Oracle分析函数六——数据分布函数及报表函数

Oracle分析函数——数据分布函数及报表函数

CUME_DIST

功能描述：计算一行在组中的相对位置，CUME_DIST总是返回大于0、小于或等于1的数，该数表示该行在N行中的位置。例如，在一个3行的组中，返回的累计分布值为1/3、2/3、3/3

SAMPLE：下例中计算每个部门的员工按薪水排序依次累积出现的分布百分比

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 CUME_DIST() OVER (PARTITION BY department_id ORDER BY salary) AS cume_dist

FROM employees

NTILE

功能描述：将一个组分为"表达式"的散列表示，例如，如果表达式=4，则给组中的每一行分配一个数（从1到4），如果组中有20行，则给前5行分配1，给下5行分配2等等。如果组的基数不能由表达式值平均分开，则对这些行进行分配时，组中就没有任何percentile的行数比其它percentile的行数超过一行，最低的percentile是那些拥有额外行的percentile。例如，若表达式=4，行数=21，则percentile=1的有5行，percentile=2的有5行等等。

SAMPLE：下例中把6行数据分为4份

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 NTILE(4) OVER (PARTITION BY department_id ORDER BY salary DESC) AS quartile

 FROM employees

 

PERCENT_RANK

功能描述：和CUME_DIST（累积分配）函数类似，对于一个组中给定的行来说，在计算那行的序号时，先减1，然后除以n-1（n为组中所有的行数）。该函数总是返回0～1（包括1）之间的数。

SAMPLE：下例中如果Khoo的salary为2900，则pr值为0.6，因为RANK函数对于等值的返回序列值是一样的

 

SELECT  

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary) AS pr

FROM employees

ORDER BY department_id,salary;

 

PERCENTILE_DISC

功能描述：返回一个与输入的分布百分比值相对应的数据值，分布百分比的计算方法见函数CUME_DIST，如果没有正好对应的数据值，就取大于该分布值的下一个值。

注意：本函数与PERCENTILE_CONT的区别在找不到对应的分布值时返回的替代值的计算方法不同

 

SAMPLE：下例中0.7的分布值在部门30中没有对应的Cume_Dist值，所以就取下一个分布值0.83333333所对应的SALARY来替代

 

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 PERCENTILE_DISC(0.7) WITHIN GROUP (ORDER BY salary ) OVER (PARTITION BY department_id) "Percentile_Disc",

 CUME_DIST() OVER (PARTITION BY department_id ORDER BY salary) "Cume_Dist"

FROM employees<!--[if !vml]--><!--[endif]-->

PERCENTILE_CONT

功能描述：返回一个与输入的分布百分比值相对应的数据值，分布百分比的计算方法见函数PERCENT_RANK，如果没有正好对应的数据值，就通过下面算法来得到值：

RN = 1+ (P*(N-1))其中P是输入的分布百分比值，N是组内的行数

CRN = CEIL(RN) FRN = FLOOR(RN)

if (CRN = FRN = RN) then

(value of expression from row at RN)

else

(CRN - RN) * (value of expression for row at FRN) +

(RN - FRN) * (value of expression for row at CRN)

注意：本函数与PERCENTILE_DISC的区别在找不到对应的分布值时返回的替代值的计算方法不同

算法太复杂，看不懂了L

SAMPLE：在下例中，对于部门60的Percentile_Cont值计算如下：

P=0.7 N=5 RN =1+ (P*(N-1)=1+(0.7*(5-1))=3.8 CRN = CEIL(3.8)=4

FRN = FLOOR(3.8)=3

（4 - 3.8）* 4800 + (3.8 - 3) * 6000 = 5760

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,  

 PERCENTILE_DISC(0.7) WITHIN GROUP (ORDER BY salary) OVER (PARTITION BY department_id) "Percentile_Disc",

 PERCENTILE_CONT(0.7) WITHIN GROUP (ORDER BY salary) OVER (PARTITION BY department_id) "Percentile_Cont",

 PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary) "Percent_Rank"

 FROM employees

总案例

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 CUME_DIST() OVER (PARTITION BY department_id ORDER BY salary) AS cume_dist, --数据分布百分比

 NTILE(4) OVER (PARTITION BY department_id ORDER BY salary) AS quartile,    --数据分布，以NTILE中的exp来计算

 PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary) AS pr,    --数据分布百分比，从0开始计

 PERCENTILE_DISC(0.7) WITHIN GROUP (ORDER BY salary ) OVER (PARTITION BY department_id) "Percentile_Disc", --输入的分布百分比值相对应的数据值

 PERCENTILE_CONT(0.7) WITHIN GROUP (ORDER BY salary) OVER (PARTITION BY department_id) "Percentile_Cont"   --表达式太复杂了，...

FROM employees

 

RATIO_TO_REPORT

功能描述：该函数计算expression/(sum(expression))的值，它给出相对于总数的百分比，即当前行对sum(expression)的贡献。

SAMPLE：下例计算每个员工的工资占该类员工总工资的百分比

SELECT

 department_id,

 first_name||' '||last_name employee_name,

 salary,

 RATIO_TO_REPORT(salary) OVER () AS rr

FROM employees

WHERE job_id = 'PU_CLERK';

REGR_ (Linear Regression) Functions

功能描述：这些线性回归函数适合最小二乘法回归线，有9个不同的回归函数可使用。

REGR_SLOPE：返回斜率，等于COVAR_POP(expr1, expr2) / VAR_POP(expr2)

REGR_INTERCEPT：返回回归线的y截距，等于

AVG(expr1) - REGR_SLOPE(expr1, expr2) * AVG(expr2)

REGR_COUNT：返回用于填充回归线的非空数字对的数目

REGR_R2：返回回归线的决定系数，计算式为：

If VAR_POP(expr2) = 0 then return NULL

If VAR_POP(expr1) = 0 and VAR_POP(expr2) != 0 then return 1

If VAR_POP(expr1) > 0 and VAR_POP(expr2 != 0 then

return POWER(CORR(expr1,expr),2)

REGR_AVGX：计算回归线的自变量(expr2)的平均值，去掉了空对(expr1, expr2)后，等于AVG(expr2)

REGR_AVGY：计算回归线的应变量(expr1)的平均值，去掉了空对(expr1, expr2)后，等于AVG(expr1)

REGR_SXX：返回值等于REGR_COUNT(expr1, expr2) * VAR_POP(expr2)

REGR_SYY：返回值等于REGR_COUNT(expr1, expr2) * VAR_POP(expr1)

REGR_SXY:返回值等于REGR_COUNT(expr1, expr2) * COVAR_POP(expr1, expr2)

 

（下面的例子都是在SH用户下完成的）

SAMPLE 1：下例计算1998年最后三个星期中两种产品（260和270）在周末的销售量中已开发票数量和总数量的累积斜率和回归线的截距

 

SELECT t.fiscal_month_number "Month", t.day_number_in_month "Day",

REGR_SLOPE(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_month_desc, t.day_number_in_month) AS CUM_SLOPE,

REGR_INTERCEPT(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_month_desc, t.day_number_in_month) AS CUM_ICPT

FROM sales s, times t

WHERE s.time_id = t.time_id

AND s.prod_id IN (270, 260)

AND t.fiscal_year=1998

AND t.fiscal_week_number IN (50, 51, 52)

AND t.day_number_in_week IN (6,7)

ORDER BY t.fiscal_month_desc, t.day_number_in_month;

 

SAMPLE 2：下例计算1998年4月每天的累积交易数量

 

SELECT UNIQUE t.day_number_in_month,

REGR_COUNT(s.amount_sold, s.quantity_sold)

OVER (PARTITION BY t.fiscal_month_number ORDER BY t.day_number_in_month)

"Regr_Count"

FROM sales s, times t

WHERE s.time_id = t.time_id

AND t.fiscal_year = 1998 AND t.fiscal_month_number = 4;

 

SAMPLE 3：下例计算1998年每月销售量中已开发票数量和总数量的累积回归线决定系数

 

SELECT t.fiscal_month_number,

REGR_R2(SUM(s.amount_sold), SUM(s.quantity_sold))

OVER (ORDER BY t.fiscal_month_number) "Regr_R2"

FROM sales s, times t

WHERE s.time_id = t.time_id

AND t.fiscal_year = 1998

GROUP BY t.fiscal_month_number

ORDER BY t.fiscal_month_number;

 

SAMPLE 4：下例计算1998年12月最后两周产品260的销售量中已开发票数量和总数量的累积平均值

 

SELECT t.day_number_in_month,

REGR_AVGY(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_month_desc, t.day_number_in_month)

"Regr_AvgY",

REGR_AVGX(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_month_desc, t.day_number_in_month)

"Regr_AvgX"

FROM sales s, times t

WHERE s.time_id = t.time_id

AND s.prod_id = 260

AND t.fiscal_month_desc = '1998-12'

AND t.fiscal_week_number IN (51, 52)

ORDER BY t.day_number_in_month;

 

SAMPLE 5：下例计算产品260和270在1998年2月周末销售量中已开发票数量和总数量的累积REGR_SXY, REGR_SXX, and REGR_SYY统计值

 

SELECT t.day_number_in_month,

REGR_SXY(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_year, t.fiscal_month_desc) "Regr_sxy",

REGR_SYY(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_year, t.fiscal_month_desc) "Regr_syy",

REGR_SXX(s.amount_sold, s.quantity_sold)

OVER (ORDER BY t.fiscal_year, t.fiscal_month_desc) "Regr_sxx"

FROM sales s, times t

WHERE s.time_id = t.time_id

AND prod_id IN (270, 260)

AND t.fiscal_month_desc = '1998-02'

AND t.day_number_in_week IN (6,7)

ORDER BY t.day_number_in_month;