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R is a computer programming language that is also a software programming environment which is used for statistical calculations and graphical methods. Two computer scientists Ross Ihaka and Robert Gentleman developed this language in the year 1993. Some of the statistical methods used in development of R are linear regression, machine learning algorithm, statistical reference and time series. This programming language is not used for research and scientific study, but it is also employed by few business giants like Google, Airbnb, Uber and Facebook. A series of steps are followed in order to perform data analysis using R. This includes processes like programming, transforming, discovering, designing, modeling and communicating.
One of the main advantages of using R programming language is that it is easily accessible. Since R is registered under General Public License, it can be downloaded for free. Commercial applications can also use them since most of the R packages are also available under the same license.
Although R is a domain-specific programming language, it has got a good ranking every year. According to the list of most popular programming languages published by IEEE, R was ranked 5th in 2016 and 6th in 2015. Due to its popularity, R is being widely used in the field of data sciences and machine learning.
Be it Windows, Mac or Linux, you can find an appropriate distribution of R for every operating system. Cross-platform compatibility is an important consideration while choosing a programming language. The code that is used for one platform can be easily modified according to the requirements for another platform.
R is programming language that offers greater potential and scope for innovation. This is why majority of the tech giants in the world have adopted R programming for their applications. With the right mix of power and simplicity, R enables companies to make calculated business decisions.
R is programming language that offers greater potential and scope for innovation. This is why majority of the tech giants in the world have adopted R programming for their applications. With the right mix of power and simplicity, R enables companies to make calculated business decisions.
R programming is a language that is quite simple and easy to learn. The main purpose of using R is to solve data analysis issues. Data and clear objectives are two main prerequisites to learn R programming in the right way. A well written set of goals or objective are required in order to perform the analysis and draw the appropriate conclusions. It is important to keep in mind that R is a programming that is created based on language S. Unlike other programming languages like Java, PHP and Python, the syntax of R is unique and different. Also, not many codes and loops are involved while writing the R language. When there is bulk data, R offers many tools like lists, frames, data tables and matrices. These features make the analysis faster and easier.
R is an interpreted language. All the commands given through the key board are executed directly and there is no need to build a specific program like other commonly used programming languages. The syntax used in R is intuitive in nature. A function that needs to be executed is always written in parenthesis even when there is nothing between them. For example,ls (). If parenthesis is not used, R displays the content of the function. Whenever R program is running, the data, variables, functions and results are stored in the memory of the computer in the form of objects. No temporary files are required for the smooth functioning of R.
When the user executes a function using -commands, the results are displayed on the screen and stored in the objects. R also enables data files to read and processed from local disk and remote servers with the help of internet. For efficient analysis of data, R also offers flexible graphical solutions. There are two types of graphical solutions- high level plotting functions and low level plotting functions. The former helps in creating a new graph while the latter helps in adding new elements to the existing graph. The creation of graphs takes place in accordance with the graphical parameters that are defined by default. These can also be modified depending upon the requirements of the function.
Majority of the financial institutions employ data sciences to make informed business decisions. In this regard, R programming provides advanced statistical suite to perform all the financial processes in an efficient manner. It also provides scope for financial institutions to monitor risk performance and create visualizations like density plots and candlestick charts. Time series models of R can be used to predict the prices of stock market.
R programming is widely employed in the banking sector for credit risk management and risk analytics. It is easy for banks to enable mortgage haircut model is crucial to perform functions like volatility of market price, distribution of sales price and rate of loan defaults.
R programming is extremely useful to crunch data and process information in the areas like genetics, epidemiology, drug invention and bioinformatics. The advanced suites in R programming are beneficial for performing tasks like pre-clinical trials and drug safety testing. The bioconductor package in R can be used to analyze genomic data.
The data found in social platforms are usually unstructured in nature. By merging various statistical tools like sentimental analysis along with R, data mining from different social media channels have become much easier. R programming is also being widely employed to study social media analytics, demographic segmentation of audience and targeting the potential customers. Companies can also build effective R models to study user sentiments and buying patterns. It is also effective in enhancing the reach and lead generation of digital marketing campaigns.
The e-commerce industry literally thrives on data science. R is the standard tool that is being used in analyzing the structured and unstructured data in online shopping portals. It is also extensively used for cross-selling products to customers. Cross-selling is done by studying the additional products that the customer has searched or looked for. This can be done by using the specialized features in R. R programming is an important tool employed for descriptive analysis. This is a section of statistics that helps you to understand the main aspects of your data. It also helps you to find measures like central tendency, kutosis and skewness. R can also easily analyze exploratory data due to its aesthetics and interactivity. Analyzing discrete and continuous probability distribution has also become easier with the use of R programming.
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