R Markdown

#install.packages("RPostgres")
#install.packages("DBI")

# Load the DBI and RPostgres libraries
library(DBI)
## Warning: package 'DBI' was built under R version 4.3.3
library(RPostgreSQL)
## Warning: package 'RPostgreSQL' was built under R version 4.3.3
library(RPostgres)
## Warning: package 'RPostgres' was built under R version 4.3.3
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
con <- dbConnect(RPostgres::Postgres(), 
                 dbname = "undatascience-db", 
                 host = "localhost", 
                 port = 5432, 
                 user = "undatascience-user", 
                 password = "undatascience-password")

dbListTables(conn = con)

Crear las tablas

## [1] 0
## [1] 0
## [1] 0

#Llenar tabla de facilities

sql_query <- "
INSERT INTO facilities (facid, name, membercost, guestcost, initialoutlay, monthlymaintenance) VALUES
(0, 'Tennis Court 1', 5, 25, 10000, 200),
(1, 'Tennis Court 2', 5, 25, 8000, 200),
(2, 'Badminton Court', 0, 15.5, 4000, 50),
(3, 'Table Tennis', 0, 5, 320, 10),
(4, 'Massage Room 1', 35, 80, 4000, 3000),
(5, 'Massage Room 2', 35, 80, 4000, 3000),
(6, 'Squash Court', 3.5, 17.5, 5000, 80),
(7, 'Snooker Table', 0, 5, 450, 15),
(8, 'Pool Table', 0, 5, 400, 15);
"
dbExecute(con, sql_query)
## [1] 9
knitr::include_graphics("C:/MariaP/Visualizacion Cientifica/PostgreSQL/R/Facilities.png")

#Llenar los datos en la tabla members

sql_query <- "
INSERT INTO members (memid, surname, firstname, address, zipcode, telephone, recommendedby, joindate) VALUES
(0, 'GUEST', 'GUEST', 'GUEST', 0, '(000) 000-0000', NULL, '2012-07-01 00:00:00'),
(1, 'Smith', 'Darren', '8 Bloomsbury Close, Boston', 4321, '555-555-5555', NULL, '2012-07-02 12:02:05'),
(2, 'Smith', 'Tracy', '8 Bloomsbury Close, New York', 4321, '555-555-5555', NULL, '2012-07-02 12:08:23'),
(3, 'Rownam', 'Tim', '23 Highway Way, Boston', 23423, '(844) 693-0723', NULL, '2012-07-03 09:32:15'),
(4, 'Joplette', 'Janice', '20 Crossing Road, New York', 234, '(833) 942-4710', 1, '2012-07-03 10:25:05'),
(5, 'Butters', 'Gerald', '1065 Huntingdon Avenue, Boston', 56754, '(844) 078-4130', 1, '2012-07-09 10:44:09'),
(6, 'Tracy', 'Burton', '3 Tunisia Drive, Boston', 45678, '(822) 354-9973', NULL, '2012-07-15 08:52:55'),
(7, 'Dare', 'Nancy', '6 Hunting Lodge Way, Boston', 10383, '(833) 776-4001', 4, '2012-07-25 08:59:12'),
(8, 'Boothe', 'Tim', '3 Bloomsbury Close, Reading, 00234', 234, '(811) 433-2547', 3, '2012-07-25 16:02:35'),
(9, 'Stibbons', 'Ponder', '5 Dragons Way, Winchester', 87630, '(833) 160-3900', 6, '2012-07-25 17:09:05'),
(10, 'Owen', 'Charles', '52 Cheshire Grove, Winchester, 28563', 28563, '(855) 542-5251', 1, '2012-08-03 19:42:37'),
(11, 'Jones', 'David', '976 Gnats Close, Reading', 33862, '(844) 536-8036', 4, '2012-08-06 16:32:55'),
(12, 'Baker', 'Anne', '55 Powdery Street, Boston', 80743, '844-076-5141', 9, '2012-08-10 14:23:22'),
(13, 'Farrell', 'Jemima', '103 Firth Avenue, North Reading', 57392, '(855) 016-0163', NULL, '2012-08-10 14:28:01'),
(14, 'Smith', 'Jack', '252 Binkington Way, Boston', 69302, '(822) 163-3254', 1, '2012-08-10 16:22:05'),
(15, 'Bader', 'Florence', '264 Ursula Drive, Westford', 84923, '(833) 499-3527', 9, '2012-08-10 17:52:03'),
(16, 'Baker', 'Timothy', '329 James Street, Reading', 58393, '833-941-0824', 13, '2012-08-15 10:34:25'),
(17, 'Pinker', 'David', '5 Impreza Road, Boston', 65332, '811 409-6734', 13, '2012-08-16 11:32:47'),
(20, 'Genting', 'Matthew', '4 Nunnington Place, Wingfield, Boston', 52365, '(811) 972-1377', 5, '2012-08-19 14:55:55'),
(21, 'Mackenzie', 'Anna', '64 Perkington Lane, Reading', 64577, '(822) 661-2898', 1, '2012-08-26 09:32:05'),
(22, 'Coplin', 'Joan', '85 Bard Street, Bloomington, Boston', 43533, '(822) 499-2232', 16, '2012-08-29 08:32:41'),
(24, 'Sarwin', 'Ramnaresh', '12 Bullington Lane, Boston', 65464, '(822) 413-1470', 15, '2012-09-01 08:44:42'),
(26, 'Jones', 'Douglas', '976 Gnats Close, Reading', 11986, '844 536-8036', 11, '2012-09-02 18:43:05'),
(27, 'Rumney', 'Henrietta', '3 Burkington Plaza, Boston', 78533, '(822) 989-8876', 20, '2012-09-05 08:42:35'),
(28, 'Farrell', 'David', '437 Granite Farm Road, Westford', 43532, '(855) 755-9876', NULL, '2012-09-15 08:22:05'),
(29, 'Worthington-Smyth', 'Henry', '55 Jagbi Way, North Reading', 97676, '(855) 894-3758', 2, '2012-09-17 12:27:15'),
(30, 'Purview', 'Millicent', '641 Drudgery Close, Burnington, Boston', 34232, '(855) 941-9786', 2, '2012-09-18 19:04:01'),
(33, 'Tupperware', 'Hyacinth', '33 Cheerful Plaza, Drake Road, Westford', 68666, '(822) 665-5327', NULL, '2012-09-18 19:32:05'),
(35, 'Hunt', 'John', '5 Bullington Lane, Boston', 54333, '(899) 720-6978', 30, '2012-09-19 11:32:45'),
(36, 'Crumpet', 'Erica', 'Crimson Road, North Reading', 75655, '(811) 732-4816', 2, '2012-09-22 08:36:38'),
(37, 'Smith', 'Darren', '3 Funktown, Denzington, Boston', 66796, '(822) 577-3541', NULL, '2012-09-26 18:08:45');
"
dbExecute(con, sql_query)
## [1] 31
knitr::include_graphics("C:/MariaP/Visualizacion Cientifica/PostgreSQL/R/Members.png")

##Crear datos de la tabla bookings

sql_query <- "
INSERT INTO bookings (bookid, facid, memid, starttime, slots) VALUES
(0, 3, 1, '2012-07-03 11:00:00', 2),
(1, 4, 1, '2012-07-03 08:00:00', 2),
(2, 6, 0, '2012-07-03 18:00:00', 2),
(3, 7, 1, '2012-07-03 19:00:00', 2),
(4, 8, 1, '2012-07-03 10:00:00', 1),
(5, 8, 1, '2012-07-03 15:00:00', 1),
(6, 0, 2, '2012-07-04 09:00:00', 3),
(7, 0, 2, '2012-07-04 15:00:00', 3),
(8, 4, 3, '2012-07-04 13:30:00', 2),
(9, 4, 0, '2012-07-04 15:00:00', 2),
(10, 4, 0, '2012-07-04 17:30:00', 2),
(11, 6, 0, '2012-07-04 12:30:00', 2),
(12, 6, 0, '2012-07-04 14:00:00', 2),
(13, 6, 1, '2012-07-04 15:30:00', 2),
(14, 7, 2, '2012-07-04 14:00:00', 2),
(15, 8, 2, '2012-07-04 12:00:00', 1),
(16, 8, 3, '2012-07-04 18:00:00', 1),
(17, 1, 0, '2012-07-05 17:30:00', 3),
(18, 2, 1, '2012-07-05 09:30:00', 3),
(19, 3, 3, '2012-07-05 09:00:00', 2),
(20, 3, 1, '2012-07-05 19:00:00', 2),
(21, 4, 3, '2012-07-05 18:30:00', 2),
(22, 6, 0, '2012-07-05 13:00:00', 2),
(23, 6, 1, '2012-07-05 14:30:00', 2),
(24, 7, 2, '2012-07-05 18:30:00', 2),
(25, 8, 3, '2012-07-05 12:30:00', 1),
(26, 0, 0, '2012-07-06 08:00:00', 3),
(27, 0, 0, '2012-07-06 14:00:00', 3),
(28, 0, 2, '2012-07-06 15:30:00', 3),
(29, 2, 1, '2012-07-06 17:00:00', 3),
(30, 3, 1, '2012-07-06 11:00:00', 2),
(31, 4, 3, '2012-07-06 12:00:00', 2),
(32, 6, 1, '2012-07-06 14:00:00', 2),
(33, 7, 2, '2012-07-06 08:30:00', 2),
(34, 7, 2, '2012-07-06 13:30:00', 2),
(35, 8, 3, '2012-07-06 15:30:00', 1),
(36, 0, 2, '2012-07-07 08:30:00', 3),
(37, 0, 0, '2012-07-07 12:30:00', 3),
(38, 0, 2, '2012-07-07 14:30:00', 3),
(39, 1, 3, '2012-07-07 08:30:00', 3),
(40, 2, 1, '2012-07-07 09:00:00', 3),
(41, 2, 1, '2012-07-07 11:30:00', 3),
(42, 2, 1, '2012-07-07 16:00:00', 3),
(43, 3, 2, '2012-07-07 12:30:00', 2),
(44, 4, 3, '2012-07-07 11:30:00', 2),
(45, 4, 3, '2012-07-07 14:00:00', 2),
(46, 4, 0, '2012-07-07 17:30:00', 2),
(47, 6, 0, '2012-07-07 08:30:00', 2),
(48, 6, 1, '2012-07-07 10:30:00', 2),
(49, 6, 1, '2012-07-07 14:30:00', 2),
(50, 6, 0, '2012-07-07 16:00:00', 2),
(51, 7, 2, '2012-07-07 11:30:00', 2),
(52, 8, 3, '2012-07-07 16:00:00', 1),
(53, 8, 3, '2012-07-07 17:30:00', 2),
(54, 0, 3, '2012-07-08 13:00:00', 3),
(55, 0, 2, '2012-07-08 17:30:00', 3),
(56, 1, 1, '2012-07-08 15:00:00', 3),
(57, 1, 1, '2012-07-08 17:30:00', 3),
(58, 3, 1, '2012-07-08 11:30:00', 2),
(59, 3, 3, '2012-07-08 18:30:00', 2),
(60, 3, 1, '2012-07-08 19:30:00', 2),
(61, 4, 0, '2012-07-08 11:00:00', 2),
(62, 4, 2, '2012-07-08 16:30:00', 2),
(63, 4, 0, '2012-07-08 18:00:00', 2),
(64, 4, 0, '2012-07-08 19:30:00', 2),
(65, 6, 0, '2012-07-08 14:00:00', 2),
(66, 6, 0, '2012-07-08 18:30:00', 2),
(67, 7, 2, '2012-07-08 11:00:00', 2),
(68, 7, 1, '2012-07-08 16:30:00', 2),
(69, 8, 3, '2012-07-08 10:00:00', 1),
(70, 8, 3, '2012-07-08 16:30:00', 1),
(71, 0, 2, '2012-07-09 12:30:00', 3),
(72, 0, 2, '2012-07-09 15:30:00', 3),
(73, 0, 2, '2012-07-09 19:00:00', 3),
(74, 1, 0, '2012-07-09 13:00:00', 3),
(75, 1, 1, '2012-07-09 19:00:00', 3),
(76, 2, 1, '2012-07-09 09:00:00', 6),
(77, 2, 0, '2012-07-09 19:00:00', 3),
(78, 3, 3, '2012-07-09 17:00:00', 2),
(79, 3, 3, '2012-07-09 18:30:00', 2),
(80, 4, 2, '2012-07-09 11:00:00', 2),
(81, 4, 3, '2012-07-09 14:30:00', 2),
(82, 6, 0, '2012-07-09 14:30:00', 2),
(83, 7, 1, '2012-07-09 15:30:00', 2),
(84, 7, 0, '2012-07-09 18:30:00', 4),
(85, 8, 3, '2012-07-09 09:30:00', 1),
(86, 8, 3, '2012-07-09 16:30:00', 1),
(87, 8, 3, '2012-07-09 20:00:00', 1),
(88, 0, 0, '2012-07-10 11:30:00', 3),
(89, 0, 0, '2012-07-10 16:00:00', 3),
(90, 3, 2, '2012-07-10 08:00:00', 2),
(91, 3, 1, '2012-07-10 11:00:00', 2),
(92, 3, 3, '2012-07-10 15:30:00', 2),
(93, 3, 2, '2012-07-10 16:30:00', 2),
(94, 3, 1, '2012-07-10 18:00:00', 2),
(95, 4, 0, '2012-07-10 10:00:00', 2),
(96, 4, 4, '2012-07-10 11:30:00', 2),
(97, 4, 0, '2012-07-10 15:00:00', 2),
(98, 4, 3, '2012-07-10 17:00:00', 4),
(99, 5, 0, '2012-07-10 08:30:00', 2),
(100, 6, 0, '2012-07-10 14:30:00', 2),
(101, 6, 0, '2012-07-10 19:00:00', 2),
(102, 7, 4, '2012-07-10 08:30:00', 2),
(103, 7, 2, '2012-07-10 17:30:00', 2),
(104, 8, 0, '2012-07-10 11:30:00', 1),
(105, 8, 3, '2012-07-10 12:00:00', 1),
(106, 8, 3, '2012-07-10 19:30:00', 1),
(107, 0, 4, '2012-07-11 08:00:00', 3),
(108, 0, 2, '2012-07-11 10:00:00', 3),
(109, 0, 0, '2012-07-11 12:00:00', 3),
(110, 0, 0, '2012-07-11 14:00:00', 3),
(111, 0, 2, '2012-07-11 15:30:00', 3),
(112, 0, 2, '2012-07-11 18:30:00', 3),
(113, 1, 0, '2012-07-11 12:30:00', 3),
(114, 1, 0, '2012-07-11 16:00:00', 3),
(115, 4, 1, '2012-07-11 08:00:00', 2),
(116, 4, 0, '2012-07-11 09:00:00', 2),
(117, 4, 3, '2012-07-11 11:00:00', 2),
(118, 4, 0, '2012-07-11 15:00:00', 2),
(119, 5, 4, '2012-07-11 17:00:00', 2),
(120, 6, 0, '2012-07-11 14:00:00', 2),
(121, 6, 0, '2012-07-11 19:30:00', 2),
(122, 7, 0, '2012-07-11 08:00:00', 2),
(123, 7, 0, '2012-07-11 14:00:00', 2),
(124, 7, 0, '2012-07-11 16:30:00', 2),
(125, 8, 4, '2012-07-11 11:00:00', 1),
(126, 8, 3, '2012-07-11 13:00:00', 1),
(127, 0, 0, '2012-07-12 13:30:00', 3),
(128, 0, 2, '2012-07-12 16:30:00', 3),
(129, 1, 1, '2012-07-12 11:30:00', 3),
(130, 2, 1, '2012-07-12 09:00:00', 3),
(131, 2, 1, '2012-07-12 18:30:00', 3),
(132, 3, 3, '2012-07-12 18:00:00', 2),
(133, 4, 1, '2012-07-12 16:00:00', 2),
(134, 6, 0, '2012-07-12 12:00:00', 4),
(135, 7, 2, '2012-07-12 08:00:00', 2),
(136, 7, 4, '2012-07-12 13:30:00', 2),
(137, 7, 4, '2012-07-12 16:00:00', 2),
(138, 8, 3, '2012-07-12 16:30:00', 1),
(139, 0, 2, '2012-07-13 10:30:00', 3),
(140, 0, 4, '2012-07-13 14:00:00', 3),
(141, 0, 3, '2012-07-13 17:00:00', 3),
(142, 1, 1, '2012-07-13 15:00:00', 3),
(143, 2, 1, '2012-07-13 09:00:00', 3),
(144, 2, 0, '2012-07-13 15:00:00', 3),
(145, 2, 1, '2012-07-13 16:30:00', 3),
(146, 4, 0, '2012-07-13 11:00:00', 2),
(147, 4, 0, '2012-07-13 13:30:00', 2),
(148, 4, 0, '2012-07-13 15:00:00', 2),
(149, 4, 3, '2012-07-13 16:00:00', 2),
(150, 4, 4, '2012-07-13 17:30:00', 2),
(151, 6, 0, '2012-07-13 09:30:00', 2),
(152, 7, 0, '2012-07-13 08:00:00', 2),
(153, 7, 1, '2012-07-13 11:00:00', 2),
(154, 7, 4, '2012-07-13 12:30:00', 2),
(155, 8, 0, '2012-07-13 15:30:00', 1),
(156, 8, 2, '2012-07-13 18:30:00', 1),
(157, 0, 2, '2012-07-14 08:30:00', 3),
(158, 0, 4, '2012-07-14 11:30:00', 3),
(159, 0, 3, '2012-07-14 15:00:00', 3),
(160, 1, 3, '2012-07-14 10:30:00', 3),
(161, 1, 3, '2012-07-14 12:30:00', 3),
(162, 1, 0, '2012-07-14 14:30:00', 3),
(163, 2, 1, '2012-07-14 08:30:00', 3),
(164, 3, 2, '2012-07-14 16:00:00', 2),
(165, 4, 3, '2012-07-14 08:00:00', 2),
(166, 4, 1, '2012-07-14 14:30:00', 2),
(167, 6, 0, '2012-07-14 09:30:00', 2),
(168, 6, 1, '2012-07-14 12:30:00', 2),
(169, 6, 0, '2012-07-14 15:00:00', 2),
(170, 7, 2, '2012-07-14 12:30:00', 2),
(171, 7, 2, '2012-07-14 15:00:00', 2),
(172, 7, 4, '2012-07-14 16:30:00', 2),
(173, 7, 1, '2012-07-14 19:00:00', 2),
(174, 8, 3, '2012-07-14 09:00:00', 1),
(175, 8, 1, '2012-07-14 17:00:00', 1),
(176, 0, 2, '2012-07-15 08:00:00', 3),
(177, 0, 0, '2012-07-15 16:00:00', 3),
(178, 0, 2, '2012-07-15 19:00:00', 3),
(179, 1, 0, '2012-07-15 10:00:00', 3),
(180, 1, 0, '2012-07-15 12:00:00', 3),
(181, 1, 3, '2012-07-15 15:30:00', 3),
(182, 2, 1, '2012-07-15 13:00:00', 3),
(183, 3, 1, '2012-07-15 17:30:00', 2),
(184, 4, 3, '2012-07-15 11:30:00', 2),
(185, 4, 0, '2012-07-15 15:00:00', 2),
(186, 4, 3, '2012-07-15 17:30:00', 2),
(187, 7, 4, '2012-07-15 14:30:00', 2),
(188, 7, 4, '2012-07-15 17:00:00', 2),
(189, 8, 4, '2012-07-15 10:00:00', 1),
(190, 8, 2, '2012-07-15 12:00:00', 1),
(191, 8, 3, '2012-07-15 12:30:00', 1),
(192, 8, 3, '2012-07-15 13:30:00', 1),
(193, 0, 5, '2012-07-16 11:00:00', 3),
(194, 0, 5, '2012-07-16 19:00:00', 3),
(195, 1, 1, '2012-07-16 08:00:00', 3),
(196, 1, 0, '2012-07-16 12:30:00', 3),
(197, 2, 1, '2012-07-16 16:30:00', 3),
(198, 4, 3, '2012-07-16 09:00:00', 2),
(199, 4, 1, '2012-07-16 11:00:00', 2),
(200, 4, 3, '2012-07-16 12:00:00', 2),
(201, 4, 3, '2012-07-16 17:30:00', 2),
(202, 6, 0, '2012-07-16 18:30:00', 2),
(203, 7, 4, '2012-07-16 08:00:00', 2),
(204, 7, 2, '2012-07-16 11:30:00', 2),
(205, 7, 4, '2012-07-16 12:30:00', 2),
(206, 7, 5, '2012-07-16 14:00:00', 2),
(207, 8, 4, '2012-07-16 12:00:00', 1),
(208, 8, 1, '2012-07-16 15:00:00', 1),
(209, 8, 4, '2012-07-16 18:00:00', 1),
(210, 8, 3, '2012-07-16 19:30:00', 1),
(211, 0, 5, '2012-07-17 12:30:00', 3),
(212, 0, 5, '2012-07-17 18:00:00', 3),
(213, 1, 1, '2012-07-17 10:00:00', 3),
(214, 1, 4, '2012-07-17 14:30:00', 3),
(215, 2, 5, '2012-07-17 10:30:00', 3),
(216, 2, 1, '2012-07-17 12:30:00', 3),
(217, 2, 1, '2012-07-17 15:30:00', 3),
(218, 2, 2, '2012-07-17 19:00:00', 3),
(219, 3, 1, '2012-07-17 14:00:00', 2),
(220, 3, 2, '2012-07-17 15:00:00', 2),
(221, 4, 0, '2012-07-17 09:00:00', 2),
(222, 4, 3, '2012-07-17 10:30:00', 2),
(223, 4, 3, '2012-07-17 12:00:00', 2),
(224, 4, 5, '2012-07-17 16:00:00', 2),
(225, 4, 3, '2012-07-17 18:30:00', 2),
(226, 5, 0, '2012-07-17 13:30:00', 2),
(227, 6, 4, '2012-07-17 12:00:00', 2),
(228, 6, 0, '2012-07-17 14:00:00', 2),
(229, 7, 4, '2012-07-17 08:00:00', 2),
(230, 7, 5, '2012-07-17 14:00:00', 2),
(231, 7, 4, '2012-07-17 16:00:00', 2),
(232, 8, 3, '2012-07-17 08:30:00', 1),
(233, 8, 2, '2012-07-17 11:00:00', 1),
(234, 8, 3, '2012-07-17 11:30:00', 1),
(235, 8, 3, '2012-07-17 14:30:00', 1),
(236, 8, 0, '2012-07-17 15:00:00', 1),
(237, 8, 3, '2012-07-17 15:30:00', 1),
(238, 8, 3, '2012-07-17 18:00:00', 1),
(239, 8, 3, '2012-07-17 20:00:00', 1),
(240, 0, 5, '2012-07-18 13:00:00', 3),
(241, 0, 5, '2012-07-18 17:30:00', 3),
(242, 1, 0, '2012-07-18 14:00:00', 3),
(243, 1, 0, '2012-07-18 16:30:00', 3),
(244, 2, 1, '2012-07-18 14:00:00', 3),
(245, 3, 2, '2012-07-18 11:30:00', 2),
(246, 3, 3, '2012-07-18 19:00:00', 2),
(247, 4, 1, '2012-07-18 08:30:00', 2),
(248, 4, 4, '2012-07-18 10:00:00', 2),
(249, 4, 5, '2012-07-18 19:00:00', 2),
(250, 5, 0, '2012-07-18 14:30:00', 2),
(251, 6, 0, '2012-07-18 10:30:00', 2),
(252, 6, 0, '2012-07-18 13:00:00', 2),
(253, 6, 0, '2012-07-18 15:00:00', 2),
(254, 6, 1, '2012-07-18 19:30:00', 2),
(255, 7, 4, '2012-07-18 08:30:00', 2),
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(4016, 6, 0, '2012-09-30 15:30:00', 2),
(4017, 6, 35, '2012-09-30 16:30:00', 2),
(4018, 6, 0, '2012-09-30 17:30:00', 2),
(4019, 6, 0, '2012-09-30 19:00:00', 2),
(4020, 7, 27, '2012-09-30 08:30:00', 2),
(4021, 7, 33, '2012-09-30 09:30:00', 2),
(4022, 7, 33, '2012-09-30 11:00:00', 2),
(4023, 7, 5, '2012-09-30 14:30:00', 2),
(4024, 7, 15, '2012-09-30 16:30:00', 2),
(4025, 7, 24, '2012-09-30 17:30:00', 2),
(4026, 7, 5, '2012-09-30 19:00:00', 2),
(4027, 8, 16, '2012-09-30 08:00:00', 1),
(4028, 8, 21, '2012-09-30 08:30:00', 2),
(4029, 8, 3, '2012-09-30 10:30:00', 1),
(4030, 8, 16, '2012-09-30 11:00:00', 1),
(4031, 8, 3, '2012-09-30 11:30:00', 1),
(4032, 8, 17, '2012-09-30 12:00:00', 1),
(4033, 8, 21, '2012-09-30 12:30:00', 1),
(4034, 8, 3, '2012-09-30 13:00:00', 1),
(4035, 8, 29, '2012-09-30 13:30:00', 1),
(4036, 8, 28, '2012-09-30 14:30:00', 1),
(4037, 8, 29, '2012-09-30 15:30:00', 1),
(4038, 8, 29, '2012-09-30 16:30:00', 2),
(4039, 8, 29, '2012-09-30 18:00:00', 1),
(4040, 8, 21, '2012-09-30 18:30:00', 1),
(4041, 8, 16, '2012-09-30 19:00:00', 1),
(4042, 8, 29, '2012-09-30 19:30:00', 1),
(4043, 8, 5, '2013-01-01 15:30:00', 1);
"
dbExecute(con, sql_query)
## [1] 4044
knitr::include_graphics("C:/MariaP/Visualizacion Cientifica/PostgreSQL/R/Bookings.png")

##Simple SQL Queries ###Retrieve everything from a table

exercises <- dbGetQuery(con, "SELECT * FROM facilities")
print(exercises)
##   facid            name membercost guestcost initialoutlay monthlymaintenance
## 1     0  Tennis Court 1        5.0      25.0         10000                200
## 2     1  Tennis Court 2        5.0      25.0          8000                200
## 3     2 Badminton Court        0.0      15.5          4000                 50
## 4     3    Table Tennis        0.0       5.0           320                 10
## 5     4  Massage Room 1       35.0      80.0          4000               3000
## 6     5  Massage Room 2       35.0      80.0          4000               3000
## 7     6    Squash Court        3.5      17.5          5000                 80
## 8     7   Snooker Table        0.0       5.0           450                 15
## 9     8      Pool Table        0.0       5.0           400                 15

###Retrieve specific columns from a table

exercises <- dbGetQuery(con,"SELECT name, membercost FROM facilities")
print(exercises)
##              name membercost
## 1  Tennis Court 1        5.0
## 2  Tennis Court 2        5.0
## 3 Badminton Court        0.0
## 4    Table Tennis        0.0
## 5  Massage Room 1       35.0
## 6  Massage Room 2       35.0
## 7    Squash Court        3.5
## 8   Snooker Table        0.0
## 9      Pool Table        0.0

###Control which rows are retrieved

exercises<- dbGetQuery(con," SELECT * FROM facilities WHERE membercost > 0")
print(exercises)
##   facid           name membercost guestcost initialoutlay monthlymaintenance
## 1     0 Tennis Court 1        5.0      25.0         10000                200
## 2     1 Tennis Court 2        5.0      25.0          8000                200
## 3     4 Massage Room 1       35.0      80.0          4000               3000
## 4     5 Massage Room 2       35.0      80.0          4000               3000
## 5     6   Squash Court        3.5      17.5          5000                 80

###Control which rows are retrieved - part 2

exercises<- dbGetQuery(con, "SELECT facid, name, membercost, monthlymaintenance 
  FROM facilities
  WHERE membercost >0 AND membercost<monthlymaintenance/50")
print(exercises)
##   facid           name membercost monthlymaintenance
## 1     4 Massage Room 1         35               3000
## 2     5 Massage Room 2         35               3000

###Basic string searches

exercises<- dbGetQuery(con,"SELECT * FROM facilities
  WHERE name LIKE '%Tennis%'")
print(exercises)
##   facid           name membercost guestcost initialoutlay monthlymaintenance
## 1     0 Tennis Court 1          5        25         10000                200
## 2     1 Tennis Court 2          5        25          8000                200
## 3     3   Table Tennis          0         5           320                 10

###Matching against multiple possible values

exercises<- dbGetQuery(con,"SELECT * from facilities
                       WHERE facid = 5 OR facid = 1")
print(exercises)
##   facid           name membercost guestcost initialoutlay monthlymaintenance
## 1     1 Tennis Court 2          5        25          8000                200
## 2     5 Massage Room 2         35        80          4000               3000

###Classify results into buckets

exercises<- dbGetQuery(con,"SELECT name, 
                 CASE WHEN monthlymaintenance > 100 THEN 'expensive' 
                      ELSE 'cheap' END AS cost
          FROM facilities")
print(exercises)
##              name      cost
## 1  Tennis Court 1 expensive
## 2  Tennis Court 2 expensive
## 3 Badminton Court     cheap
## 4    Table Tennis     cheap
## 5  Massage Room 1 expensive
## 6  Massage Room 2 expensive
## 7    Squash Court     cheap
## 8   Snooker Table     cheap
## 9      Pool Table     cheap

###Removing duplicates, and ordering results

exercises<- dbGetQuery(con,
  "SELECT DISTINCT surname
          FROM members
          ORDER BY surname
          LIMIT 10")
print(exercises)
##    surname
## 1    Bader
## 2    Baker
## 3   Boothe
## 4  Butters
## 5   Coplin
## 6  Crumpet
## 7     Dare
## 8  Farrell
## 9  Genting
## 10   GUEST

###Combining results from multiple queries

exercises<- dbGetQuery(con,"
          SELECT surname AS name FROM members
          UNION
          SELECT name FROM facilities")
print(exercises)
##                 name
## 1       Table Tennis
## 2         Tupperware
## 3     Massage Room 2
## 4              Jones
## 5             Boothe
## 6     Massage Room 1
## 7              Smith
## 8               Owen
## 9            Purview
## 10     Snooker Table
## 11          Joplette
## 12              Hunt
## 13             GUEST
## 14   Badminton Court
## 15           Butters
## 16            Coplin
## 17           Genting
## 18             Bader
## 19 Worthington-Smyth
## 20            Pinker
## 21           Farrell
## 22              Dare
## 23      Squash Court
## 24    Tennis Court 1
## 25            Sarwin
## 26             Baker
## 27    Tennis Court 2
## 28            Rownam
## 29        Pool Table
## 30          Stibbons
## 31            Rumney
## 32             Tracy
## 33           Crumpet
## 34         Mackenzie

###Simple aggregation

exercises <- dbGetQuery(con,"
          SELECT MAX(joindate) AS latest
          FROM members")
print(exercises)
##                latest
## 1 2012-09-26 18:08:45

###More aggregation

exercises <- dbGetQuery(con,"
          SELECT firstname, surname, joindate from members
          WHERE joindate = (SELECT max(joindate) FROM members)")
print(exercises)
##   firstname surname            joindate
## 1    Darren   Smith 2012-09-26 18:08:45

##Joins and Subqueries

###Retrieve the start times of members’ bookings

exercises <- dbGetQuery(con,"select bks.starttime 
    from 
        bookings bks
        inner join members mems
            on mems.memid = bks.memid
    where 
        mems.firstname='David' 
        and mems.surname='Farrell' ")
print(exercises)
##              starttime
## 1  2012-09-18 09:00:00
## 2  2012-09-18 17:30:00
## 3  2012-09-18 13:30:00
## 4  2012-09-18 20:00:00
## 5  2012-09-19 09:30:00
## 6  2012-09-19 15:00:00
## 7  2012-09-19 12:00:00
## 8  2012-09-20 15:30:00
## 9  2012-09-20 11:30:00
## 10 2012-09-20 14:00:00
## 11 2012-09-21 10:30:00
## 12 2012-09-21 14:00:00
## 13 2012-09-22 08:30:00
## 14 2012-09-22 17:00:00
## 15 2012-09-23 08:30:00
## 16 2012-09-23 17:30:00
## 17 2012-09-23 19:00:00
## 18 2012-09-24 08:00:00
## 19 2012-09-24 16:30:00
## 20 2012-09-24 12:30:00
## 21 2012-09-25 15:30:00
## 22 2012-09-25 17:00:00
## 23 2012-09-26 13:00:00
## 24 2012-09-26 17:00:00
## 25 2012-09-27 08:00:00
## 26 2012-09-28 11:30:00
## 27 2012-09-28 09:30:00
## 28 2012-09-28 13:00:00
## 29 2012-09-29 16:00:00
## 30 2012-09-29 10:30:00
## 31 2012-09-29 13:30:00
## 32 2012-09-29 14:30:00
## 33 2012-09-29 17:30:00
## 34 2012-09-30 14:30:00

###Work out the start times of bookings for tennis courts

exercises <- dbGetQuery(con,"SELECT b.starttime, f.name
    FROM bookings b
    JOIN facilities f ON b.facid = f.facid
          WHERE f.name ILIKE '%Tennis Court%'  AND DATE(b.starttime) = '2012-09-21'
          ORDER BY b.starttime, f.name")
print(exercises)
##              starttime           name
## 1  2012-09-21 08:00:00 Tennis Court 1
## 2  2012-09-21 08:00:00 Tennis Court 2
## 3  2012-09-21 09:30:00 Tennis Court 1
## 4  2012-09-21 10:00:00 Tennis Court 2
## 5  2012-09-21 11:30:00 Tennis Court 2
## 6  2012-09-21 12:00:00 Tennis Court 1
## 7  2012-09-21 13:30:00 Tennis Court 1
## 8  2012-09-21 14:00:00 Tennis Court 2
## 9  2012-09-21 15:30:00 Tennis Court 1
## 10 2012-09-21 16:00:00 Tennis Court 2
## 11 2012-09-21 17:00:00 Tennis Court 1
## 12 2012-09-21 18:00:00 Tennis Court 2

###Produce a list of all members who have recommended another member

exercises <- dbGetQuery(con,"SELECT DISTINCT mem.firstname, mem.surname 
    FROM members mem
        INNER JOIN members rem
            ON mem.memid = rem.recommendedby
ORDER BY surname, firstname")
print(exercises)
##    firstname  surname
## 1   Florence    Bader
## 2    Timothy    Baker
## 3     Gerald  Butters
## 4     Jemima  Farrell
## 5    Matthew  Genting
## 6      David    Jones
## 7     Janice Joplette
## 8  Millicent  Purview
## 9        Tim   Rownam
## 10    Darren    Smith
## 11     Tracy    Smith
## 12    Ponder Stibbons
## 13    Burton    Tracy

###Produce a list of all members, along with their recommender

exercises <- dbGetQuery(con,"SELECT mem.firstname AS memfname, mem.surname AS memsname, recs.firstname as recfname, recs.surname as recsname
    FROM members mem
    LEFT OUTER JOIN members recs
    ON recs.memid = mem.recommendedby
ORDER BY memsname, memfname")
print(exercises)
##     memfname          memsname  recfname recsname
## 1   Florence             Bader    Ponder Stibbons
## 2       Anne             Baker    Ponder Stibbons
## 3    Timothy             Baker    Jemima  Farrell
## 4        Tim            Boothe       Tim   Rownam
## 5     Gerald           Butters    Darren    Smith
## 6       Joan            Coplin   Timothy    Baker
## 7      Erica           Crumpet     Tracy    Smith
## 8      Nancy              Dare    Janice Joplette
## 9      David           Farrell      <NA>     <NA>
## 10    Jemima           Farrell      <NA>     <NA>
## 11   Matthew           Genting    Gerald  Butters
## 12     GUEST             GUEST      <NA>     <NA>
## 13      John              Hunt Millicent  Purview
## 14     David             Jones    Janice Joplette
## 15   Douglas             Jones     David    Jones
## 16    Janice          Joplette    Darren    Smith
## 17      Anna         Mackenzie    Darren    Smith
## 18   Charles              Owen    Darren    Smith
## 19     David            Pinker    Jemima  Farrell
## 20 Millicent           Purview     Tracy    Smith
## 21       Tim            Rownam      <NA>     <NA>
## 22 Henrietta            Rumney   Matthew  Genting
## 23 Ramnaresh            Sarwin  Florence    Bader
## 24    Darren             Smith      <NA>     <NA>
## 25    Darren             Smith      <NA>     <NA>
## 26      Jack             Smith    Darren    Smith
## 27     Tracy             Smith      <NA>     <NA>
## 28    Ponder          Stibbons    Burton    Tracy
## 29    Burton             Tracy      <NA>     <NA>
## 30  Hyacinth        Tupperware      <NA>     <NA>
## 31     Henry Worthington-Smyth     Tracy    Smith

###Produce a list of all members who have used a tennis court

exercises <- dbGetQuery(con,"SELECT DISTINCT mems.firstname || ' ' || mems.surname as member, 
    facs.name as facility
    FROM members mems
    INNER JOIN bookings bks
    ON mems.memid = bks.memid
    INNER JOIN facilities facs
    ON facs.facid = bks.facid
    
WHERE facs.name ILIKE '%Tennis Court%'
ORDER BY member, facility")
print(exercises)
##               member       facility
## 1         Anne Baker Tennis Court 1
## 2         Anne Baker Tennis Court 2
## 3       Burton Tracy Tennis Court 1
## 4       Burton Tracy Tennis Court 2
## 5       Charles Owen Tennis Court 1
## 6       Charles Owen Tennis Court 2
## 7       Darren Smith Tennis Court 2
## 8      David Farrell Tennis Court 1
## 9      David Farrell Tennis Court 2
## 10       David Jones Tennis Court 1
## 11       David Jones Tennis Court 2
## 12      David Pinker Tennis Court 1
## 13     Douglas Jones Tennis Court 1
## 14     Erica Crumpet Tennis Court 1
## 15    Florence Bader Tennis Court 1
## 16    Florence Bader Tennis Court 2
## 17    Gerald Butters Tennis Court 1
## 18    Gerald Butters Tennis Court 2
## 19       GUEST GUEST Tennis Court 1
## 20       GUEST GUEST Tennis Court 2
## 21  Henrietta Rumney Tennis Court 2
## 22        Jack Smith Tennis Court 1
## 23        Jack Smith Tennis Court 2
## 24   Janice Joplette Tennis Court 1
## 25   Janice Joplette Tennis Court 2
## 26    Jemima Farrell Tennis Court 1
## 27    Jemima Farrell Tennis Court 2
## 28       Joan Coplin Tennis Court 1
## 29         John Hunt Tennis Court 1
## 30         John Hunt Tennis Court 2
## 31   Matthew Genting Tennis Court 1
## 32 Millicent Purview Tennis Court 2
## 33        Nancy Dare Tennis Court 1
## 34        Nancy Dare Tennis Court 2
## 35   Ponder Stibbons Tennis Court 1
## 36   Ponder Stibbons Tennis Court 2
## 37  Ramnaresh Sarwin Tennis Court 1
## 38  Ramnaresh Sarwin Tennis Court 2
## 39        Tim Boothe Tennis Court 1
## 40        Tim Boothe Tennis Court 2
## 41     Timothy Baker Tennis Court 1
## 42     Timothy Baker Tennis Court 2
## 43        Tim Rownam Tennis Court 1
## 44        Tim Rownam Tennis Court 2
## 45       Tracy Smith Tennis Court 1
## 46       Tracy Smith Tennis Court 2

###Produce a list of costly bookings

exercises <- dbGetQuery(con,"SELECT mems.firstname || ' ' || mems.surname as member, 
    facs.name as facility,
    CASE 
        when mems.memid = 0 then
            bks.slots*facs.guestcost
        else
            bks.slots*facs.membercost
    end as cost
    
    from members mems                                
                inner join bookings bks
                        on mems.memid = bks.memid
                inner join facilities facs
                        on bks.facid = facs.facid
        where
        bks.starttime >= '2012-09-14' and 
        bks.starttime < '2012-09-15' and (
            (mems.memid = 0 and bks.slots*facs.guestcost > 30) or
            (mems.memid != 0 and bks.slots*facs.membercost > 30)
        )
order by cost desc ")
print(exercises)
##             member       facility cost
## 1      GUEST GUEST Massage Room 2  320
## 2      GUEST GUEST Massage Room 1  160
## 3      GUEST GUEST Massage Room 1  160
## 4      GUEST GUEST Massage Room 1  160
## 5      GUEST GUEST Tennis Court 2  150
## 6   Jemima Farrell Massage Room 1  140
## 7      GUEST GUEST Tennis Court 1   75
## 8      GUEST GUEST Tennis Court 1   75
## 9      GUEST GUEST Tennis Court 2   75
## 10 Ponder Stibbons Massage Room 1   70
## 11  Jemima Farrell Massage Room 1   70
## 12    Burton Tracy Massage Room 1   70
## 13 Matthew Genting Massage Room 1   70
## 14  Florence Bader Massage Room 2   70
## 15      Jack Smith Massage Room 1   70
## 16     GUEST GUEST   Squash Court   70
## 17     GUEST GUEST   Squash Court   35
## 18     GUEST GUEST   Squash Court   35

###Produce a list of all members, along with their recommender, using no joins.

exercises <- dbGetQuery(con,"
                        select distinct mems.firstname || ' ' ||  mems.surname as member,
    (select recs.firstname || ' ' || recs.surname as recommender 
        from members recs 
        where recs.memid = mems.recommendedby
    )
    from 
        members mems
order by member")   

###Produce a list of costly bookings, using a subquery

exercises <- dbGetQuery(con,"select member, facility, cost from (
    select 
        mems.firstname || ' ' || mems.surname as member,
        facs.name as facility,
        case
            when mems.memid = 0 then
                bks.slots*facs.guestcost
            else
                bks.slots*facs.membercost
        end as cost
        from
            members mems
            inner join bookings bks
                on mems.memid = bks.memid
            inner join facilities facs
                on bks.facid = facs.facid
        where
            bks.starttime >= '2012-09-14' and
            bks.starttime < '2012-09-15'
    ) as bookings
    where cost > 30
order by cost desc")
print(exercises)
##             member       facility cost
## 1      GUEST GUEST Massage Room 2  320
## 2      GUEST GUEST Massage Room 1  160
## 3      GUEST GUEST Massage Room 1  160
## 4      GUEST GUEST Massage Room 1  160
## 5      GUEST GUEST Tennis Court 2  150
## 6   Jemima Farrell Massage Room 1  140
## 7      GUEST GUEST Tennis Court 1   75
## 8      GUEST GUEST Tennis Court 1   75
## 9      GUEST GUEST Tennis Court 2   75
## 10 Ponder Stibbons Massage Room 1   70
## 11  Jemima Farrell Massage Room 1   70
## 12    Burton Tracy Massage Room 1   70
## 13 Matthew Genting Massage Room 1   70
## 14  Florence Bader Massage Room 2   70
## 15      Jack Smith Massage Room 1   70
## 16     GUEST GUEST   Squash Court   70
## 17     GUEST GUEST   Squash Court   35
## 18     GUEST GUEST   Squash Court   35

Modifying Data

Insert some data into a table

query_result <- dbExecute(con, "
  INSERT INTO facilities (facid, name, membercost, guestcost, initialoutlay, monthlymaintenance) 
  VALUES (9, 'Spa', 20, 30, 100000, 800);")
print(query_result)
## [1] 1

Insert multiple rows of data into a table

query_result <- dbExecute(con, "DELETE FROM facilities WHERE facid = 9")
query_result <- dbExecute(con, "
INSERT INTO facilities (facid, name, membercost, guestcost, initialoutlay, monthlymaintenance) VALUES (9, 'Spa', 20, 30, 100000, 800), (10, 'Squash Court 2', 3.5, 17.5, 5000, 80);")

print(query_result)
## [1] 2

Insert calculated data into a table

query_result <- dbExecute(con, "INSERT INTO facilities (facid, name, membercost, guestcost, initialoutlay, monthlymaintenance) SELECT MAX(facid) + 1, 'Spa', 20, 30, 100000, 800 FROM facilities;")

print(query_result)
## [1] 1

Update some existing data

query_result <- dbExecute(con, "UPDATE facilities SET initialoutlay = 10000 WHERE name = 'Tennis Court 2';")
print(query_result)
## [1] 1

Update multiple rows and columns at the same time

query_result <- dbExecute(con, "UPDATE facilities SET membercost = 6, guestcost = 30 WHERE name LIKE 'Tennis Court%';")
print(query_result)
## [1] 2

Update a row based on the contents of another row

query_result <- dbExecute(con, 
"UPDATE facilities 
 SET membercost = (SELECT membercost FROM facilities WHERE name = 'Tennis Court 1') * 1.10, 
     guestcost = (SELECT guestcost FROM facilities WHERE name = 'Tennis Court 1') * 1.10 
 WHERE name = 'Tennis Court 2';")
print(query_result)
## [1] 1

Delete all bookings

Representación de cómo eliminar una tabla, no se eliminará porque se usa posteriormente.

#query_result <- dbExecute(con, "DELETE FROM bookings;")
#print(query_result)

Delete a member from the members table

Representación de cómo eliminar una tabla, no se eliminará porque se usa posteriormente.

#query_result <- dbExecute(con, "DELETE FROM members WHERE memid = 37;")
#print(query_result)

Delete based on a subquery

Representación de cómo eliminar una tabla, no se eliminará porque se usa posteriormente.

#query_result <- dbExecute(con, "DELETE FROM members WHERE memid NOT IN (SELECT memid FROM bookings);")
#print(query_result)

##Aggregation ### Count the number of facilities

query_result <- dbGetQuery(con, "SELECT count(*) FROM facilities;")
print(query_result)
##   count
## 1    12

Count the number of expensive facilities

query_result <- dbGetQuery(con, "SELECT count(*) FROM facilities WHERE guestcost >= 10;")
print(query_result)
##   count
## 1     9

Count the number of recommendations each member makes.

query_result <- dbGetQuery(con, 
"SELECT recommendedby, count(*) 
 FROM members
 WHERE recommendedby IS NOT NULL
 GROUP BY recommendedby
 ORDER BY recommendedby;")
print(query_result)
##    recommendedby count
## 1              1     5
## 2              2     3
## 3              3     1
## 4              4     2
## 5              5     1
## 6              6     1
## 7              9     2
## 8             11     1
## 9             13     2
## 10            15     1
## 11            16     1
## 12            20     1
## 13            30     1

List the total slots booked per facility

query_result <- dbGetQuery(con, 
"SELECT facid, sum(slots) as \"Total Slots\"
 FROM bookings
 GROUP BY facid
 ORDER BY facid;")
print(query_result)
##   facid Total Slots
## 1     0        1320
## 2     1        1278
## 3     2        1209
## 4     3         830
## 5     4        1404
## 6     5         228
## 7     6        1104
## 8     7         908
## 9     8         911

List the total slots booked per facility in a given month

query_result <- dbGetQuery(con, 
"SELECT facid, sum(slots) as \"Total Slots\"
 FROM bookings
 WHERE
    starttime >= '2012-09-01'
    AND starttime < '2012-10-01'
 GROUP BY facid
 ORDER BY sum(slots);")
print(query_result)
##   facid Total Slots
## 1     5         122
## 2     3         422
## 3     7         426
## 4     8         471
## 5     6         540
## 6     2         570
## 7     1         588
## 8     0         591
## 9     4         648

List the total slots booked per facility per month

query_result <- dbGetQuery(con, 
"SELECT facid, extract(month from starttime) as month, sum(slots) as \"Total Slots\"
 FROM bookings
 WHERE extract(year from starttime) = 2012
 GROUP BY facid, month
 ORDER BY facid, month;")
print(query_result)
##    facid month Total Slots
## 1      0     7         270
## 2      0     8         459
## 3      0     9         591
## 4      1     7         207
## 5      1     8         483
## 6      1     9         588
## 7      2     7         180
## 8      2     8         459
## 9      2     9         570
## 10     3     7         104
## 11     3     8         304
## 12     3     9         422
## 13     4     7         264
## 14     4     8         492
## 15     4     9         648
## 16     5     7          24
## 17     5     8          82
## 18     5     9         122
## 19     6     7         164
## 20     6     8         400
## 21     6     9         540
## 22     7     7         156
## 23     7     8         326
## 24     7     9         426
## 25     8     7         117
## 26     8     8         322
## 27     8     9         471

Find the count of members who have made at least one booking

query_result <- dbGetQuery(con, "SELECT count(distinct memid) FROM bookings;")
print(query_result)
##   count
## 1    30

List facilities with more than 1000 slots booked

query_result <- dbGetQuery(con, 
"SELECT facid, sum(slots) as \"Total Slots\"
 FROM bookings
 GROUP BY facid
 HAVING sum(slots) > 1000
 ORDER BY facid;")
print(query_result)
##   facid Total Slots
## 1     0        1320
## 2     1        1278
## 3     2        1209
## 4     4        1404
## 5     6        1104

Find the total revenue of each facility

query_result <- dbGetQuery(con, 
"SELECT facs.name, sum(slots * case
        when memid = 0 then facs.guestcost
        else facs.membercost
    end) as revenue
 FROM bookings bks
 INNER JOIN facilities facs
    ON bks.facid = facs.facid
 GROUP BY facs.name
 ORDER BY revenue;")
print(query_result)
##              name revenue
## 1    Table Tennis   180.0
## 2   Snooker Table   240.0
## 3      Pool Table   270.0
## 4 Badminton Court  1906.5
## 5    Squash Court 13468.0
## 6  Massage Room 2 15810.0
## 7  Tennis Court 1 16632.0
## 8  Tennis Court 2 18889.2
## 9  Massage Room 1 72540.0

Find facilities with a total revenue less than 1000

query_result <- dbGetQuery(con, 
"SELECT name, revenue FROM (
    SELECT facs.name, sum(case 
                when memid = 0 then slots * facs.guestcost
                else slots * facs.membercost
            end) as revenue
        FROM bookings bks
        INNER JOIN facilities facs
            ON bks.facid = facs.facid
        GROUP BY facs.name
    ) as agg WHERE revenue < 1000
ORDER BY revenue;")
print(query_result)
##            name revenue
## 1  Table Tennis     180
## 2 Snooker Table     240
## 3    Pool Table     270

Output the facility id that has the highest number of slots booked

query_result <- dbGetQuery(con, 
"SELECT facid, sum(slots) as \"Total Slots\"
 FROM bookings
 GROUP BY facid
 ORDER BY sum(slots) DESC
 LIMIT 1;")
print(query_result)
##   facid Total Slots
## 1     4        1404

List the total slots booked per facility per month, part 2

query_result <- dbGetQuery(con, 
"SELECT facid, extract(month from starttime) as month, sum(slots) as slots
 FROM bookings
 WHERE
    starttime >= '2012-01-01'
    AND starttime < '2013-01-01'
 GROUP BY ROLLUP(facid, month)
 ORDER BY facid, month;")
print(query_result)
##    facid month slots
## 1      0     7   270
## 2      0     8   459
## 3      0     9   591
## 4      0    NA  1320
## 5      1     7   207
## 6      1     8   483
## 7      1     9   588
## 8      1    NA  1278
## 9      2     7   180
## 10     2     8   459
## 11     2     9   570
## 12     2    NA  1209
## 13     3     7   104
## 14     3     8   304
## 15     3     9   422
## 16     3    NA   830
## 17     4     7   264
## 18     4     8   492
## 19     4     9   648
## 20     4    NA  1404
## 21     5     7    24
## 22     5     8    82
## 23     5     9   122
## 24     5    NA   228
## 25     6     7   164
## 26     6     8   400
## 27     6     9   540
## 28     6    NA  1104
## 29     7     7   156
## 30     7     8   326
## 31     7     9   426
## 32     7    NA   908
## 33     8     7   117
## 34     8     8   322
## 35     8     9   471
## 36     8    NA   910
## 37    NA    NA  9191

List the total hours booked per named facility

query_result <- dbGetQuery(con, 
"SELECT facs.facid, facs.name,
 trim(to_char(sum(bks.slots)/2.0, '9999999999999999D99')) as \"Total Hours\"
 FROM bookings bks
 INNER JOIN facilities facs
    ON facs.facid = bks.facid
 GROUP BY facs.facid, facs.name
 ORDER BY facs.facid;")
print(query_result)
##   facid            name Total Hours
## 1     0  Tennis Court 1      660.00
## 2     1  Tennis Court 2      639.00
## 3     2 Badminton Court      604.50
## 4     3    Table Tennis      415.00
## 5     4  Massage Room 1      702.00
## 6     5  Massage Room 2      114.00
## 7     6    Squash Court      552.00
## 8     7   Snooker Table      454.00
## 9     8      Pool Table      455.50

List each member’s first booking after September 1st 2012

query_result <- dbGetQuery(con, 
"SELECT mems.surname, mems.firstname, mems.memid, min(bks.starttime) as starttime
 FROM bookings bks
 INNER JOIN members mems ON
    mems.memid = bks.memid
 WHERE bks.starttime >= '2012-09-01'
 GROUP BY mems.surname, mems.firstname, mems.memid
 ORDER BY mems.memid;")
print(query_result)
##              surname firstname memid           starttime
## 1              GUEST     GUEST     0 2012-09-01 08:00:00
## 2              Smith    Darren     1 2012-09-01 09:00:00
## 3              Smith     Tracy     2 2012-09-01 11:30:00
## 4             Rownam       Tim     3 2012-09-01 16:00:00
## 5           Joplette    Janice     4 2012-09-01 15:00:00
## 6            Butters    Gerald     5 2012-09-02 12:30:00
## 7              Tracy    Burton     6 2012-09-01 15:00:00
## 8               Dare     Nancy     7 2012-09-01 12:30:00
## 9             Boothe       Tim     8 2012-09-01 08:30:00
## 10          Stibbons    Ponder     9 2012-09-01 11:00:00
## 11              Owen   Charles    10 2012-09-01 11:00:00
## 12             Jones     David    11 2012-09-01 09:30:00
## 13             Baker      Anne    12 2012-09-01 14:30:00
## 14           Farrell    Jemima    13 2012-09-01 09:30:00
## 15             Smith      Jack    14 2012-09-01 11:00:00
## 16             Bader  Florence    15 2012-09-01 10:30:00
## 17             Baker   Timothy    16 2012-09-01 15:00:00
## 18            Pinker     David    17 2012-09-01 08:30:00
## 19           Genting   Matthew    20 2012-09-01 18:00:00
## 20         Mackenzie      Anna    21 2012-09-01 08:30:00
## 21            Coplin      Joan    22 2012-09-02 11:30:00
## 22            Sarwin Ramnaresh    24 2012-09-04 11:00:00
## 23             Jones   Douglas    26 2012-09-08 13:00:00
## 24            Rumney Henrietta    27 2012-09-16 13:30:00
## 25           Farrell     David    28 2012-09-18 09:00:00
## 26 Worthington-Smyth     Henry    29 2012-09-19 09:30:00
## 27           Purview Millicent    30 2012-09-19 11:30:00
## 28        Tupperware  Hyacinth    33 2012-09-20 08:00:00
## 29              Hunt      John    35 2012-09-23 14:00:00
## 30           Crumpet     Erica    36 2012-09-27 11:30:00

Produce a list of member names, with each row containing the total member count

query_result <- dbGetQuery(con, 
"SELECT count(*) OVER(), firstname, surname
 FROM members
 ORDER BY joindate;")
print(query_result)
##    count firstname           surname
## 1     31     GUEST             GUEST
## 2     31    Darren             Smith
## 3     31     Tracy             Smith
## 4     31       Tim            Rownam
## 5     31    Janice          Joplette
## 6     31    Gerald           Butters
## 7     31    Burton             Tracy
## 8     31     Nancy              Dare
## 9     31       Tim            Boothe
## 10    31    Ponder          Stibbons
## 11    31   Charles              Owen
## 12    31     David             Jones
## 13    31      Anne             Baker
## 14    31    Jemima           Farrell
## 15    31      Jack             Smith
## 16    31  Florence             Bader
## 17    31   Timothy             Baker
## 18    31     David            Pinker
## 19    31   Matthew           Genting
## 20    31      Anna         Mackenzie
## 21    31      Joan            Coplin
## 22    31 Ramnaresh            Sarwin
## 23    31   Douglas             Jones
## 24    31 Henrietta            Rumney
## 25    31     David           Farrell
## 26    31     Henry Worthington-Smyth
## 27    31 Millicent           Purview
## 28    31  Hyacinth        Tupperware
## 29    31      John              Hunt
## 30    31     Erica           Crumpet
## 31    31    Darren             Smith

Produce a numbered list of members

query_result <- dbGetQuery(con, 
"SELECT row_number() OVER(ORDER BY joindate) AS row, firstname, surname
 FROM members
 ORDER BY joindate;")
print(query_result)
##    row firstname           surname
## 1    1     GUEST             GUEST
## 2    2    Darren             Smith
## 3    3     Tracy             Smith
## 4    4       Tim            Rownam
## 5    5    Janice          Joplette
## 6    6    Gerald           Butters
## 7    7    Burton             Tracy
## 8    8     Nancy              Dare
## 9    9       Tim            Boothe
## 10  10    Ponder          Stibbons
## 11  11   Charles              Owen
## 12  12     David             Jones
## 13  13      Anne             Baker
## 14  14    Jemima           Farrell
## 15  15      Jack             Smith
## 16  16  Florence             Bader
## 17  17   Timothy             Baker
## 18  18     David            Pinker
## 19  19   Matthew           Genting
## 20  20      Anna         Mackenzie
## 21  21      Joan            Coplin
## 22  22 Ramnaresh            Sarwin
## 23  23   Douglas             Jones
## 24  24 Henrietta            Rumney
## 25  25     David           Farrell
## 26  26     Henry Worthington-Smyth
## 27  27 Millicent           Purview
## 28  28  Hyacinth        Tupperware
## 29  29      John              Hunt
## 30  30     Erica           Crumpet
## 31  31    Darren             Smith

Output the facility id that has the highest number of slots booked, again

query_result <- dbGetQuery(con, 
"SELECT facid, total FROM (
    SELECT facid, sum(slots) as total, rank() OVER (ORDER BY sum(slots) DESC) as rank
    FROM bookings
    GROUP BY facid
) as ranked
WHERE rank = 1;")
print(query_result)
##   facid total
## 1     4  1404

Rank members by (rounded) hours used

query_result <- dbGetQuery(con, 
"SELECT firstname, surname,
 ((sum(bks.slots)+10)/20)*10 as hours,
 rank() OVER (ORDER BY ((sum(bks.slots)+10)/20)*10 DESC) as rank
 FROM bookings bks
 INNER JOIN members mems
    ON bks.memid = mems.memid
 GROUP BY mems.memid
 ORDER BY rank, surname, firstname;")
print(query_result)
##    firstname           surname hours rank
## 1      GUEST             GUEST  1200    1
## 2     Darren             Smith   340    2
## 3        Tim            Rownam   330    3
## 4        Tim            Boothe   220    4
## 5      Tracy             Smith   220    4
## 6     Gerald           Butters   210    6
## 7     Burton             Tracy   180    7
## 8    Charles              Owen   170    8
## 9     Janice          Joplette   160    9
## 10      Anne             Baker   150   10
## 11   Timothy             Baker   150   10
## 12     David             Jones   150   10
## 13     Nancy              Dare   130   13
## 14  Florence             Bader   120   14
## 15      Anna         Mackenzie   120   14
## 16    Ponder          Stibbons   120   14
## 17      Jack             Smith   110   17
## 18    Jemima           Farrell    90   18
## 19     David            Pinker    80   19
## 20 Ramnaresh            Sarwin    80   19
## 21   Matthew           Genting    70   21
## 22      Joan            Coplin    50   22
## 23     David           Farrell    30   23
## 24     Henry Worthington-Smyth    30   23
## 25      John              Hunt    20   25
## 26   Douglas             Jones    20   25
## 27 Millicent           Purview    20   25
## 28 Henrietta            Rumney    20   25
## 29     Erica           Crumpet    10   29
## 30  Hyacinth        Tupperware    10   29

Find the top three revenue generating facilities

query_result <- dbGetQuery(con, 
"SELECT name, rank FROM (
    SELECT facs.name as name, rank() OVER (ORDER BY sum(case
                when memid = 0 then slots * facs.guestcost
                else slots * membercost
            end) DESC) as rank
    FROM bookings bks
    INNER JOIN facilities facs
        ON bks.facid = facs.facid
    GROUP BY facs.name
) as subq
WHERE rank <= 3
ORDER BY rank;")
print(query_result)
##             name rank
## 1 Massage Room 1    1
## 2 Tennis Court 2    2
## 3 Tennis Court 1    3

Classify facilities by value

query_result <- dbGetQuery(con, 
"SELECT name, CASE WHEN class = 1 THEN 'high'
        WHEN class = 2 THEN 'average'
        ELSE 'low'
    END as revenue
FROM (
    SELECT facs.name as name, ntile(3) OVER (ORDER BY sum(case
                when memid = 0 then slots * facs.guestcost
                else slots * membercost
            end) DESC) as class
    FROM bookings bks
    INNER JOIN facilities facs
        ON bks.facid = facs.facid
    GROUP BY facs.name
) as subq
ORDER BY class, name;")
print(query_result)
##              name revenue
## 1  Massage Room 1    high
## 2  Tennis Court 1    high
## 3  Tennis Court 2    high
## 4 Badminton Court average
## 5  Massage Room 2 average
## 6    Squash Court average
## 7      Pool Table     low
## 8   Snooker Table     low
## 9    Table Tennis     low

Calculate the payback time for each facility

query_result <- dbGetQuery(con, 
"SELECT facs.name as name,
 facs.initialoutlay/((sum(case
        when memid = 0 then slots * facs.guestcost
        else slots * membercost
    end)/3) - facs.monthlymaintenance) as months
FROM bookings bks
INNER JOIN facilities facs
    ON bks.facid = facs.facid
GROUP BY facs.facid
ORDER BY name;")
print(query_result)
##              name    months
## 1 Badminton Court 6.8317677
## 2  Massage Room 1 0.1888574
## 3  Massage Room 2 1.7621145
## 4      Pool Table 5.3333333
## 5   Snooker Table 6.9230769
## 6    Squash Court 1.1339583
## 7    Table Tennis 6.4000000
## 8  Tennis Court 1 1.8712575
## 9  Tennis Court 2 1.6403123

Calculate a rolling average of total revenue

query_result <- dbGetQuery(con, 
"SELECT dategen.date,
 (
    select sum(case
        when memid = 0 then slots * facs.guestcost
        else slots * membercost
    end) / 15 as revenue
    from bookings bks
    inner join facilities facs
        on bks.facid = facs.facid
    where bks.starttime > dategen.date - interval '14 days'
        and bks.starttime < dategen.date + interval '1 day'
 ) as revenue
from
 (
    select cast(generate_series(timestamp '2012-08-01',
        '2012-08-31', '1 day') as date) as date
 ) as dategen
order by dategen.date;")
print(query_result)
##          date  revenue
## 1  2012-08-01 1191.793
## 2  2012-08-02 1219.320
## 3  2012-08-03 1229.460
## 4  2012-08-04 1245.207
## 5  2012-08-05 1228.933
## 6  2012-08-06 1254.360
## 7  2012-08-07 1249.187
## 8  2012-08-08 1238.640
## 9  2012-08-09 1218.507
## 10 2012-08-10 1240.673
## 11 2012-08-11 1245.593
## 12 2012-08-12 1266.427
## 13 2012-08-13 1290.640
## 14 2012-08-14 1324.867
## 15 2012-08-15 1352.180
## 16 2012-08-16 1364.113
## 17 2012-08-17 1409.947
## 18 2012-08-18 1458.933
## 19 2012-08-19 1490.587
## 20 2012-08-20 1512.627
## 21 2012-08-21 1534.013
## 22 2012-08-22 1629.380
## 23 2012-08-23 1656.540
## 24 2012-08-24 1683.333
## 25 2012-08-25 1710.393
## 26 2012-08-26 1723.480
## 27 2012-08-27 1751.673
## 28 2012-08-28 1775.800
## 29 2012-08-29 1771.993
## 30 2012-08-30 1746.053
## 31 2012-08-31 1791.840

Working with Timestamps!

Produce a timestamp for 1 a.m. on the 31st of August 2012

query_result <- dbGetQuery(con, "SELECT timestamp '2012-08-31 01:00:00';")
print(query_result)
##             timestamp
## 1 2012-08-31 01:00:00

Subtract timestamps from each other

query_result <- dbGetQuery(con, "SELECT timestamp '2012-08-31 01:00:00' - timestamp '2012-07-30 01:00:00' as interval;")
print(query_result)
##   interval
## 1  32 days

Generate a list of all the dates in October 2012

query_result <- dbGetQuery(con, "SELECT generate_series(timestamp '2012-10-01', timestamp '2012-10-31', interval '1 day') as ts;")
print(query_result)
##            ts
## 1  2012-10-01
## 2  2012-10-02
## 3  2012-10-03
## 4  2012-10-04
## 5  2012-10-05
## 6  2012-10-06
## 7  2012-10-07
## 8  2012-10-08
## 9  2012-10-09
## 10 2012-10-10
## 11 2012-10-11
## 12 2012-10-12
## 13 2012-10-13
## 14 2012-10-14
## 15 2012-10-15
## 16 2012-10-16
## 17 2012-10-17
## 18 2012-10-18
## 19 2012-10-19
## 20 2012-10-20
## 21 2012-10-21
## 22 2012-10-22
## 23 2012-10-23
## 24 2012-10-24
## 25 2012-10-25
## 26 2012-10-26
## 27 2012-10-27
## 28 2012-10-28
## 29 2012-10-29
## 30 2012-10-30
## 31 2012-10-31

Get the day of the month from a timestamp

query_result <- dbGetQuery(con, "SELECT extract(day from timestamp '2012-08-31');")
print(query_result)
##   extract
## 1      31

Work out the number of seconds between timestamps

query_result <- dbGetQuery(con, "SELECT extract(epoch from (timestamp '2012-09-02 00:00:00' - '2012-08-31 01:00:00'));")
print(query_result)
##   extract
## 1  169200

Work out the number of days in each month of 2012

query_result <- dbGetQuery(con, "SELECT extract(month from cal.month) as month,
    (cal.month + interval '1 month') - cal.month as length
    from
    (
        select generate_series(timestamp '2012-01-01', timestamp '2012-12-01', interval '1 month') as month
    ) cal
order by month;")
print(query_result)
##    month  length
## 1      1 31 days
## 2      2 29 days
## 3      3 31 days
## 4      4 30 days
## 5      5 31 days
## 6      6 30 days
## 7      7 31 days
## 8      8 31 days
## 9      9 30 days
## 10    10 31 days
## 11    11 30 days
## 12    12 31 days

Work out the number of days remaining in the month

query_result <- dbGetQuery(con, "SELECT (date_trunc('month', ts.testts) + interval '1 month') 
        - date_trunc('day', ts.testts) as remaining
    from (select timestamp '2012-02-11 01:00:00' as testts) ts")
print(query_result)
##   remaining
## 1   19 days

Work out the end time of bookings

query_result <- dbGetQuery(con, "SELECT starttime, starttime + slots*(interval '30 minutes') endtime
    from bookings
    order by endtime desc, starttime desc
    limit 10")
print(query_result)
##              starttime             endtime
## 1  2013-01-01 15:30:00 2013-01-01 16:00:00
## 2  2012-09-30 19:30:00 2012-09-30 20:30:00
## 3  2012-09-30 19:00:00 2012-09-30 20:30:00
## 4  2012-09-30 19:30:00 2012-09-30 20:00:00
## 5  2012-09-30 19:00:00 2012-09-30 20:00:00
## 6  2012-09-30 19:00:00 2012-09-30 20:00:00
## 7  2012-09-30 18:30:00 2012-09-30 20:00:00
## 8  2012-09-30 18:30:00 2012-09-30 20:00:00
## 9  2012-09-30 19:00:00 2012-09-30 19:30:00
## 10 2012-09-30 18:30:00 2012-09-30 19:30:00

Return a count of bookings for each month

query_result <- dbGetQuery(con, "SELECT date_trunc('month', starttime) as month, count(*)
    from bookings
    group by month
    order by month")
print(query_result)
##        month count
## 1 2012-07-01   658
## 2 2012-08-01  1472
## 3 2012-09-01  1913
## 4 2013-01-01     1

Work out the utilisation percentage for each facility by month

query_result <- dbGetQuery(con, "SELECT name, month, 
    round((100*slots)/
        cast(
            25*(cast((month + interval '1 month') as date)
            - cast (month as date)) as numeric),1) as utilisation
    from  (
        select facs.name as name, date_trunc('month', starttime) as month, sum(slots) as slots
            from bookings bks
            inner join facilities facs
                on bks.facid = facs.facid
            group by facs.facid, month
    ) as inn
order by name, month")
print(query_result)
##               name      month utilisation
## 1  Badminton Court 2012-07-01        23.2
## 2  Badminton Court 2012-08-01        59.2
## 3  Badminton Court 2012-09-01        76.0
## 4   Massage Room 1 2012-07-01        34.1
## 5   Massage Room 1 2012-08-01        63.5
## 6   Massage Room 1 2012-09-01        86.4
## 7   Massage Room 2 2012-07-01         3.1
## 8   Massage Room 2 2012-08-01        10.6
## 9   Massage Room 2 2012-09-01        16.3
## 10      Pool Table 2012-07-01        15.1
## 11      Pool Table 2012-08-01        41.5
## 12      Pool Table 2012-09-01        62.8
## 13      Pool Table 2013-01-01         0.1
## 14   Snooker Table 2012-07-01        20.1
## 15   Snooker Table 2012-08-01        42.1
## 16   Snooker Table 2012-09-01        56.8
## 17    Squash Court 2012-07-01        21.2
## 18    Squash Court 2012-08-01        51.6
## 19    Squash Court 2012-09-01        72.0
## 20    Table Tennis 2012-07-01        13.4
## 21    Table Tennis 2012-08-01        39.2
## 22    Table Tennis 2012-09-01        56.3
## 23  Tennis Court 1 2012-07-01        34.8
## 24  Tennis Court 1 2012-08-01        59.2
## 25  Tennis Court 1 2012-09-01        78.8
## 26  Tennis Court 2 2012-07-01        26.7
## 27  Tennis Court 2 2012-08-01        62.3
## 28  Tennis Court 2 2012-09-01        78.4

##String Operations

###Format the names of members

exercises <- dbGetQuery(con, "select surname || ', ' || firstname as name from members")
print(exercises)             
##                        name
## 1              GUEST, GUEST
## 2             Smith, Darren
## 3              Smith, Tracy
## 4               Rownam, Tim
## 5          Joplette, Janice
## 6           Butters, Gerald
## 7             Tracy, Burton
## 8               Dare, Nancy
## 9               Boothe, Tim
## 10         Stibbons, Ponder
## 11            Owen, Charles
## 12             Jones, David
## 13              Baker, Anne
## 14          Farrell, Jemima
## 15              Smith, Jack
## 16          Bader, Florence
## 17           Baker, Timothy
## 18            Pinker, David
## 19         Genting, Matthew
## 20          Mackenzie, Anna
## 21             Coplin, Joan
## 22        Sarwin, Ramnaresh
## 23           Jones, Douglas
## 24        Rumney, Henrietta
## 25           Farrell, David
## 26 Worthington-Smyth, Henry
## 27       Purview, Millicent
## 28     Tupperware, Hyacinth
## 29               Hunt, John
## 30           Crumpet, Erica
## 31            Smith, Darren

###Find facilities by a name prefix

exercises <- dbGetQuery(con, "select * from facilities where name like 'Tennis%'")
print(exercises)  
##   facid           name membercost guestcost initialoutlay monthlymaintenance
## 1     0 Tennis Court 1        6.0        30         10000                200
## 2     1 Tennis Court 2        6.6        33         10000                200

###Perform a case-insensitive search

exercises <- dbGetQuery(con,
                        "select * from facilities where upper(name) like 'TENNIS%' ")
print(exercises)  
##   facid           name membercost guestcost initialoutlay monthlymaintenance
## 1     0 Tennis Court 1        6.0        30         10000                200
## 2     1 Tennis Court 2        6.6        33         10000                200

###Find telephone numbers with parentheses

exercises <- dbGetQuery(con,"select memid, telephone from members where telephone ~ '[()]'        ")
print(exercises)
##    memid      telephone
## 1      0 (000) 000-0000
## 2      3 (844) 693-0723
## 3      4 (833) 942-4710
## 4      5 (844) 078-4130
## 5      6 (822) 354-9973
## 6      7 (833) 776-4001
## 7      8 (811) 433-2547
## 8      9 (833) 160-3900
## 9     10 (855) 542-5251
## 10    11 (844) 536-8036
## 11    13 (855) 016-0163
## 12    14 (822) 163-3254
## 13    15 (833) 499-3527
## 14    20 (811) 972-1377
## 15    21 (822) 661-2898
## 16    22 (822) 499-2232
## 17    24 (822) 413-1470
## 18    27 (822) 989-8876
## 19    28 (855) 755-9876
## 20    29 (855) 894-3758
## 21    30 (855) 941-9786
## 22    33 (822) 665-5327
## 23    35 (899) 720-6978
## 24    36 (811) 732-4816
## 25    37 (822) 577-3541

###Pad zip codes with leading zeroes

exercises <- dbGetQuery(con,"select lpad(cast(zipcode as char(5)),5,'0') zip from members order by zip    ")
print(exercises) 
##      zip
## 1  00000
## 2  00234
## 3  00234
## 4  04321
## 5  04321
## 6  10383
## 7  11986
## 8  23423
## 9  28563
## 10 33862
## 11 34232
## 12 43532
## 13 43533
## 14 45678
## 15 52365
## 16 54333
## 17 56754
## 18 57392
## 19 58393
## 20 64577
## 21 65332
## 22 65464
## 23 66796
## 24 68666
## 25 69302
## 26 75655
## 27 78533
## 28 80743
## 29 84923
## 30 87630
## 31 97676

###Count the number of members whose surname starts with each letter of the alphabet

exercises <- dbGetQuery(con,"
select substr (mems.surname,1,1) as letter, count(*) as count 
    from members mems
    group by letter
    order by letter   ")
print(exercises) 
##    letter count
## 1       B     5
## 2       C     2
## 3       D     1
## 4       F     2
## 5       G     2
## 6       H     1
## 7       J     3
## 8       M     1
## 9       O     1
## 10      P     2
## 11      R     2
## 12      S     6
## 13      T     2
## 14      W     1

###Clean up telephone numbers

exercises <- dbGetQuery(con,"
select memid, translate(telephone, '-() ', '') as telephone
    from members
    order by memid")
print(exercises)
##    memid  telephone
## 1      0 0000000000
## 2      1 5555555555
## 3      2 5555555555
## 4      3 8446930723
## 5      4 8339424710
## 6      5 8440784130
## 7      6 8223549973
## 8      7 8337764001
## 9      8 8114332547
## 10     9 8331603900
## 11    10 8555425251
## 12    11 8445368036
## 13    12 8440765141
## 14    13 8550160163
## 15    14 8221633254
## 16    15 8334993527
## 17    16 8339410824
## 18    17 8114096734
## 19    20 8119721377
## 20    21 8226612898
## 21    22 8224992232
## 22    24 8224131470
## 23    26 8445368036
## 24    27 8229898876
## 25    28 8557559876
## 26    29 8558943758
## 27    30 8559419786
## 28    33 8226655327
## 29    35 8997206978
## 30    36 8117324816
## 31    37 8225773541

##Recursive Queries

###Find the upward recommendation chain for member ID 27

exercises <- dbGetQuery(con,"
with recursive recommenders(recommender) as (
    select recommendedby from members where memid = 27
    union all
    select mems.recommendedby
        from recommenders recs
        inner join members mems
            on mems.memid = recs.recommender
)
select recs.recommender, mems.firstname, mems.surname
    from recommenders recs
    inner join members mems
        on recs.recommender = mems.memid
order by memid desc")
print(exercises)
##   recommender firstname surname
## 1          20   Matthew Genting
## 2           5    Gerald Butters
## 3           1    Darren   Smith

###Find the downward recommendation chain for member ID 1

exercises <- dbGetQuery(con,"
with recursive recommendeds(memid) as (
    select memid from members where recommendedby = 1
    union all
    select mems.memid
        from recommendeds recs
        inner join members mems
            on mems.recommendedby = recs.memid
)
select recs.memid, mems.firstname, mems.surname
    from recommendeds recs
    inner join members mems
        on recs.memid = mems.memid
order by memid  ")
print(exercises)
##    memid firstname   surname
## 1      4    Janice  Joplette
## 2      5    Gerald   Butters
## 3      7     Nancy      Dare
## 4     10   Charles      Owen
## 5     11     David     Jones
## 6     14      Jack     Smith
## 7     20   Matthew   Genting
## 8     21      Anna Mackenzie
## 9     26   Douglas     Jones
## 10    27 Henrietta    Rumney

###Produce a CTE that can return the upward recommendation chain for any member

exercises <- dbGetQuery(con,"
with recursive recommenders(recommender, member) as (
    select recommendedby, memid
        from members
    union all
    select mems.recommendedby, recs.member
        from recommenders recs
        inner join members mems
            on mems.memid = recs.recommender
)
select recs.member member, recs.recommender, mems.firstname, mems.surname
    from recommenders recs
    inner join members mems     
        on recs.recommender = mems.memid
    where recs.member = 22 or recs.member = 12
order by recs.member asc, recs.recommender desc")
print(exercises)
##   member recommender firstname  surname
## 1     12           9    Ponder Stibbons
## 2     12           6    Burton    Tracy
## 3     22          16   Timothy    Baker
## 4     22          13    Jemima  Farrell

dbDisconnect(con)