R for Data Science 

R is a free analytics tool or programming language in data science. It is an open-source software used in data statistics, data science, and data visualization projects. It is the most popular programming language among data scientists and statisticians. It is powerful, versatile, flexible, and can be integrated with different business integration or BI tools, such as Sisense, to sort critical business data.

These integrations involve predictive models like linear regression and statistical functions. R enables data scientists to create and execute statistical models through Sisense data. Aspiring data scientists can join data science online courses in India to know and learn more about R. 

Diving Deep into R 

Data science is among the popular fields in the modern world. Industries use it to construct and analyze data and convert raw data into accessible information. For this, they require numerous programming tools. R is one such tool that creates an intensive environment to transform, process, visualize and research information. 

The programming language is focused on data mining and statistical analysis. R can help create applications and software that can undergo statistical analysis. Further, it has a graphical interface and a variety of analytical modeling tools, such as linear and non-linear modeling, classical statistical tests, time-series analysis, and data clustering. 

Data science freaks can check out data science Bootcamp reviews to explore more about R courses. 

R & Python Programming 

Python is R’s sister language. Both these programming languages are powerful tools that help maximize data reporting. Data science professionals can integrate R directly with the analytics stack instead of using development tools like Jupyter Notebooks and R Studio to predict business results, create statistical models and make interactive dashboards. Combining Python and R means that analytics can be done faster with updated and accurate data. However, they have significant differences that are given below. 

Features of R in Data Science

Some of the common features of R programming in data science are; 

• It provides support for statistical modeling. 
• It provides visualization tools for data science applications. 
• It is used in data science-driven applications to extract, transform and load (ETL) information. 
• R sets up interfaces for spreadsheets and various databases like SQL. 
• It offers packages for data wrangling. 
• Data scientists can use R to apply machine learning-based algorithms to understand future events. 
• It analyzes unsorted data and offers an interface for NoSQL databases. 

R Packages & Libraries in Data Science 

There are numerous packages and libraries that data science professionals may consider installing. These include; 

• Ggplot2- It is a visualization library in R. It gives an aesthetical and interactive appeal to graphics. Ggplot2 implements an approach of “grammar of graphics” that allows producing visualizations by expressing the attributes of data and graphical representations. 
• Dplyr- The package is suitable for data analysis and data wrangling. It facilitates functions in R for data frames. It allows working with local data frames and remote database tables. 
• Tidyr- This package is used for cleaning and tidying data. In this package, each row symbolizes an observation and each variable symbolizes a column. 
• Esquisse- The package consists of important features of Tableau associated with R. It is an advanced version of ggplot2. It enables drawing curves, scatter plots, bar graphs, and histograms. 
• Shiny- The package allows sharing files and makes them visible so they can be easily found. 
• Mlr- This package performs machine learning tasks. It contains useful algorithms to do machine learning projects. Moreover, it provides extensible frameworks for regression, clustering, classification, multi-classification, and survival analysis. 
• E1071- The package helps implement functions such as Naive Bayes, SVM, clustering, and Fourier Transform. 
• Caret- Caret means regression and classification training. With this, data scientists can make classification problems and complex regressions. 

Applications of R Programming

Some applications of R programming in the data science field include; 

• Finance 

R is popularly used in the financial industry. It comes with a statistical suite that helps perform financial tasks. Financial institutions rely on R to do downside risk management, use visualization, such as density plots, candlestick charts, and graphs, and adjust risk performance. It provides tools for auto-regressions, moving averages, and time-series analysis that forms the foundation of finance applications. Portfolio management firms widely use R for credit risk analysis. Further, it enables financial data mining through R packages. Shiny, an R package, helps show the financial products through engaging graphics and visuals. 

• Medical 

R is used in several medical domains, including drug discovery, bioinformatics, genetics epidemiology, etc. With R, healthcare companies can process information that can help in further analysis. For advanced processes like drug discoveries, R can analyze data to ensure drug safety followed by performing clinical trials. Further, R’s famous package, Bioconductor, facilitates analyzing genomic data. R is used in epidemiology for statistical modeling that allows data scientists to predict and analyze the spread of any disease.

• Banking 

Banking institutions use R for risk analysis and credit risk modeling. Some of them use it to perform financial reporting, analyze financial losses, and access visualization tools. They use R in combination with SAS tools to set loan defaults, manage sales price distribution and calculate probabilities for a shortfall. It also facilitates the analysis of customer segmentation, quality, and retention. 

• Social media

Social Media platforms are a playground for R beginners. Data mining forms and sentiment analysis are some of the statistical tools used with R. Social media is a challenge for data science as a large fraction of the information is available in unstructured forms. R can help analyze social media, identify potential buyers and target them to sell products. Additionally, it allows companies to create statistical tools that can analyze customer sentiments and provide a better user experience. 

• Manufacturing 

Global manufacturing companies use R to analyze user sentiments. This makes them understand their customers’ needs and interests so they can optimize their products to improve their services. With this, they can increase profits and decrease the production of untrendy products. 

• E-commerce 

Data science is a significant aspect of e-commerce, and R is a standard tool used in the industry. Since these companies have to manage and deal with various data forms (structured and unstructured) and data sources, such as databases (NoSQL and SQL) and spreadsheets, R is the best option to consider. It helps these companies in analyzing and recommending cross-selling items to the consumers. Cross-selling refers to suggesting extra products that complement a customer’s original purchase. Moreover, various R statistical processes like linear modeling can help in predicting future product sales by understanding the customers’ behavior. R can allow undergoing A/B testing analysis across product pages which makes it popular among e-commerce organizations. 

Conclusion 

In data science, R is a programming language that allows analyzing and sorting of data. It has various other features and advantages which make it accepted in all data science-based industries. 

You can always learn data science by taking up the best online courses from the top institutes. The courses will not only give you special recognition in the industry but also bump up your salary. 

Read More: How To Become a Data Scientist?