Dive into our exclusive selection of e-books, created by a single expert to inspire and educate you! From deep insights to practical tips, explore a variety of topics designed to help you grow, both personally and professionally.
.png)
This book will teach 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 and visualize.
This website is and will always be free, licensed under the CC BY-NC-ND 3.0 License. If you’d like a physical copy of the book, you can order it on Amazon
.png)
This book provides hands-on modules for many of the most common machine learning methods to include: Generalized low rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines, Deep neural networks, Stacking / super learners, and more — all built and tuned using scalable R packages.
References
Efron, Bradley, and Trevor Hastie. 2016. Computer Age Statistical Inference. Vol. 5. Cambridge University Press.Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. The Elements of Statistical Learning. Vol. 1. Springer Series in Statistics New York, NY, USA:Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. Vol. 1. MIT Press Cambridge.Wickham, Hadley. 2014. Advanced R. Chapman; Hall/CRC.Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.This website is (and will always be) free to use, and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. If you’d like a physical copy of the book, you can order it from amazon. If you’d like to give back please make a donation to Kākāpō Recovery: the kākāpō (which appears on the cover of R4DS) is a critically endangered native NZ parrot; there are only 213 left.
.png)
The book is different from other forecasting textbooks in several ways.It is free and online, making it accessible to a wide audience.
It uses R, which is free, open-source, and extremely powerful software.
The online version is continuously updated. You don’t have to wait until the next edition for errors to be removed or new methods to be discussed. We will update the book frequently.
There are dozens of real data examples taken from our own consulting practice. We have worked with hundreds of businesses and organisations helping them with forecasting issues, and this experience has contributed directly to many of the examples given here, as well as guiding our general philosophy of forecasting.
We emphasise graphical methods more than most forecasters. We use graphs to explore the data, analyse the validity of the models fitted and present the forecasting results.
.png)
R Graphics Cookbook, a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. 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.
.png)
R4DS 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, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.
This website is (and will always be) free to use, and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License. If you’d like a physical copy of the book, you can order it from amazon. If you’d like to give back please make a donation to Kākāpō Recovery: the kākāpō (which appears on the cover of R4DS) is a critically endangered native NZ parrot; there are only 213 left.
This is the online version of “Modern Data Visualization with R”, published by CRC Press. A print version is also available from Amazon.R is an amazing platform for data analysis, capable of creating almost any type of graph. This book helps you create the most popular visualizations - from quick and dirty plots to publication-ready graphs. The text relies heavily on the ggplot2 package for graphics, but other approaches are covered as well.
The online version of this book is free to use and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
.png)
This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language.
This website is (and will always be) free to use, and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. If you’d like a physical copy of the book, you can order it from amazon; it was published by O’Reilly in July 2014. If you’d like to give back, please consider reporting a typo or leaving a pull request at github.com/rstudio-education/hopr.The book is written in RMarkdown with bookdown.
.png)
While this book gives some details on the basics of ggplot2, its primary focus is explaining the Grammar of Graphics that ggplot2 uses, and describing the full details. It is not a cookbook, and won’t necessarily help you create any specific graphic that you need. But it will help you understand the details of the underlying theory, giving you the power to tailor any plot specifically to your needs.The book is written by Hadley Wickham, Danielle Navarro, and Thomas Lin Pedersen.
.png)
In this book, you’ll gain insight and practical skills for creating interactive and dynamic web graphics for data analysis from R. It makes heavy use of plotly for rendering graphics, but you’ll also learn about other R packages that augment a data science workflow, such as the tidyverse and shiny.
An online version of this book, available at https://plotly-r.com, is free to use and is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License.
.png)
This book introduces quantitative research in the scientific and health disciplines, with an emphasis on introductory statistics. Unlike many introductory statistics textbooks, this textbook gives context to the statistics by first covering the basics of the research design process; it connects the research question with the means to answer that question. I believe this is crucial to understanding the need and purpose of using statistics.
Peter K. Dunn (2024). Scientific Research and Methodology: An introduction to quantitative research in science and health. https://bookdown.org/pkaldunn/SRM-TextbookThe online version of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You can purchase a printed copy or PDF copy from CRC Press in 2025.
.png)
Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data. These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries.
The full reference is: Pebesma, E.; Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). 314 pages. Chapman and Hall/CRC, Boca Raton.
This book introduces sits, an open-source R package of big Earth observation data analysis using satellite image time series. Users build regular data cubes from cloud services such as Amazon Web Services, Microsoft Planetary Computer, Copernicus Data Space Ecosystem, NASA Harmonized Landsat-Sentinel, Brazil Data Cube, Swiss Data Cube, Digital Earth Australia, and Digital Earth Africa.
This book is licensed as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) by Creative Commons. The sits package is licensed under the GNU General Public License, version 3.0.
This e-book provides an in-depth exploration of how to integrate and optimize open-source tools like GDAL/OGR and SQLite with R to elevate your Geographic Information Systems (GIS) workflows.
This guide walks you through the step-by-step process of creating your own QGIS plugin to export vector layer data to CSV files. Learn how to utilize Python and the QGIS API to develop custom tools that simplify your workflows and enhance your GIS projects.
.png)
This e-book provides a practical and straightforward guide to using the stringr package in R, making string manipulation more efficient. With clear and applicable examples, it teaches how to detect patterns, extract substrings, and apply regular expressions, making it ideal for those looking to enhance their data analysis skills in R
.png)
This e-book, provides a concise guide to using the sf package in R for spatial data analysis. Learn the basics, advanced operations, data integration, and visualization techniques to enhance your spatial analysis workflow.
.png)
Explore the use of thematic maps with the tmap package in R. This comprehensive guide covers how to visualize spatial data, customize maps, and integrate external data to enhance your geospatial analysis. Ideal for data scientists, geographers, and urban planners.
.png)
Create dynamic reports by combining R code with formatted text! Learn to generate interactive documents, dashboards, and presentations with automatic updates.
.png)
This concise guide offers practical tips and real-world examples to help you clean, organize, and analyze data efficiently using R. Perfect for beginners and pros alike, this e-book is your go-to resource for mastering data wrangling techniques.
.png)
Optimize your AI, Big Data, and analytics with Geoeasy’s expert strategies. Ensure accuracy, enrich insights, and make data-driven decisions confidently. Download now and unlock the power of high-quality data.
.png)
Elevate your GIS projects with Terra, the cutting-edge tool for efficient spectral band manipulation and advanced spatial analysis.
.png)
Master the art of date and time manipulation with Lubridate, the essential R package for temporal data. Streamline conversions, manage time zones, and enhance your time series analysis effortlessly. Perfect for analysts, data scientists, and anyone working with time-sensitive data.
Ronaldo Menezes brings decades of expertise to the field of geotechnology. Now, he's sharing his vast knowledge through exclusive courses and in-depth e-books. Get ready to master spatial and statistical analysis techniques, and raise your professional level.
And that's not all! Introducing GeoEasy, a revolutionary platform that simplifies the way you access and manipulate geographic data. No more scattered information and complicated software downloads. With GeoEasy, everything you need is just a click away.
GeoEasy not only brings together information from multiple sources in one place, but also offers exclusive microservices in its subscription plan. Imagine being able to crop satellite images using shapefiles without needing additional software or advanced technical knowledge. With GeoEasy, all of this is possible.
.jpg)
.png)
.png)
