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Appendix C: R fragments

  • Page ID
    3616
    • C.1: R and databases
      There are many interfaces which connect R with different database management software and there is even package sqldf which allows to work with R data frames through commands from SQL language.
    • C.2: R and time
    • C.3: R and Bootstrapping
      All generalities like standard deviation and mean are normally taken from sample but meant to represent the whole statistical population. Therefore, it is possible that these estimations could be seriously wrong. Statistical techniques like bootstrapping were designed to minimize the risk of these errors. Bootstrap is based only on the given sample but try to estimate the whole population.
    • C.4: R and shape
      Analysis of biological shape is a really useful technique. Inspired with highly influential works of D’Arcy Thompson, it takes into account not the linear measurements but the whole shape of the object: contours of teeth, bones, leaves, flower petals, and even 3D objects like skulls or beaks. Naturally, shape is not exactly measurement data, it should be analyzed with special approaches. There are methods based on the analysis of curves  and methods which use landmarks and thin-plate splines (T
    • C.5: R and Bayes
    • C.6: R, DNA and evolution
    • C.7: R and reporting
    • C.8: Answers to exercises