Data, also known as the backbone of research, is a base, on which the entire research depends on. After months of backbreaking research, scholars gather huge amounts of data (relevant as well as irrelevant). The collected data must be integrated and organised in an apt manner. And this can be done through the data analysis process. Data analysis, the process of evaluating data using statistical and analytical tools to obtain meaningful information is an ordeal in every scholar life. 

It an integral part of a research and breaks down the complex problem into simple ones, provides a theoretical base to the study and lends credibility to researched data. Today, there are several tool/software available out there which lets you conduct data analysis. This includes SPSS, Excel, Tableau public, and many more. Among all these, the most popular data analysis tool is R language which is widely used in research and academics.

R, developed in the 90s, is a robust language that is used to evaluate and organise data in a research. R offers several statistical techniques such as linear & non-linear modelling, time series analysis, clustering, etc. One of the strengths of R is that it lets you develop quality plots and contains plenty of mathematical symbols and formulae required to conduct the test. 

However, R requires an integrated software for data calculation, manipulation, and graphical display. The environment includes

• Data handling and storage facility
•  Operators for calculations on arrays
•  Coherent and integrated collection of data analysis intermediate tools 
•  A simple and well-developed programming language consisting of conditional loop and input/output facilities.

Of the several benefits of R programming language for data analysis, a few are:

1. Missing values - Real data consists of missing values. However, missing values are a significant part of the R language.  R includes several functions that have argument and knows how to handle the missing values.
2. Interactive language - Data analysis is an interactive process. What you see/done at one stage determines the next and hence, interactivity is important; which is present in R.
3. Functions as a first class object - Functions such as mean and median, are objects that can be used like a data. R lets you change your analysis to utilise the median function rather than the mean, at ease.
4. Graphics - Graphics are the crucial feature of the data analysis. This is because, it is effortless to explore data by developing the relevant graphs. R has several graphic function which lets you convey the important features of data. 
5. Data wrangling - R has several packages that simplifies the task of preparing the data for data analysis. The data cleaning as well as transformation is also a straightforward process in R thereby helping you to reduce the time spent on them.  

R has numerous features that can assist you to perform your data analysis at ease and accurately. Hence, spend some time on learning this language in the initial stage and then clean, manipulate and conduct data analysis efficiently.

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