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Mar 14, 2017 2:00 PM
(1500 views)

I was disappointed to find out that JMP resricts us from stacking just one column. I understand that stacking just one column doesn't make sense, however, since it's just a data transform it does make sense to want to do it.

Anyways, I wanted to open up the conversation and see who can come up with a better work around than I did. Here's mine:

```
Names Default to Here(1);
//Example Data Table
dt = Open( "$SAMPLE_DATA/Decathlon.jmp" );
//col list is list of columns from 1 to n (never an empty list)
colList = {"Javelin", "Pole Vault"};
//Stack columns to prepare data for analysis
if(nitems(colList) > 1,
analysisColsExpr = Expr(columns());
for(i=1; imax=nitems(colList), i<=imax, i++,
col = Column(dt, colList[i]);
insert into(analysisColsExpr, col);
);
dtAnalysis = Eval(Eval Expr(dt << Stack(
Expr( Name Expr( analysisColsExpr )),
Source Label Column( "AnalysisType" ),
Stacked Data Column( "AnalysisValue" ),
Output Table( "Stacked Data" )
)));
close(dt, nosave);
,
//Can't stack only one column adjust columns to match above strategy
dt << New Column("AnalysisType", Character, Nominal, Formula(colList[1]));
column(dt, colList[1]) << Set Name("AnalysisValue");
dt << Set Name("Stacked Data");
dtAnalysis = dt;
);
//Run Analysis
oneway = dtAnalysis << Oneway( Y( :AnalysisValue ), X( :AnalysisType ) );
```

Good Luck!

- Tags:
- stacking columns

3 REPLIES

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Here's an alternative that does not mess up the original table if there is only one column, i.e. creates a new table (like Stack() does) instead of adapting the original table to the approach.

```
Names Default To Here(1);
//Example Data Table
dt = Open("$SAMPLE_DATA/Decathlon.jmp");
//col list is list of columns from 1 to n (never an empty list)
colList = {"Javelin", "Pole Vault"};
If(N Items(colList) > 1,
dtAnalysis = dt << Stack(
Columns(colList),
Source Label Column("AnalysisType"),
Stacked Data Column("AnalysisValue"),
Output Table("Stacked Data")
),
dtAnalysis = New Table("Stacked Data",
addrows(N Rows(dt)),
New Column("AnalysisType", character, set each value(colList[1])),
New Column("AnalysisValue", numeric, values(Column(dt, colList[1]) << getvalues))
)
);
//Run Analysis
oneway = dtAnalysis << Oneway(Y(:AnalysisValue), X(:AnalysisType));
```

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Another approach without a conditional (assuming original data table has >1 rows):

```
Names Default To Here(1);
//Example Data Table
dt = Open("$SAMPLE_DATA/Decathlon.jmp");
//col list is list of columns from 1 to n (never an empty list)
colList = {"Javelin", "Pole Vault"};
dtTemp = dt << Transpose(Private, Columns(colList), Label Column Name("AnalysisType"));
stackList = (dtTemp << get column names)[2 :: N Row(dt) + 1];
dtAnalysis = dtTemp << Stack(
Columns(stackList),
Stacked Data Column("AnalysisValue"),
Output Table("Stacked Data")
);
//Run Analysis
oneway = dtAnalysis << Oneway(Y(:AnalysisValue), X(:AnalysisType));
```

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This is a great strategy and works perfectly in JMP 13, however, in JMP 12 since Name is a Label column it automatically throws it in as a label column for the Transpose function--short circuiting the strategy. Not sure if that was a bug, but glad it is fixed in JMP 13. Unfortunately, I'm the only one on my team that has JMP 13 currently...