2  Understanding R Applications in Data Science

In this lesson, we unpack the role of R in the field of Data Science.

2.1 The Role of R

R is a programming language that is widely used in the field of Data Science. Its role in Data Science is multifaceted and can be summarized as follows:

  • Data Wrangling: R has a powerful set of libraries that allow you to manipulate and transform data, which is a critical step in any Data Science project.

  • Statistical Analysis: R has a rich set of statistical libraries that allow you to perform a wide range of statistical analyses, including hypothesis testing, regression analysis, and time series analysis.

  • Data Visualization: R has an extensive set of libraries for creating high-quality data visualizations, such as plots, charts, and graphs, that enable you to communicate insights effectively.

  • Machine Learning: R has a comprehensive set of libraries for building and deploying machine learning models, such as decision trees, random forests, and neural networks.

  • Reproducibility: R provides a framework for creating reproducible data analyses, which is essential for collaborating with others and ensuring that your work can be verified and replicated.

2.1.1 R v.s. Python

R vs. Python.
Figure 2.1: R vs. Python

You may wondering why we choose R over another popular language - Python, in this course. The short answer is the choice ultimately depends on the specific needs of the data scientist and the project at hand.

Ask yourself: what kind of data scientist you want to become? R is hands down the best option when you focus on statistics and probabilities. It has a large community of statisticians that can answer your questions. But, if you want to develop applications that process enormous amounts of data, Python is your best option. It has a more extensive ecosystem of developers, and it’s easier to find people willing to collaborate with you.

Technical Differences 1. Syntax: R has a syntax that is tailored for statistical analysis and modelling, with many built-in functions and operators specifically designed for this purpose. Python, on the other hand, has a more general-purpose syntax that can be used for a wide range of tasks beyond statistical analysis.

  1. Libraries and Packages: Both R and Python have extensive libraries and packages for data science, but they differ in their focus and scope. R has a strong emphasis on statistical modelling and analysis, with packages like ggplot2, dplyr, and tidyr. Python, on the other hand, has a broader range of applications, including web development, scientific computing, and machine learning, with packages like NumPy, Pandas, and Scikit-learn.

  2. Community: Both R and Python have large and active communities, but they differ in their backgrounds and focus. R has historically been used more by statisticians and data analysts, while Python has been more popular among software engineers and developers.

  3. Learning Curve: R is generally considered to have a steeper learning curve than Python, especially for those who are new to programming. However, once you become familiar with R’s syntax and packages, it can be a very powerful and efficient tool for statistical analysis.

  4. Visualisation: R has a strong focus on Visualisation, with packages like ggplot2 and lattice that make it easy to create high-quality plots and charts. Python also has Visualisation packages like Matplotlib and Seaborn, but they may require more customisation and tweaking to get the desired output.