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This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.
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TwitterSubscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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15 Global import shipment records of R Salt with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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TwitterSubscribers can access export and import data for 80 countries using HS codes or product names-ideal for informed market analysis.
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TwitterLlc R Prof Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.
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437 Global import shipment records of R Door Mat with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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TwitterMichael R Hanono Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.
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19 Global import shipment records of R Human with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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18 Global import shipment records of R Coat with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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TwitterRosendo R Rodriguez Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.
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TwitterSubscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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TwitterSubscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.
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7055 Global import shipment records of R Diesel Engine with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.
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TwitterView details of Sweet Potato Noodle Import Data of R And G Corp Supplier to US with product description, price, date, quantity, major us ports, countries and more.
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TwitterExplore detailed Work Light import data of R And R Products Inc in the USA—product details, price, quantity, origin countries, and US ports.
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TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.