Introduction
R is a software language used to analyse scientific data of market research, data mining, surveys etc., this statistical analysis provides indication of rise and fall of demand and supply of a particular product in the market. R process voluminous data.
Processing voluminous data is a complex task. R can analyze data, transform data and can project data patterns. In R, analytical results can be presented using different graphical representations in the form that can be easily understood by the user. The features that make R useful in data handling are:
- Data Transformation: Voluminous data vary in type as it is obtained from different sources. To process voluminous data it is critical to transform it into a common type. R support features to give specifications according to which data transformation can take place.
- Correlation and Regression Analysis: R use Correlation and Regression analysis for prediction and forecasting. Prediction and forecasting is an essential component of machine learning. Thus, use of R in machine learning is noticeable. Correlation and Regression analysis is also used to find causal relationships. R is able to find causal relationships between independent and dependent variables in the domain of epidemiology, social sciences, psychology and statistics.
- Analysis of variance: R uses analysis of variance to process voluminous data. To process voluminous data effectively it needs to be grouped based on desired characteristics. Processing multiple groups and projecting hidden variability among them is an important aspect because of which R is used.
- Multivariate analysis of variance: R uses multivariate analysis of variance to conduct comparative analysis of data of random variables. Often the value of random variables is unknown. R works on different types of population and enables users to define attributes on the basis of which different types of population can be categorized.
- T-tests: When a set of two populations is used and it is required to find differences between them R use T-test. t-Test is used to find the mean difference between the two sets of population and on the basis of which project the different pattern that exists between the two sets of population.
Application of R Programming
R can execute machine learning algorithms, can conduct text analysis and can work on big data. R can perform statistical analysis on small projects as well as large projects having variable complexity. With the use of R, organizations can improve efficiency and minimize risk in decision taking.
R can be used to test research hypotheses and can perform top-down statistical analysis and can predict hidden patterns and models in the data set. R can work with different data file formats and can establish connections with ODBC to manage and manipulate data. R can integrate Python extensions to work with machine learning algorithms such as association, segmentation and classification algorithms.
Application of R includes:
- Industries
- Research Projects
- For Teaching and Learning
Industries:
R is used in market research, health care, government and retail to conduct statistical analysis, reporting and deployment. With the use of R industries are do predictive analysis and can decrease risk in making decisions.
In market research R can analyze trends, forecast and plan to reach a conclusion having high accuracy. In market research identifying and predicting relationships in association with structural equation modelling is required and R fulfills this requirement.
Governments require in-depth analysis of citizen needs, analytical reports of government policies and statistical analysis of budget allocations, to do this R is used. R can perform exploratory data analysis by working on a number of variables simultaneously. Citizen preferences depend on different variables and to predict citizen preferences with accuracy there is a need to study the relationship between different variables to do this R is used. Government uses R to mitigate fraud and threats. R uses Linear regression, Monte Carlo simulation and geographical analysis to conduct in-depth analysis of data to improve citizen life.
In healthcare, R can enable us to overcome barriers of problematic data conventions and outdated practices. R can analyze patient data with the help of linear regression, Monte Carlo simulations and geographical analysis. Using correlation and regression R can predict patient treatment and dose responses in clinical trials. Univariate and multivariate modelling techniques of R can be used to conclude patterns with high percentage of accuracy when working with patient data. Using R doctors can prioritize treatment and determine the severity of each case.
In retails R can be used to do customer analysis and customize deals to customers. R can project selling patterns to make appropriate decisions. R can drill into browsing and purchase history of customers, and can drill into customer preferences. R is able to project market offers to maximize profit.
Research Projects:
Survey research is gaining importance as it helps in making decisions. Survey research produces voluminous data that can be easily analysed using R. Research is conducted in business, in the public sector and in the private sector to understand hidden patterns and designs. Research is conducted on population samples and these samples are collected using design techniques such as stratified, clustered or multistage sampling. R process data set considering these sampling techniques and project relationships that exist in data and predict results. R includes techniques such as statistical learning and preference scaling enhancing its practical applicability to research.
Teaching and Learning:
R has the capability to lead the domain of data science. R is designed considering its academic use thus it has an easy-to-use software interface. R has learning material that can develop skills in students to use R and improve their decision-making process when working with voluminous data. Industries are in search of students that possess data science skills. A student having analytical skills in processing voluminous data is preferred by industry.
Conclusion
Working domain of R is large. R supports data scientists, marketing executives, industry research analysts and different government agencies in collection of data, analysis of data and report generation.
R can be used to identify internal data structures, can be used in student assessment and can provide hidden patterns of industrial growth.
