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Data Science

Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and;

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Doing Meta-Analysis with R

* Contains two introductory chapters on how to set up an R environment and do basic importsmanipulation of meta-analysis data, including;

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Beginning Data Science in R 4

. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for;

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Statistical Analysis of Network Data with R

Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the;

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Data Manipulation with R

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled;

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Data Munging with R

Data Munging with R shows readers how to take raw data and transform it for use in computations, tables, graphs, and more. Whether they;

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A Tour of Data Science

, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.;

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A Tour of Data Science

to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation;

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Beginning Data Science in R 4

. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for;

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Quantitative Corpus Linguistics with R

corpus linguists, it introduces R programming with emphasis on: data processing and manipulation in general; text processing with and;

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Quantitative Corpus Linguistics With R

corpus linguists, it introduces R programming with emphasis on: data processing and manipulation in general; text processing with and;

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Getting Started with R

R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural;

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Instant Heat Maps in R

manipulation. Heat Maps in R How-to is the book for you if you want to make use of this free and open source software to get the most out of your data;

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Advancing into Analytics

about data manipulation into R and Python programming. This practical book guides you through: Foundations of analytics in Excel: Use;

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Statistics And Data With R

and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R. Assuming no previous knowledge;

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R for Health Data Science

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life;

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R for Health Data Science

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life;

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R for Data Science Cookbook

resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the;

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Biostatistics and Computer-based Analysis of Health Data using R

Biostatistics and Computer-Based Analysis of Health Data Using the R Software addresses the concept that many of the actions performed;

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Data Wrangling with R

wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the;

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Python for R Users

scientists and data scientists that know R and would like to learn Python or are familiar with Python and want to learn R. It also functions as;

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Practical R 4

many examples and projects, this book teaches you how to get data into R and how to work with that data using R. Once grounded in the;

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R Machine Learning By Example

Learn * Utilize the power of R to handle data extraction, manipulation, and exploration techniques * Use R to visualize data spread across;

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Geospatial Health Data

geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and;

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Manipulation

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SAP Lumira, Discovery Edition

charts and infographs. Start by acquiring your data, either offline or live, and explore the available data manipulation actions. Next, create;

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Basics of Matrix Algebra for Statistics with R

A Thorough Guide to Elementary Matrix Algebra and Implementation in R Basics of Matrix Algebra for Statistics with R provides a;

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