Название: Deep R ProgrammingАвтор: Marek Gagolewski
Издательство: Independently published
Год: 2023-11-30 (v1.0.0.9002)
Страниц: 457
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for Data Science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and Machine Learning.
R is not just a statistical package. It is a general-purpose, high-level programming language that happens to be very powerful for numerical, data-intense computing activities of any kind. It offers extensive support for statistical, Machine Learning, data analysis, data wrangling, and data visualisation applications, but there is much more.
As we detail below, R has a long history. It is an open-source version of the S environment, which was written for statisticians, by statisticians. Therefore, it is a free, yet often more capable alternative to other software (but without any strings attached). Unlike in some of them, in R, a spreadsheet-like GUI is not the main gateway for performing computations on data. Here, we must write code to get things done. Despite the beginning of the learning curve’s being a little steeper for non-programmers, in the long run, R empowers us more because we are not limited to tackling the most common scenarios. If some functionality is missing or does not suit our needs, we can easily (re)implement it ourselves.
R is thus very convenient for rapid prototyping. It helps turn our ideas into fully operational code that can be battle-tested, extended, polished, run in production, and otherwise enjoyed. As an interpreted language, it can be executed not only in an interactive read-eval-print loop (command–result, question–answer, …), but also in batch mode (running standalone scripts).
Therefore, we would rather position R amongst such environments for numerical or scientific computing as Python with NumPy and Pandas, Julia, GNU Octave, Scilab, and MATLAB. However, it is more specialised in Data Science applications than any of them. Hence, it provides a much smoother experience. This is why, over the years, R has become the de facto standard in statistics and related fields.
R is a whole ecosystem. Apart from the R language interpreter, it features advanced:
- graphics capabilities,
- a consistent, well-integrated help system,
- ways for convenient interfacing with compiled code,
- a package system and centralised package repositories (such as CRAN and Bioconductor),
- a lively community of users and developers – curious and passionate people, like you and yours cordially.
Our aim is to learn the fundamentals of base R, which constitutes the ligua franca for all R users. We want to be able to indite code that everybody should understand; code that will work without modifications ten years from now (no slang!).
This graduate-level textbook is for readers who:
- would like to experience the joy of solving problems by programming,
- want to become independent users of the R environment,
- can appreciate a more cohesively and comprehensively organised material,
- do not mind a slightly steeper learning curve at the beginning,
- do not want to be made obsolete by artificial “intelligence” in the future.
Some readers will benefit from its being their first introduction to R (yet, without all the pampering). For others, this will be a fine course from intermediate to advanced (do not skip the first chapters, though).
Either way, we should not forget to solve all the prescribed exercises.
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