Data Analysis with R Programming

R programming language is one of many important tools in data analytics, especially for statistics purposes. This powerful statistical programming tool, that is written by Statisticians for Statisticians, is called as a king of statistical computing languages for analyzing and visualizing big data. For that reason, it would be nice to know about this programming language that rules Data Science. Let’s start with the basic!

Getting started

Compared to Python, another popular programming language in the data analytics world, R actually has slightly different characteristics. R is used by professionals who have a statistical-oriented approach to solving problems; for example scientists, statisticians, and engineers. Python is used by professionals looking for solutions in the data analytics or those who have to heavily mine data for answers; for example data scientists, machine learning specialists, and software developers. The comparison between R and Python is shown below.

If we are more familiar with Microsoft Excel or SQL, R also has a commonality with them such as they all use functions. In spreadsheets, we use functions in the formulas, and in SQL, we include them in queries. In R, we will use functions in the code. In terms of the differences, we can see the table below.