Statistics and graphs are staples in the field of data analysis. Many solutions today are obtained by observing patterns and associations in large volumes of data in real time. With increasing volumes and sophistication, it is necessary to be able to programmatically analyze statistical data. R is one that comes as the solution.
What exactly is R?
R is a programming language and software environment that facilitates computing in an expert statistical manner along with the help of graphics. It is a GNU project that has been written mainly in C and FORTRAN. It is effectively a command line interface based language. However, with the increasing popularity of graphical user interfaces, there are now many available for use.
Characteristics
The statistical features provided are very intuitive. It includes various features such as non-linear format modeling, linear format modeling, time series analysis, object classification and categorization into groups, etc. Features can be easily extended through functions and extensions. A community called the R community is actively involved in contributing packages. You can write code in C, C++, and FORTRAN for computationally intensive tasks. Using the provided static graphics, publication-quality graphics can be created. Dynamic charts can be created by using extensions.
R supports matrix arithmetic. It has a wide variety of data structures including scalars, vectors, matrices, and data frames. Procedural programming with functions and object-oriented programming with generic functions are supported.
Training
When it comes to learning the programming language, there are some prerequisites that need to be met. One should have basic knowledge of statistics on topics such as the t-test, the chi-square test, and regression. It is important to know the difference between descriptive and inferential statistics. Previous knowledge of day-to-day programming is also required.
Objectives include mastering the use of the R console, R flow control, and data structures. You will also learn to use vector calculations. Knowledge of R functions, basic R graphing, installing R packages, exploring R documentation, learning to avoid pitfalls, using R for descriptive and inferential statistics, and writing statistical models will also be covered.
You might start with the history and overview of R, its advantages and disadvantages, installation steps, and browsing the documentation. Then one can proceed to learn how to use the R Console, how to get help and explore documentation, write and run scripts.
Proceeding to programmatic constructs with variables and data structures, data types and other basic programming constructs like functions are next on the chart. One can move on to control flow, functions, and branching.
After that, one must learn how to handle embedded data and read local data and web data. After that, one moves on to the use of inferential and descriptive statistical features and regression models that are linear and non-linear.
Then one can learn the software aspects of the language, how to install the software and its extensions. Graphics and labels are also learned and exported.
A Online training has been made available in many centers. One of the pioneering statistical programming languages, R is widely learned for research and development purposes.
