Learn R - 10 Code Examples & CST Typing Practice Test
R is a high-level, interpreted programming language and environment specifically designed for statistical computing, data analysis, and graphical representation. It provides a rich ecosystem of packages and functions for statistical modeling, data visualization, and reproducible research.
Learn R with Real Code Examples
Updated Nov 21, 2025
Installation Setup
Download and install R from CRAN (https://cran.r-project.org/)
Install RStudio for a full IDE experience (optional but recommended)
Set library paths for custom package storage
Use `install.packages()` to add new packages
Verify installation with `R --version` and a test script
Environment Setup
Install R from CRAN
Install RStudio IDE
Set library paths for package storage
Install essential packages
Test R scripts and plots
Config Files
.Rprofile for environment setup
.Renviron for environment variables
R scripts (.R) and notebooks (.Rmd)
Project-specific library paths
Package DESCRIPTION and NAMESPACE files
Cli Commands
R - start interactive REPL
Rscript file.R - run scripts
install.packages('pkg') - install package
update.packages() - update installed packages
R CMD check package - validate package
Internationalization
UTF-8 support
Handles multi-language datasets
CRAN packages support various locales
Reports can be generated in multiple languages
Community examples worldwide
Accessibility
Cross-platform compatibility
Open-source and free
Accessible to statisticians and analysts
Readable syntax for domain-specific tasks
Community support for beginners
Ui Styling
Console-based output
Plots with ggplot2/base graphics
Interactive Shiny dashboards
R Markdown reports (HTML/PDF/Word)
Minimal native GUI, relies on IDE or Shiny
State Management
Global and local variables
Function environments
Mutable lists and data frames
Package namespaces
R session and workspace management
Data Management
Vectors, matrices, lists, data frames
dplyr and data.table for manipulation
tidyr for reshaping
Reading/writing CSV, Excel, databases
Serialization with RDS or feather files
Frequently Asked Questions about R
What is R?
R is a high-level, interpreted programming language and environment specifically designed for statistical computing, data analysis, and graphical representation. It provides a rich ecosystem of packages and functions for statistical modeling, data visualization, and reproducible research.
What are the primary use cases for R?
Statistical modeling and hypothesis testing. Data visualization and reporting. Machine learning and predictive analytics. Bioinformatics and genomic data analysis. Financial and econometric analysis
What are the strengths of R?
Excellent for statistical analysis and data visualization. Vast ecosystem of specialized packages. Strong community support for data science. Open-source with extensive documentation. Highly reproducible workflows using R Markdown
What are the limitations of R?
Slower than compiled languages for large datasets. Memory-intensive with very large data. Steeper learning curve for programming beginners. Less suited for general-purpose software development. Graphical performance can lag behind modern GUI frameworks
How can I practice R typing speed?
CodeSpeedTest offers 10+ real R code examples for typing practice. You can measure your WPM, track accuracy, and improve your coding speed with guided exercises.