R Programming — Complete Guide
In this tutorial, you will learn about R Programming. We cover key concepts, practical examples, and best practices to help you master this topic.
R is a programming language and environment dedicated to statistical computing and data visualization. Master data analysis, ggplot2, and the tidyverse with hands-on tutorials.
1. What Is R?
Origins, philosophy, and use cases
2. Vectors
Creating, indexing, and vectorized operations
3. Data Frames
Tabular data, subsetting, and manipulation
4. Factors and Categorical Data
Storing and working with categories
5. Lists and Matrices
Multi-dimensional data structures
6. dplyr for Data Wrangling
Filter, select, mutate, summarise, arrange
7. tidyr for Data Tidying
Pivot, spread, gather, and clean data
8. ggplot2 Visualization
Grammar of graphics, layers, and themes
9. Statistical Analysis
t-tests, ANOVA, correlation, and regression
10. Linear Regression
Model fitting, diagnostics, and prediction
11. R Markdown
Reproducible reports and documentation
12. Shiny Web Apps
Interactive dashboards and applications
13. Data Import and Export
Reading CSV, Excel, JSON, and databases
14. The Tidyverse Ecosystem
Purrr, stringr, lubridate, and forcats
15. Advanced Visualization
Interactive plots, maps, and animations
16. Machine Learning
caret, tidymodels, and model evaluation
17. Writing Functions
Functions, scoping, and error handling
18. R Packages
Creating and sharing R packages
19. data.table for Big Data
Fast data manipulation on large datasets
20. Real-World Projects
Data analysis and reporting projects
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All 61 topics in R Programming — Complete Guide are published.