Data science requires mastery in various fields like machine learning, R programming, Python, deep learning, and many more. Among all these, one of the basic key programming languages required for every data scientist is R programming. These programming languages help a data scientist to collect data, create visualizations, perform predictive and statistical analysis, and communicate the course of results to stakeholders.
Basics of the R programming language:
It is a programming language developed by Robert Gentleman and Ross Ihaka. It deals with various concepts of graphical and statistical methods. Includes statistical interference, linear regression and machine learning algorithms, time series, and much more. Detailed analysis, such as data reduction, correlation, and data clustering, is easily done with R programming. R’s data handling process is very effective compared to the rest. Critical programming language concepts are easily analyzed with R. It also provides a large, integrated, and coherent collection of transition tools, particularly used for data analysis. Graphic facilities, flexible tools, along with well-structured programs are its specialties. For extensive programs, C, C++, and FORTRAN often accompany each other in the runtime process.
R can be useful for analyzing data that takes place in the following series of steps:
- Programming: R is an accessible and transparent programming tool
- Transforming: is a collection of various library tools that can help us solve problems particularly related to data science.
- Discover: Data must be refined, analyzed, and investigated.
- Modeling: R captures the correct model of the data using a wide range of tools.
- Communicate: Integrate graphics, outputs, and code to share the data outputs.
R programming is used by numerous industries, namely academic, agriculture, healthcare, government, insurance, finance, retail, manufacturing, and much more. Major companies like Google, Facebook, Microsoft, Uber, Twitter, HP, Ford, and Novartis use multipurpose R. Apart from these, R is also used by IT, analytics, financial institutions, and consulting firms for better performance. Economic forecasting, behavioral analysis, advertising effectiveness, data visualization, statistical analysis, and credit risk modeling are some of the main uses of R programming.
R provides several methods to simplify the statistical analysis of big data by using the following features:
- Linear and nonlinear modeling
- Time series analysis
- Group
- Interfaces and extensibility to other programming languages
- Shareable code packages
The charting and graphing capabilities of this language are unmatched. Initially, R gained popularity in academia where data had to be constantly reproduced, and it became a key to credibility. Reproducible work has been advantageous in the business sector where bits of data were analyzed from time to time. Every tool required by a data scientist to evaluate and manipulate data is included in the R programming language.
Data science is redefining the way companies run their businesses. Learning a language like R can help you in a number of ways, such as giving you the best understanding of data, which eventually makes you a successful data analyst.