R Programming
R for statistical computing: data manipulation with tidyverse, visualization with ggplot2, and time series analysis.
R was built for statistical analysis and remains the tool of choice for many statisticians and researchers. This section covers R fundamentals, the tidyverse for data manipulation, ggplot2 for creating publication-quality visualizations, and time series analysis for financial data.
Why R?
R was designed by statisticians for statistics. It has unmatched depth in statistical methods, from basic tests to advanced econometrics. The tidyverse makes data manipulation intuitive, and ggplot2 produces publication-ready charts with minimal code. Many academic papers and research still use R.
What You'll Learn
You'll start with R syntax and RStudio, then learn the tidyverse ecosystem (dplyr, tidyr, readr) for data wrangling. The visualization course covers ggplot2 in depth. You'll create charts that would take hours in Excel. Finally, you'll apply R to financial time series analysis.
R and Python Together
Many professionals use both. R excels at statistical modeling and visualization; Python is better for automation and deployment. Learning R gives you access to specialized statistical packages and a different way of thinking about data problems.
