Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
R for Geographic Data Science
🌠 Top Free C++ Books - 100% Free or Open Source!
  • Title: R for Geographic Data Science
  • Author(s): Stefano De Sabbata
  • Publisher: GitHub; eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Paperback/Hardcover: N/A
  • eBook: HTML
  • Language: English
  • ISBN-10: N/A
  • ISBN-13: N/A
  • Share This:  

Book Description

This book is an introduction to geographic data science using R, covers the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses.

About the Authors
  • Stefano De Sabbata is an Associate Professor of Geographical Information Science, University of Leicester.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Geographic Data Science with R: Visualizing and Analyzing

    This book provides a series of tutorials aimed at teaching good practices for using Time Series and geospatial data to address topics related to environmental change. It is based on the R language and aims at data scientists.

  • Geographic Data Science with Python (Sergio Rey, et al.)

    Provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data, by using geographical and computational reasoning to unlock new insights hidden within data.

  • Spatial Data Science: With Applications in R

    Introduces and explains the concepts underlying Spatial Data, gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions.

  • Geocomputation with R (Robin Lovelace, et al.)

    This book is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities.

  • 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.

  • 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.

  • Geospatial Analysis with Python (Ujaval Gandhi)

    Suitable for GIS practitioners with no programming background or python knowledge. The course will introduce basic Python programming concepts, libraries for spatial analysis, geospatial APIs and techniques for building spatial data processing pipelines.

  • Python Scripting for Spatial Data Processing (Pete Bunting, et al)

    This book is a Python tutorial for beginners aiming at teaching spatial data processing. It is used as part of the courses taught in Remote Sensing and GIS, using psycopg2, and ogr2ogr, etc., at Aberystwyth University, UK.

  • Geospatial Analysis: Principles, Techniques and Software Tools

    This book provides a compehensive guide to concepts, methods and tools, with many examples being provided using a variety of software tools such as ArcGIS, Idrisi, Grass, Surfer and many others to clarify the concepts discussed

  • Efficient R Programming: Practical Guide to Smarter Programming

    This book is about increasing the amount of work you can do with R in a given amount of time. It's about both computational and programmer efficiency. This book is for anyone who wants to make their use of R more reproducible, scalable, and faster.

  • Cookbook for R: Best R Programming TIPs (Winston Chang)

    The goal of this cookbook is to provide solutions to common tasks and problems in analyzing data. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.

  • 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 Packages: Organize, Test, Document, and Share Your Code

    Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author's package development philosophy.

  • Forecasting, Principles and Practice, Using R (R. J. Hyndman)

    This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.

Book Categories
:
Other Categories
Resources and Links