Scripting Guide - SAS

Chapter 9 Data Tables 303
Calculations
Example
Take advantage of this method to develop a compact formula to distinguish females and males with Xs and
Ys, while excluding females from calculations, hiding males from plots, and assigning different colors for
each age.
Recall that the logical == operator is an equality test that returns 1 for true and 0 for false.
dt=open("$SAMPLE_DATA/Big Class.JMP");
for each row(
rowstate() = as row state(
(sex=="F")*2 + // exclude F
(sex=="M")*4 + // hide M
((sex=="F")*2+(sex=="M")*6) * 16 +
// marker 2 for F, 6 for M
(age-11)*256));
// colors 1 through 6
Calculations
This section discusses functions for pre-evaluated columnwise and rowwise statistics and shows how JSL
expressions work behind the scenes in the JMP formula calculator. See the earlier discussion of “Summarize”
on page 252, for a way to gather summary statistics on a data table.
Pre-Evaluated Statistics
JMP has Col Maximum, Col Mean, Col Minimum, Col N Missing, Col Number, Col Quantile, CV
(Coefficient of Variation), Col Standardize, Col Std Dev, Col Sum, Maximum, Mean, Minimum,
NMissing, Number, Std Dev, and Sum functions, which are special “pre-evaluated” functions.
Note: Statistics are also computed with Summarize (p. 252). Although the named arguments to
Summarize have the same names as these pre-evaluated statistic functions, they are not calling the
pre-evaluated statistic functions. The resemblance is purely coincidental.
All the statistics are pre-evaluated. That is, JMP calculates them once over the rows or columns specified and
thereafter uses the results as constants. Because they are computed once and then used over and over again,
they are more efficient to use in calculations than an equivalent formula-calculated results.
When JMP encounters a pre-evaluated function in a script, it immediately evaluates the function and then
uses the result as a constant thereafter. Thus, pre-evaluated functions enable you to use columnwise results
for rowwise calculations. For example, if you use Col Mean inside a column formula, it first evaluates the
mean for the column specified and then uses that result as a constant in evaluating the rest of the formula on
each row. For example, a formula might standardize a column using its preevaluated mean and standard
deviation:
(Height-Col Mean(Height))/Col Std Dev(Height)