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# Ntile

Assign the rows of a column to a given number of ranks.

### Usage

###### Ntile(<ranks>,<field>,<direction  ”asc” | “desc”>)

ranks (required) Number of ranks. Must be an integer greater than 0.

field (required) The column used to order the table.

direction (optional) The direction to sort the input column. Enter is “asc” to sort ascending and “desc” to sort descending. Default sort is ascending.

NOTE: An equal number of rows will be given each Ntile rank. The Ntile ranks are assigned in order.

### Examples

###### Ntile(4, [Population 2010])

An equal number of rows will be ranked 1, 2, 3 and 4 according to the size of their population. The lowest quartile of values in [Population 2010] will be ranked 1. The Highest quartile of values will be ranked 4.

### Window Functions

Window Functions are special functions where the result is dependent on the order and grouping of rows. The “window” for a Window Function is defined by the grouping at that level. The result of a window function is determined only by the rows within the window. For example, given a table of city populations, if you grouped by State and then Rank'ed the cities by Population, you would have an independent ranking for each State. If there is no grouping, the “window” is the entire table.

For most Window Functions, the results are dependent on how the rows within the window are sorted. Many Window Functions will only work properly if the table is uniquely sorted by a column in the same “window” as the Window Function. To ensure that there is a unique sort order without any duplicate values, it is often useful to use multi-column sorting criteria to clearly define how to handle duplicate values.

The complexity of Window Functions makes them a little harder to use, but they are also a very powerful tool.

#### Cumulative Window Functions

Cumulative Window Functions are aggregate functions that apply to all of the rows up-to and including the current row.

The window that Cumulative Window Functions are applied in must be uniquely sorted for the function to work properly.

#### Moving Window Functions

Moving Window Functions are aggregate functions which apply to a “window” bracketing the current row. A common moving Window Function  is a “moving average”.

The window is specified as a certain number of rows above and below the current row, constrained by the grouping.

The window that Moving Window Functions are applied in must be uniquely sorted for the function to work properly.

#### Shifting Window Functions

Shifting Window functions either shift the values in a column or repeat a specified value. The functions are useful when calculating month over month data.

The window that Shifting Window Functions are applied in must be uniquely sorted for the function to work properly.

#### Ranking Window Functions

Ranking Window Functions apply a rank to each row based on the criteria in a row provided to the function.

Ranking functions are not dependent on the sort order of the rows in the window it is applied in.