r programming


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R programming for data science
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I started using R in 1998 when I was a college undergraduate working on my senior thesis. The version was 0.63. I was an applied mathematics major with a statistics concentration and I was working with Dr. Nicolas Hengartner on an analysis of word frequencies in classic

Programming with big data in R
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Page 1. Programming with Big Data in R George Ostrouchov Oak Ridge National Laboratory and University of Tennessee Page 2. Introduction to R and HPC Why R : Programming with Data Chambers. Computational Methods for Data Analysis. Wiley, 1977

The art of R programming
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This book is for those who wish to write code in R, as opposed to those who use R mainly for a sequence of separate, discrete statistical operations, plotting a histogram here, performing a regression analysis there. The readers level of programming background range from

Monte Carlo Simulation Studies in Item Response Theory with the R Programming Language R Programlama Dili ile Madde Tepki Kuraminda Monte Carlo
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Monte Carlo simulation studies play an important role in operational and academic research in educational measurement and psychometrics. Item response theory (IRT) is a psychometric area in which researchers and practitioners often use Monte Carlo simulations

A survey on classification in R programming using data mining
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A long standing problem in Data mining is that data to be found unstructured due to its different format. Classification is a crucial analysis topic in data mining. At a present time, data mining sector presents a brand new data model. The activation values of the hidden

Understanding decision tree algorithm by using R programming language
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Decision Tree is one of the most efficient technique to carry out data mining, which can be easily implemented by using R, a powerful statistical tool which is used by more than 2 million statisticians and data scientists worldwide. Decision trees can be used in a variety of

Notes on R: a programming environment for data analysis and graphics
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Page 1. Notes on R: A Programming Environment for Data Analysis and Graphics Bill Venables Dave Smith Department of Statistics The University of Adelaide Robert Gentleman Ross Ihaka Department of Statistics University of Auckland Martin Mächler

Notes on R: a programming environment for data analysis and graphics
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Page 1. Notes on R: A Programming Environment for Data Analysis and Graphics Bill Venable Dave Smith Department of Statistics The University of Adelaide Robert Gentleman Ro Ihaka Department of Statistics University of Auckland Martin Miachler

Object-oriented programming in R
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class. The model does support object references, and the methods can alter an object remotely. In this sense, the model is still sharply different from ordinary R programming , and we do not propose it as a replacement. However

Application of analytics using r programming for forecasting day-ahead electricity demand
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Analytics technologies are becoming increasingly important for the organizations to mine and get insights into large amount of structured and unstructured data, and also to enable effective decision making for the management. One of the challenges for power industry is to

Programming in R
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Page 1. Programming in R A Short Introduction Thomas Girke December 2014 Programming in R Slide 1/65 Page 2. Programming in R Control Structures Loops Functions Useful Utilities Calling External Software Running R Scripts Building R Packages Exercises

Good Practices in R Programming
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1. Work with Source files 2. Keep R source code well readable and maintainable 3. Do read the documentation 4. Do learn from the masters Read R (package) sources 5. Do not Copy Paste! Modularize into (small) Functions 6. Strive for clarity and simplicity 7. Test your

Performance Visualization of Bubble Sort in Worst Case Using R Programming in Personal Computer
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The present work attempts to perform a visual analysis of the performance of Bubble sort in the worst case in a personal computer (laptop). The said algorithm is implemented using R programming language and the run time of the Bubble sort in the worst case for the data size

Teaching and learning statistics and experimental analysis for environmental science students, through programming activities in R
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Abstract We present a constructionist approach for the teaching and learning of statistics and experimental analysis for environmental and biological science students, through computational activities based on the R programming environment4

R Programming Tips
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Upon the divestiture of ATT in 198 the source code for the S language became proprietary, whereas the C language remained in the public domain. The GCC compiler which was developed by Richard Stallman was licensed under the General Public License

R Programming II: Data Manipulation and Functions
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Page 1. UCLA Department of Statistics Statistical Consulting Center R Programming II: Data Manipulation and Functions Denise Ferrari denise@stat.ucla.edu April 0 2010 Denise Ferrari denise@stat.ucla.edu R Programming II: Data Manipulation and Functions UCLA SCC Page

Improving the reversible programming language r and its supporting tools
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Finally, the emulator was updated to support the new language features. The results of this project serve as a step in the evolution of the R programming tools. Suggestions for future work are presented. 1 Introduction 1.1 Background

Simulating Confidence Intervals for Mean and Variance using Real Data in R Programming Environment
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Research has shown that the use of computer simulation methods as an alternative to traditional methods enhances the understanding of the statistical concepts. The increasing availability of technology allows instructors and students to use computationally intensive

Introduction to Programming in R
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Page 1. Introduction to Programming in R Course Website: http://bcb.dfci.harvard.edu/˜ aedin/courses/R/ CENTERS FOR DISEASE CONTROL AND PREVENTION 26-28TH JUNE 2012 Aedin Culhane Email: aedin@jimmy.harvard

Application of Data Mining with R Programming to Implement ERP for Digitization of Valuation Reports on Commercial Vehicles and Passenger Cars
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This paper proposes an approach towards data mining with R programming implemented in ERP for complete digitization of valuation reports for commercial vehicles and passenger cars. This paper entails entry of basis data, customary for the operating system of ERP. This CSE PROJECTS

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