Select and learn appropriate statistical software for your research project

Yuqi He

she/hers

My job is to support your research. Email/call me your questions or to schedule a personal consultation in-person or via Zoom. I'm eager to assist!

King Library, Office 4041

yuqi.he@sjsu.edu

King Library, Office 4041

yuqi.he@sjsu.edu

(408) 808-2044

R is an open-source statistical programming language. It can be used for a variety of tasks, including (but not limited to): data cleaning/manipulation, regression, clustering, classification, data visualization, etc..

Visit r-project.org to learn more context about R, and to download the programming language. I also recommend downloading Rstudio, and integrated development environment (IDE) for R.

- UCLA Institute for Digital Research and EducationIncludes FAQs, learning modules, sample code and data analysis examples.
- Princeton Library Data and Statistical ServicesResources in more specific applications of R, such as time series.
- Intro to R VideosShort videos about developing in R; created by Google Developers.
- R ManualsDocumentation edited by the R Development Core Team.
- R Courses on UdemyOnline courses covering a variety of topics (e.g. machine learning, visuals, etc.) in R.

Note: You need to pay for these classes. - R Courses on Lynda.comOnline courses covering a variety of topics (e.g. machine learning, visuals, etc.) in R.

Note: Lynda.com is provided by San Jose Public Library. You will need to sign up for a public library card in order to access Lynda.com for free. - R Courses on Coursera.comOnline courses covering a variety of topics (e.g. machine learning, visuals, etc.) in R.

Note: To access full course materials, must pay.

In OneSearch, search the *Subject* *Contains* "R (Computer Program Language)":

- Linear Regression Using R by David LiljaISBN: 9781946135001Publication Date: 2016-11-01Linear Regression Using R: An Introduction to Data Modeling presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models. Key modeling and programming concepts are intuitively described using the R programming language. All of the necessary resources are freely available online.
- The Book of R by Tilman M. DaviesISBN: 9781593276515Publication Date: 2016-07-16The Book of R is a comprehensive, beginner-friendly guide to R, the world's most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you'll find everything you need to begin using R effectively for statistical analysis. You'll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling.
- The Essential R Reference by Mark GardenerISBN: 9781118391419Publication Date: 2012-11-19The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses.

- Mastering Scientific Computing with R by Paul GerrardISBN: 1783555262Publication Date: 2015-01-01If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
- Machine Learning Cookbook by Atul TripathiISBN: 9781785280511Publication Date: 2017-04-14This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected. The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.
- Metaprogramming in R: Advanced Statistical Programming for Data Science, Analysis and Finance by Thomas MailundISBN: 9781484228814Learn how to manipulate functions and expressions to modify how the R language interprets itself. This book is an introduction to metaprogramming in the R language, so you will write programs to manipulate other programs. Metaprogramming in R shows you how to treat code as data that you can generate, analyze, or modify.

- Biostatistical Design and Analysis Using R by Murray LoganISBN: 9781405190084Publication Date: 2010-05-10R -- the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research.
- Automated Trading with R: Quantitative Research and Platform Development by Chris ConlanISBN: 9781484221785Automated Trading with R explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage.
- Computational Methods for Numerical Analysis with R by James P. Howard, IIISBN: 9781498723640Publication Date: 2017-06-19This book provides an overview of the standard topics of numerical analysis but uses R as the underlying programming language. There are many textbooks for numerical analysis, many of them using MATLAB, but many people are now increasingly using R. This book covers the major topics - including error analysis, linear algebra, interpolation, integration, optimization, and differential equations - focussing in depth on a selection of sub-topics, with simple examples and R code integrated throughout. It is not intended to be a comprehensive textbook, but a reference to enable readers to get started on using R for numerical analysis.

- Last Updated: Sep 15, 2023 10:04 AM
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