Col Standardize

Col Standardize using For

Example 1

Summary: Generates a customized data table by matching values from a specified list and inserting corresponding data into an empty list.

Code:

dt = Open("data_table.jmp");
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    y = "sex";
    If( i == 3,
        y = "height";
        Row() = 1;
    );
    Match( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3], Insert Into( testVar, Eval List( {i, y, Round( Col Standardize( Column( dt, y ) ), 3 )} ) ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data table;
2. Initialize xlist with four strings.
3. Initialize empty list testVar.
4. Start loop with i from 1 to 4.
5. Set y to "sex".
6. If i is 3, set y to "height" and reset row to 1.
7. Match xlist[i] value.
8. Insert {i, y} into testVar for first two matches.
9. For third match, insert {i, y, standardized height rounded to 3}.
10. Insert {i, y} into testVar for fourth match.

Example 2

Summary: Generates a distribution analysis for continuous variables in a specified data table using MatchMZ and conditional logic.

Code:

dt = Open("data_table.jmp");
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    y = "sex";
    If( i == 3,
        y = "height";
        Row() = 1;
    );
    MatchMZ( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3], Insert Into( testVar, Eval List( {i, y, Round( Col Standardize( Column( dt, y ) ), 3 )} ) ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data_table data
2. Define xlist with values.
3. Initialize empty testVar list.
4. Start loop over xlist items.
5. Set y variable to "sex".
6. Change y to "height" for third iteration.
7. Reset row to 1 if y changed.
8. Use MatchMZ for conditional logic.
9. Insert {i, y} into testVar for first two xlist items.
10. Insert {i, y, standardized height} for third item.
11. Insert {i, y} into testVar for fourth item.
12. Handle any unmatched cases with error message.

Example 3

Summary: Generates a list containing variables and their corresponding values from a specified data table, utilizing a For loop and MatchV3 function.

Code:

dt = Open("data_table.jmp");
xlist = {"1", "2", "3", "4"};
testVar = {};
For( i = 1, i <= N Items( xlist ), i++,
    y = "sex";
    If( i == 3,
        y = "height";
        Row() = 1;
    );
    MatchV3( xlist[i],
        xlist[1], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[2], Insert Into( testVar, Eval List( {i, y} ) ),
        xlist[3], Insert Into( testVar, Eval List( {i, y, Round( Col Standardize( Column( dt, y ) ), 3 )} ) ),
        xlist[4], Insert Into( testVar, Eval List( {i, y} ) ),
        Insert Into( testVar, Eval List( {i, y, "error"} ) )
    );
);

Code Explanation:

1. Open data table.
2. Define variable xlist.
3. Initialize empty list testVar.
4. Start loop over xlist.
5. Set default variable y to "sex".
6. Change y to "height" on third iteration.
7. Reset row index to 1 on third iteration.
8. Match value in xlist.
9. Insert evaluated list into testVar.
10. End loop.