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R Programming
R programming deals with statistical computation to define the user graphic interface which provides the opensource and free environment development for the implementation of the problems.
The important topics include data mining and warehousing,Hypothesis test,Clustering,Time series analysis,Regression modeling
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R Programming Assignment help :
 RStudio,R Markdown,data types,operations,numbers,characters ,composites,Vectors,creating sequences,common functions,tabular data,continuous data,R style,data frames,Multivariate statistical summaries ,ggplot2 graphics,QQ plots,ANOVA,Linear regression,multicollinearity,Diagnosing,interpreting regression,plyr package,splitapplycombine
 language elements ,R+Knitr+Markdown+GitHub,Data input,output,Data storage formats,Subsetting objects,Vectorization,Control structures,Functions,Scoping Rules,Loop functions,data manipulation ,dplyr ,profiling,Statistical simulation ,S3,S4,Reference classes,Performance profiling.
 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 kfold crossvalidation,,Detecting Fraudulent Transactions,Clustering methods,,Classification methods,,Imbalanced class distributions and methods ,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,kNearest neighbors
 R programming techniques, statistical analyses,data objects,loops,importing/exporting datasets,graphics,ttests,ANOVA,linear regression,non parametric tests,logistic regression

Overview of R, R data types and objects, reading and writing data ,Control structures, functions, scoping rules, dates and times ,Loop functions, debugging tools ,Simulation, code profiling ,swirl Programming

R statistical package for data analysis,R for descriptive statistics , graphics, inferential statistical analyses ,regression analysis, read and write data files, data manipulations ,R script files, R functions