data_analytics_with_r.md

Notes About R

[This is work in progress. To be fixed into categories of books on R.]

References

Citations/References that use the LaTeX/BibTeX notation are taken from my BibTeX database (set of BibTeX entries).

Books to Reference

• https://link.springer.com/book/10.1007/978-3-319-45599-0 Data Wrangling with R Bradley C. Boehmke, Ph.D. in Use R! (2016)
• https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
• https://www.amazon.com/Art-Programming-Statistical-Software-Design/dp/1593273843/
• https://www.amazon.com/Book-First-Course-Programming-Statistics/dp/1593276516/
• https://www.amazon.com/dp/1786460114/
• https://www.amazon.com/dp/1788398319/

Books on R:

• [156] John M. Chambers. Programming with Data. Springer, New York, 1998.
• [157] John M. Chambers and Trevor J. Hastie. Statistical Models in S. Chapman & Hall, London, 1992.
• [158] Richard A. Becker, John M. Chambers, and Allan R. Wilks. The New S Language. Chapman & Hall, London, 1988.

Books to Get

Books on R:

• [61] John M. Quick. The Statistical Analysis with R Beginners Guide. Packt Publishing, 2010. Book
• [62] Francois Husson, Sébastien Lê, and Jérôme Pagès. Exploratory Multivariate Analysis by Example Using R. Computer Sciences and Data Analysis. Chapman & Hall/CRC, 2010. See books 1 and 2
• [66] Carlo Gaetan and Xavier Guyon. Spatial Statistics and Modeling. Springer Series in Statistics. Springer, 2010.
• [68] Hrishikesh D. Vinod, editor. Advances in Social Science Research Using R. Lecture Notes in Statistics. Springer, 2010. http://link.springer.com/book/10.1007%2F978-1-4419-1764-5
• [69] Victor Bloomfield. Computer Simulation and Data Analysis in Molecular Biology and Biophysics: An Introduction Using R. Springer, 2009. Book
• [80] Kai Velten. Mathematical Modeling and Simulation: Introduction for Scientists and Engineers. Wiley-VCH, 2009.
• [81] 2nd edition
• [84] Günther Sawitzki. Computational Statistics. Chapman & Hall/CRC Press, Boca Raton, FL, 2009.
• [88] Ewout W. Steyerberg. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. SBH/Statistics for Biology and Health. Springer, 2009.
• [93] Alain Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveiliev, and Graham M. Smith. Mixed Effects Models and Extensions in Ecology with R. Springer, New York, 2009.
• [95] Stefano M. Iacus. Simulation and Inference for Stochastic Differential Equations: With R Examples. Springer, New York, 2008.
• [97] Deepayan Sarkar. Lattice: Multivariate Data Visualization with R. Springer, New York, 2008.
• [100] Robert Gentleman. Bioinformatics with R. Chapman & Hall/CRC, Boca Raton, FL, 2008.
• [101] Robert Gentleman. R Programming for Bioinformatics. Computer Science & Data Analysis. Chapman & Hall/CRC, Boca Raton, FL, 2008. Another book.
• [104] Peter Dalgaard. Introductory Statistics with R. Springer, 2nd edition, 2008. ISBN 978-0-387-79053-4.
• [106] Luke Keele. Semiparametric Regression for the Social Sciences. Wiley, Chichester, UK, 2008. and web page
• [108] John M. Chambers. Software for Data Analysis: Programming with R. Springer, New York, 2008.
• [111] Clemens Reimann, Peter Filzmoser, Robert Garrett, and Rudolf Dutter. Statistical Data Analysis Explained: Applied Environmental Statistics with R. Wiley, Chichester, UK, 2008 and web page.
• [114] Benjamin M. Bolker. Ecological Models and Data in R. Princeton University Press, 2008. and web page
• [115] W. John Braun and Duncan J. Murdoch. A First Course in Statistical Programming with R. Cambridge University Press, Cambridge, 2007. and web page
• [116] Scott M. Lynch. Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. Springer, New York, 2007.
• [117] Sandrine Dudoit and Mark J. van der Laan. Multiple Testing Procedures and Applications to Genomics. Springer Series in Statistics. Springer, 2007
• [118] Philip J. Boland. Statistical and Probabilistic Methods in Actuarial Science. Chapman & Hall/CRC, Boca Raton, FL, 2007.
• [119] Michael Greenacre. Correspondence Analysis in Practice, Second Edition. Chapman & Hall/CRC, Boca Raton, FL, 2007.
• [120] John Maindonald and John Braun. Data Analysis and Graphics Using R. Cambridge University Press, Cambridge, 2nd edition, 2007. and web page
• [121] Jean-Michel Marin and Christian P. Robert. Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New York, 2007.
• [123] David Siegmund and Benjamin Yakir. The Statistics of Gene Mapping. Springer, New York, 2007.
• [126] Peter J. Diggle and Paulo Justiniano Ribeiro. Model-based Geostatistics. Springer, 2006.
• [127] Nhu D. Le and James V. Zidek. Statistical Analysis of Environmental Space-Time Processes. Springer, 2006.
• [129] Julian J. Faraway. Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC, Boca Raton, FL, 2006. and web page
• [130] Jana Jureckova and Jan Picek. Robust Statistical Methods with R. Chapman & Hall/CRC, Boca Raton, FL, 2006.
• [137] Fionn Murtagh. Correspondence Analysis and Data Coding with JAVA and R. Chapman & Hall/CRC, Boca Raton, FL, 2005. and web page
• [138] Brian S. Everitt. An R and S-Plus Companion to Multivariate Analysis. Springer, 2005. and \url{http://biostatistics.iop.kcl.ac.uk/publications/everitt/}
• [140] Robert Gentleman, Vince Carey, Wolfgang Huber, Rafael Irizarry, and Sandrine Dudoit, editors. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, 2005.
• [142] Richard M. Heiberger and Burt Holland. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer, 2004. and web page
• [145] Sylvie Huet, Annie Bouvier, Marie-Anne Gruet, and Emmanuel Jolivet. Statistical Tools for Nonlinear Regression. Springer, New York, 2003.
• [147] Giovanni Parmigiani, Elizabeth S. Garrett, Rafael A. Irizarry, and Scott L. Zeger. The Analysis of Gene Expression Data. Springer, New York, 2003.
• [148] William N. Venables and Brian D. Ripley. Modern Applied Statistics with S. Fourth Edition. Springer, New York, 2002. and web page
• [149] John Fox. An R and S-Plus Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, USA, 2002.
• [151] Frank E. Harrell. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. Springer, 2001. and web page
• [152] William N. Venables and Brian D. Ripley. S Programming. Springer, New York, 2000.
• [153] Terry M. Therneau and Patricia M. Grambsch. Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer, 2000.
• [154] Jose C. Pinheiro and Douglas M. Bates. Mixed-Effects Models in S and S-Plus. Springer, 2000.
• [155] Deborah Nolan and Terry Speed. Stat Labs: Mathematical Statistics Through Applications. Springer Texts in Statistics. Springer, 2000. and web page.

zz books

• "Data Science at the Command Line" \cite{Janssens2015}
• "Getting Started with Data Science" \cite{Haider2015}

http://www.wzchen.com/data-science-books

Author Information

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