R can be found on all of the Oceanographic Campus Library's computer stations. This includes RStudio.

**R** is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

R may be downloaded here: https://cran.r-project.org/mirrors.html Select the mirror in the USA closest to your location.

RStudio may be downloaded here: https://rstudio.com/products/rstudio/download/#download

- Environmental and Ecological Statistics with R, Second Edition by Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Editionfocuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.Call Number: GE45.S73 Q25 2017ISBN: 9781498728720Publication Date: 2016-10-21
- Discovering Statistics Using R by Lecturers - request an e-inspection copy of this text or contact your local SAGE representative to discuss your course needs. Watch Andy Field's introductory video to Discovering Statistics Using R Keeping the uniquely humorous and self-deprecating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioural sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next you discover the importance of exploring and graphing data, before moving onto statistical tests that are the foundations of the rest of the book (for example correlation and regression). You will then stride confidently into intermediate level analyses such as ANOVA, before ending your journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help you gain the necessary conceptual understanding of what you're doing, the emphasis is on applying what you learn to playful and real-world examples that should make the experience more fun than you might expect. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, together with hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more. Given this book's accessibility, fun spirit, and use of bizarre real-world research it should be essential for anyone wanting to learn about statistics using the freely-available R software. Available with Perusall--an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more. Call Number: HA32 .F537 2012ISBN: 9781446200469Publication Date: 2012-04-05
- Statistical Computing with R by Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with Rcovers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing.Call Number: QA276.45.R3 R59 2008ISBN: 9781584885450Publication Date: 2007-11-15
- Community Ecology by Interactions between species are of fundamental importance to all living systems and the framework we have for studying these interactions is community ecology. This is important to our understanding of the planets biological diversity and how species interactions relate to the functioning of ecosystems at all scales. Species do not live in isolation and the study of community ecology is of practical application in a wide range of conservation issues. The study of ecological community data involves many methods of analysis. In this book you will learn many of the mainstays of community analysis including: diversity, similarity and cluster analysis, ordination and multivariate analyses. This book is for undergraduate and postgraduate students and researchers seeking a step-by-step methodology for analysing plant and animal communities using R and Excel. Microsoft's Excel spreadsheet is virtually ubiquitous and familiar to most computer users. It is a robust program that makes an excellent storage and manipulation system for many kinds of data, including community data. The R program is a powerful and flexible analytical system able to conduct a huge variety of analytical methods, which means that the user only has to learn one program to address many research questions. Its other advantage is that it is open source and therefore completely free. Novel analytical methods are being added constantly to the already comprehensive suite of tools available in R. Mark Gardener is both an ecologist and an analyst. He has worked in a range of ecosystems around the world and has been involved in research across a spectrum of community types. His knowledge of R is largely self-taught and this gives him insight into the needs of students learning to use R for complicated analyses.Call Number: QH541 .G374 2014ISBN: 9781907807626Publication Date: 2014-02-01
- Statistics for Ecologists Using R and Excel by This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference - t-test and U-test; correlation - Spearman's rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal-Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. - Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel - Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. - Amazon 5-star review It has been very easy to follow and will be perfect for anyone. - Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. - Goodreads, 4-star reviewCall Number: QH541.15.S72 G37 2017ISBN: 9781784271398Publication Date: 2017-01-09
- Mixed Effects Models and Extensions in Ecology with R by This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.Call Number: QH541.15.S72 M59 2009ISBN: 9780387874579Publication Date: 2009-03-12
- A Primer in Biological Data Analysis and Visualization Using R by R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R.Call Number: QA276.45.R3 H37 2014ISBN: 9780231166980Publication Date: 2014-02-18

- Beginning R by Beginning R, Second Edition is a hands-on book showing howto use the R language, write and save R scripts, read in data files, and writecustom statistical functions as well as use built in functions. This book showsthe use of R in specific cases such as one-way ANOVA analysis, linear andlogistic regression, data visualization, parallel processing, bootstrapping,and more. It takes a hands-on, example-based approach incorporating bestpractices with clear explanations of the statistics being done. It has beencompletely re-written since the first edition to make use of the latestpackages and features in R version 3. R is a powerful open-source language and programmingenvironment for statistics and has become the de facto standard for doing,teaching, and learning computational statistics. R is both an object-oriented language and afunctional language that is easy to learn, easy to use, and completely free. Alarge community of dedicated R users and programmers provides an excellentsource of R code, functions, and data sets, with a constantly evolvingecosystem of packages providing new functionality for data analysis. R has alsobecome popular in commercial use at companies such as Microsoft, Google, andOracle. Your investment in learning R is sure to pay off in the long term as Rcontinues to grow into the go to language for data analysis and research. What You Will Learn: How to acquire and install R Hot to import and export data and scripts How to analyze data and generate graphics How to program in R to write custom functions Hot to use R for interactive statistical explorations How to conduct bootstrapping and other advancedtechniquesCall Number: E-BOOKISBN: 9781484203743Publication Date: 2015-10-13
- Functional Programming in R by Master functions and discover how to write functional programs in R. In this concise book, you'll make your functions pure by avoiding side-effects; you'll write functions that manipulate other functions, and you'll construct complex functions using simpler functions as building blocks. In Functional Programming in R, you'll see how we can replace loops, which can have side-effects, with recursive functions that can more easily avoid them. In addition, the book covers why you shouldn't use recursion when loops are more efficient and how you can get the best of both worlds. Functional programming is a style of programming, like object-oriented programming, but one that focuses on data transformations and calculations rather than objects and state. Where in object-oriented programming you model your programs by describing which states an object can be in and how methods will reveal or modify that state, in functional programming you model programs by describing how functions translate input data to output data. Functions themselves are considered to be data you can manipulate and much of the strength of functional programming comes from manipulating functions; that is, building more complex functions by combining simpler functions. What You'll Learn Write functions in R including infix operators and replacement functions Create higher order functions Pass functions to other functions and start using functions as data you can manipulate Use Filer, Map and Reduce functions to express the intent behind code clearly and safely Build new functions from existing functions without necessarily writing any new functions, using point-free programming Create functions that carry data along with them Who This Book Is For Those with at least some experience with programming in R.Call Number: E-BOOKISBN: 9781484227459Publication Date: 2017-03-29
- Statistics for Censored Environmental Data Using Minitab and R by Praise for the First Edition " . . . an excellent addition to an upper-level undergraduate course on environmental statistics, and . . . a 'must-have' desk reference for environmental practitioners dealing with censored datasets." --Vadose Zone Journal Statistical Methods for Censored Environmental Data Using Minitab#65533; and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies. This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab#65533; into the discussed analyses, the book features newly developed and updated material including: A new chapter on multivariate methods for censored data Use of interval-censored methods for treating true nondetects as lower than and separate from values between the detection and quantitation limits ("remarked data") A section on summing data with nondetects A newly written introduction that discusses invasive data, showing why substitution methods fail Expanded coverage of graphical methods for censored data The author writes in a style that focuses on applications rather than derivations, with chapters organized by key objectives such as computing intervals, comparing groups, and correlation. Examples accompany each procedure, utilizing real-world data that can be analyzed using the Minitab#65533; and R software macros available on the book's related website, and extensive references direct readers to authoritative literature from the environmental sciences. Statistics for Censored Environmental Data Using Minitab#65533; and R, Second Edition is an excellent book for courses on environmental statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for#65533;environmental professionals, biologists, and ecologists who focus on the water sciences, air quality, and soil science.Call Number: E-BOOKISBN: 9780470479889Publication Date: 2012-02-01
- The R Book by Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: '...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' (The American Statistician, August 2008) 'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' (Professional Pensions, July 2007) Call Number: E-BOOKISBN: 9781118448960Publication Date: 2012-11-07
- Environmental and Ecological Statistics with R, Second Edition by Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Editionfocuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.Call Number: E-BOOKISBN: 9781498728720Publication Date: 2016-10-21
- Understanding and Applying Basic Statistical Methods Using R by Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox' Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations. Call Number: E-BOOKISBN: 9781119061397Publication Date: 2016-06-07
- Comparative Approaches to Using R and Python for Statistical Data Analysis by The application of statistics has proliferated in recent years and has become increasingly relevant across numerous fields of study. With the advent of new technologies, its availability has opened into a wider range of users. Comparative Approaches to using R and Python for Statistical Data Analysis is a comprehensive source of emerging research and perspectives on the latest computer software and available languages for the visualization of statistical data. By providing insights on relevant topics, such as inference, factor analysis, and linear regression, this publication is ideally designed for professionals, researchers, academics, graduate students, and practitioners interested in the optimization of statistical data analysis.Call Number: E-BOOKISBN: 9781683180166Publication Date: 2017-01-06

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- Learning RJoin author Barton Poulson as he introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions.

The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages. - R Statistics Essential TrainingR is the language of big data—a statistical programming language that helps describe, mine, and test relationships between large amounts of data. Author Barton Poulson shows how to use R to model statistical relationships using graphs, calculations, tests, and other analysis tools. Learn how to enter and modify data; create charts, scatter plots, and histograms; examine outliers; calculate correlations; and compute regressions, bivariate associations, and statistics for three or more variables. Challenge exercises with step-by-step solutions allow you to test your skills as you progress.
- Code Clinic: RSuccessful programmers know more than a computer language. They also know how to think about solving problems. They use "computational thinking": breaking a problem down into segments that lend themselves to technical solutions. Code Clinic is a series of ten courses where authors solve the same problems using different programming languages. Here, Mark Niemann-Ross works with R.

Mark introduces challenges and then provides an overview of his solutions in R. Challenges include topics such as statistical analysis, searching directories for images, and accessing peripheral devices.

Visit other courses in the series to see how to solve the exact same challenges in languages like C++, C#, JavaScript, PHP, Python, Ruby, and Swift. - Data Wrangling in RTidy data is a data format that provides a standardized way of organizing data values within a dataset. By leveraging tidy data principles, statisticians, analysts, and data scientists can spend less time cleaning data and more time tackling the more compelling aspects of data analysis. In this course, learn about the principles of tidy data, and discover how to create and manipulate data tibbles—transforming them from source data into tidy formats. Instructor Mike Chapple uses the R programming language and the tidyverse packages to teach the concept of data wrangling—the data cleaning and data transformation tasks that consume a substantial portion of analysts' time. He wraps up with three hands-on case studies that help to reinforce the data wrangling principles and tactics covered in this course.
- Logistic Regression in R and ExcelBusiness decisions are often binary: take on this project or put it off for a year; extend credit to this customer or insist on cash; open a new retail outlet in a particular location or find another spot. When an outcome is a continuous variable such as revenue, ordinary regression is often a good technique, but when there are only two outcomes, logistic regression usually offers better tools.

Learn how to use R and Excel to analyze data in this course with Conrad Carlberg. He takes you through advanced logistic regression, starting with odds and logarithms and then moving on into binomial distribution and converting predicted odds back to probabilities. After this foundation is established, he shifts the focus to inferential statistics, likelihood ratios, and multinomial regression. Conrad's comprehensive coverage of how to perform logistic regression includes tackling common problems, explaining relationships, reviewing outcomes, and interpreting results. - R for Excel UsersData scientists who use Excel realize that R is emerging as the new standard for statistical wrangling (especially for larger data sets). This course serves as the perfect bridge for the many Excel-reliant data analysts and business users who need to update their data science skills by learning R.

Much of the course focuses on how crucial statistical tasks and operations are done in R—often with the DescTools package—as contrasted with Excel's functions and Data Analysis add-in, and then scales up from there, showing R's more powerful features. Conrad Carlberg will help you effectively toggle between both programs, moving data back and forth so you can get the best of both worlds. Start by learning how to install R and the DescTools package, and the data files used in all the hands-on exercises. Then learn about calculating descriptive statistics on numeric and nominal variables, and running bivariate analyses in both Excel and R. In the "Next steps" video, Conrad breaks down the pros and cons of Excel vs. R and provides tips for learning more about statistics in each application.

- Statistics and RWe will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts. - Introduction to R for Data ScienceThis course is part of the Microsoft Professional Program Certificate in Data Science.

R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs.

This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform. Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.

Enjoy! - Programming with R for Data ScienceThis course is part of the Microsoft Professional Program Certificate in Data Science.

In this computer science course from Microsoft, developed in collaboration with the Technical University of Denmark (DTU), get the knowledge and skills you need to use R, the statistical programming language for data scientists, in the field of your choice.

In this course you will learn all you need to get up to speed with programming in R. Explore R data structures and syntaxes, see how to read and write data from a local file to a cloud-hosted database, work with data, get summaries, and transform them to fit your needs. Plus, find out how to perform predictive analytics using R and how to create visualizations using the popular ggplot2 package. - Data Science: R BasicsThe demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science Series prepares you with the necessary knowledge base and skills to tackle real-world data analysis challenges. The series covers concepts such as probability, inference, regression and machine learning and helps you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. In the R Basics course, we learn the basic building blocks of R. As done in all our courses, we use motivating case studies, we ask specific questions, and learn by answering these through data analysis. Our assessments use code checking technology that will permit you to get hands-on practice during the courses.

Throughout the series, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. In this course, we will introduce the necessary basic R syntax to get you going. However, rather than cover every R skill you need, we introduce just enough so you can continue learning in the next courses, which will provide more in depth coverage. We believe that you can better retain R knowledge when you learn it to solve a specific problem. The motivating question in this course relates to crime in the United States and we provide a relevant dataset. You will learn some basic R skills to permit us to answer specific questions about differences across the different states.

HarvardX has partnered with DataCamp for all assignments. This allows students to program directly in a browser-based interface. You will not need to download any special software, but an up-to-date browser is recommended. - Statistical Analysis in BioinformaticsImprovements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment.

For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to use statistical tools to gain meaningful information from them.

In this course, part of the Bioinformatics MicroMasters program, you will learn about the R language and environment and how to use it to perform statistical analyses on biological big datasets. - Explore Statistics with RDo you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics!

Skilled professionals who can process and analyze data are in great demand today. In this course you will explore concepts in statistics to make sense out of data. You will learn the practical skills necessary to find, import, analyze and visualize data. We will take a look under the hood of statistics and equip you with broad tools for understanding statistical inference and statistical methods. You will also perform some really complicated calculations and visualizations, following in the footsteps of Karolinska Institute’s researchers.

Statistical programming is an essential skill in our golden age of data abundance. Health science has become a field of big data, just like so many other fields of study. New techniques make it possible and affordable to generate massive data sets in biology. Researchers and clinicians can measure the activity for each of 30000 genes of a patient. They can read the complete genome sequence of a patient. Thanks to another trend of the decade, open access publishing, the results of such large scale health science are very often published for you to read free of charge. You can even access the raw data from open databases such as the gene expression database of the NCBI, National Center for Biotechnology Information.

We will dive into this data together. Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course.

- R Programming Software and Statistics TutorialsProgramming tutorials for the statistical software R, presented for beginners. A complete set of videos for learning how to use the Statistical Software R. Discusses importing data, getting started, descriptive statistics, and bivariate hypothesis tests, both parametric and non-parametric. These videos are intended to pair well with an introductory statistics course.
- R Tutorial VideosThis R Tutorial Videos playlist will help you in understanding the various fundamentals of R programming with examples in detail. It takes you through R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio.
- R tutorial - Learn R ProgrammingIn this R tutorial you learn how to write your first code in R. Check the full course with interactive coding exercises at https://www.datacamp.com/courses/free-introduction-to-r
- R Tutorials for Non-Programmer DatascientistsA step-by-step R programming tutorials from scratch for beginners.

R is a leading open source software for statistical and advanced analysis. It is increasingly adopted by companies world-wide as the prime tool to solve advanced analytics oriented business problems.

R Programming Language is used widely in applied research in Finance, Genetics, Forecasting, Optimization, Machine Learning, Clinical trials etc. Visit http://cran.r-project.org/web/views/ for more info.

Take this course and learn the most coveted skill that companies are eager to hire.

- Choosing the Correct Statistical Test in SAS, STAT, SPSS and RThe UCLA Institute for Digital Research & Education offers a table that you can use to choose statistical tests, as well as instructions on how to run the tests in SAS, STATA, SPSS, and R.
- Introduction to Statistics and Data Science - A moderndive into R and the tidyverseThis open textbook assumes no prerequisites: no algebra, no calculus, and no prior programming/coding experience. This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way statisticians, data scientists, data journalists, and other researchers would.
- Quick-RR is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. Rob Kabacoff created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. His goal is to help you quickly access this language in your work.