Books that discuss BOTH R and SQL are a very small subset and assume some knowledge of both. R INTRODUCTORY BOOKS 1. Peter Dalgaard, "Introductory Statistics with R", 2002. "The book is based upon a set of notes developed for the course in Basic Statistics for Health Researchers at the Faculty of Health Sciences of the University of Copenhagen. This course had as its primary target.. students for the Ph.D. degree in medicine." Intro page viii. body mass index (BMI) and age of menarche. 2. Jared Lander, "R for Everyone", 2014. More modern, but less focused on health and a little more scattershot.
R AUTHORITATIVE REFERENCE 1. Brian Ripley and William Venables, "Modern Applied Statistics with S", 2002. Anything by John Chambers, Robert Gentleman or Brian Ripley or any member of the "R Core Development Team" can be considered authoritative (the stuff you can footnote without frowns) on R. Also, if you are going to use the R mailing list read all of the PDFs that come with the base installation of R. Its better now, but the R mailing list used to have a very strong "RTFM" attitude and did not want to explain anything that was clearly covered in the manuals. Especially read the "R Import/Export Manual" PDF. ADVANCED R (with SQL) Depends on what you are doing. If you working with health surveys, Thomas Lumley's "Complex Surveys" is invaluable!!!! One of Lumley's examples is the CDC's BRFSS, "The Behavioral Risk Factor Surveillance System (BRFSS) is the world's largest, on-going telephone health survey system." (from CDC website). Which in Lumley's example is: - The BRFSS 2007 data as a HUGE (245Mb) SQLite database <http://r-survey.r-forge.r-project.org/svybook/brfss07.db>. ?"? 1. Thomas Lumley, "Complex Surveys: A Guide to Health Analysis Using R", http://r-survey.r-forge.r-project.org/svybook/index.html On the other hand, if you are dealing with biological data such as trying to match results from GeneChips with existing reference sources you might prefer Robert Gentleman's "R Programming for Bioinformatics" especially, Chapter 8 "Data Technologies". 1. Robert Gentleman's "R Programming for Bioinformatics", 2009. "We begin our discussion by describing a range of tools that have been implemented in R and that can be used to process and transform data. Next we discuss the different interfaces to databases that are available, but focus our discussion on SQLite as it is used extensively within the Bioconductor Project." page 229 The databases discussion resumes on page 238, Section 8.4, discusses SQLite on page 241 including a specific example: "In the code below we load the SQLite package, initialize a driver and open a dataase that has been supplied with the RBionf [R] package that accompanies this volume. The database contains a number of tables that map between identifers on the Affymetrix HG-U95v2 GeneChip and different quantities of interest such as GO categories or PubMed IDs (that map published papers that discuss the corresponding genes). We then list the tables in that database." Sometimes we get tired of reading dry tomes and we prefer something more chatty and amusing. For R and other tools I enjoy reading: Cathy O'Neil's and Rachel Schutt's "Doing Data Science: Straight Talk from the Frontline", 2013. It's an O'Reilly book. For SQLite, I enjoy Michael Owen's, "The Definitive Guide to SQLite", 2006. -- maybe not the whole book, but the Chapter 4 example page 75 "Foods mentioned in episodes of the Seinfield sitcom" is a hoot (and turned out to help me solve an real world problem). If you are doing anything beyond Stats 101 classical statistics it helps to understand the Bayesian bogeyman. A fascinating, non-technical, historical account is provided by Sharon Bertsch McGrayne, in her book "The Theory that would not Die...". BAYESIAN STATISTICS (HISTORY) Sharon Bertsch McGrayne, "The Theory That Would Not Die How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy" ?, 2011. http://yalepress.yale.edu/book.asp?isbn=9780300169690 "For the student who is being exposed to Bayesian statistics for the first time, McGrayne?s book provides a wealth of illustrations to whet his or her appetite for more. It will broaden and deepen the field of reference of the more experienced statistician, and the general reader will find an understandable, well-written, and fascinating account of a scientific field of great importance today. " http://www.ams.org/notices/201205/rtx120500657p.pdf All the more timely with the release of the movie "The Imitation Game", because Turing & Co. cracked the German Enigma code using Bayesian statistics.? There few specific "Bayesian" packages in R (an interface to BUGS); but it lurks in the background of many of them -- any use of the word "prior". Hope this helps. Jim On Wed, Feb 25, 2015 at 11:28 AM, VASILEIOU Eleftheria <E.Vasileiou at ed.ac.uk > wrote: > Hi, > > I would need to use R for my analysis for my Project and my supervisor > suggested me to learn the SQL language for R. > Could you please provide me some resources for learning SQL and R? > > > Thanks in advance, > Eleftheria > > Eleftheria Vasileiou BSc, MPH > Research Student, Centre for Population Health Sciences > Room 815, Old Medical School, University of Edinburgh > > E.Vasileiou at ed.ac.uk > > The University of Edinburgh is a charitable body, registered in > Scotland, with registration number SC005336. > > _______________________________________________ > sqlite-users mailing list > sqlite-users at mailinglists.sqlite.org > http://mailinglists.sqlite.org/cgi-bin/mailman/listinfo/sqlite-users > >
