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In this Program we provide knowledge about following topics –

  1. Data Objects- In this section there is description about the different aspects of data types and structures in R. Operators such as c and : will be used in this section.
  2. Importing data- The first thing in a statistical data analysis system is to import data. R provides a few methods to import data.
  3. Data Manipulation- The programming language in R provides many different functions and mechanisms to manipulate and extract data.
  4. Writing functions- Most tasks are performed by calling a function in R. In fact, everything which have done so far is calling an existing function which then performed a certain task resulting in some kind of output. A function can be regarded as a collection of statements and is an object in R of class `function’. One of the strengths of R is the ability to extend R by writing new functions.
  5. Efficient calculations
  6. Graphics – One of the strengths of R above SAS or SPSS is its graphical system, there are numerous functions. You can create `standard’ graphs, use the R syntax to modify existing graphs or create completely new graphs.R offers a remarkable variety of graphics. There are two kinds of graphical functions: the high-level plotting functions which create a new graph, and the low-level plotting functions which add elements to an existing graph. The graphs are produced with respect to graphical parameters which are needed by default and can be modified with the function par.
  7. Statistics- The base installation of R contains many functions for calculating statistical summaries, data analysis and statistical modeling.The package stats contains functions for a wide range of basic statistical analyses: classical tests, linear models (including least-squares regression, generalized linear models, and analysis of variance), distributions, summary statistics, hierarchical clustering, time-series analysis, nonlinear least squares, and multivariate analysis. Other statistical methods are available in a large number of packages. Some of them are distributed with a base installation of R and are labelled recommanded, and many other packages are contributed and must be installed by the user.
  8. Object Oriented Programming- The programming language in R is object-oriented, In R this means:
    1. All objects in R are members of a certain class.
    2. There are generic methods that will pass an object to its specific method.
    3. The user can create a new classes, new generic and speci c methods.
  9. R Language objects
  10. Calling R from SAS- The SAS system provides many routines for data manipulations and data analysis. It may be hard to convince a `long-time’ SAS user to use R for data manipulation or statistics. However, the graphics in R are superior compared to what SAS/GRAPH can offer. Some graphs are unavailable in SAS/GRAPH or very time consuming to program.
  11. Defaults and preferences in R, Starting R
  12. Creating an R package- R packages are (after a short learning phase) a comfortable way to maintain collections of R functions and data sets. As an article distributes scientific ideas to others, a package distributes statistical methodology to others. An R package can be thought of as the software equivalent of a scientific article.
  13. Calling R from Java- The package `R Java’ can be used to call java code within R. This become useful when there is need to extend java programs with the numerical power of R, or build java GUI’s around R.
  14. Creating fancy output and reports

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