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Autor(en): 
  • Thomas Mailund
  • Advanced Object-Oriented Programming in R: Statistical Programming for Data Science, Analysis and Finance 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2017  
    Genre:  EDV / Informatik 
     
    B / Compilers & interpreters / Computer programming / Mathematical & statistical software / Mathematical statistics / Maths for computer scientists / Probability and Statistics in Computer Science / Professional and Applied Computing
    ISBN:  9781484229187 
    EAN-Code: 
    9781484229187 
    Verlag:  Springer EN 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  2058 gr 
    Seiten:  110 
    Illustration:  XV, 110 p. 10 illus., schwarz-weiss Illustrationen 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software.

    After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding endeavors. You'll then be able to visualize your data as objects that have state and then manipulate those objects with polymorphic or generic methods. Your projects will benefit from the high degree of flexibility provided by polymorphism, where the choice of concrete method to execute depends on the type of data being manipulated. 

    You will:
    • Define and use classes and generic functions using R 
    • Work with the R class hierarchies
    • Benefit from implementation reuse
    • Handle operator overloading
    • Apply the S4 and R6 classes 

      



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