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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.
The important topics include data mining and warehousing,Hypothesis test,Clustering,Time series analysis,Regression modeling
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R Programming Assignment help :
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- 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 ,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
- R programming techniques, statistical analyses,data objects,loops,importing/exporting datasets,graphics,t-tests,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