I always use strings as my column base and then build my references from them how ever I need. I have found this to be the most flexible way of handling different types of referencing.
For example, if I take your script and make some assumptions of its purpose (starting from third column go through all of them, create a new formula column in which you change values to numeric, compare missing values and perform actions based on the result) I would write it like this in JMP18:
Names Default To Here(1);
dtTOSA = New Table("Untitled",
Add Rows(4),
Compress File When Saved(1),
New Column("Column 1",
Numeric,
"Continuous",
Format("Best", 12),
Set Values([1, 2, 3, 4])
),
New Column("Column 2", Character, "Nominal", Set Values({"A", "1", "2", "3"})),
New Column("Column 3", Character, "Nominal", Set Values({"1", "2", "3", "4"})),
New Column("Column 4", Character, "Nominal", Set Values({"A", "B", "C", "D"}))
);
wait(1); // demo purposes
cols = dtTOSA << Get Column Names("String");
// Remove From(cols, 1, 2); // skipping this for demo purposes
For Each({colname}, cols,
// colname = cols[3]; // for testing
Eval(EvalExpr(
newcol = dtTOSA << New Column("Temp", numeric, Formula(
Num(Expr(Name Expr(As Column(dtTOSA, colname))))
));
));
newcol << Run Formulas;
newcol << delete formula();
orig_vals = Col Number(Column(dtTOSA, colname));
new_vals = Col Number(Column(dtTOSA, (newcol << get name)));
If(orig_vals == new_vals,
dtTOSA << Delete Columns(colname);
newcol << set name(colname);
,
dtTOSA << Delete Columns(newcol)
);
);
And if I were to do this from the beginning but still using a bit similar idea as you have, I would (at least at this second) do something like this
Names Default To Here(1);
dt = New Table("Untitled",
Add Rows(4),
Compress File When Saved(1),
New Column("Column 1",
Numeric,
"Continuous",
Format("Best", 12),
Set Values([1, 2, 3, 4])
),
New Column("Column 2", Character, "Nominal", Set Values({"A", "1", "2", "3"})),
New Column("Column 3", Character, "Nominal", Set Values({"1", "2", "3", "4"})),
New Column("Column 4", Character, "Nominal", Set Values({"A", "B", "C", "D"}))
);
wait(1); // demo purposes
cols = dt << Get Column Names("Character", "String"); // We only need to check character columns
For Each({colname}, cols,
dt_summary = dt << Summary(
Group(Eval(colname)),
Freq("None"),
Weight("None"),
Link to original data table(0),
output table name("Summar of " || colname),
Private
);
orig_vals = Col Number(Column(dt_summary, 1));
Column(dt_summary, 1) << Set Data Type("Numeric");
new_vals = Col Number(Column(dt_summary, 1));
Close(dt_summary, no save);
If(orig_vals == new_vals, // we can change
Column(dt, colname) << Set Data Type("Numeric");
Column(dt, colname) << Set Modeling Type("Continuous");
);
);
I tend to do these utilizing Transform Each and Num, but sometimes it might be enough if you let JMP do the conversions (there are case where you might not want to do this though..., see Num() from scripting index and how it behaves with Num("5%") for example. << Restrict avoids this behaviour)
-Jarmo
