Statistics and graphs are staples in the data analytics field. Many solutions today are obtained by observing patterns and associations in bulk volumes of real time data. With growing volumes and sophistication, one needs to be able to programmatically analyse the statistical data. R is one that comes as the solution.
What exactly is R?
R is a programming language and a software environment that facilitates computing in an adept statistical manner along with help of graphics. It is a GNU project that has been primarily written in C and FORTRAN. It is predominantly a command line interface based language. However, with increasing popularity for graphical user interfaces, there are many available for use now.
Features
The statistical features provided are very intuitive. It includes varied features like modelling in non-linear format, modelling in linear format, analysis of time series, classification of objects and categorizing them into clusters etc. The features can be extended easily through functions and extensions. A community called the R community is involved actively in contributing packages. You can write code in C, C++ and FORTRAN for computer intensive tasks. Using the static graphics provided, one can create publication quality graphs. Dynamic graphs can be created by using extensions.
R supports matrix arithmetic. It has a wide variety of data structures that include scalars, vectors, matrices and data frames. Procedural programming with functions and object-oriented programming with generic functions is supported.
Training
When it comes to learning the programming language, there are some prerequisites that need to be satisfied. One should possess basic knowledge of statistics on topics like t-test, chi-square test and regression. Knowing the difference between descriptive and inferential statistics is important. Prior day-to-day programming knowledge is also required.
The objectives include mastering the use of the R console, R's flow control and data structures. You will also be able to learn to use vectored calculations. Knowing R functions, basic R graphics, installing R packages, exploring the R documentation, learning to avoid pitfalls, using R for descriptive and inferential statistics, writing statistical models are also to be covered.
One could start with the history and overview of R, its advantages and disadvantages, installation steps and exploration of the documentation. Then one can proceed to learning to use the R Console, how to get help and exploring documentation, writing and executing 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 onto control flow, functions and branching.
After that, one should learn to handle built in data and read local data and web data. Following that, one moves onto using features of inferential and descriptive statistics and regression models that are linear and non-linear.
Then one can learn the software aspects of the language like installing the software and its extensions. One also learns the graphics and labels and exporting them.
R Online training has been facilitated at many centres across. One of the pioneer statistical programming languages, R is learnt extensively for research and development purposes.
What exactly is R?
R is a programming language and a software environment that facilitates computing in an adept statistical manner along with help of graphics. It is a GNU project that has been primarily written in C and FORTRAN. It is predominantly a command line interface based language. However, with increasing popularity for graphical user interfaces, there are many available for use now.
Features
The statistical features provided are very intuitive. It includes varied features like modelling in non-linear format, modelling in linear format, analysis of time series, classification of objects and categorizing them into clusters etc. The features can be extended easily through functions and extensions. A community called the R community is involved actively in contributing packages. You can write code in C, C++ and FORTRAN for computer intensive tasks. Using the static graphics provided, one can create publication quality graphs. Dynamic graphs can be created by using extensions.
R supports matrix arithmetic. It has a wide variety of data structures that include scalars, vectors, matrices and data frames. Procedural programming with functions and object-oriented programming with generic functions is supported.
Training
When it comes to learning the programming language, there are some prerequisites that need to be satisfied. One should possess basic knowledge of statistics on topics like t-test, chi-square test and regression. Knowing the difference between descriptive and inferential statistics is important. Prior day-to-day programming knowledge is also required.
The objectives include mastering the use of the R console, R's flow control and data structures. You will also be able to learn to use vectored calculations. Knowing R functions, basic R graphics, installing R packages, exploring the R documentation, learning to avoid pitfalls, using R for descriptive and inferential statistics, writing statistical models are also to be covered.
One could start with the history and overview of R, its advantages and disadvantages, installation steps and exploration of the documentation. Then one can proceed to learning to use the R Console, how to get help and exploring documentation, writing and executing 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 onto control flow, functions and branching.
After that, one should learn to handle built in data and read local data and web data. Following that, one moves onto using features of inferential and descriptive statistics and regression models that are linear and non-linear.
Then one can learn the software aspects of the language like installing the software and its extensions. One also learns the graphics and labels and exporting them.
R Online training has been facilitated at many centres across. One of the pioneer statistical programming languages, R is learnt extensively for research and development purposes.
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