R is an open-source coding language that is used for data visualization, statistical analysis, and data science. As R is open-source, it has massive communities that are working continuously to enhance its environment and help worldwide members innovate and improve. R is highly flexible and compatible with various technologies. R consists of more than 10,000 packages and libraries to improve and add to its significant capabilities. It also has libraries for dynamic as well as static graphics. Now, this blog will help you to know where one utilizes the uses of R in different sectors.
Such as social media, research & academics, and much more. We have listed some of the uses of this programming language by which one can easily understand its importance.
Importance of R Programming
Table of Contents
Data-Driven Decision Making: Data is considered as a vital tool for organizational decision making processes in the current society that is reinforced by technology. R can offer a strong environment for analysing big data which can be useful for developing business strategies for any company.
Widely Used in Academia: R has evenly solidified its role in academic research in a number of areas, including statistics, data science, economics and biology. That is why it is an open-source language that can provide unique opportunities for teaching and carrying out research.
Cross-Platform Support: R can run on Windows, Mac OS, as well as linux operating systems and above all is compatible with all the three systems. This makes it ideal because it is able to run on any environment without a lot of changes be made by data professionals.
Community Support: R has a large number of users and quite often users create new packages, tools and libraries in R. Such a wide network makes sure that R is up to date with the evolution of data science.
Visualization and Reporting: R is very good when it comes to data visualization. It uses the libraries such as ggplot2, shiny, and plotly to help the users to develop greatly detailed and even interactive visualizations from large data and convert the data into spectacular and enthralling stories.
Uses of r programming language in real life
The use of R in research & academics
R is one of the statistical research tools that is used by several students and statisticians to perform several statistical analyses and computations. The statistical techniques might be non-linear and linear modeling, classification, clustering, time-series analysis, classical statistic tests, and other all executions of R and its libraries.
This programming language also uses for deep learning and for machine learning researches as well. With the help of its library, one can facilitate monitoring and non-monitoring learnings. R is one of the commonly used programming languages for learning machine programming.
Another research includes large data sets such as finding genetic patterns and anomalies, big data, several drug compositions, all of them the uses of R carried out the relevancy of the large data types and finally draw the meaningful summarizes from the data.
The use of R in IT sectors
Not only IT companies using the R programming language but it also provides services to small, medium and large scale businesses for their business intelligence. These companies use it for machine learning products as it can build statistical data handling products and computational tools which can create data manipulated services.
There are various IT companies that use R, such as:
- IBM.
- Paytm.
- Wipro.
- Accenture.
- Infosys.
- Tata Consultancy Services.
- Microsoft.
- Google.
The use of R in finance
Being a statistical coding language, R is used in those finance sectors that deal with more statistical data. Data science and R have their own widespread uses in finance sectors. To carry out different financial computations and tasks, R can be used for advanced statistical suites. With the help of R and its libraries, one can easily do a risk assessment, stock-market modeling, auto-regression, moving averages, time-series analysis, financial data ming and much more.
This language also supports the process of business decision-making. The data visualization of R can describe the outcomes of data analysis into various graphical formats such as density plots, candlestick charts, and high-quality drawdown plots. It helps the business tycoons to link the technical data analysis aspects and their results. The R is used by companies such as Bajaj Allianz Insurance, American Express, and much more.
The use of R for E-Commerce
The companies of E-Commerce use R to enhance the experiences of users on their website and for finance and marketing purposes. With the help of R, they can enhance cross-product selling if a client buys a product from their site, it will suggest some additional products as per their original purchases. These suggestions also benefit past customers. Online-based companies collect and process unstructured and structured data from varying sources. Here, R plays an important role, and it is used for targeted advertising, financial data processing, market strategies, and sales modeling.
The use of R for social media
Social media companies such as Facebook use the R programming language to analyze various behaviors and sentiments. One can change and enhance the suggestions for the users that are based on the history of the users and tone and mood of the recent posts and seen content. According to the user’s history and sentiments, the ads can be adjusted for the users. Several programmers use R to analyze user sessions, traffic, and content that can improve the experience of the users.
The use of R in banking
There are several banking firms that use R for risk analytics and risk modeling. Various banks use R with its proprietary software such as SAS. It is used for mortgage haircut modeling, volatility modeling, client assessment, fraud detection, stat modeling, loan stress test simulation, and much more. Besides statistic analysis, it is used for data visualization, business intelligence, calculation of customer quality, customer segmentation, and customer retention.
The use of R in healthcare
With the help of R, one can easily process data that provides a backdrop to data processing and analysis. There are some fields where R is used heavily, such as genetics, bioinformatics, drug discovery, epidemiology, and much more. R programming language is used to check and predict the spreading of several diseases. We also use it to check drug-safety data, analyze genetic sequences, and various combinations and permutations of chemicals and drugs. The Bioconductor packages of r offer facilities to analyze genomic information.
The use of R in manufacturing
Several manufacturing companies use this programming language to complement their business and marketing strategies. They consider their client’s feedback to streamlines and enhance the products. These companies use information to support market strategies. Market trends and predicting demand for adjusting the manufacturing practices.
The use of R in the governmental department
R is used to keep the records and process their censuses in the governmental department. It helps them with effective governance and law-making. It also used for services like weather forecasting, drug regulations, and disaster-impact analysis.
Advantages And Disadvantages Of R Programming
Advantages:
Rich Statistical Functionality: A truly important language for statistical computations as well as for model building and testing, analysis and forecasting is R.
Strong Data Visualization: Visualization packages in R let the user plot graph and make aesthetically pleasing and intricate Two Way plots. This is important especially in presentation of results in order to influence a change.
Large Community and Resources: Technically, R is rich with numerous online communities, including tutorials, question and answer forums, and prodigiously documented helping both newcomers and old timers.
Extensive Libraries: The libraries and packages available in R are vast, which makes it an all-in-one tool to solve many data science issues. Main area sr packages from data analysis level to machine learning level needs are supported almost all.
Platform Independence: R can be installed on any operating system and thus can be easily used by professionals who work in different systems.
High Flexibility: This makes it easy to create new, unique functions and packages. In other words, R is very flexible to meet one’s demands.
Disadvantages:
Steep Learning Curve: However, R is also very strong and the usage of R for data analysis may be difficult especially for first time users who have little knowledge in statistics. It has its own syntax and needs time to know how to use the packages properly.
Memory Consumption: R primarily works in memory (RAM), which makes it problematic in the sense of handling large data sets: such computations can be slowed, or a memory error may occur.
Speed Limitations: R of course is not the fastest language when it comes to powerful calculation compared with languages like Python or C++.
Poor Support for Big Data: R is very useful when working with relative small amounts of data, but can cause issues when working with really huge amount of data without using additional software like Apache Spark or Hadoop.
Lack of Standardized Code Practices:
The one disadvantage of R is that being an open source language, there are no standard protocols guiding the language to set it a practice of setting and adhering to certain coding standards across projects, and then differing.
Conclusion
In this blog, you have learned various industries and sectors that use the R programming language. This blog also illustrates several purposes of uses of R. Earlier R is used for academics and research work but now it is used for several industrial purposes such as IT, Finance, Healthcare, and much more.
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