Learn R with Real Code Examples

Plotting a histogram with ggplot2

Performing linear regression on a dataset

Clustering with k-means

Building a Shiny interactive dashboard

Analyzing genomic or financial datasets

Check package dependencies

Ensure correct R version for package compatibility

Debug vectorized operations carefully

Monitor memory usage with large datasets

Use `traceback()` and `debug()` for error inspection

Unit testing with testthat package

Validate functions on sample datasets

Check package dependencies

Automate testing in R scripts or CI pipelines

Monitor code coverage and reproducibility

R scripts for batch execution

Shiny apps for web deployment

R Markdown to generate HTML, PDF, or Word reports

Package development for CRAN/Bioconductor

Docker containers for reproducible environments

RStudio IDE

R Markdown for reporting

Shiny for interactive web apps

CRAN and Bioconductor package repositories

Data manipulation libraries: dplyr, data.table

Python via reticulate

C/C++ via Rcpp

Databases: MySQL, PostgreSQL, SQLite

Big data platforms: Spark, Hadoop

Visualization frameworks: ggplot2, plotly

Use RStudio keyboard shortcuts

Leverage R Markdown for documentation

Reuse functions and packages

Automate repetitive analyses

Regularly update packages and R version

Optimize memory usage for large datasets

Debug complex vectorized operations

Integrate R with other languages

Automate reproducible reports

Deploy Shiny apps securely