Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Text Mining with R: A Tidy Approach
Top Free Unix/Linux Books 🌠 - 100% Free or Open Source!
  • Title: Text Mining with R: A Tidy Approach
  • Author(s) Julia Silge and David Robinson
  • Publisher: O'Reilly Media; 1 edition (July 2, 2017); eBook (Creative Commons Licensed, 2024-02-02)
  • License: CC BY-NC-SA 3.0 US
  • Paperback: 194 pages
  • eBook: HTML and PDF
  • Language: English
  • ISBN-10: 1491981652
  • ISBN-13: 978-1491981658
  • Share This:  

Book Description

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

About the Authors
  • Julia Silge is a data scientist at Stack Overflow;
  • David Robinson is a data scientist at Stack Overflow with a PhD in Quantitative and Computational Biology from Princeton University.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Tidy Modeling with R: A Framework for Modeling in the Tidyverse

    This book shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work. It demonstrate ways to create models by focusing on an R dialect called the Tidyverse.

  • Tidyverse Skills for Data Science (Carrie Wright, et al)

    Intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages, covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

  • R for Data Science: Visualize, Model, Transform, Tidy, Import

    This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

  • Theory and Applications for Advanced Text Mining (S. Sakurai)

    This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.

  • Data-Intensive Text Processing with MapReduce (Jimmy Lin)

    This free book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.

  • Text Algorithms (Maxime Crochemore, et al)

    This much-needed book on the design of algorithms and data structures for text processing emphasizes both theoretical foundations and practical applications. The core is the material on suffix trees and subword graphs, applications of these data structures.

  • Hands-On Programming with R: Functions and Simulations

    This book not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time.

  • R Programming for Data Science (Roger D. Peng)

    This book is about the fundamentals of R programming. Get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. You will have a solid foundation on data science toolbox.

  • R Graphics Cookbook: Practical Recipes for Visualizing Data

    This cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly - without having to comb through all the details of R's graphing systems.

  • An Introduction to R (Alex Douglas, et al.)

    The main aim of this book is to help you climb the initial learning curve and provide you with the basic skills and experience (and confidence!) to enable you to further your experience in using R.

  • Advanced R, Second Edition (Hadley Wickham)

    This book helps you understand how R works at a fundamental level. Designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages to understand what makes R different and special.

  • R for Data Science: Visualize, Model, Transform, Tidy, Import

    This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.

  • Text Processing in Python (David Mertz)

    This book is an example-driven, hands-on tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. It provides efficient and effective solutions to specific text processing problems.

Book Categories
:
Other Categories
Resources and Links