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Udemy - R Graph Essentials
Author: naag

Udemy - R Graph Essentials
MP4 | Video: 1280x720 | 73 kbps | 48 KHz | Duration: 2 Hours | 285 MB
Genre: eLearning | Language: English

A visual and practical approach to learning how to create statistical graphs using R Details
R Graph Cookbook (repost)
Author: interes

Hrishi Mittal, "R Graph Cookbook"
2011 | ISBN: 1849513066 | 272 pages | PDF | 4 MB

Detailed hands-on recipes for creating the most useful types of graphs in R - starting from the simplest versions to more advanced applications
Learn to draw any type of graph or visual data representation in R
Filled with practical tips and techniques for creating any type of graph you need; not just theoretical explanations
All examples are accompanied with the corresponding graph images, so you know what the results look like
Each recipe is independent and contains the complete explanation and code to perform the task as efficiently as possible
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R Graph Cookbook (repost)
Author: interes

R Graph Cookbook by Hrishi Mittal
English | 2011 | ISBN: 1849513066 | 272 pages | PDF | 4 MB
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Beginning R: The Statistical Programming Language
Author: Bayron

Beginning R: The Statistical Programming Language by Mark Gardener
English | 2012 | ISBN: 111816430X | 504 pages | PDF | 18 MB
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R Graph Cookbook
Author: tot167

Hrishi Mittal, "R Graph Cookbook"
Pa ckt Pub lishing | 2011 | ISBN: 1849513066 | 272 pages | PDF | 7,7 MB
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R Graphs Cookbook - Second Edition
Author: AlenMiler

R Graphs Cookbook - Second Edition by Jaynal Abedin
English | 30 Oct. 2014 | ISBN: 1783988789 | 368 Pages | EPUB/MOBI/PDF (True) | 42.2 MB
With: Code Files

Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries. Details
R for Statistics
Author: arundhati

Pierre-Andre Cornillon, Arnaud Guyader, "R for Statistics"
2012 | ISBN-10: 1439881456 | 320 pages | PDF | 10 MB
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R Programming LiveLessons : Fundamentals to Advanced (Full)
Author: Max-X

R Programming LiveLessons : Fundamentals to Advanced (Full)
English | .MP4 | h264, yuv420p, 960x540, 30.00 fps(r) | aac, 48000 Hz, stereo | 8.55 GB
Genre: E-learning

R Programming: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization. data munging, regression, classification, clustering, modern machine learning and more. Details
Udemy - R - Business Analytics Using R Programming [repost]
Author: house23

Udemy - R - Business Analytics Using R Programming
MP4 | AVC 470kbps | English | 1280x720 | 30fps | 17 hours | AAC stereo 59kbps | 2.68 GB
Genre: Video Training

This course ensure quick learning in a simplified way. It explains the most important aspects of working on data and conduct analysis through example. You will start by learning how to install and navigate R studio. Learn Data/Object Types and Operations, Importing into R, and Loops and Conditions. you will be introduced to the use of R in Analytics, where you will learn a little about each object type in R and use that in Data Mining/Analytical Operations. learn the use of R in Statistics, using R to evaluate Descriptive Statistics, Probability Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear Models, Non-Linear Regression, and Trees. Learn to create 2-dimensional Univariate and Multi-variate plots. Details
Learning Path: R Programming for Data Analysts
Author: FenixN

Learning Path: R Programming for Data Analysts
HDRips | MP4/AVC, ~1500 kb/s | 960x528 | Duration: 15:12:01 | English: AAC, 128 kb/s (2 ch) | 5,46 GB
Genre: Development / Programming

R Programming Data Analyst Learning Path, is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning, network analysis, web graphics, and techniques for dealing with large data, both in memory and in databases.
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