# EXAMPLE - RANK Functions

This example demonstrates you to generate a ranked order of values.

**Functions**:

Item | Description |
---|---|

RANK Function | Computes the rank of an ordered set of value within groups. Tie values are assigned the same rank, and the next ranking is incremented by the number of tie values. |

DENSERANK Function | Computes the rank of an ordered set of value within groups. Tie values are assigned the same rank, and the next ranking is incremented by 1. |

**Source:**

The following dataset contains lap times for three racers in a four-lap race. Note that for some racers, there are tie values for lap times.

Runner | Lap | Time |
---|---|---|

Dave | 1 | 72.2 |

Dave | 2 | 73.31 |

Dave | 3 | 72.2 |

Dave | 4 | 70.85 |

Mark | 1 | 71.73 |

Mark | 2 | 71.73 |

Mark | 3 | 72.99 |

Mark | 4 | 70.63 |

Tom | 1 | 74.43 |

Tom | 2 | 70.71 |

Tom | 3 | 71.02 |

Tom | 4 | 72.98 |

**Transformation:**

You can apply the `RANK()`

function to the `Time`

column, grouped by individual runner:

Transformation Name | |
---|---|

Parameter: Formulas | RANK() |

Parameter: Group by | Runner |

Parameter: Order by | Time |

You can use the `DENSERANK()`

function on the same column, grouping by runner:

Transformation Name | |
---|---|

Parameter: Formulas | DENSERANK() |

Parameter: Group by | Runner |

Parameter: Order by | Time |

**Results:**

After renaming the columns, you have the following output:

Runner | Lap | Time | Rank | Rank-Dense |
---|---|---|---|---|

Mark | 4 | 70.63 | 1 | 1 |

Mark | 1 | 71.73 | 2 | 2 |

Mark | 2 | 71.73 | 2 | 2 |

Mark | 3 | 72.99 | 4 | 3 |

Tom | 2 | 70.71 | 1 | 1 |

Tom | 3 | 71.02 | 2 | 2 |

Tom | 4 | 72.98 | 3 | 3 |

Tom | 1 | 74.43 | 4 | 4 |

Dave | 4 | 70.85 | 1 | 1 |

Dave | 1 | 72.2 | 2 | 2 |

Dave | 3 | 72.2 | 2 | 2 |

Dave | 2 | 73.31 | 4 | 3 |