, including what they are, when you should use them, how to implement them, and how they differ from histograms.