How to Extract Data from a Multiple Column Table

This article describes how to go from 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. While the cursor is blinking (active) in the R Code editor, 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 Appearance > Appearance.

Method 2 - By name

1. Select your table.

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

3. Select Calculation > Custom Code from the toolbar.

4. In the R Code editor, 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 will return all the 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 Appearance > Appearance.

 

Method 3 - By index

1. Select your table.

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

3. Select Calculation > Custom Code from the toolbar.

4. In the R Code editor, 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 Appearance > Appearance.

See also

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