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#r-language

Statistical computing, data visualization, and reproducible research with the R programming language.

📄 2,847 articles
👥 342 contributors
📅 Updated 2h ago

R is a free, open-source programming language and software environment designed for statistical computing, bioinformatics, and data visualization. Developed by Ross Ihaka and Robert Gentleman at the University of Auckland in 1993, R has become the standard language for data scientists, statisticians, and researchers across academia and industry.

R is renowned for its extensive ecosystem of CRAN packages (over 20,000), powerful graphics system (ggplot2), and seamless integration with TinyTeX for reproducible research via R Markdown. It supports everything from basic descriptive statistics to complex machine learning pipelines.

# Basic R example: linear regression with visualization
library(ggplot2)
library(tidyverse)

# Fit model
model <- lm(mpg ~ wt + hp, data = mtcars)
summary(model)

# Visualize
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE)
2,847 results
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A Complete Introduction to R for Statistical Computing

Master the fundamentals of R programming — from basic data types and control structures to writing reusable functions. This comprehensive guide covers everything you need to start your journey in statistical computing.

✍️ Dr. Elena Vasquez 👁️ 48.2K views 💬 234 comments 📅 Dec 15, 2024 ⏱️ 22 min read
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ggplot2 Masterclass: The Grammar of Graphics in R

Deep dive into the ggplot2 package — Hadley Wickham's revolutionary implementation of Leland Wilkinson's Grammar of Graphics. Learn to create publication-quality visualizations from simple scatter plots to complex multi-panel figures.

✍️ Marcus Chen 👁️ 35.7K views 💬 189 comments 📅 Jan 3, 2025 ⏱️ 31 min read
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Tidyverse Tutorial: Data Manipulation with dplyr and tidyr

Learn the pipe operator, filtering, selecting, mutating, grouping, and summarizing data using the tidyverse toolkit. Includes hands-on exercises with real-world datasets.

✍️ Prof. Sarah Mitchell 👁️ 29.1K views 💬 156 comments 📅 Nov 28, 2024 ⏱️ 45 min read
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R Markdown: Reproducible Research from Analysis to Publication

Combine R code, narrative text, and output into beautiful HTML, PDF, or Word documents. This guide covers knitr integration, chunk options, dynamic reports, and deploying Shiny apps alongside analyses.

✍️ James O'Brien 👁️ 22.4K views 💬 98 comments 📅 Jan 8, 2025 ⏱️ 28 min read
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Gapminder Dataset: Classic R Visualization Example

Explore life expectancy, GDP per capita, and population data for 142 countries from 1952 to 2007. Perfect for practicing ggplot2, dplyr pipelines, and animated data stories.

📦 gapminder package 👁️ 18.9K views 💬 72 comments 📅 Oct 5, 2024 📊 1,704 rows × 6 cols
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Machine Learning in R: A Practical Guide with caret and tidymodels

Compare classification algorithms including random forests, support vector machines, and neural networks. Learn cross-validation, hyperparameter tuning, and model evaluation using the caret and tidymodels frameworks.

✍️ Dr. Priya Sharma 👁️ 16.3K views 💬 112 comments 📅 Dec 1, 2024 ⏱️ 38 min read
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Building Interactive Web Applications with Shiny

Create dynamic, interactive dashboards and web apps directly in R without writing HTML, CSS, or JavaScript. Covers reactive programming, layouts, inputs, outputs, and deploying to Shiny Server.

✍️ Alex Rodriguez 👁️ 14.8K views 💬 87 comments 📅 Nov 12, 2024 ⏱️ 52 min read
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Bayesian Statistics in R: From BUGS to Stan and brms

Explore Bayesian inference using R interfaces to JAGS, Stan, and the brms package. Covers posterior sampling, MCMC diagnostics, hierarchical models, and Bayesian A/B testing with real-world examples.

✍️ Prof. David Kowalski 👁️ 11.2K views 💬 64 comments 📅 Oct 22, 2024 ⏱️ 41 min read
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