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R programming deals with statistical computation to define the user graphic interface which provides the open-source and free environment development for the implementation of the problems. It is the language oriented optimization in which there is an availability of the unaltered code of R for programs which create the differences in the graphics of S.
It includes the methodology which is highly extensible in providing the routes of open source for the graphical techniques. For the implementation of the programming, it involves the platform for Unix, Ms-dos, Macros windows.
The important topics included in this are given as follow:
Data mining and warehousing
Hypothesis test
Time series analysis
Regression modeling
R’s interface involves two function i.e. R-wrapper and C functions for the declaration of a initial functions in any C language. Further, it includes a data type that takes as input an expression in SEXP’s and always returns an output for the objects.
R implementation provides a scheme which is being developed to share the efficient scoping of semantics in terms of lexical analysis. Moreover, it works on the language like C and FORTRAN. It provides the interface for graphical user to develop command lines for numerous data oriented language. We use conceptual parts of source code for its implementation so that it can also work in the field of research analysis for the projects as well as medicine and clinical operation trial. It is further available on project that works on general public references to provide the multi paradigm and object oriented sources.
The general public license includes the publication quality which acts as a strength in the field of source code in terms of symbols and formulas. The applications to be worked on are included as:
Data structures
Documentation and compilation of functions
Probability distributions
Simulations and OO guide field
R environment works in the field of calculating the quality of graphics which integrate the facility of data handling and storage. It provides the on-line formats and hardcopy for the conceptual documentation of latex in order to provide the supplement of various programs. It includes the recursive functions for defining the input and output for the users which can help to develop the loops and conditions for the respective individual programming language.
Integrated development environment i.e. IDE provides a way for the open source data to give a platform to develop a framework for the different version of RSTUDIO server and desktop design. It involves the resemblance of regular application to run a program with the help of web browsers.
The major software that would be available for accessing the imperative details of the R programming language includes as following:
RCPP :-It is an application program interface that access the declarations and attribute efficiently used for code integration. It extensibility provides the interchanging of the objects for the easy RCPP facility of language.
RCPP Eigen:-It is the optimization of the Eigen values to generate the linear algebra for the sparse matrices in order to obtain the high performance and decompositions for the matrix methods.
CRAN is the set of protocols which include the mirror for the network to be minimized for load which in turn use the package to download the data for the programming details of the consistent language. It includes the file transfer protocols which help to transfer the store files to get the update of the latest versions for the documentation. It provides the various key roles which are described as:
Array and matrices
Integration of coherent tools
Graphical representation of language
Big data in R provides the series of the high performance for data miners for developing the packages that can efficiently work on the processors based on MPI for the analysis of batch code. It initiates the modern computation to be focused on multi core machines for the process of parallelism to obtain the further final solutions. Moreover, the implementation of parallelism includes:
RMPI:- In this, there is one processor that has more power to control the efficient task of other processor in terms of clusters and involves the simulation of applied statistics which give useful production like bootstrap method.
PBDR:- It works for homogeneous environment that attain the singular value matrix for the large data which has further no restrictions to work in SPMD parallelism.
Functional programming is the differential analysis which works on the different aspect of factors to obtain the vectors and variables. It helps to manipulate creation of functions tools which involve the storage factors to be included in the list to pass the variables for the inbuilt functions.
RMETRICES is an open development project which is available for finance. The main feature does not need to be accessed on C++ or C language which involve statistical component package to increment the molecules for the R-forge. It also involves the advantage which leads to the project development are:
Object oriented language
Data visualization
Pathbreaking research in parallel computation
Complexity in finance
Prototype for simulation documentation
Packaging for statistical model
In this, Data structures provide a subtype with a variant variable in order to obtain the garbage collector complication which is used to create and delete the unmatched object.
The advanced topics included are given in the following as:
Robust error handling
Bayesian probability
Mapping and profiling of graphics
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Topics for Assignment help :

R Markdown
data types
creating sequences
common functions
tabular data
continuous data
R style
data frames
If/else statements
For/while loops
Multivariate statistical summaries
ggplot2 graphics
QQ plots
Linear regression
interpreting regression
plyr package

R programming environment
language elements
data structures
Data input
Data storage formats
Subsetting objects
Control structures
Scoping Rules
Loop functions
data manipulation
Statistical simulation


R's environment
Object oriented programming
Reference classes
Performance profiling
Exception handling
Debugging R code
Creating R packages
Unit testing
C++ coding
Calling dynamically
loaded libraries
Writing C/C++ code
compiling C/C++ code
R's performance
C++ linear algebra library 

Predicting Algae Blooms

  • Descriptive statistics
  • Data visualization
  • Strategies to handle unknown variable values
  • Regression tasks
  • Evaluation metrics for regression tasks
Predicting Algae Blooms
  • Multiple linear regression
  • Regression trees
  • Model selection/comparison through k-fold cross-validation
Detecting Fraudulent Transactions
  • Clustering methods
  • Classification methods
  • Imbalanced class distributions and methods for handling this type of problems
  • Naive Bayes classifiers
  • Precision/recall and precision/recall curves
Classifying Microarray Samples
  • Feature selection methods for problems with a very large number of predictors
  • Random forests
  • k-Nearest neighbors

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