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Ross Ihaka and Robert Gentleman developed the R Programming Language in 1993 to facilitate statistical computing. R programming language and software environment encapsulates a wide range of statistical and graphical models, which are used by data miners and statisticians to develop statistical software and perform data analysis. It covers machine learning algorithms, time series, linear regression, statistical inference, and so on. R programming language is particularly useful in data analysis, which is carried out in five steps, namely, programming, transformation, discovery, modelling, and communication of the results. R libraries are mostly coded using R programming language, however, when heavy computational functions are involved, Fortran, C, and C++ codes are used. Presently, R Programming language is used by top global companies including Google, Microsoft, Facebook, Uber, Airbnb, and so on.
R PROGRAMMING LANGUAGE IS OPEN SOURCE: Being an open source programming language, it makes it possible for all potential users to benefit from the utilities offered by it without having to bear any cost or acquire a license. As an open source, it can also be modified continuously since anyone can develop it further by customizing packages, solving problems, and adding new features and packages. It garners an unlimited possibility for growth.
HUGE NUMBER OF PACKAGES: R Programming language comprises of a huge number of packages, exceeding 10,000 at present, and new packages are being added every day. These packages can be used by diverse industries from finance to healthcare, which makes R an appealing language in several areas of operation.
IDEAL FOR DATA WRANGLING: So far as data analysis is concerned, R is considered to be the best language and environment because of the advanced data wrangling packages it offers. These packages are particularly helpful in sorting through enormous databases consisting of unorganized data. Dplyr and readr are two of the best liked packages for data wrangling.
FACILITATES GRAPHING AND PLOTTING: R is ideal for statistical computing and data analysis because it provides great options for visual representation by enabling customized graphing and plotting. There are numerous libraries with aesthetic visual options to choose from, the most popular ones being plotly and ggplot2.
ACCOMMODATES MACHINE LEARNING OPERATIONS: Advanced machine learning operations which are an integral part of data analysis are offered by R, allowing users to perform regression, classification, and helping in the development of artificial neural networks.
COMPATIBLE WITH ALL PLATFORMS: R programming language is compatible with all the leading operating systems including Windows, Linux, and Mac. This improves the usability of the programming language since users who work across different platforms can co-ordinate when using the R language.
COMPATIBLE WITH OTHER PROGRAMMING LANGUAGES: The flexibility of R programming language is one of its prominent features and owing to its high compatibility, it can be combined with the most popular programming languages which are Python, Java, Fortran, C, and C++. In addition, database management system like Hadoop can also be integrated with R.
EASILY PRESENTABLE REPORTS: Data analysis is followed by presentation of reports, which is made incredibly easy by R since it offers packages like Markdown, Shiny, and others. These packages reduce the work for users by customizing presentations in a chosen format. These reports can comprise of plots, data, and chosen R scripts. In addition, R programming language makes is possible to create web apps that the users’ audience can use to go through the data and results or to manipulate them to further their own studies.
IDEAL FOR STATISTICS: The tools, packages, and assistance that R programming language and environment offers for the development of statistical software/ data analysis is unparalleled, which is why it is called the lingua franca of statistics and is preferred by statisticians over all other programming languages.
R PROGRAMMING LANGUAGE HELP IN ACADEMIC RESEARCH: R programming language is used as a statistical research tool by students and researchers alike for the purpose of statistical computation and analysis. R libraries house several important statistical techniques which reduce the time taken to organise and interpret data, thereby helping researchers, and these tools include classification, clustering, time series analysis, non-linear and linear modelling, and other popular statistical tests. Any research that involves big data is made easy by R, which helps to go through huge amount of data to derive relevant conclusions. Some of the researches that are largely dependant on R are tests of drug compositions, genetic anomalies, genetic patterns, and so on. R is also used in academia for machine learning and deep learning, with R libraries offering both unmonitored and monitored machine learning options.
R PROGRAMMING HELP IN FINANCE: Financial industry is probably the one that is most concerned with statistics and R programming language is a crucial tool at their disposal to help with financial tasks such as data mining, auto regression, moving averages, risk assessment, time series analysis, and so on. R plays a central role in the decision-making process of financial organizations where they use visual representation tools accorded by R to disseminate knowledge among the management regarding the position of variables, as revealed by the interpreted data. The data is represented in comprehensible forms which include density plots, candlestick charts, and draw down plots to name a few. International financial organizations like American Express, Standard Chartered, and JP Morgan depend on R for their financial analysis.
R PROGRAMMING HELP IN HEALTHCARE: R helps in crunching data and using available information to cast analysis which can be used to make future decisions, and this predictability enabled by R is largely useful in healthcare. R can be used to analyse and predict the spread of infections, safety quotient of the drugs under use, allergic responses of patients with certain medical histories to particular drug composition, genetic formation of living beings including microorganisms, and various possible permutation and combination of drugs which could lead to the discovery of new or faster cures. The two fields of medicine that are most heavily dependant on R programming language are genetics where genomic data can be analysed using R’s Bioconductor package, and drug development where R helps in performing pre-clinical trials without risking resources. R is also used in epidemiology, bioinformatics, and various other areas of medical science.
R PROGRAMMING HELP IN E-COMMERCE: In E-commerce industry, R is used not only for financial risk assessment and devising marketing strategies, but e-commerce companies use R to improve the experience of users on their websites. For instance, R programming language enables cross marketing, where while a potential buyer is searching for a specific product, they are recommended other similar or complementary products. Besides, the data about previous purchases of a given user is used to analyse their preferences, and recommendations are made accordingly, increasing the chances of sale. Not only do these sites use data gathered from their own websites, but the users’ activities on other similar websites, or on search engines like google are used as input data based on which recommendations are made. R is capable of organizing and processing huge quantity of structured and unstructured data from multiple sources, which improves the users’ experience while benefitting the e-commerce companies. Data analysis and R is used by Amazon to improve their cross-product references. Flipkart improves their targeted ads by employing R for predictive analysis. Companies like Thomas Cook use R for customization of their client specific offers and automation of their last minute price settings.
R PROGRAMMING HELP IN IT SECTOR: IT companies enhance business intelligence for themselves by crunching data using R and simultaneously, they offer these services to other companies, where they develop data managing products and statistical tools as per the needs of their customers. R also helps IT companies in machine learning, and is used by leading organizations including IBM, Accenture, Wipro and many others. In fact, R is used by Google for improving their search suggestions and search results, to better target online advertisements, to estimate the ROI of ad campaigns, and to estimate their financial gains. Browsers like Mozilla Firefox use R for the visualization of web activities. Microsoft relies on R as a statistical engine within their Machine Learning ventures.
R PROGRAMMING HELP IN MANUFACTURING: The statistical tools extended by R is used by manufacturing companies to boost their business and marketing strategies. For instance, the feedback received from customers is used for streamlining and improving their products. Descriptive analysis is used to estimate the past performance of their lines of products, and predictive analysis is used to attain a glimpse of possible future trends. The information thus derived is used to make decisions regarding future productions, such as, which products are to be dropped and what new products could be added to their lines to improve business. R is also used to assess the target market, especially by international brands, to determine which products ought to be launched in which locations. Ford Motors Company depends on R for analysing their business strategies, they use R to gauge customer response to their products, which is used to plan future designs for their products. John Deere trusts R to analyse demands, forecast production capabilities, and assess market risks.
R PROGRAMMING HELP IN BANKING: Banking sector is largely dependent on R for client-specific credit risk analysis and for market risk analysis in general. Before sanctioning loan to a customer, they run thorough checks to estimate the credibility of the client, and data is acquired from myriad sources for the purpose. This largely unstructured data is organized and analysed using statistical tools provided by R, and with this the banks can perform fraud detection, loan stress test simulation, volatility modelling, stat modelling, mortgage haircut modelling, and so on. R is also used for the purposes of client retention by customizing services, for determining customer priority and segmentation. In addition, the banking sector also uses R for improving their business intelligence, marketing strategies, and data visualization. R is used by Bank of America to calculate financial loses and create financial reports.
R PROGRAMMING HELP IN SOCIAL MEDIA: Social media uses R for performing complex user-oriented study, that is, user sentiment and behaviour analysis. Based on interpretations, recommendations and ads made are upgraded. Not only does this focus on the searches conducted by the users but it also gauges the tone and subject of the posts that the user recently shared or interacted with. The knowledge is used to streamline the content that is shown to users and the advertisements that are presented to them. Social media sites not only gather input data based on users’ activity on the given site, but on related social forums as well, which is why a conversation on WhatsApp regarding a trip often results in sudden holiday package advertisements popping up on one’s Facebook feed. Besides, social sites also use R for analysing user sessions, traffic, and so on. Facebook’s ‘people you may know’ recommendation is facilitated by R, which predicts colleague or peer interaction and updates its networking graphs accordingly. Twitter uses R for data visualization and semantic clustering.
R PROGRAMMING HELP USED BY GOVERNMENTS: Government departments need to deal with Big Data and R is of great use to them; it helps them to maintain records, to organize, process, and analyse their censuses, and perform various other tasks that involve data analysis. R plays a crucial role in decision-making processes including economic and healthcare policies, social welfare policies, law making, and governance. It is used for assessing past performance to estimate the progress, success, or failure of policies based on which necessary changes are made. R is also used for weather analysis, passing drug regulations, predicting disaster and implementing disaster prevention, analysing disaster impact, and so on. FDA uses R for pre-clinical testing, drug evaluation, estimation of possible reactions that could be caused by certain chemicals or food products. National Weather Service uses R for forecasting weather, visualizing the forecasts, and analysing conditions of areas affected by disasters. Apart from the abovementioned industries, several others use R, such as the print media: New York Times uses R to analyse data and prepare graphs before they are printed.
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