Relative frequency distributions : COUNT « Aggregate Functions « SQL / MySQL






  1. SQL / MySQL
  2. Aggregate Functions
  3. COUNT

Relative frequency distributions

       
mysql>
mysql> CREATE TABLE testscore
    -> (
    ->  subject INT UNSIGNED NOT NULL AUTO_INCREMENT,
    ->  age             INT UNSIGNED NOT NULL,
    ->  sex             ENUM('M','F') NOT NULL,
    ->  score   INT,
    ->  PRIMARY KEY (subject)
    -> );
Query OK, 0 rows affected (0.00 sec)

mysql>
mysql> INSERT INTO testscore (age,sex,score)
    ->  VALUES
    ->  (5,'M',5),
    ->  (5,'M',4),
    ->  (5,'F',6),
    ->  (5,'F',7),
    ->  (6,'M',8),
    ->  (6,'M',9),
    ->  (6,'F',4),
    ->  (6,'F',6),
    ->  (7,'M',8),
    ->  (7,'M',6),
    ->  (7,'F',9),
    ->  (7,'F',7),
    ->  (8,'M',9),
    ->  (8,'M',6),
    ->  (8,'F',7),
    ->  (8,'F',10),
    ->  (9,'M',9),
    ->  (9,'M',7),
    ->  (9,'F',10),
    ->  (9,'F',9)
    -> ;
Query OK, 20 rows affected (0.00 sec)
Records: 20  Duplicates: 0  Warnings: 0

mysql>
mysql>
mysql> CREATE TABLE ref (score INT);
Query OK, 0 rows affected (0.01 sec)

mysql> INSERT INTO ref (score)
    -> VALUES(0),(1),(2),(3),(4),(5),(6),(7),(8),(9),(10);
Query OK, 11 rows affected (0.00 sec)
Records: 11  Duplicates: 0  Warnings: 0

mysql>
mysql> SELECT subject, age, sex, score FROM testscore ORDER BY subject;
+---------+-----+-----+-------+
| subject | age | sex | score |
+---------+-----+-----+-------+
|       1 |   5 | M   |     5 |
|       2 |   5 | M   |     4 |
|       3 |   5 | F   |     6 |
|       4 |   5 | F   |     7 |
|       5 |   6 | M   |     8 |
|       6 |   6 | M   |     9 |
|       7 |   6 | F   |     4 |
|       8 |   6 | F   |     6 |
|       9 |   7 | M   |     8 |
|      10 |   7 | M   |     6 |
|      11 |   7 | F   |     9 |
|      12 |   7 | F   |     7 |
|      13 |   8 | M   |     9 |
|      14 |   8 | M   |     6 |
|      15 |   8 | F   |     7 |
|      16 |   8 | F   |    10 |
|      17 |   9 | M   |     9 |
|      18 |   9 | M   |     7 |
|      19 |   9 | F   |    10 |
|      20 |   9 | F   |     9 |
+---------+-----+-----+-------+
20 rows in set (0.00 sec)

mysql>
mysql> SELECT @n := COUNT(score) FROM testscore;
+--------------------+
| @n := COUNT(score) |
+--------------------+
|                 20 |
+--------------------+
1 row in set (0.00 sec)

mysql>
mysql> SELECT ref.score, (COUNT(testscore.score)*100)/@n AS percent
    -> FROM ref LEFT JOIN testscore ON ref.score = testscore.score
    -> GROUP BY ref.score;
+-------+---------+
| score | percent |
+-------+---------+
|     0 |  0.0000 |
|     1 |  0.0000 |
|     2 |  0.0000 |
|     3 |  0.0000 |
|     4 | 10.0000 |
|     5 |  5.0000 |
|     6 | 20.0000 |
|     7 | 20.0000 |
|     8 | 10.0000 |
|     9 | 25.0000 |
|    10 | 10.0000 |
+-------+---------+
11 rows in set (0.00 sec)

mysql>
mysql> drop table testscore;
Query OK, 0 rows affected (0.00 sec)

mysql> drop table ref;
Query OK, 0 rows affected (0.00 sec)








Related examples in the same category

1.COUNT([DISTINCT] { | *})
2.Get the number of items for each type of wood
3.Get the number of woods for each type of item
4.Counting
5.How many times did drivers travel more than 200 miles in a day?
6.COUNT and GROUP BY
7.Count by group
8.COUNT(expr) doesn't count NULL values is useful when producing multiple counts from the same set of values.
9.The different forms of COUNT( ) can be very useful for counting missing values
10.Put the COUNT( ) expression in a HAVING clause instead.
11.Tracking Down Duplicates
12.Queries counting duplicate records have the following form
13.Counting and Identifying Duplicates
14.Count number of rows containing duplicated names
15.To see which names are duplicated in the cat_mailing table, use a summary query that displays the non-unique v
16.Eliminating Duplicates from a Query Result
17.Removing Duplicates of a Particular Row
18.Count duplicate records
19.How many days did Suzi drive?
20.How many messages were sent by each message sender
21.How many states did the United States consist of at the beginning of the 20th century?
22.Or to count weekend versus weekday trips
23.Count each value and see which one is most common
24.Generating Frequency Distributions
25.Counting Missing Values
26.Count them directly using SUM(ISNULL(score)).
27.Count the total number of rows
28.Categorizing Non-Categorical Data
29.Total number of the table vs unique values count
30.To produce a count-per-author summary that includes even authors with no books in the book table, use a LEFT J