Why Markov Chains Work Well in R Programming

Evolutionary changes to programming has helped data scientists create better models within R programming

The neat aspect of data science is that innovative developers and analysts are finding new ways of applying well-researched statistical concepts. The growth of R programming has raised the opportunity to develop data models for machine learning. Markov Chain models in no exception. How R is arranged around matrices is part of that opportunity.

Matrices work well in R because the underlying programming structure is designed to interpret data as vectors. As I mentioned in my post about matrices, vectors are the basic data object in R. That basic structure allows you to model any dataset into a table with a degree of efficiency. There are also specialty vector objects, such as time series objects that support statistical predictive models involving time series data.

The data objects are significantly larger than a typical spreadsheet. An Excel spreadsheet at its maximum can hold 13,000 columns and over a million rows. R objects can hold 2^30–1 fields for a vector. This makes R more than sufficient for a data table of 2 million rows.

You Can Do More With Data in General