Introduction to R and Kepler QA/QC - LTER Information Management

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R Basics • R is an open source statistical language • “Atomic” types: logical, integer, real, complex, string (or character) and raw • Data in R is stored in one of several types of objects Scalar : myVar
R Workspaces • All the variables and functions defined during a session are part of the “Workspace” • R Workspaces can be saved for later use When you come back, everything is the same as when the workspace was saved
Page 1: John Porter, University of Virginia
Page 5 and 6: Quick Exercise - Run these in R # a
Page 7 and 8: Reading Data into Data Frames • A
Page 10 and 11: R Data Structures • A lot of the
Page 12 and 13: Using Data Frames A
Page 14 and 15: R Help R has a number of ways of ca
Page 16 and 17: QA/QC - Typical Tests • Identific
Page 18 and 19: R code for Spike Detection ## ident
Page 20 and 21: “Lattice” Plots • R excels in
Page 22 and 23: Kepler Configuration • Kepler doe
Page 24 and 25: Typical connections for basic RExpr
Page 28 and 29: Hook up some input and output actor
Page 30: Results of Running Workflow R Listi
Page 33 and 34: Trick 1 - select the right object t
Page 35 and 36: Trick 2 - Trap R errors • Normall
Page 37 and 38: Now we get an informative message
Page 39 and 40: Exercise - A succession of workflow
Page 41 and 42: 2_Ft_Monroe_GoodData.kar
Page 43 and 44: Kepler Stuff to Note • Parameters
Page 45 and 46: Using a Parameter Value • Add a $
Page 47 and 48: R stuff to Note • This workflow u
Page 49 and 50: R+Kepler vs. R Alone • Given that
Page 51 and 52: Example of Collaboration • I coul
Page 53 and 54:
A more complex and general workflow
Page 55 and 56:
Passing R Workspaces • This workf
Page 57 and 58:
A conversion problem Temperature an
Page 59 and 60:
Factors This is the mean of the IND
Page 61:
Your Final Challenge • As it’s
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