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
Learning Path
Learn R syntax and basic data structures
Practice data manipulation with dplyr
Explore visualization with ggplot2
Study statistical modeling and machine learning
Build reproducible reports and Shiny apps
Skill Improvement Plan
Week 1: R basics and vectors
Week 2: Data frames, lists, and matrices
Week 3: Functions, control flow, and packages
Week 4: Visualization and reporting
Week 5: Statistical analysis and real datasets
Interview Questions
What are R’s primary strengths in data analysis?
Explain the difference between a vector, list, and data frame in R.
How do you create a plot with ggplot2?
Describe how to build a Shiny app.
What are R packages and how do you manage them?
Cheat Sheet
x <- 10 - variable assignment
c(1,2,3) - vector creation
data.frame() - create data frame
lm(y ~ x, data=df) - linear regression
library(ggplot2) - load package
Books
R for Data Science
Advanced R
Hands-On Programming with R
The Art of R Programming
R Graphics Cookbook
Tutorials
R basics and data types
Data manipulation with dplyr
Visualization with ggplot2
Statistical modeling and machine learning
Building Shiny dashboards
Official Docs
R Project official website
R manuals and reference guides
CRAN package documentation
Community Links
RStudio Community
Stack Overflow R tag
CRAN mailing lists
Bioconductor forums
Data science and analytics forums
Community Support
R Project official website
RStudio community forums
Stack Overflow R tag
CRAN and Bioconductor communities
Data science and academic R groups
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.