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Using R for Data Management, Statistical

Using R for Data Management, Statistical

Using R for Data Management, Statistical Analysis, and Graphics by Nicholas J. Horton, Ken Kleinman

Using R for Data Management, Statistical Analysis, and Graphics



Download Using R for Data Management, Statistical Analysis, and Graphics




Using R for Data Management, Statistical Analysis, and Graphics Nicholas J. Horton, Ken Kleinman ebook
Publisher: CRC
Page: 296
Format: pdf
ISBN: 1439827559, 9781439827550


R is a free and open language and environment for statistical computing and graphics. The aim of the workshop is to provide an introduction to R for academics, researchers and professionals and provide insights into possibilities of data analysis and statistics with case studies and examples. Information graphics are, more Though his book can get extremely technical and is geared toward someone with significant programming experience (as well as statistical analysis skills), his Seven Stages of Visualizing Data are relevant to anyone interested in displaying data. The breadth of both data management capabilities, statistical analysis, and graphics are much larger in SPSS than in Excel. This course outline includes R introduction (including getting unstuck), Data Management, Graphics, and Statistical Analysis and Data Mining. The primary aim of the course is to learn methods in R for: 1) data manipulation 2) exploratory data analysis, 3) data analysis using standard statistical methods, and 4) graphical presentation of data and results. An information graphic generally deals with answering and analyzing knowledge, whereas a data visualization is typically a more complex examination of a set of statistics. More detail on the workshop is available in the website: http://www.iiitmk.ac.in/Rworkshop/. I have recently started a PhD in Hydrology and have chosen R to perform the majority of my statistical analyses and to produce publication quality graphics. ACM San Francisco Bay Area Professional Chapter course. I also use it to write code (Making good progress but still learning) for managing and cleaning up hydrological and climate data (data aggregation, missing data, extracting indicators for testing etc.).

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