Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.

The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.

Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base.

Includes built-in methods for generating long 'SQL' CASE statements,
             and other 'SQL' statements that may otherwise be arduous to construct
             by hand.The generated statement can easily be concatenated to string
             literals to form queries to 'SQL'-like databases, such as when using 
             the 'RODBC' package. The current methods include casewhen() for 
             building CASE statements, inlist() for building IN statements, and
             updatetable() for building UPDATE statements.

The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.