Rank() - the rank function is used to grade data depending on certain criteria.
1. Used in joins , where, group by and having clause.
2. Used in predceeding operaions;
3.Formations of partitioning in partitioning expression.
4. Evaluation of analytical functions
5. always process by Order by query.
"
example:
RANK()
- OVER ( [ partition_by_clause ] order_by_clause )
- partition_by_clause divides the result set produced by the FROM clause into partitions to which the function is applied. If not specified, the function treats all rows of the query result set as a single group. order_by_clause determines the order of the data before the function is applied. The order_by_clause is required. The <rows or range clause> of the OVER clause cannot be specified for the RANK function
select first_name, department_id, salary, RANK( ) OVER (ORDER BY department_id) as r from employees
FIRST_NAME DEPARTMENT_ID SALARY R-------------------- ------------- ---------- ----------
Jennifer 10 4577.76 1
Michael 20 13525.2 2
Pat 20 6242.4 2
Den 30 11444.4 4
Alexander 30 3225.24 4
Shelli 30 3017.16 4
Sigal 30 2913.12 4
Guy 30 2705.04 4
Karen 30 2601 4
Susan 40 6762.6 10
Matthew 50 8323.2 11
FIRST_NAME DEPARTMENT_ID SALARY R
-------------------- ------------- ---------- ----------
Adam 50 8531.28 11
Payam 50 8219.16 11
Shanta 50 6762.6 11
Kevin 50 6034.32 11
Julia 50 3329.28 11
Irene 50 2809.08 11
James 50 2496.96 11
Steven 50 2288.88 11
Laura 50 3433.32 11
Mozhe 50 2913.12 11
James 50 2601 11
SQL> select first_name, salary, rank() over (order by salary desc) from (selec
first_name, last_name, salary from employees);
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Steven 24969.6 1
Neena 17686.8 2
Lex 17686.8 2
John 14565.6 4
Karen 14045.4 5
Michael 13525.2 6
Nancy 12493.12 7
Shelley 12493.12 7
Alberto 12484.8 9
Lisa 11964.6 10
Den 11444.4 11
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Gerald 11444.4 11
Ellen 11444.4 11
Eleni 10924.2 14
Clara 10924.2 14
Janette 10404 16
Peter 10404 16
Hermann 10404 16
Harrison 10404 16
Tayler 9987.84 20
Danielle 9883.8 21
David 9883.8 21
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Patrick 9883.8 21
Peter 9363.6 24
Alexander 9363.6 24
Allan 9363.6 24
Daniel 9363.6 24
Alyssa 9155.52 28
Jonathon 8947.44 29
Jack 8739.36 30
William 8635.32 31
Adam 8531.28 32
John 8531.28 32
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Matthew 8323.2 34
Lindsey 8323.2 34
Christopher 8323.2 34
Payam 8219.16 37
Jose Manuel 8115.12 38
Ismael 8011.08 39
Louise 7803 40
Nanette 7803 40
William 7698.96 42
Elizabeth 7594.92 43
Mattea 7490.88 44
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Oliver 7282.8 45
Kimberely 7282.8 45
Sarath 7282.8 45
Luis 7178.76 48
David 7074.72 49
Susan 6762.6 50
Shanta 6762.6 50
Sundar 6658.56 52
Charles 6450.48 53
Amit 6450.48 53
Sundita 6346.44 55
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Pat 6242.4 56
Bruce 6242.4 56
Kevin 6034.32 58
Valli 4993.92 59
David 4993.92 59
Jennifer 4577.76 61
Nandita 4369.68 62
Diana 4369.68 62
Alexis 4265.64 64
Sarah 4161.6 65
Britney 4057.56 66
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Kelly 3953.52 67
Jennifer 3745.44 68
Renske 3745.44 68
Trenna 3641.4 70
Julia 3537.36 71
Jason 3433.32 72
Laura 3433.32 72
Julia 3329.28 74
Samuel 3329.28 74
Winston 3329.28 74
Stephen 3329.28 74
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Alexander 3225.24 78
Alana 3225.24 78
Jean 3225.24 78
Curtis 3225.24 78
Kevin 3121.2 82
Anthony 3121.2 82
Shelli 3017.16 84
Timothy 3017.16 84
Michael 3017.16 84
Sigal 2913.12 87
Vance 2913.12 87
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Girard 2913.12 87
Mozhe 2913.12 87
John 2809.08 91
Irene 2809.08 91
Guy 2705.04 93
Douglas 2705.04 93
Donald 2705.04 93
Randall 2705.04 93
Karen 2601 97
James 2601 97
Randall 2601 97
FIRST_NAME SALARY RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------
Peter 2601 97
Martha 2601 97
Joshua 2601 97
Ki 2496.96 103
James 2496.96 103
Hazel 2288.88 105
Steven 2288.88 105
TJ 2184.84 107
107 rows selected.
SQL>
=======================
DENSE_RANK()
dense rank - there are no gaps between the ranks:
SQL> select first_name, salary, dense_rank() over (order by salary desc) from (s
elect first_name, last_name, salary from employees);
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Steven 24969.6 1
Neena 17686.8 2
Lex 17686.8 2
John 14565.6 3
Karen 14045.4 4
Michael 13525.2 5
Nancy 12493.12 6
Shelley 12493.12 6
Alberto 12484.8 7
Lisa 11964.6 8
Den 11444.4 9
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Gerald 11444.4 9
Ellen 11444.4 9
Eleni 10924.2 10
Clara 10924.2 10
Janette 10404 11
Peter 10404 11
Hermann 10404 11
Harrison 10404 11
Tayler 9987.84 12
Danielle 9883.8 13
David 9883.8 13
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Patrick 9883.8 13
Peter 9363.6 14
Alexander 9363.6 14
Allan 9363.6 14
Daniel 9363.6 14
Alyssa 9155.52 15
Jonathon 8947.44 16
Jack 8739.36 17
William 8635.32 18
Adam 8531.28 19
John 8531.28 19
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Matthew 8323.2 20
Lindsey 8323.2 20
Christopher 8323.2 20
Payam 8219.16 21
Jose Manuel 8115.12 22
Ismael 8011.08 23
Louise 7803 24
Nanette 7803 24
William 7698.96 25
Elizabeth 7594.92 26
Mattea 7490.88 27
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Oliver 7282.8 28
Kimberely 7282.8 28
Sarath 7282.8 28
Luis 7178.76 29
David 7074.72 30
Susan 6762.6 31
Shanta 6762.6 31
Sundar 6658.56 32
Charles 6450.48 33
Amit 6450.48 33
Sundita 6346.44 34
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Pat 6242.4 35
Bruce 6242.4 35
Kevin 6034.32 36
Valli 4993.92 37
David 4993.92 37
Jennifer 4577.76 38
Nandita 4369.68 39
Diana 4369.68 39
Alexis 4265.64 40
Sarah 4161.6 41
Britney 4057.56 42
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Kelly 3953.52 43
Jennifer 3745.44 44
Renske 3745.44 44
Trenna 3641.4 45
Julia 3537.36 46
Jason 3433.32 47
Laura 3433.32 47
Julia 3329.28 48
Samuel 3329.28 48
Winston 3329.28 48
Stephen 3329.28 48
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Alexander 3225.24 49
Alana 3225.24 49
Jean 3225.24 49
Curtis 3225.24 49
Kevin 3121.2 50
Anthony 3121.2 50
Shelli 3017.16 51
Timothy 3017.16 51
Michael 3017.16 51
Sigal 2913.12 52
Vance 2913.12 52
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Girard 2913.12 52
Mozhe 2913.12 52
John 2809.08 53
Irene 2809.08 53
Guy 2705.04 54
Douglas 2705.04 54
Donald 2705.04 54
Randall 2705.04 54
Karen 2601 55
James 2601 55
Randall 2601 55
FIRST_NAME SALARY DENSE_RANK()OVER(ORDERBYSALARYDESC)
-------------------- ---------- -----------------------------------
Peter 2601 55
Martha 2601 55
Joshua 2601 55
Ki 2496.96 56
James 2496.96 56
Hazel 2288.88 57
Steven 2288.88 57
TJ 2184.84 58
107 rows selected.
SQL>
==================================
Top n queries:
below queries will give you the top salary from rows.
SQL> select * from (select first_name, salary, dense_rank() over (order by salar
y desc) as top_n from (select first_name, last_name, salary from employees)) whe
re top_n <4;
FIRST_NAME SALARY TOP_N
-------------------- ---------- ----------
Steven 24969.6 1
Neena 17686.8 2
Lex 17686.8 2
John 14565.6 3
SQL>
=======================
2nd example : top n queries:
SQL> select * from (select first_name, salary, rank() over (order by salary desc
) as top_n from (select first_name, last_name, salary from employees)) where top
_n < 10;
FIRST_NAME SALARY TOP_N
-------------------- ---------- ----------
Steven 24969.6 1
Neena 17686.8 2
Lex 17686.8 2
John 14565.6 4
Karen 14045.4 5
Michael 13525.2 6
Nancy 12493.12 7
Shelley 12493.12 7
Alberto 12484.8 9
9 rows selected.
SQL>
========================
Cume_dist()
it will use for calculate the fraction of number of rows.
SQL> select * from (select salary, first_name,cume_dist() over(order by salary)a
s rank from employees) where rank<20;
SALARY FIRST_NAME RANK
---------- -------------------- ----------
2184.84 TJ .009345794
2288.88 Hazel .028037383
2288.88 Steven .028037383
2496.96 Ki .046728972
2496.96 James .046728972
2601 Karen .102803738
2601 James .102803738
2601 Randall .102803738
2601 Peter .102803738
2601 Martha .102803738
2601 Joshua .102803738
SALARY FIRST_NAME RANK
---------- -------------------- ----------
2705.04 Guy .140186916
2705.04 Douglas .140186916
2705.04 Donald .140186916
2705.04 Randall .140186916
2809.08 John .158878505
2809.08 Irene .158878505
2913.12 Sigal .196261682
2913.12 Vance .196261682
2913.12 Girard .196261682
2913.12 Mozhe .196261682
3017.16 Shelli .224299065
SALARY FIRST_NAME RANK
---------- -------------------- ----------
3017.16 Timothy .224299065
3017.16 Michael .224299065
3121.2 Kevin .242990654
3121.2 Anthony .242990654
3225.24 Alexander .280373832
3225.24 Alana .280373832
3225.24 Jean .280373832
3225.24 Curtis .280373832
3329.28 Julia .317757009
3329.28 Samuel .317757009
3329.28 Winston .317757009
SALARY FIRST_NAME RANK
---------- -------------------- ----------
3329.28 Stephen .317757009
3433.32 Jason .336448598
3433.32 Laura .336448598
3537.36 Julia .345794393
3641.4 Trenna .355140187
3745.44 Jennifer .373831776
3745.44 Renske .373831776
3953.52 Kelly .38317757
4057.56 Britney .392523364
4161.6 Sarah .401869159
4265.64 Alexis .411214953
SALARY FIRST_NAME RANK
---------- -------------------- ----------
4369.68 Nandita .429906542
4369.68 Diana .429906542
4577.76 Jennifer .439252336
4993.92 Valli .457943925
4993.92 David .457943925
6034.32 Kevin .46728972
6242.4 Pat .485981308
6242.4 Bruce .485981308
6346.44 Sundita .495327103
6450.48 Charles .514018692
6450.48 Amit .514018692
SALARY FIRST_NAME RANK
---------- -------------------- ----------
6658.56 Sundar .523364486
6762.6 Susan .542056075
6762.6 Shanta .542056075
7074.72 David .551401869
7178.76 Luis .560747664
7282.8 Oliver .588785047
7282.8 Kimberely .588785047
7282.8 Sarath .588785047
7490.88 Mattea .598130841
7594.92 Elizabeth .607476636
7698.96 William .61682243
SALARY FIRST_NAME RANK
---------- -------------------- ----------
7803 Louise .635514019
7803 Nanette .635514019
8011.08 Ismael .644859813
8115.12 Jose Manuel .654205607
8219.16 Payam .663551402
8323.2 Matthew .691588785
8323.2 Lindsey .691588785
8323.2 Christopher .691588785
8531.28 Adam .710280374
8531.28 John .710280374
8635.32 William .719626168
SALARY FIRST_NAME RANK
---------- -------------------- ----------
8739.36 Jack .728971963
8947.44 Jonathon .738317757
9155.52 Alyssa .747663551
9363.6 Alexander .785046729
9363.6 Daniel .785046729
9363.6 Peter .785046729
9363.6 Allan .785046729
9883.8 Patrick .813084112
9883.8 Danielle .813084112
9883.8 David .813084112
9987.84 Tayler .822429907
SALARY FIRST_NAME RANK
---------- -------------------- ----------
10404 Hermann .859813084
10404 Harrison .859813084
10404 Janette .859813084
10404 Peter .859813084
10924.2 Clara .878504673
10924.2 Eleni .878504673
11444.4 Gerald .906542056
11444.4 Den .906542056
11444.4 Ellen .906542056
11964.6 Lisa .91588785
12484.8 Alberto .925233645
SALARY FIRST_NAME RANK
---------- -------------------- ----------
12493.12 Shelley .943925234
12493.12 Nancy .943925234
13525.2 Michael .953271028
14045.4 Karen .962616822
14565.6 John .971962617
17686.8 Lex .990654206
17686.8 Neena .990654206
24969.6 Steven 1
107 rows selected.
percent_rank()
calculate the percetlie value of row.
SQL> select * from (select salary, first_name,percent_rank() over(order by salar
y)as rank from employees) where rank<20;
SALARY FIRST_NAME RANK
---------- -------------------- ----------
2184.84 TJ 0
2288.88 Hazel .009433962
2288.88 Steven .009433962
2496.96 Ki .028301887
2496.96 James .028301887
2601 Karen .047169811
2601 James .047169811
2601 Randall .047169811
2601 Peter .047169811
2601 Martha .047169811
2601 Joshua .047169811
SALARY FIRST_NAME RANK
---------- -------------------- ----------
2705.04 Guy .103773585
2705.04 Douglas .103773585
2705.04 Donald .103773585
2705.04 Randall .103773585
2809.08 John .141509434
2809.08 Irene .141509434
2913.12 Sigal .160377358
2913.12 Vance .160377358
2913.12 Girard .160377358
2913.12 Mozhe .160377358
3017.16 Shelli .198113208
SALARY FIRST_NAME RANK
---------- -------------------- ----------
3017.16 Timothy .198113208
3017.16 Michael .198113208
3121.2 Kevin .226415094
3121.2 Anthony .226415094
3225.24 Alexander .245283019
3225.24 Alana .245283019
3225.24 Jean .245283019
3225.24 Curtis .245283019
3329.28 Julia .283018868
3329.28 Samuel .283018868
3329.28 Winston .283018868
SALARY FIRST_NAME RANK
---------- -------------------- ----------
3329.28 Stephen .283018868
3433.32 Jason .320754717
3433.32 Laura .320754717
3537.36 Julia .339622642
3641.4 Trenna .349056604
3745.44 Jennifer .358490566
3745.44 Renske .358490566
3953.52 Kelly .377358491
4057.56 Britney .386792453
4161.6 Sarah .396226415
4265.64 Alexis .405660377
SALARY FIRST_NAME RANK
---------- -------------------- ----------
4369.68 Nandita .41509434
4369.68 Diana .41509434
4577.76 Jennifer .433962264
4993.92 Valli .443396226
4993.92 David .443396226
6034.32 Kevin .462264151
6242.4 Pat .471698113
6242.4 Bruce .471698113
6346.44 Sundita .490566038
6450.48 Charles .5
6450.48 Amit .5
SALARY FIRST_NAME RANK
---------- -------------------- ----------
6658.56 Sundar .518867925
6762.6 Susan .528301887
6762.6 Shanta .528301887
7074.72 David .547169811
7178.76 Luis .556603774
7282.8 Oliver .566037736
7282.8 Kimberely .566037736
7282.8 Sarath .566037736
7490.88 Mattea .594339623
7594.92 Elizabeth .603773585
7698.96 William .613207547
SALARY FIRST_NAME RANK
---------- -------------------- ----------
7803 Louise .622641509
7803 Nanette .622641509
8011.08 Ismael .641509434
8115.12 Jose Manuel .650943396
8219.16 Payam .660377358
8323.2 Matthew .669811321
8323.2 Lindsey .669811321
8323.2 Christopher .669811321
8531.28 Adam .698113208
8531.28 John .698113208
8635.32 William .716981132
SALARY FIRST_NAME RANK
---------- -------------------- ----------
8739.36 Jack .726415094
8947.44 Jonathon .735849057
9155.52 Alyssa .745283019
9363.6 Alexander .754716981
9363.6 Daniel .754716981
9363.6 Peter .754716981
9363.6 Allan .754716981
9883.8 Patrick .79245283
9883.8 Danielle .79245283
9883.8 David .79245283
9987.84 Tayler .820754717
SALARY FIRST_NAME RANK
---------- -------------------- ----------
10404 Hermann .830188679
10404 Harrison .830188679
10404 Janette .830188679
10404 Peter .830188679
10924.2 Clara .867924528
10924.2 Eleni .867924528
11444.4 Gerald .886792453
11444.4 Den .886792453
11444.4 Ellen .886792453
11964.6 Lisa .91509434
12484.8 Alberto .924528302
SALARY FIRST_NAME RANK
---------- -------------------- ----------
12493.12 Shelley .933962264
12493.12 Nancy .933962264
13525.2 Michael .952830189
14045.4 Karen .962264151
14565.6 John .971698113
17686.8 Lex .981132075
17686.8 Neena .981132075
24969.6 Steven 1
107 rows selected.
SQL>
=====================================================================
SQL> select salary, first_name,percent_rank() over(order by salary)as rank from
employees;
SALARY FIRST_NAME RANK
---------- -------------------- ----------
2184.84 TJ 0
2288.88 Hazel .009433962
2288.88 Steven .009433962
2496.96 Ki .028301887
2496.96 James .028301887
2601 Karen .047169811
2601 James .047169811
2601 Randall .047169811
2601 Peter .047169811
2601 Martha .047169811
2601 Joshua .047169811
=================================================
NTILE (N),
this will be use for calculate tertiles, quartiles and other common statistics. the function will divide into N buckets and distributes the rows in the buckets according to rank.
SQL> select salary, first_name, NTILE (4) over (order by salary) as NTILE,per
t_rank() over(order by salary)as rank from employees;
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
2184.84 TJ 1 0
2288.88 Hazel 1 .009433962
2288.88 Steven 1 .009433962
2496.96 Ki 1 .028301887
2496.96 James 1 .028301887
2601 Karen 1 .047169811
2601 James 1 .047169811
2601 Randall 1 .047169811
2601 Peter 1 .047169811
2601 Martha 1 .047169811
2601 Joshua 1 .047169811
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
2705.04 Guy 1 .103773585
2705.04 Douglas 1 .103773585
2705.04 Donald 1 .103773585
2705.04 Randall 1 .103773585
2809.08 John 1 .141509434
2809.08 Irene 1 .141509434
2913.12 Sigal 1 .160377358
2913.12 Vance 1 .160377358
2913.12 Girard 1 .160377358
2913.12 Mozhe 1 .160377358
3017.16 Shelli 1 .198113208
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
3017.16 Timothy 1 .198113208
3017.16 Michael 1 .198113208
3121.2 Kevin 1 .226415094
3121.2 Anthony 1 .226415094
3225.24 Alexander 1 .245283019
3225.24 Alana 2 .245283019
3225.24 Jean 2 .245283019
3225.24 Curtis 2 .245283019
3329.28 Julia 2 .283018868
3329.28 Samuel 2 .283018868
3329.28 Winston 2 .283018868
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
3329.28 Stephen 2 .283018868
3433.32 Jason 2 .320754717
3433.32 Laura 2 .320754717
3537.36 Julia 2 .339622642
3641.4 Trenna 2 .349056604
3745.44 Jennifer 2 .358490566
3745.44 Renske 2 .358490566
3953.52 Kelly 2 .377358491
4057.56 Britney 2 .386792453
4161.6 Sarah 2 .396226415
4265.64 Alexis 2 .405660377
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
4369.68 Nandita 2 .41509434
4369.68 Diana 2 .41509434
4577.76 Jennifer 2 .433962264
4993.92 Valli 2 .443396226
4993.92 David 2 .443396226
6034.32 Kevin 2 .462264151
6242.4 Pat 2 .471698113
6242.4 Bruce 2 .471698113
6346.44 Sundita 2 .490566038
6450.48 Charles 2 .5
6450.48 Amit 3 .5
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
6658.56 Sundar 3 .518867925
6762.6 Susan 3 .528301887
6762.6 Shanta 3 .528301887
7074.72 David 3 .547169811
7178.76 Luis 3 .556603774
7282.8 Oliver 3 .566037736
7282.8 Kimberely 3 .566037736
7282.8 Sarath 3 .566037736
7490.88 Mattea 3 .594339623
7594.92 Elizabeth 3 .603773585
7698.96 William 3 .613207547
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
7803 Louise 3 .622641509
7803 Nanette 3 .622641509
8011.08 Ismael 3 .641509434
8115.12 Jose Manuel 3 .650943396
8219.16 Payam 3 .660377358
8323.2 Matthew 3 .669811321
8323.2 Lindsey 3 .669811321
8323.2 Christopher 3 .669811321
8531.28 Adam 3 .698113208
8531.28 John 3 .698113208
8635.32 William 3 .716981132
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
8739.36 Jack 3 .726415094
8947.44 Jonathon 3 .735849057
9155.52 Alyssa 3 .745283019
9363.6 Alexander 3 .754716981
9363.6 Daniel 4 .754716981
9363.6 Peter 4 .754716981
t_rank() over(order by salary)as rank from employees;
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
2184.84 TJ 1 0
2288.88 Hazel 1 .009433962
2288.88 Steven 1 .009433962
2496.96 Ki 1 .028301887
2496.96 James 1 .028301887
2601 Karen 1 .047169811
2601 James 1 .047169811
2601 Randall 1 .047169811
2601 Peter 1 .047169811
2601 Martha 1 .047169811
2601 Joshua 1 .047169811
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
2705.04 Guy 1 .103773585
2705.04 Douglas 1 .103773585
2705.04 Donald 1 .103773585
2705.04 Randall 1 .103773585
2809.08 John 1 .141509434
2809.08 Irene 1 .141509434
2913.12 Sigal 1 .160377358
2913.12 Vance 1 .160377358
2913.12 Girard 1 .160377358
2913.12 Mozhe 1 .160377358
3017.16 Shelli 1 .198113208
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
3017.16 Timothy 1 .198113208
3017.16 Michael 1 .198113208
3121.2 Kevin 1 .226415094
3121.2 Anthony 1 .226415094
3225.24 Alexander 1 .245283019
3225.24 Alana 2 .245283019
3225.24 Jean 2 .245283019
3225.24 Curtis 2 .245283019
3329.28 Julia 2 .283018868
3329.28 Samuel 2 .283018868
3329.28 Winston 2 .283018868
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
3329.28 Stephen 2 .283018868
3433.32 Jason 2 .320754717
3433.32 Laura 2 .320754717
3537.36 Julia 2 .339622642
3641.4 Trenna 2 .349056604
3745.44 Jennifer 2 .358490566
3745.44 Renske 2 .358490566
3953.52 Kelly 2 .377358491
4057.56 Britney 2 .386792453
4161.6 Sarah 2 .396226415
4265.64 Alexis 2 .405660377
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
4369.68 Nandita 2 .41509434
4369.68 Diana 2 .41509434
4577.76 Jennifer 2 .433962264
4993.92 Valli 2 .443396226
4993.92 David 2 .443396226
6034.32 Kevin 2 .462264151
6242.4 Pat 2 .471698113
6242.4 Bruce 2 .471698113
6346.44 Sundita 2 .490566038
6450.48 Charles 2 .5
6450.48 Amit 3 .5
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
6658.56 Sundar 3 .518867925
6762.6 Susan 3 .528301887
6762.6 Shanta 3 .528301887
7074.72 David 3 .547169811
7178.76 Luis 3 .556603774
7282.8 Oliver 3 .566037736
7282.8 Kimberely 3 .566037736
7282.8 Sarath 3 .566037736
7490.88 Mattea 3 .594339623
7594.92 Elizabeth 3 .603773585
7698.96 William 3 .613207547
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
7803 Louise 3 .622641509
7803 Nanette 3 .622641509
8011.08 Ismael 3 .641509434
8115.12 Jose Manuel 3 .650943396
8219.16 Payam 3 .660377358
8323.2 Matthew 3 .669811321
8323.2 Lindsey 3 .669811321
8323.2 Christopher 3 .669811321
8531.28 Adam 3 .698113208
8531.28 John 3 .698113208
8635.32 William 3 .716981132
SALARY FIRST_NAME NTILE RANK
---------- -------------------- ---------- ----------
8739.36 Jack 3 .726415094
8947.44 Jonathon 3 .735849057
9155.52 Alyssa 3 .745283019
9363.6 Alexander 3 .754716981
9363.6 Daniel 4 .754716981
9363.6 Peter 4 .754716981
9363.6 Allan 4 .754716981
9883.8 Patrick 4 .79245283
9883.8 Danielle 4 .79245283
9883.8 David 4 .79245283
9987.84 Tayler 4 .820754717
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