{"id":2374,"date":"2014-04-20T10:58:45","date_gmt":"2014-04-20T05:28:45","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=2374"},"modified":"2017-04-19T23:00:43","modified_gmt":"2017-04-19T17:30:43","slug":"learn-r-12-books-and-online-resources","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/learn-r-12-books-and-online-resources\/","title":{"rendered":"Learn R : 12 Free Books and Online Resources"},"content":{"rendered":"
\n
Please read the disclaimer about the Free PDF Books in this article at the bottom<\/pre>\n
R, an open-source statistical and data mining programming language, is slowly but surely catching up in its race with commercial software like SAS & SPSS. I believe R will eventually replace SAS as the language of choice for modeling and analysis for most organizations. The primary reason for this is plainly commercial. Most organizations are questioning the heavy annual cost of SAS on their P&L statement. This is escalated with the presence of R as a free and viable replacement. R is a highly advanced language with over 5000 add-on packages to assist in data management and analysis. Most senior analysts and analytics leaders have already started polishing their skills on R. In this article, I will introduce the books and online resource that will help you to learn\u00a0R and its applications. Before introducing these resources, let me elucidate why you need many resources for self-learning.<\/p>\n
\"The<\/a>
Human Obsession with Linearity – by Roopam<\/p><\/div>\n
Non-Linear Self-Learning<\/span>
\n<\/span><\/h2>\n
Humans are obsessed with linearity. Look at our houses, furniture, televisions, photo frames or cabinets, they all follow linear designs. The reason is\u00a0 linearity is simple, however, it is certainly not natural. Outside our houses nature is flourishing with non-linearity – trees, mountains, rivers and the human body all follow non-linear patterns and dynamics (to explore more read about fractal geometry and chaos theory, or we will discuss it in some later articles on YOU CANalytics).<\/p>\n
Learning \/ teaching in schools and universities usually take the linear path, however, self-learning, in my opinion, is highly non-linear. Unlike school-learning, self-learning is driven by purpose and need, hence one tends to hop between books, chapters, and the internet – I say this from experience. Let me present the resources that have helped me the most to\u00a0learn R. I have divided these resources into the following 5 categories<\/p>\n
    \n
  1. R for Reference <\/strong>: these books cover most essential aspects about R and also serve well as reference books<\/li>\n
  2. R with Theory <\/strong>: these books are great if you want to understand fundamentals of statistics and machine learning while using R as the tool
    \n<\/strong><\/li>\n
  3. R with Applications <\/strong>: these books use case studies or applications based learning
    \n<\/strong><\/li>\n
  4. R Graphic and Programming <\/strong>: focus of these books is on R Graphics or programming
    \n<\/strong><\/li>\n
  5. Online Resource <\/strong>: short online courses and computer-based learning tools (I have also included the most important online data repository over here)<\/li>\n<\/ol>\n\n\n\n
    \"python\"<\/a> Also, check out some awesome free books on\u00a0Python for data science<\/a>.<\/strong>\u00a0Both R and Python are essential in a\u00a0data scientist’s toolkit.<\/span><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
     1. R for Reference<\/span><\/h2>\n
    \n
    \"r-for-data-science\"<\/a>
    Click to Read<\/span><\/p><\/div>\n
    R for Data Science<\/strong>\u00a0– Hadley Wickham & Garrett Grolemund<\/em><\/span><\/p>\n
    YOU CANalytics Book Rating \"5 (5 \/ 5)<\/p><\/blockquote>\n
    First of all, thanks to Jared for recommending this book in the comments section of this article. I have spent last hour or so skimming through the book and believe this book deserves a place right at the top. This is an extremely well written and practical reference book. Moreover, I believe, for beginners to R this is a good book to start.<\/p>\n
    Read the Full Book<\/a>: R for Data Science<\/strong><\/p>\n
    \n
    \"R<\/a>
    Click to Read<\/span><\/p><\/div>\n
    R in Action –<\/strong>\u00a0Robert Kabacoff<\/em><\/p>\n
    YOU CANalytics Book Rating \"5 (5 \/ 5)<\/p><\/blockquote>\n
    Here is another exceptional book to start learning R on your own. I must say Robert Kabacoff, the author of this book, has done a phenomenal job with this book. The organization of the book is immaculate and the presentation is friendly. I will highly recommend either this book or\u00a0R for Data Science<\/em>\u00a0to start your journey to learn R.<\/p>\n
    Read Full PDF<\/a>: R in Action<\/strong><\/p>\n
    \n
    \"r<\/a>
    Click to Read<\/p><\/div>\n
    R for Everyone: <\/strong>Advanced Analytics and Graphics – Jared P. Lander<\/em><\/p>\n
    YOU CANalytics Book Rating \"5 (5 \/ 5)<\/p><\/blockquote>\n
    Jared Lander, in his book, wastes no time on basic graphic (comes pre-installed with R) but jumps directly to ggplot2 package (a much advanced and sleek graphical package). This sets the tone for this book i.e. don’t learn things you won’t use in real life applications later. I recommend this book for a fast-paced experience to learn\u00a0R.<\/p>\n
    Read Partial PDF to Evaluate Table of Contents<\/a>: R for Everyone<\/strong><\/p>\n
    \n
    \"The<\/a>
    Click to Read<\/span><\/p><\/div>\n
    The R Book <\/strong>– Michael J. Crawley
    \n<\/strong><\/em><\/p>\n
    YOU CANalytics Book Rating \"4.8 (4.8 \/ 5)<\/p><\/blockquote>\n
    With close to a thousand pages and vast coverage, ‘The R Book’ could be called the Bible for R.\u00a0 This book starts with simple concepts in R and gradually move to highly advanced topics. The breadth of the book can be estimated through the presence of dedicated chapters on topics as diverse as data frames, graphics, Bayesian statistics, and survival analysis. Essentially this is a must-have reference book for any wannabe R programmer. But for a beginner, the thickness of the book could be intimidating.<\/p>\n
    Read Full PDF<\/a>: The R Book<\/strong><\/p>\n
     <\/p>\n
    2. R with Theory<\/span><\/h2>\n
    \n
    \"R<\/a>
    Click to Read<\/span><\/p><\/div>\n
    An Introduction to Statistical Learning: <\/strong>with Applications in R\u00a0– Gareth James et al.
    \n<\/strong><\/em><\/p>\n
    YOU CANalytics Book Rating \"5 (5 \/ 5)<\/p><\/blockquote>\n
    This book is a high-quality statistical text with R as the software of choice. If you want to be comfortable with fundamental concepts in parallel with learning R, then this is the book for you. Having said this, you will love this book even if you have studied advanced statistics. The book also covers some advanced machine learning concepts such as support machine learning (SVM) and regularization. A great book by all means.<\/p>\n
    Read Full PDF<\/a>:\u00a0An Introduction to Statistical Learning<\/strong><\/p>\n
    \n
    \"machine<\/a>
    Click to Read<\/span><\/p><\/div>\n
    Machine Learning with R\u00a0<\/strong>– Brett Lantz
    \n<\/strong><\/em><\/p>\n
    YOU CANalytics Book Rating \"4.5 (4.5 \/ 5)<\/p><\/blockquote>\n
    If you want to learn R from the machine learning perspective, then this is the book for you. Some people take a lot of interest in the fine demarcation between statistics and machine learning; however, for me, there is too much overlap between the topics. I have given up on the distinction as it makes no difference from the applications perspective. The book introduces R-Weka package \u2013 Weka is another open source software used extensively in academic research.<\/p>\n
    Read Full PDF<\/a>:\u00a0Machine Learning with R<\/strong><\/p>\n
    3. R with Applications<\/strong><\/span><\/h2>\n
    \n
    \"r<\/a>
    Click to Read<\/span><\/p><\/div>\n
    R and Data Mining: <\/strong>Examples and Case Studies – Yanchang Zhao
    \n<\/strong><\/em><\/p>\n
    YOU CANalytics Book Rating \"4.3 (4.3 \/ 5)<\/p><\/blockquote>\n
    There are other books that use case studies approach for readers to learn R. I like this book because of the interesting topics this book covers including text mining, social network analysis and time series modeling. Having said this, the author could have put in some effort on the formatting of this book which is pure ugly. At times you will feel you are reading a masters level project report while skimming through the book. However, once you get over this aspect the content is really good to learn R.<\/p>\n
    Read Full PDF<\/a>:\u00a0R and Data Mining<\/strong><\/p>\n
    \n
    \"R<\/a>
    Click to Read<\/span><\/p><\/div>\n
    Data Mining with Rattle and R: <\/strong>The Art of Excavating Data for Knowledge Discovery (Use R!)\u00a0– Graham Williams<\/em><\/p>\n
    YOU CANalytics Book Rating \"4.2 (4.2 \/ 5)<\/p><\/blockquote>\n
    Rattle is no SAS E-miner or SPSS modeler (both commercial GUI\u00a0based data mining tools). However trust me, apart from a few minor issues Rattle is not at all bad. The book is a great reference to Rattle (a GUI add-on package for R to mine data) for data mining. I really hope they keep working on Rattle to make it better, as it has a lot of potential.<\/p>\n
    Read Full PDF<\/a>:\u00a0Data Mining with Rattle and R<\/strong><\/p>\n
    \u00a04. R Graphics and Programming<\/span><\/h2>\n
    \n
    \n
    \"GGplot2\"<\/a>
    Click to Read<\/span><\/p><\/div>\n
    ggplot2:<\/strong> Elegant Graphics for Data Analysis (Use R!) – Hadley Wickham<\/em><\/p>\n
    YOU CANalytics Book Rating \"4 (4 \/ 5)<\/p><\/blockquote>\n<\/div>\n
    ‘ggplot 2’ is an exceptional package to create wonderful graphics on R. It is much better than the base graphics that comes pre-installed with R, so I would recommend you start directly with ggplot 2 without wasting your time on base graphics. ‘R for everyone’, the first book we discussed, has a good introduction to ggplot. However, if you want to get to further depths of ggplot-2 then this is the book for you.<\/p>\n
    Read Full PDF<\/a>:\u00a0ggplot2<\/strong><\/p>\n
    Though I prefer ggplot 2, Lattice is another package at par with ggplot 2. A good book to start with Lattice is ‘Lattice: Multivariate Data Visualization with R (Use R!)<\/span> by   Deepayan Sarkar’. Read Full PDF<\/a>:\u00a0Lattice<\/strong><\/span><\/span><\/p>\n
     <\/p>\n
    \n
    \"R<\/a>
    Click to Read<\/span><\/p><\/div>\n
    The Art of R Programming <\/strong>: A Tour of Statistical Software Design\u00a0– Norman Matloff
    \n<\/strong><\/em><\/p>\n
    YOU CANalytics Book Rating \"4.2 (4.2 \/ 5)<\/p><\/blockquote>\n
    If you want to learn programming and coding aspect of R more than the analysis aspect, then this is the book for you. The author of this book has extensive experience in R coding and that is evident when you read this book. I must warn you that at times while reading this book one wonders about the utility of some of the things Mr. Matloff talks about. Nevertheless, this is the best book in the market to learn R programming. The author also touches on the issues of parallel computing in R – a topic highly relevant in the day and age of big data.<\/p>\n
    Read Full PDF<\/a>:\u00a0The Art of R Programming<\/strong><\/p>\n
    5. Online Resource<\/span><\/h2>\n
    \"Slide1\"<\/a>C<\/strong>ode School <\/strong>: Try R<\/strong><\/p>\n
    YOU CANalytics Resource Rating \"4.9 (4.9 \/ 5)<\/p><\/blockquote>\n
    This is a wonderful place to learn R programming. Before jumping to the books, I recommend you take this free online course. You don’t need to install R on your system to complete this course. It will take you less than an hour to complete this course but will prepare you well for further learning. (Link<\/a><\/strong>)<\/p>\n
     <\/p>\n
    \"Slide2\"<\/a>Coursera <\/strong>: R Programming\u00a0<\/strong>– Roger D. Peng<\/em><\/p>\n
    YOU CANalytics Resource Rating \"3.5 (3.5 \/ 5)<\/p><\/blockquote>\n
    I had really high expectations from this course on coursera.com. Expectations were high since Dr. Andrew Ng is associated with this site and his course on machine learning<\/a> is delightful. However, the course by Dr. Roger D. Peng fell short of my expectations by some margin. The instructor is a good communicator, an expert in R and the topics of this course are highly relevant for learning R. The biggest problem for me with this course is its tone which is highly didactic. If Dr. Peng could slightly redesign this course around applications and examples it will become a fantastic course. (Link<\/a>)<\/strong><\/p>\n
     <\/p>\n
    \"lynda\"<\/a>Lynda.com<\/strong> : R Statistics Essential Training\u00a0<\/a><\/strong> – Barton Poulson<\/span><\/p>\n
    YOU CANalytics Resource Rating \"4.5 (4.5 \/ 5)<\/p><\/blockquote>\n
    This course is not as comprehensive as the above course on coursera. However, the tone of the course is much more applied and learner-friendly. (Link<\/a><\/strong>)<\/p>\n
     <\/p>\n
    \"Presentation1\"<\/a>UCI Machine Learning Repository<\/strong><\/p>\n
    YOU CANalytics Resource Rating \"5 (5 \/ 5)<\/p><\/blockquote>\n
    UCI machine learning repository has tons of freely available datasets. This site is not associated with R. However, ‘datasets’ package in R has many of the datasets taken from this site. The reason you may still want to go this site is because they have provided links to research papers that have used these datasets. (Link<\/a><\/strong>).<\/p>\n
     <\/p>\n
    A few more great online resources to learn R \r\n<\/strong><\/span>1) Datacamp (Link<\/a><\/strong>): Great courses on R, try this site for some interactive courses on R\r\n2) Open Intro (Link<\/strong><\/a>): This site has some really good tutorials for doing basic statistics on R\r\n3) R-tutor (Link<\/a><\/strong>): This is a good site to start learning R from scratch\r\n4) R-bloggers (Link<\/a><\/strong>): A great culminations of blogs for R, may not be the place you want to visit first up\r\n5) Kaggle (Link<\/strong><\/a>): This link has 3 good tutorials to learn R\r\n<\/span><\/pre>\n
    Sign-off Note<\/span><\/h4>\n
    Let me create a loose parallel between Excel and R to offer you an advice about learning R. As I have mentioned earlier, R has more than 5000 add-on packages on CRAN library and millions of functions for data analysis. This may sound a bit daunting to a new learner. Luckily the online resource is quite powerful hence number of functions won’t be a challenge. Moreover, if you have worked on Excel, you will know that there are just a handful of functions that you use repeatedly based on your style of analysis. This same pattern will emerge with R as well. Hence, don’t get intimidated by the number of functions.<\/p>\n
    Enjoy learning R! It is good fun.<\/p>\n
    Disclaimer<\/strong> : Roopam Upadhyay or YOU CANalytics has no affiliation to either the authors of the books or the web-sites hosting these PDF books shared in this post. I am assuming that none of the PDF file links I have shared in this article is a copyright infringement since they are among the top Google search results. Several of these files are from either the authors' webpages or from scholarly links. In case you believe otherwise about any link please let me know I will remove that link.\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"
    Please read the disclaimer about the Free PDF Books in this article at the bottom R, an open-source statistical and data mining programming language, is slowly but surely catching up in its race with commercial software like SAS & SPSS. I believe R will eventually replace SAS as the language of choice for modeling and<\/p>\n
    Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":460,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[68,62,79],"tags":[33,27,7,6,10],"jetpack_publicize_connections":[],"yoast_head":"\nLearn R : 12 Free Books and Online Resources – YOU CANalytics |<\/title>\n<meta name=\"description\" content=\"Learn R: this article will introduce books and online resources to learn R - an open-source statistical and data mining programming language.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/learn-r-12-books-and-online-resources\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Learn R : 12 Free Books and Online Resources – YOU CANalytics |\" \/>\n<meta property=\"og:description\" content=\"Learn R: this article will introduce books and online resources to learn R - 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