Quick instructions for the Data selection form

• Data is typically (but not always) stored in the rightmost columns of a CSV file. Shift click to set the top left cell of the data. Columns to the left will be marked as axes.
• Select ``axis'' in the Dimension dropdown to include a column as an axis. Column data turns blue.
• Select ``ignore'' in the Dimension dropdown to exclude a column. Column data turns red.
• Select ``data'' in the Dimension dropdown to treat a column as a data column. Column data turns black.
• Select Type for included axis columns. Select one of three types:

  string

  most general type, data treated as is.

  value

  value data must be numerical, e.g. 1, 2, and not quoted (as in ``1'', ``2'', etc.)

  time

  data must refer to date-time data. The format field may be used to control interpretation of this data, though if this field is left blank, Ravel will interpret the data format itself. Blank format assumes data contains year/month/day/hour/minute/second separated by some non-numerical character. If fewer than 6 numerical fields present (from year to second), smallest units are set to minimum value (0 or 1 respectively).

• The header row can be set by clicking in the left hand margin.
• The column names can be quickly populated from the header row from a context menu.
• Multiple columns can have their types changed simultaneously by click dragging to select, then changing the value on one of the columns.
• When more than one data column exists, it creates a ``horizontal dimension''. This has a name, type and format string, just like a regular axis column.
• The ``unique values'' row shows the number of unique values in that column. This can be used to determine columns to exclude (via the ``ignore'' option. This becomes important when the hypercube size is too large to be imported. Ravel uses 64 bit numbers to index into the data cube, and so if the hypercube has more than elements, Ravel will not be able to import the data. The hypercube size is rendered in red if that is the case. Good columns to ignore are things like unique keys exported from a database.