Advanced R 原版PDF by Wickham

With more than 10 years experience programming in R, I’ve had the luxury of being able to spend a lot of time trying to figure out and understand how the language works. This book is my attempt to pass on what I’ve learned so that you can quickly become an effective R programmer. Reading it will help you avoid the mistakes I’ve made and dead ends I’ve gone down, and will teach you useful tools, techniques, and idioms that can help you to attack many types of problems. In the process, I hope to show that, despite its frustrating quirks, R is, at its heart, an elegant and beautiful language, well tailored for data analysis and statistics

R for Data Science 原版PDF by Wickham & Grolemund

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of R for Data Science is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.

Data Mining with R Learning with Case Studies 2nd 原版PDF by Torgo

The main goal of this book is to introduce the reader to the use of R as a tool for data mining. R is a freely downloadable1 language and environment for statistical computing and graphics. Its capabilities and the large set of available add-on packages make this tool an excellent alternative to many existing (and expensive!) data mining tools.

R Cookbook 原版pdf by Teetor

R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 2,000 available add-ons, and R is a serious rival to all commercial statistical packages. But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones. The simple tasks are easy once you know how, yet figuring out that “how” can be maddening.

Introducing Monte Carlo Methods with R 原版PDF by Robert & Casella

The purpose of this book is to provide a self-contained entry into Monte Carlo computational techniques. First and foremost, it must not be confused with a programming addendum to our earlier book Monte Carlo Statistical Meth- ods whose second edition came out in 2004. The current book has a dierent purpose, namely to make a general audience familiar with the programming aspects of Monte Carlo methodology through practical implementation. Not only have we introduced R at the core of this book, but the emphasis and contents have changed drastically from Monte Carlo Statistical Methods, even though the overall vision remains the same. Theoretical foundations are intentionally avoided in the current book.

Analysis of Integrated and Cointegrated Time Series with R 原版PDF by Pfaff

This book’s title is the synthesis of two influential and outstanding entities. To quote David Hendry in the Nobel Memorial Prize lecture for Clive W. J. Granger,“[the] modeling of non-stationary macroeconomic time series [. . . ] has now become the dominant paradigm in empirical macroeconomic research” (Hendry [2004]). Hence, a thorough command of integration and cointegration analysis is a must for the applied econometrician. On the other side is the open-source statistical programming environment R. Since the mid-1990s, it has grown steadily out of infancy and can now be considered mature, flexible, and powerful software with more than 600 contributed packages. However, it is fair to say that R has not yet received the attention among econometricians it deserves. This book tries to bridge this gap by showing how easily the methods and tools encountered in integration and cointegration analysis are implemented in R.

The Art of R Programming 原版PDF by Matloff

R is a scripting language for statistical data manipulation and analysis. It was inspired by, and is mostly compatible with, the statistical language S developed by AT&T. The name S, for statistics, was an allusion to another programming language with a one-letter name developed at AT&T—the famous C language. S later was sold to a small firm, which added a graphical user interface (GUI) and named the result S-Plus.

R High Performance Programming 原版PDF by Lim & Tjhi

In a world where data is becoming increasingly important, business people and scientists need tools to analyze and process large volumes of data efficiently. R is one of the tools that have become increasingly popular in recent years for data processing, statistical analysis, and data science. While R has its roots in academia, it is now used by organizations across a wide range of industries and geographical areas.

Programming Graphical User Interfaces in R 原版PDF by Lawrence & Verzani,

Two common types of user interfaces in statistical computing are the command line interface (CLI) and the graphical user interface (GUI). The usual CLI consists of a textual console in which the user types a sequence of commands at a prompt, and the output of the commands is printed to the console as text. The R console is an example of a CLI. A GUI is the primary means of interacting with desktop environments, such as Windows and Mac OS X, and statistical software, such as JMP. GUIs are contained within windows, and resources, such as documents, are represented by graphical icons. User controls are packed into hierarchical drop-down menus, buttons, sliders, etc. The user manipulates the windows, icons, and menus with a pointer device, such as a mouse.

Machine Learning with R 2nd 原版PDF by Lantz

Machine learning, at its core, is concerned with the algorithms that transform information into actionable intelligence. This fact makes machine learning well-suited to the present-day era of big data. Without machine learning, it would be nearly impossible to keep up with the massive stream of information. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start using machine learning. R offers a powerful but easy-to-learn set of tools that can assist you with finding data insights