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Data Manipulation Steve Allison

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Presentation on theme: "Data Manipulation Steve Allison"— Presentation transcript:

1 Data Manipulation Steve Allison allisons@uci.edu

2 Language R… – is an interpreted language – is highly interactive – is case sensitive (mydata ≠ Mydata ≠ myData) – stores data in a variety of classes (more on this…) – language is fully documented (available here) and open sourceavailable here

3 Coding vs. Menus Efficiency of complicated procedures Ease of reproducing output Readability Customization

4 VocabulaRy Object Function Workspace Working directory History Package Call

5 A brief tour of RStudio RStudio is a GUI for R Panes (Source, Console, Workspace, Utilities) Code completion Projects Check out the screencast at http://rstudio.org/http://rstudio.org/

6 Rules for naming data structures Must begin with a letter, no spaces, no _ good.name1 vs. 2_poor name Assignment syntax: <- (not = ) Formally reserved words if else repeat while function for in next breakTRUE FALSE NULL Inf NaN NA NA_integer_ NA_real_ NA_complex_ NA_character_ Informally reserved words data file formula summary

7 Packages “Currently, the CRAN package repository features 3732 available packages.”

8 Many Types of Data Import User input Text files Excel files (uses gdata or xlsx ) Others

9 Data Classes Vectors Matrices Arrays Data Frames Lists Factors Various model objects

10 Finding Help Calling a help file: ?seq#function name help(aov)#function name ??tukey#fuzzy match example(lm)#worked example Online resources – Quick R Quick R – UCLA stats department UCLA stats department – CRAN CRAN – R for SAS Users R for SAS Users – UseR! UseR!


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