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13.2: R Resources

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    The following books, which I have found useful, either provide a broad introduction to the R programming language, or a more targeted coverage of a particular application. The texts published by O'Reilly have on-line versions made available for free; there entries here provide links to the on-line versions.

    • Chambers, J. M. Software for Data Analysis: Programming with R, Springer: New York, 2008.
    • Chang, W. R Graphics Cookbook, O'Reilly, 2013.
    • Gardner, M. Beginning R: The Statistical Programming Language, Wiley, 2012.
    • Gillespie, C.; Lovelace, R. Efficient R Programming, O'Reilly, 2020.
    • Grolemund, G. Hands-On Programming with R, O'Reilly, 2014.
    • Horton, N. J.; Kleinman, K. Using R and RStudio for Data Management, Statistical Analysis, and Graphics, 2nd Edition, CRC Press, 2015.
    • James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning with Applications in R, Springer, 2013.
    • Lander, J. P. R for Everyone: Advanced Analytics and Graphics, Addison Wesley, 2014.
    • Kabacoff, Robert I. R in Action: Data Analysis and Graphics with R, Manning, 2011.
    • Maindonald, J.; Braun, J. Data Analysis and Graphics Using R, Cambridge University Press: Cambridge, UK, 2003.
    • Matloff, N. The Art of R Programming, No Starch Press, 2011.
    • Sarkar, D. Lattice: Multivariate Data Visualization With R, Springer: New York, 2008.
    • Vaughn, S. Scientific Inference, Cambridge, 2013.
    • Wickham, H. ggplot2, Springer, 2009.
    • Wickham, H.; Grolemund, G. R for Data Science, O'Reilly, 2017.


    • Doi, J.; Potter, G.; Wong, J. "Web Application Teaching Tools for Statistics Using R and Shiny", Technology Innovations in Statistics Education, 2016, 9.

    This page titled 13.2: R Resources is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by David Harvey.

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