How to Extract Data from a Multiple Column Table

This article explains how to convert a multiple-column table...

...to returning only the cells you need.

Requirements

• A multiple-column table with a single statistic.

Method 1 - Point-and-click

1. Click on Calculation > Custom Code from the toolbar.

2. Click on the page to insert the object on the page.

3. Click into the Code panel, and while the cursor is blinking (active), click on a part of a table that you'd like to extract.

See Bespoke Analyses for examples and a demo of the feature.

4. OPTIONAL: Add a percentage sign or adjust the number of decimal places via Properties  > Appearance > Appearance.

Method 2 - By name

1. Select your table.

2. Copy the name from Properties   >General > General > Name.

3. Select Calculation > Custom Code from the toolbar.

4. In the Code  panel, add a line in the format of table_name[row_name, column_name]

Using our example, the code to return just the second row of the Male column is:

table.age.gender["25 to 29","Male"]

And the results are:

To instead return values from the first three rows of the Male column is:

table.age.gender[c("18 to 24","25 to 29","30 to 34"),"Male"]

And the results are:

Leaving one of these arguments empty returns all rows or columns, respectively, from the table. Below, the column argument has been removed from above (table.age.gender[c("18 to 24","25 to 29","30 to 34"),]), so it returns both columns:

5. OPTIONAL: Add a percentage sign or adjust the number of decimal places via Properties > Appearance > Appearance.

Method 3 - By index

1. Select your table.

2. Copy the name from Properties > General > General > Name.

3. Select Calculation > Custom Code from the toolbar.

4. In the Code panel, add a line in the format of table_name[row_index, column_index]

Using our example, the code to return the value from the second row of the first column is: table.age.gender[2,1]

And the results are:

To instead return values from the first three rows of the first column is: table.age.gender[c(1,2,3),1]

And the results are:

Alternatively, you can use index ranges: table.age.gender[1:3,1]

You can even reverse this to remove the other rows instead: table.age.gender[c(-4:-10,1)]

You can additionally remove the last row by using the NROW argument:

table.age.gender[-NROW(table.age.gender),]

And the results are:

5. OPTIONAL: Add a percentage sign or adjust the number of decimal places via Properties  > Appearance > Appearance.

Next

How to Extract Data from a Single Column Summary Table

How to Extract Data from a Multiple Column Table with Multiple Statistics

How to Extract Data from a Multiple Column Table with Nested Data