other spreadsheets call pivot tables. Under any name, they are a tool
for extracting and summarizing the information contained in spreadsheet
cells in a more convenient form. Using a DataPilot, you can immediately
see relationships between different pieces of data that would be
difficult -- if not impossible -- to find using formulas, and tedious
to extract manually. In effect, a DataPilot gives you something of the
power of using a database without actually switching out of a
spreadsheet. Small wonder, then, that over half of spreadsheet users
are said to use datapilots or pivot tables.
To understand the usefulness of DataPilots, imagine that you are a
manufacturer selling Ogg Vorbis music players in the North American
market. Your product comes in two colors, beige and black, and in 80,
150 and 300 megabyte sizes, and is sold in sales packs. For each sale,
you record the number of display packs sold, and the total price. On a
spreadsheet, some of your data might look like this:
Notice that the columns all have labels. These labels are a pre-requisite for working with DataPilots.
When you come to analyze this data, you might want to know such
questions as how many units of each color you sold, or how many units
in each country. You could find this information by setting up a
combination of filters and formulas, but creating a DataPilot is much
For example, to quickly find out how many sales packs you sold in each country, you could create the following DataPilot:
Sales for all countries are shown by default. However, if you use
the Country filter at the top of the DataPilot, you can see the sales
only for Canada:
The gray cells in each DataPilot represent filters that you can use
to modify it. By clicking the one marked Filter, you can change the
information displayed in the DataPilot.
In addition, you can drag the other filters into new positions to
change the information display. For example, if you drag the Country
filter to the right of the Quantity column in the existing datapilot,
it now displays the quantity sold broken down by country, with the
first few rows reading:
As you can see, an datapilot is an ideal way of obtaining new perspectives on your data with a minimum of effort.
Creating a DataPilot
To begin creating an datapilot, highlight the range of cells you
want to base it upon, then select Data -> DataPilot -> Start to
open the DataPilot dialog window. Alternatively, choose the same menu
item, then select a data source that you have already registered with
OpenOffice.org using File -> New -> Database and a range of cells
The DataPilot window gives you a diagram of the DataPilot that you
are creating, and a list of columns from the data source. To create the
general layout for the DataPilot, all you have to do is drag the
columns to one of the blank spaces on the diagram. If you drag a column
name to the Column fields or Row fields space, then it becomes the
first cell in a row or column, just as you might expect from the name
(in the first DataPilot above, Quantity was selected as the column, and
no row was chosen). Similarly, if you drag a column name to the Data
fields, it becomes the data in the DataPilot (in the first example
above, the Price). The only potentially puzzling choice is the Page
Fields, which is actually just the custom filter for changing the
contents of the DataPilot on the fly (in the first example, the
Country). If you make a mistake, you can drag the column back to the
list of column building blocks on the right.
Once you have done the basic setup, you can also choose what
function to use in the DataPilot. In the examples above, I simply used
the default Sum function, which for many purposes is all that you need.
However, you can also use another ten basic functions: Count, Average,
Max, Min, Product, Count (Numbers Only), StDev (Sample), StDevP
(Population), Var (Sample) and Var (Population). If necessary, you can
find details about what these functions do in OpenOffice.org's online
By default, the DataPilot is written directly below the range it is
based upon. However, if you select the More button in the dialog
window, you can set the cells or sheet to which it is inserted. You can
also set additional options, such as ignoring empty rows. However, for
the most part, you can safely ignore these options, especially when
just starting to work with DataPilots, because the defaults are the
ones you are likely to want most of the time anyway.
After you create the DataPilot, selecting part of it enables Data
->DataPilot -> Refresh if you need to update it because of a
change to the source information. The same sub-menu contains a Delete
item that you can use when you no longer need the DataPilot.
Learning to work with DataPilots can take some time. New users need to
learn that columns, not rows, should contain labels. They also have to
keep track of which columns contain data and which do not: If those
that do not are dragged to the Data fields in the dialog window, then
the result will be nonsense. However, for most people, only a few trial
and errors should be needed to master the basics of DataPilots.
Soon, you'll be reading your data in ways you never considered.
After that, what you do with DataPilots is up to your imagination.
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