├── .gitignore ├── IRE2016 ├── part1 │ ├── data │ │ ├── contributions.txt │ │ ├── famous_donors.txt │ │ └── scripts │ │ │ └── utility │ │ │ ├── Gemfile │ │ │ ├── filter_contribs.rb │ │ │ └── fix_names.rb │ ├── images │ │ ├── create_contributions_table.png │ │ ├── customize-menu.png │ │ ├── execute_sql.png │ │ ├── ff-menu.png │ │ ├── import.png │ │ ├── import_icon.png │ │ ├── new_database.png │ │ └── new_table.png │ ├── part1_steps.html │ └── part1_steps.textile └── part2 │ ├── data │ ├── candidates.txt │ └── contributions.txt │ ├── images │ ├── ff-menu.png │ ├── import_icon.png │ └── new_database.png │ └── part2.textile ├── NICAR2015 ├── candidates.txt ├── committees.txt ├── contributions.txt ├── images │ └── create_contributions_table.png ├── part1_steps.textile └── part2_steps.textile ├── NICAR2016 ├── part1 │ ├── data │ │ ├── .#famous_donors.txt │ │ ├── contributions.txt │ │ ├── famous_donors.txt │ │ └── scripts │ │ │ └── utility │ │ │ ├── Gemfile │ │ │ ├── filter_contribs.rb │ │ │ └── fix_names.rb │ ├── images │ │ ├── create_contributions_table.png │ │ ├── customize-menu.png │ │ ├── execute_sql.png │ │ ├── ff-menu.png │ │ ├── import.png │ │ ├── import_icon.png │ │ ├── new_database.png │ │ └── new_table.png │ ├── part1_steps.html │ └── part1_steps.textile └── part3 │ ├── data │ ├── candidates.txt │ └── contributions.txt │ ├── images │ ├── ff-menu.png │ ├── import_icon.png │ └── new_database.png │ ├── part3_steps.html │ ├── part3_steps.textile │ └── sql_scripts │ ├── create_candidates.sql │ └── create_contributions.sql ├── quick_steps.textile ├── quick_steps2.textile ├── readme.textile └── tutorial_files ├── candidates.txt ├── contributors.txt ├── contributors_candidates.sqlite ├── contributors_with_candidate_id.txt ├── images ├── BUFFET.png ├── alabama.png ├── alert_aggregate.png ├── amount_by_city_state.png ├── amount_by_state.png ├── and_or_sans_parens.png ├── and_statment.png ├── avg_amt.png ├── between.png ├── browse_and_search.png ├── browse_candidates.png ├── browse_contributors.png ├── buffet_by_id.png ├── city_state.png ├── city_state_by_amount_desc.png ├── concat_city_state.png ├── connect_db.png ├── count_ca.png ├── create_table.png ├── delete_prep.png ├── distinct_state_space.png ├── distinct_zip.png ├── execute_sql.png ├── first_and.png ├── group_by_join.png ├── group_by_join_results.png ├── group_by_left_outer_join.png ├── group_by_without_city.png ├── having_amount_greater.png ├── import.png ├── import_confirm.png ├── import_confirm_rows.png ├── import_contributors.png ├── import_icon.png ├── in.png ├── is_not_null.png ├── last_name_NULL.png ├── lewis_or_null.png ├── like_initial.png ├── max_amount.png ├── multiple_joins.png ├── new_database.png ├── new_table.png ├── not_lewis.png ├── open_sqlite.png ├── or_statement.png ├── order_by.png ├── order_by_desc.png ├── order_by_multiple.png ├── select_all.png ├── select_contributors_candidates_names.png ├── select_distinct.png ├── select_distinct_lname.png ├── select_distinct_state_normal.png ├── select_substr.png ├── self_join.png ├── sqlite_manager_empty.png ├── sqlite_manager_ff.png ├── sqlite_open_last_db.png ├── state_after_update.png ├── state_column.png ├── state_ga.png ├── subquery_max.png ├── sum_ga.png ├── sum_top_20.png ├── tool_menu.jpg ├── top_twenty_contributors.png ├── va_contribs.png ├── warning_aggregate_where.png ├── where_clause1.png ├── where_gt_2300.png ├── where_gt_2300_with_group.png ├── where_plus_having.png └── zip_LIKE.png ├── part1.textile ├── part2.textile ├── part3.textile ├── part4.textile ├── sql_keywords.rtf ├── sql_keywords.txt ├── sql_tutorial.txt ├── sqlite_install.textile ├── sqlite_keywords.rtf └── test.sqlite /.gitignore: -------------------------------------------------------------------------------- 1 | *~ 2 | thumbs.db 3 | .DS_Store -------------------------------------------------------------------------------- /IRE2016/part1/data/contributions.txt: -------------------------------------------------------------------------------- 1 | id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount 2 | |C00573519|IND|ST. 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APPRAISER|2015-04-13|500 120 | |C00575795|IND|ALFERNESS|JOY||SAN FRANCISCO|CA|941072734|NOT EMPLOYED|HUMANITARIAN|2015-05-02|1700 121 | |C00575795|IND|FLYNN|JOHN||VIENNA|VA|221816131|N/A|RETIRED|2015-05-14|500 122 | |C00575795|IND|KASSAN|ALEX||NEW YORK|NY|10013|ANCHORAGE MEDIA GROUP|PRODUCTION|2015-04-28|2700 123 | |C00575795|IND|SENATORE|BRAD||BOSTON|MA|021162671|SELF-EMPLOYED|FASHION DESIGN|2015-06-30|1000 124 | |C00575795|IND|HELLER TRIPENY|MARY||CORNING|NY|148309481|SELF-EMPLOYED||2015-05-08|2700 125 | |C00575795|IND|FINKEL|JUDITH||HOUSTON|TX|770245404|N/A|RETIRED|2015-06-01|2700 126 | |C00575795|IND|FORD|ARLENE||BALTIMORE|MD|212101526|N/A|RETIRED|2015-06-19|300 127 | |C00575795|IND|NEWMAN|MELISSA||ALEXANDRIA|VA|223142414|CENTURYLINK|ATTORNEY|2015-04-20|1000 128 | |C00575795|IND|CANTU|ALONZO||MCALLEN|TX|785042202|CANTU CONSTRUCTION & DEVELOPMENT|OWNER/DEVELOPER|2015-06-30|2700 129 | |C00575795|IND|HARRIS|MAYA||NEW YORK|NY|100366812|HILLARY FOR AMERICA|SENIOR POLICY ADVISOR|2015-06-30|2700 130 | |C00575795|IND|OTTINGER|BARBARA||PASADENA|CA|911051342|||2015-05-13|250 131 | |C00575795|IND|HAIRSTON|JAMES||NEWARK|NJ|071121708|N/A|RETIRED|2015-04-30|225 132 | |C00575795|IND|ELLISON|CARL||NEW YORK|NY|101281248|N/A|RETIRED|2015-06-23|2700 133 | |C00575795|IND|MARDIROSSIAN|SHANT||KATONAH|NY|105362919|KOHLBERG & CO., LLC|PARTNER AND COO|2015-06-28|2700 134 | |C00575795|IND|RATNER|TAWNY||CLEVELAND HEIGHTS|OH|441063138|||2015-04-26|2700 135 | |C00575795|IND|LINDEN|MARTHA||ANCHORAGE|AK|995073970|OWEN R. BELL, MD, APC|CERTIFIED NURSE MIDWIFE|2015-04-13|250 136 | |C00581876|IND|DEMLER|JOHN||NORTHFIELD|IL|600933117|RETIRED|SALES|2015-09-29|500 137 | |C00581876|IND|HUMPHREY|GORDON||CHICHESTER|NH|032586102|RETIRED|RETIRED|2015-09-29|2700 138 | |C00581876|IND|PELLEGRINO|BOB||CINCINNATI|OH|452433220|RETIRED|INVESTOR|2015-09-29|250 139 | |C00581876|IND|BOND|JAMES|D|FARGO|ND|581047058|COLLINS & COMPANY|EXECUTIVE|2015-09-29|250 140 | |C00581876|IND|PALAFOUTAS|JUDITH||ALEXANDRIA|VA|223144761|GEORGETOWN UNIVERSITY|RESEARCH DIRECTOR|2015-09-29|1000 141 | |C00581876|IND|WINSLOW|CLARK||BELVEDERE TIBURON|CA|949202426|WINSLOW CAPITAL|INVESTMENT MGMT|2015-09-29|1000 142 | |C00581876|IND|YOUNG|STEWART||WASHINGTON|DC|200024489|CORLEY CONSULTING|LOBBYIST|2015-09-29|250 143 | |C00581876|IND|BATTOCLETTI|DAVID||COLUMBUS|OH|432157513|ICE MILLER WHITEBOARD|LOBBYIST|2015-09-29|1000 144 | |C00581876|IND|COLEMAN|GEORGE||FAIRFIELD|CT|068245649|CREDIT SUISSE|BANKER|2015-09-29|500 145 | |C00581876|IND|SWALDO|THEODORE||NORTH CANTON|OH|447208807|RETIRED|RETIRED|2015-09-29|250 146 | |C00581876|IND|OSTERTHALER|ROBERT||VIENNA|VA|221821368|RETIRED|CEO|2015-09-29|900 147 | |C00581876|IND|BERGAN|PHILIP||CHARLESTON|SC|294012403|RETIRED|RETIRED|2015-09-29|500 148 | |C00581876|IND|DUBRAVCIC|STEPHEN||BRONWOOD|GA|398260083|DARKOTECH, INC|MANAGER|2015-08-07|250 149 | |C00581876|IND|BRISTOW|JULIE||CINCINNATI|OH|452083332|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-07|2700 150 | |C00581876|IND|WASSERSTROM|ALAN||COLUMBUS|OH|432072167|WASSERSTROM COMPANIES|CEO|2015-08-07|2700 151 | |C00581876|IND|SANDRIDGE|RHONDA|L|WESTFIELD CENTER|OH|442519801|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-07|2700 152 | |C00581876|IND|WILSON|RONALD||SOLON|OH|441395077|GENERAL ELECTRIC|GENERAL MANAGER|2015-08-07|500 153 | |C00581876|IND|BRISTOW|WILLIAM|S|CINCINNATI|OH|452083332|JEFF WYLER AUTOMATIVE FAMILY|PRESIDENT|2015-08-07|2700 154 | |C00581876|IND|FETTER|STEVEN||PORT TOWNSEND|WA|983683058|SELF-EMPLOYED|UTILITY CONSULTANT|2015-08-07|2700 155 | |C00581876|IND|BECHTOLD|TIMOTHY||COLUMBUS|OH|432156108|VORYS SATER|LOBBYIST|2015-08-07|500 156 | |C00581876|IND|STEWART|JENNIFER||NEW HOPE|PA|189389246|STUDENT|STUDENT|2015-08-07|250 157 | |C00581876|IND|ELSANT|MARTIN||WOODMERE|NY|115982909|NORTH SHORE HEALTH SYSTEM|RADIOLOGIST|2015-08-07|200 158 | |C00581876|IND|THOMPSON|LUCINA||DALLAS|TX|752303038|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-17|2700 159 | |C00581876|IND|EARL|SUSAN||COLUMBUS|OH|432061275|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-17|250 160 | |C00581876|IND|MAUER|VINCENT||CINCINNATI|OH|452443604|BROCK BROWN COD|LAWYER|2015-08-18|250 161 | |C00580399|IND|COHEN|LARRY||NY|NY|100143744|SEVEN BRIDGES ADVISORS|FINANCIAL ADVISOR|2015-08-13|2700 162 | |C00580399|IND|COHEN|STEVE||GREENWICH|CT|068360142|POINT72 ASSET MANAGEMENT|CHAIRMAN/CEO|2015-09-03|859 163 | |C00580399|IND|COHEN|STEVE||GREENWICH|CT|068360142|POINT72 ASSET MANAGEMENT|CHAIRMAN/CEO|2015-08-15|1840 164 | |C00580399|IND|GUREGHIAN|VAHAN|H|GLADWYNE|PA|190351507|CSMI LLC|FOUNDER AND CEO|2015-09-30|2700 165 | |C00580399|IND|PJETERNIKAJ|LILA||SADDLE RIVER|NJ|074583012|||2015-08-04|1350 166 | |C00580399|IND|CONSIDINE|THOMAS||SPRING LAKE|NJ|077622369|GRAVILAUR SOLUTIONS LLC|CONSULTANT|2015-09-29|2700 167 | |C00580399|IND|CONSORTI|JASON||MONROE|NJ|088312242|CSC|IT MANAGER|2015-07-01|500 168 | |C00580399|IND|CONSTABLE|RICHARD||SOUTH ORANGE|NJ|070791141|US ATTORNEY|LAWYER/LEGAL|2015-09-09|1000 169 | |C00580399|IND|CONTI|GINA||NEW PROVIDENCE|NJ|079742117|HOMEMAKER|HOMEMAKER|2015-09-30|2500 170 | |C00580399|IND|CONTI|KURT|G|NEW PROVIDENCE|NJ|079742117|CONTI GROUP|PRESIDENT/CEO|2015-09-30|2500 171 | |C00580399|IND|CONWAY|ARTHUR|W|MAHWAH|NJ|074302040|DIALAMERICA MARKETING INC.|PRESIDENT & CEO|2015-07-16|2700 172 | |C00580399|IND|GIBBONS|MARY|M|BASKING RIDGE|NJ|079204107|ELBERON DEVELOPMENT GROUP ST. CLAIRE'|ADMINISTRATOR/SOCIAL WORKER|2015-08-20|1000 173 | |C00580399|IND|GIBLIN|BRENDON|P|SOUTHBOROUGH|MA|017721706|BRENDON PROPERTIES|DEVELOPER REAL ESTATE|2015-09-08|500 174 | |C00580399|IND|GIDWITZ|CHRISTINA||CHICAGO|IL|606065829|HOUSEWIFE|HOUSEWIFE|2015-09-27|2700 175 | |C00580399|IND|GILFILLAN|CHRISTINE|C|MORRISTOWN|NJ|079607355|THE MCJ AMELIOR FOUNDATION|PRESIDENT|2015-07-02|2700 176 | |C00580399|IND|GILFILLAN|MICHAEL|T|MADISON|NJ|079402751|BRICK CITY SPORTS CAPITAL LLC|INVESTMENTS|2015-07-02|2700 177 | |C00580399|IND|GILMAN|J|PAUL|MENDHAM|NJ|079451205|COVANTA ENERGY|SENIOR VICE PRESIDENT AND CHIEF SUSTAI|2015-07-31|2700 178 | |C00580399|IND|GILMAN|THOMAS||SCOTTSDALE|AZ|852623818|DAIMLERCHRYSLER FINANCIAL SERVICES AME|CEO|2015-07-27|2700 179 | |C00580399|IND|GILMORE|THOMAS|P|ANDOVER|NJ|078214127|GILMORE ELECTRIC INC.|ELECTRICIAN|2015-07-24|2700 180 | |C00580399|IND|CONNORS|CHARLES|T|WESTFIELD|NJ|070902902|H&M INTERNATIONAL TRANSPORTANTION|PRESIDENT & CHIEF OPERATING OFFICER|2015-07-27|2700 181 | |C00580399|IND|CONNORS|CHARLES|T|WESTFIELD|NJ|070902902|H&M INTERNATIONAL TRANSPORTANTION|PRESIDENT & CHIEF OPERATING OFFICER|2015-07-27|2700 182 | |C00580399|IND|CONSIGLIO|THOMAS||RAMSEY|NJ|074461346|RESOURCE REALTY|SALES|2015-08-03|2700 183 | |C00580399|IND|COLE|BRIAN||OAKLAND|NJ|074362350|COLE MEDICAL INC|VICE PRESIDENT|2015-07-01|2700 184 | |C00579458|IND|VILLARI|DAVID|J|FORT LAUDERDALE|FL|333061906|GIG INS GROUP|BUSINESS OWNER|2015-11-30|250 185 | |C00579458|IND|VIEIRA|SEAN||FAIRFAX|VA|220336226|SJV LLC|PRESIDENT|2015-11-29|1000 186 | |C00579458|IND|VIJAYANAGAR|RAGHAVENDRA||TAMPA|FL|336063573|R VIJAYANAGAR M.D. P.A.|SURGEON|2015-11-20|2000 187 | |C00579458|IND|VILLARREAL|AARON||MCALLEN|TX|785044405|||2015-10-30|2700 188 | |C00579458|IND|VILLAMIZAR|JOHN||ROYAL PALM BEACH|FL|334116107|ADVANCED VISION|OPTICIAN|2015-12-29|375 189 | |C00579458|IND|WARE|BLAKE|BEAKLEY|AMARILLO|TX|791092331|HOMEMAKER|HOMEMAKER|2015-11-19|2000 190 | |C00579458|IND|WARD|JIM||PHOENIX|AZ|850162363|THE PHOENIX SYMPHONY|CEO|2015-10-14|1000 191 | |C00579458|IND|WARD|JOHN|M|DALLAS|TX|752047849|RETIRED|RETIRED|2015-11-09|1000 192 | |C00579458|IND|WARE|PATRICK|O'NEILL|AMARILLO|TX|791092331|STUDENT|STUDENT|2015-11-19|4000 193 | |C00579458|IND|WARE|PATRICK|O'NEILL|AMARILLO|TX|791092331|STUDENT|STUDENT|2015-11-19|-2000 194 | |C00579458|IND|WAREHAM|KRISTINA||LAKE HIAWATHA|NJ|070342209|PRECISION CASTPARTS CORP|ENGINEER|2015-12-31|1000 195 | |C00579458|IND|ANDREWS|BARRY|G|DALLAS|TX|752052805|ANDREWS DISTRIBUTING|EXECUTIVE|2015-12-15|-2700 196 | |C00579458|IND|ANDREWS|BARRY|G|DALLAS|TX|752052805|ANDREWS DISTRIBUTING|EXECUTIVE|2015-12-15|2700 197 | |C00579458|IND|MANNING|RALPH|F|MARS|PA|160462633|TUCKER ARENSBERG PC|ATTORNEY|2015-11-10|1000 198 | |C00579458|IND|MANNING|RAMON||HOUSTON|TX|770211611|PARTNERS ENERGY|INVESTOR|2015-10-28|250 199 | |C00579458|IND|MANNING|SAM|BLAGDEN|DALLAS|TX|752195220|SELF-EMPLOYED|INVESTMENTS|2015-11-10|500 200 | |C00579458|IND|MANRIQUE|FRANCISCO||DORAL|FL|331783505|||2015-10-20|500 201 | -------------------------------------------------------------------------------- /IRE2016/part1/data/famous_donors.txt: -------------------------------------------------------------------------------- 1 | C00575795|A|Q2|P|201509039001608655|15|IND|BUFFETT, WARREN|OMAHA|NE|681313311|BERKSHIRE HATHAWAY|CHAIRMAN|04152015|2700||C51337|1024052|||4090920151249626621 2 | C00577130|N|Q2|P|201507159000153383|15E|IND|RUFFALO, MARK|NEW YORK|NY|100031526|SELF-EMPLOYED|ACTOR|06022015|250|C00401224|VPF7BDV8BV8|1015044||* EARMARKED CONTRIBUTION: SEE BELOW|4071620151247172334 3 | C00458844|A|Q2|G|201510159003030910|22Y|IND|ADELSON, SHELDON|LAS VEGAS|NV|89145|||06122015|2600||GENREF0042|1029436|||4102120151256390199 4 | -------------------------------------------------------------------------------- /IRE2016/part1/data/scripts/utility/Gemfile: -------------------------------------------------------------------------------- 1 | source 'https://rubygems.org' 2 | 3 | gem 'people' -------------------------------------------------------------------------------- /IRE2016/part1/data/scripts/utility/filter_contribs.rb: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env ruby 2 | 3 | require 'csv' 4 | 5 | committee_ids = [] 6 | CSV.foreach('candidates.txt', :headers => true, :col_sep => '|', :quote_char => "\x00") do |row| 7 | committee_ids << row[0] 8 | end 9 | 10 | skips = ['C00577981', 'C00578757', 'C00577312', 'C00575449', 'C00578658'] 11 | used = committee_ids - skips 12 | 13 | included = {} 14 | used.each {|id| included[id] = 0} 15 | 16 | CSV.foreach('itcont.txt', :col_sep => '|', :quote_char => "\x00") do |row| 17 | #skip if not a contrib 18 | next if row[5] != '15' 19 | committee_id = row[0] 20 | if used.include? committee_id 21 | if included[committee_id] < 25 22 | puts row.to_csv(:col_sep => '|') 23 | included[committee_id] += 1 24 | else 25 | used.delete committee_id 26 | break if used.length == 0 27 | end 28 | end 29 | end 30 | 31 | 32 | exit 33 | ##### 34 | ## Check the number of contributors per candidate 35 | ##### 36 | committee_counts = {} 37 | committee_ids.each do |id| 38 | committee_counts[id] = 0 39 | end 40 | 41 | 42 | committee_ids.each do |id| 43 | CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| 44 | committee_counts[id] += 1 if row[0] == id 45 | end 46 | end 47 | 48 | puts committee_counts 49 | -------------------------------------------------------------------------------- /IRE2016/part1/data/scripts/utility/fix_names.rb: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env ruby 2 | 3 | require 'csv' 4 | require 'people' 5 | 6 | np = People::NameParser.new(:case_mode => 'upper') 7 | CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| 8 | no_title = row[7].gsub(/ (MR.|MRS.|DR.|MS.)$/, '') 9 | name = np.parse(no_title) 10 | next if name[:last].empty? 11 | date_parts = row[13].match(/(\d\d)(\d\d)(\d\d\d\d)/) 12 | date_string = "#{date_parts[3]}-#{date_parts[1]}-#{date_parts[2]}" 13 | puts [name[:last], name[:first], name[:middle], row[8], row[9], row[10], row[11], row[12], date_string, row[14], row[0]].to_csv(:col_sep => '|', :quote_char => "\x00") 14 | end 15 | -------------------------------------------------------------------------------- /IRE2016/part1/images/create_contributions_table.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/create_contributions_table.png -------------------------------------------------------------------------------- /IRE2016/part1/images/customize-menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/customize-menu.png -------------------------------------------------------------------------------- /IRE2016/part1/images/execute_sql.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/execute_sql.png -------------------------------------------------------------------------------- /IRE2016/part1/images/ff-menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/ff-menu.png -------------------------------------------------------------------------------- /IRE2016/part1/images/import.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/import.png -------------------------------------------------------------------------------- /IRE2016/part1/images/import_icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/import_icon.png -------------------------------------------------------------------------------- /IRE2016/part1/images/new_database.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/new_database.png -------------------------------------------------------------------------------- /IRE2016/part1/images/new_table.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/IRE2016/part1/images/new_table.png -------------------------------------------------------------------------------- /IRE2016/part1/part1_steps.html: -------------------------------------------------------------------------------- 1 |

A Quick Introduction to SQL Using SQLite (Just the Steps)

2 |

1. Open Firefox and click on the menu icon:

3 |

4 |

Use the "customize" option to find the SQLite Manager Plugin:

5 |

6 |

You can add it to your menu by dragging or right-click to add it to the toolbar.

7 |
8 |

2. Create a new database called campaign_finance:

9 |

10 |
11 |

3. Create a new table called contributions () and give it the following field definitions:

12 |

13 |
14 |

Inserting data manually (Don't worry, you won't do this often.)

15 |

4. Insert a row of data.

16 |

Use the "Execute SQL" tab (it's in the second menu row, under the icons)
17 |

18 |

And paste or type the following statement:

19 | 
INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','BUFFETT','WARREN','','OMAHA','Nebraska','68131','BERKSHIRE HATHAWAY','CHAIRMAN','2015-04-15',2700);
20 |
21 |

5. Insert three more rows (one at a time or all at once)

22 | 
INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00577130','IND','RUFFALO','MARK','','NEW YORK','NY','|100031526','SELF-EMPLOYED','ACTOR','2015-06-02',250);
 23 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00458844','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89145','','','2015-06-12',2600);
 24 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00579458','IND','MANNING','PEYTON MR.','','CLEVELAND','OH','441141717','DENVER BRONCOS','PROFESSIONAL ATHLETE','2015-08-04',2700);
 25 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','COPPOLA','SOFIA','','NEW YORK','NY','100130778','SELF-EMPLOYED','DIRECTOR/WRITER/ACTOR','2016-01-22',2700);
26 |
27 |

The SELECT statement

28 |

6. Select everything from the contributions table:

29 | 
SELECT * FROM contributions;
30 |

Note the wildcard * character.

31 |
32 |

7. Choose some fields to select:

33 | 
SELECT city, state FROM contributions;
34 |

Note the duplicates; why are they there?

35 |
36 |

8. Get a distinct set of cities and states:

37 | 
SELECT DISTINCT city, state FROM contributions;
38 |
39 |

THE WHERE CLAUSE

40 |

9. Add some conditions to the SELECT:

41 | 
SELECT * from contributions WHERE state='NY';
42 |

Now try playing around with some of the operators:

43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 |
operatordescription
=Equal
<>Not equal*
>Greater than
<Less than
>=Greater than or equal
<=Less than or equal
75 |

Here are some examples:

76 | 
SELECT * from contributions WHERE amount > 500;
77 | 
SELECT * from contributions WHERE amount >= 500;
78 | 
SELECT last_name from contributions WHERE state <> 'NY';
79 |
80 |

10. Combine conditions with AND or OR:

81 |

Find only the big spenders from New York

82 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
83 |

Find any big spender and anyone from New York

84 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
85 |
86 |

11. But watch out for operator precedence

87 |

Looking for big spenders from Las Vegas and New York City

88 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
89 |

How did Mark Ruffalo sneak in there?

90 |

Try that again:

91 | 
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
92 |
93 |

Pick One: Using BETWEEN and IN (NOT IN)

94 |

11. Find all contributions between $500 and $1000

95 | 
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
96 |

(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.)

97 |
98 |

12. Find all contributors from a list of states:

99 | 
SELECT * from contributions WHERE state IN ('OH', 'NY');
100 |

(Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY'
101 | But the IN syntax makes things much clearer, and it's easier to write.)

102 |
103 |

13. Use NOT IN to find results where a value is not included in the given set:

104 | 
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
105 |
106 |

Sorting the results

107 |

14. Use ORDER BY to sort:

108 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
109 |

And reverse the order:

110 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
111 |

And order by more than one column:

112 | 
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
113 |
114 |

Limiting the number of rows returned

115 |

15. Return only the top two contributions:

116 | 
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
117 |
118 |

Changing values with UPDATE

119 |

16. Check the list of state names again:

120 | 
SELECT state FROM contributions;
121 |

Let's correct Nebraska:

122 | 
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
123 |

And check again:

124 | 
SELECT DISTINCT state FROM contributions;
125 |
126 |

DELETING rows

127 |

17. Let's get rid of a row:

128 | 
SELECT * FROM contributions WHERE id = 1;
129 | 
DELETE FROM contributions WHERE id = 1;
130 |
131 |

Importing data from a flat file

132 |

18. Import a delimited file:
133 | a. Go to https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt (Or grab it from the conference computer.)

134 |

b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place).

135 |

c. Click the SQLite Manager Import Wizard icon ().

136 | 143 | -------------------------------------------------------------------------------- /IRE2016/part1/part1_steps.textile: -------------------------------------------------------------------------------- 1 | h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 2 | 3 | 1. Open Firefox and click on the menu icon: 4 | 5 | !./images/ff-menu.png! 6 | 7 | Use the "customize" option to find the SQLite Manager Plugin: 8 | 9 | !./images/customize-menu.png! 10 | 11 | You can add it to your menu by dragging or right-click to add it to the toolbar. 12 | 13 |
14 | 15 | 2. Create a new database called campaign_finance: 16 | 17 | !./images/new_database.png! 18 | 19 |
20 | 21 | 3. Create a new table called contributions (!./images/new_table.png!) and give it the following field definitions: 22 | 23 | !./images/create_contributions_table.png! 24 | 25 |
26 | 27 | h3. Inserting data manually (Don't worry, you won't do this often.) 28 | 29 | 4. Insert a row of data. 30 | 31 | Use the "Execute SQL" tab (it's in the second menu row, under the icons) 32 | !./images/execute_sql.png! 33 | 34 | And paste or type the following statement: 35 | 

 36 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','BUFFETT','WARREN','','OMAHA','Nebraska','68131','BERKSHIRE HATHAWAY','CHAIRMAN','2015-04-15',2700);
 37 |
38 | 39 |
40 | 41 | 5. Insert three more rows (one at a time or all at once) 42 | 

 43 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00577130','IND','RUFFALO','MARK','','NEW YORK','NY','|100031526','SELF-EMPLOYED','ACTOR','2015-06-02',250);
 44 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00458844','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89145','','','2015-06-12',2600);
 45 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00579458','IND','MANNING','PEYTON MR.','','CLEVELAND','OH','441141717','DENVER BRONCOS','PROFESSIONAL ATHLETE','2015-08-04',2700);
 46 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','COPPOLA','SOFIA','','NEW YORK','NY','100130778','SELF-EMPLOYED','DIRECTOR/WRITER/ACTOR','2016-01-22',2700);
 47 |
48 | 49 | 50 |
51 | 52 | h3. The SELECT statement 53 | 54 | 6. Select everything from the contributions table: 55 | 56 | 
SELECT * FROM contributions;
57 | 58 | Note the wildcard * character. 59 | 60 |
61 | 62 | 7. Choose some fields to select: 63 | 64 | 
SELECT city, state FROM contributions;
65 | 66 | Note the duplicates; why are they there? 67 | 68 |
69 | 70 | 8. Get a distinct set of cities and states: 71 | 72 | 
SELECT DISTINCT city, state FROM contributions;
73 | 74 |
75 | 76 | h3. THE WHERE CLAUSE 77 | 78 | 9. Add some conditions to the SELECT: 79 | 
SELECT * from contributions WHERE state='NY';
80 | Now try playing around with some of the operators: 81 | 82 | |*operator*|*description*| 83 | |=|Equal| 84 | |<>|Not equal*| 85 | |>|Greater than| 86 | |<|Less than| 87 | |>=|Greater than or equal| 88 | |<=|Less than or equal| 89 | 90 | Here are some examples: 91 | 
SELECT * from contributions WHERE amount > 500;
92 | 
SELECT * from contributions WHERE amount >= 500;
93 | 
SELECT last_name from contributions WHERE state <> 'NY';
94 | 95 |
96 | 97 | 10. Combine conditions with AND or OR: 98 | 99 | Find only the big spenders from New York 100 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
101 | 102 | Find any big spender and anyone from New York 103 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
104 | 105 |
106 | 107 | 11. But watch out for operator precedence 108 | 109 | Looking for big spenders from Las Vegas and New York City 110 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
111 | 112 | How did Mark Ruffalo sneak in there? 113 | 114 | Try that again: 115 | 
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
116 | 117 |
118 | 119 | h3. Pick One: Using BETWEEN and IN (NOT IN) 120 | 121 | 11. Find all contributions between $500 and $1000 122 | 
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
123 | (Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) 124 | 125 |
126 | 127 | 12. Find all contributors from a list of states: 128 | 
SELECT * from contributions WHERE state IN ('OH', 'NY');
129 | 130 | (Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY' 131 | But the IN syntax makes things much clearer, and it's easier to write.) 132 | 133 |
134 | 135 | 13. Use NOT IN to find results where a value is not included in the given set: 136 | 
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
137 | 138 | 139 |
140 | 141 | 142 | h3. Sorting the results 143 | 144 | 14. Use ORDER BY to sort: 145 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
146 | And reverse the order: 147 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
148 | And order by more than one column: 149 | 
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
150 | 151 |
152 | 153 | h3. Limiting the number of rows returned 154 | 155 | 15. Return only the top two contributions: 156 | 
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
157 | 158 |
159 | 160 | h3. Changing values with UPDATE 161 | 162 | 16. Check the list of state names again: 163 | 
SELECT state FROM contributions;
164 | 165 | Let's correct Nebraska: 166 | 
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
167 | 168 | And check again: 169 | 
SELECT DISTINCT state FROM contributions;
170 | 171 | 172 |
173 | 174 | h3. DELETING rows 175 | 176 | 17. Let's get rid of a row: 177 | 
SELECT * FROM contributions WHERE id = 1;
178 | 
DELETE FROM contributions WHERE id = 1;
179 | 180 | 181 |
182 | 183 | 184 | 185 | h3. Importing data from a flat file 186 | 187 | 18. Import a delimited file: 188 | a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt] (Or grab it from the conference computer.) 189 | 190 | b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). 191 | 192 | c. Click the SQLite Manager Import Wizard icon (!./images/import_icon.png!). 193 | 194 | * Using the **Select File** button, browse for the **contributions.txt** file you just saved. 195 | * Check the "First row contains column names" check box. 196 | * Select **Pipe(|)** for "Fields separated by" 197 | * For everything else, you can keep the default selections. 198 | * You should have 204 rows of data. 199 | 200 | -------------------------------------------------------------------------------- /IRE2016/part2/data/candidates.txt: -------------------------------------------------------------------------------- 1 | id|committee_id|last_name|first_name|middle_name|party 2 | 1|C00580100|TRUMP|DONALD|J|REP 3 | 2|C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP 4 | 3|C00458844|RUBIO|MARCO||REP 5 | 4|C00581876|KASICH|JOHN|R|REP 6 | 5|C00573519|CARSON|BENJAMIN|S|REP 7 | 6|C00577130|SANDERS|BERNARD||DEM 8 | 7|C00575795|CLINTON|HILLARY|RODHAM|DEM 9 | 8|C00578658|O'MALLEY|MARTIN|JOSEPH|DEM 10 | 9|C00579458|BUSH|JEB||REP 11 | 10|C00580399|CHRISTIE|CHRISTOPHER|J|REP 12 | 11|C00575449|PAUL|RAND||REP 13 | 12|C00577312|FIORINA|CARLY||REP 14 | 13|C00578757|GRAHAM|LINDSEY|O|REP 15 | 14|C00577981|HUCKABEE|MIKE||REP 16 | -------------------------------------------------------------------------------- /IRE2016/part2/data/contributions.txt: -------------------------------------------------------------------------------- 1 | id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount|candidate_id 2 | |C00575795|IND|BUFFETT|WARREN||OMAHA|Nebraska|68131|BERKSHIRE HATHAWAY|CHAIRMAN|2015-04-15|2700|7 3 | |C00577130|IND|RUFFALO|MARK||NEW YORK|NY|100031526|SELF-EMPLOYED|ACTOR|2015-06-02|250|6 4 | |C00458844|IND|ADELSON|SHELDON||LAS VEGAS|NV|89145|||2015-06-12|2600|3 5 | |C00579458|IND|MANNING|PEYTON MR.||CLEVELAND|OH|441141717|DENVER BRONCOS|PROFESSIONAL ATHLETE|2015-08-04|2700|9 6 | |C00575795|IND|COPPOLA|SOFIA||NEW YORK|NY|100130778|SELF-EMPLOYED|DIRECTOR/WRITER/ACTOR|2016-01-22|2700|7 7 | |C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-14|200|5 8 | |C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-28|250|5 9 | |C00573519|IND|STANFIELD|AMY||FRANKLIN|TN|370675672|SELF-EMPLOYED|ARTIST|2015-03-31|250|5 10 | |C00573519|IND|STARK|CHERYL|ANN|ENGLEWOOD|FL|342245434|PATHWAY LIGHTING PRODUCTS INC.|MARKETING RESEARCH|2015-03-11|500|5 11 | |C00573519|IND|STARK|FRED||ENGLEWOOD|FL|342245434|PATHWAY LIGHTING PRODUCTS INC.|CEO|2015-03-19|1000|5 12 | |C00573519|IND|STEBBENS|KIM||EVERETT|WA|982011557|WAYA GROUP INC|EXECUTIVE|2015-03-14|250|5 13 | |C00573519|IND|STEGEMANN|BELINDA||CLINT|TX|798365308|RETIRED|RETIRED|2015-03-28|500|5 14 | |C00573519|IND|STEIGERWALD|DONALD||ESCONDIDO|CA|920263942|STEIGERWALD DOUGHERTY INC|BUILDING CONTRACTOR|2015-03-04|250|5 15 | |C00573519|IND|STEINKAMP|JEFFREY||ROCHESTER|VT|057670098|RETIRED|RETIRED|2015-03-31|1500|5 16 | |C00573519|IND|STENGEL|LINDA||SIOUX FALLS|SD|571058110|RETIRED|RETIRED|2015-03-30|250|5 17 | |C00573519|IND|STENSETHER|BARBARA|L|TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500|5 18 | |C00573519|IND|STENSETHER|JOHN||TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500|5 19 | |C00573519|IND|STEWARDSON|MELINDA||BUFFALO GAP|TX|795082140|WYLIE INDEPENDENT SCHOOL DISTRICT|READING TEACHER|2015-03-31|250|5 20 | |C00573519|IND|STOCKSTILL|JAMES||PICAYUNE|MS|394665552|RETIRED|RETIRED|2015-03-04|250|5 21 | |C00573519|IND|STODDARD|CHERYL||FREDERICKSBURG|VA|224052180|ANTHEM|REGIONAL MANAGER|2015-03-31|250|5 22 | |C00573519|IND|STOKER|RICHARD||MIAMI BEACH|FL|331417401|RETIRED|RETIRED|2015-03-24|300|5 23 | |C00573519|IND|THORNTON|JOE||DALLAS|TX|752254816|HFF|REAL ESTATE CAPITAL MARKETS|2015-03-05|2700|5 24 | |C00573519|IND|TIMBLE|JAMES||FRANKLIN GROVE|IL|610319555|RETIRED|RETIRED|2015-03-28|250|5 25 | |C00573519|IND|TOBB|RONALD||BROOKEVILLE|MD|208333208|BOWLES FLUIDICS|OPERATIONS VP|2015-03-03|250|5 26 | |C00573519|IND|TOLLAKSON|DARLENE|M|SOUTH WAYNE|WI|535879761|||2015-03-30|400|5 27 | |C00573519|IND|TOMANN|DON||BUFFALO|MN|553134253|UMC INC.|BUSINESS OWNER/PROFESSIONAL|2015-03-03|250|5 28 | |C00573519|IND|TORRENCE|DARREN||PENSACOLA|FL|325075234|SELF-EMPLOYED|OWNER|2015-03-18|250|5 29 | |C00573519|IND|TORRE|THOMAS||CUMMING|GA|300411205|AT&T|ENGINEER|2015-03-23|250|5 30 | |C00573519|IND|TRAN|VINCENT||CRESCENT CITY|CA|955319576|STATE OF CALIFORNIA|DENTIST|2015-03-04|1000|5 31 | |C00574624|IND|CHOATE|CHARLIE||BURLESON|TX|760282217|SELF EMPLOYED|SMALL BUSINESS OWNER|2015-03-31|250|2 32 | |C00574624|IND|CHOR|MARLENE||ANACONDA|MT|597112904|||2015-03-31|500|2 33 | |C00574624|IND|MACDONALD|LENNA|R|DANIEL ISLAND|SC|294927523|BVCC LLC|ENTREPRENEUR|2015-03-25|1000|2 34 | |C00574624|IND|MACH|STEVEN|P|HOUSTON|TX|772190630|MACH INDUSTRIAL GROUP LP|VP FINANCE|2015-03-23|2700|2 35 | |C00574624|IND|MACKEY|WILLIAM||MIAMI|FL|331566023|||2015-03-31|500|2 36 | |C00574624|IND|MADDOX|MATTHEW|MATT|HOUSTON|TX|770083017|BROTHERS ALVARADO; P.C.|ATTORNEY|2015-03-30|1000|2 37 | |C00574624|IND|MADSON|RYAN||EL DORADO HILLS|CA|957629665|NA|N/A|2015-03-28|1000|2 38 | |C00574624|IND|MAGGLOS|LINDA||MALIBU|CA|902653746|HOMEMAKER|HOMEMAKER|2015-03-24|1000|2 39 | |C00574624|IND|MAGNESS|SOPHIE||HOUSTON|TX|770571814|MAGNESS ORTHODONTICS|DIRECTOR OF MARKETING|2015-03-23|500|2 40 | |C00574624|IND|MALONEY|THOMAS||HAMPTON BAYS|NY|119462826|SHINNECOCK HARDWARE|OWNER|2015-03-25|1000|2 41 | |C00574624|IND|MANN|JODI||STUDIO CITY|CA|916043855|GJ SULLIVAN|BROKER|2015-03-24|1000|2 42 | |C00574624|IND|MANN|KEN||STUDIO CITY|CA|916043855|COVERX|INSURANCE|2015-03-24|1000|2 43 | |C00574624|IND|MANN|RICHARD|F|PONTE VEDRA BEACH|FL|320824609|RETIRED|RETIRED|2015-03-31|200|2 44 | |C00574624|IND|MANNING|JOHN|OWEN|AUSTIN|TX|787315848|GREAT POINT CAPITAL|TRADER|2015-03-31|5400|2 45 | |C00574624|IND|HARDY|LONNIE||SHREVEPORT|LA|711192503|SELF EMPLOYED|CPA|2015-03-23|1000|2 46 | |C00574624|IND|MARACCHINI|CHRIS||CEDAR HILL|TX|751041003|AMERICAN AIRLINES|PILOT|2015-03-24|1000|2 47 | |C00574624|IND|MARDIGIAN|DAVID||BLOOMFIELD HILLS|MI|483040934|MCM MANAGEMENT CORPORATION|CEO|2015-03-31|5400|2 48 | |C00574624|IND|HARDAWAY|MIKE||MCKINNEY|TX|750706221|RAYTHEON COMPANY|TECH WRITER/EDITOR|2015-03-24|500|2 49 | |C00574624|IND|HARIPRASAD|SATISH||EAST STROUDSBURG|PA|183027984|SELF EMPLOYED|PRINTER|2015-03-24|500|2 50 | |C00574624|IND|HARPER|JIM||BIRMINGHAM|MI|480091311|RETIRED|RETIRED|2015-03-24|1000|2 51 | |C00574624|IND|HARRELL|KEITH||MCKINNEY|TX|750717390|BUSINESS OWNER|COUNSELOR/RESTAURANT OWNER|2015-03-25|5400|2 52 | |C00574624|IND|JANES|RALPH||MARBLE FALLS|TX|786544071|RE JANES GRAVEL COMPNAY|CONSULTANT|2015-03-31|5400|2 53 | |C00574624|IND|JANES|RALPH||MARBLE FALLS|TX|786544071|RE JANES GRAVEL COMPNAY|CONSULTANT|2015-03-31|-2700|2 54 | |C00574624|IND|JANES|RALPH||MARBLE FALLS|TX|786544071|RE JANES GRAVEL COMPNAY|CONSULTANT|2015-03-31|2700|2 55 | |C00458844|IND|IRIZARRY|STEVEN||WASHINGTON|DC|200041008|ROBERTI+WHITE LLC|CONSULTANT|2014-12-22|2500|3 56 | |C00458844|IND|SIMMONS|ANNETTE|C|DALLAS|TX|752402620|HOMEMAKER|HOMEMAKER|2014-12-17|5000|3 57 | |C00458844|IND|MANTENA|RAMA|R|JUPITER|FL|334774202|P4 HEALTHCARE|CEO|2014-11-19|5000|3 58 | |C00458844|IND|PATTEN|CAROLYN|A|PORT SAINT LUCIE|FL|349863252|UNIVERSITY OF FLORIDA||2015-02-17|250|3 59 | |C00458844|IND|PETHOKOUKIS|JOHN|MR|WESTERN SPRINGS|IL|605582013|MORGAN STANLEY|FINANCIAL ADVISOR|2015-02-25|1000|3 60 | |C00458844|IND|PETHOKOUKIS|SARA||WESTERN SPRINGS|IL|605582013|HOMEMAKER|HOMEMAKER|2015-02-25|1000|3 61 | |C00458844|IND|PLOMARITIS|TITUS|MR|PELHAM|NH|030765003|RETIRED|RETIRED|2015-03-31|1000|3 62 | |C00458844|IND|REPLOGLE|STEPHEN||WASHINGTON|DC|200172912|COVE STRATEGIES|CONSULTANT|2015-02-09|5000|3 63 | |C00458844|IND|REPLOGLE|STEPHEN||WASHINGTON|DC|200172912|COVE STRATEGIES|CONSULTANT|2015-02-09|-2300|3 64 | |C00458844|IND|REPLOGLE|STEPHEN||WASHINGTON|DC|200172912|COVE STRATEGIES|CONSULTANT|2015-02-09|2300|3 65 | |C00577130|IND|HOLZHAUER|FRED||NOVATO|CA|949471909|METHOD PRODUCTS, PBC|CHEMIST|2015-06-30|250|6 66 | |C00577130|IND|HONG|PENELOPE||ROCKPORT|TX|783823610|NOT EMPLOYED|RETIRED|2015-06-30|250|6 67 | |C00577130|IND|WYATT|EDNA||SANTA FE|NM|875012242|SELF-EMPLOYED|CARPENTER|2015-05-20|300|6 68 | |C00577130|IND|YOUNKIN|RANDY||YOUNGSTOWN|OH|445052563|||2015-05-20|500|6 69 | |C00577130|IND|SHEWFELT|MATTHEW||LOS ANGELES|CA|900263322|WILSHIRE ESCROW COMPANY|ESCROW OFFICER|2015-06-18|250|6 70 | |C00577130|IND|ROTHNER|GLENN||PASADENA|CA|911052720|ROTHNER, SEGALL & GREENSTONE|ATTORNEY|2015-06-18|250|6 71 | |C00577130|IND|SMITH|PRENTISS|CHARLES|BRATTLEBORO|VT|053014232|PRENTISS SMITH & CO,. INC.|INVESTMENT MANAGER|2015-06-18|400|6 72 | |C00577130|IND|SMITH|PRENTISS|CHARLES|BRATTLEBORO|VT|053014232|PRENTISS SMITH & CO,. INC.|INVESTMENT MANAGER|2015-06-18|400|6 73 | |C00577130|IND|STRATTON|RICHARD|G|BUFFALO|NY|142163417|NICHOLS SCHOOL|TEACHER|2015-06-18|300|6 74 | |C00577130|IND|STRATTON|RICHARD|G|BUFFALO|NY|142163417|NICHOLS SCHOOL|TEACHER|2015-06-18|300|6 75 | |C00577130|IND|KAST|RICHARD||BURLINGTON|VT|054082544|SELF-EMPLOYED|PHYSICIAN|2015-06-18|212|6 76 | |C00577130|IND|KUSNERZ|PEGGY|ANN|ANN ARBOR|MI|481044303|||2015-06-18|200|6 77 | |C00577130|IND|KUSNERZ|PEGGY|ANN|ANN ARBOR|MI|481044303|||2015-06-18|200|6 78 | |C00577130|IND|MARSH|PAUL|G|HAMILTON|OH|450139476|||2015-06-18|250|6 79 | |C00577130|IND|MARSH|PAUL|G|HAMILTON|OH|450139476|||2015-06-18|250|6 80 | |C00577130|IND|MCDANIEL|MICHAEL|J|SAN DIEGO|CA|921082806|SELF|MD|2015-06-18|200|6 81 | |C00577130|IND|ALLEN|BRUCE||LAUGHLIN|NV|890280673|SELF-EMPLOYED|CLINICAL PSYCHOLOGIST|2015-06-02|2700|6 82 | |C00577130|IND|ALLEN|YOKO||LAUGHLIN|NV|890280673|N/A|HOMEMAKER|2015-06-02|2700|6 83 | |C00577130|IND|ROBERTSON|JOAN|R|MILWAUKEE|WI|532174340|||2015-06-10|1000|6 84 | |C00577130|IND|MURRAY|BRIAN||LOS ANGELES|CA|900344551|SHEPPARD MULLIN LLP|ATTORNEY|2015-06-18|250|6 85 | |C00577130|IND|OWENS|BONNIE|L|NAPERVILLE|IL|605648987|RETIRED|FINANCE DIRECTOR, LOCAL GOVERNMENT|2015-06-18|500|6 86 | |C00577130|IND|OWENS|BONNIE|L|NAPERVILLE|IL|605648987|RETIRED|FINANCE DIRECTOR, LOCAL GOVERNMENT|2015-06-18|500|6 87 | |C00577130|IND|PAUL|JANE||LOS ANGELES|CA|900662840|ANTIOCH UNIVERSITY|TEACHER|2015-06-18|250|6 88 | |C00577130|IND|PRUITT|GRETA||LA CRESCENTA|CA|912141541|NOT EMPLOYED|RETIRED|2015-06-18|250|6 89 | |C00577130|IND|PULICE|MICHAEL||LOS ANGELES|CA|900683128|HOUSEBLEND MEDIA|PRODUCTION ASSISTANT|2015-06-18|250|6 90 | |C00580100|IND|ANDALORO|JIM||MARBLEHEAD|MA|01945|METAL TRONICS, INC.|OWNER|2015-06-30|500|1 91 | |C00580100|IND|ANTON|FRANCINE||SPRING LAKE|MI|49456|RETIRED|RETIRED|2015-06-24|250|1 92 | |C00580100|IND|ATKINS|LORI||SAN ANTONIO|TX|78231|TEXAS SINUS CENTER|PHYSICIAN ASSISTANT|2015-06-20|250|1 93 | |C00580100|IND|BINGHAM|RON||JACKSON|TN|38305|EMG CLINIS OF TENNESSEE|MD|2015-06-19|500|1 94 | |C00580100|IND|BLUE|JAN||HUNTINGTON BEACH|CA|92648|HOAG HEALTH|SENIOR VICE PRESIDENT|2015-06-17|500|1 95 | |C00580100|IND|BRIDGES|PAMELA||LAUREL|MS|39443|INTERNATIONAL FIRE|PRESIDENT|2015-06-26|250|1 96 | |C00580100|IND|BYRNES|JECOAH||COLORADO SPRINGS|CO|80920|EMBREE CAPITAL MARKETS GROUP, INC.|SALES|2015-06-16|250|1 97 | |C00580100|IND|CANNON|WANDA||BEVERLY HILLS|CA|90210|RETIRED|RETIRED|2015-06-16|250|1 98 | |C00580100|IND|CARRERAS|DENNIS||COCONUT CREEK|FL|33073|BERSTONE PAYMENT SYSTEMS, LLC|CEO|2015-06-17|250|1 99 | |C00580100|IND|CARTER|SCOTT||LUBBOCK|TX|79407|OTTO'S GRANARY|MANAGER/OWNER|2015-06-19|250|1 100 | |C00580100|IND|CHAMPION|STEPHAN||DESTIN|FL|32541|AC LEGG|SALES|2015-06-17|250|1 101 | |C00580100|IND|COOKINGHAM|GAIL||ROCHESTER HILLS|MI|48307|SELF-EMPLOYED|PHYSICIAN|2015-06-30|250|1 102 | |C00580100|IND|CRAICHY|KINSEY||TAMPA|FL|33601|LIVING FUEL|CEO|2015-06-29|250|1 103 | |C00580100|IND|CRUME|W|HAYDEN|COLUMBUS|MS|39705|RETIRED|RETIRED|2015-06-16|2700|1 104 | |C00580100|IND|CRUMPLER|MARK||ASHDOWN|AR|71822|DOMTAR|MECHANIC|2015-06-29|250|1 105 | |C00580100|IND|CUGINI|CHRISTY||NAPLES|FL|34119|MILLENIUM|PHYSICIAN|2015-06-25|250|1 106 | |C00580100|IND|MCGEORGE|EDWARD||RICHMOND|VA|23238|MCGEORGE ROLLING HILLS RV|CEO|2015-06-26|2700|1 107 | |C00580100|IND|MCKAY|THOMAS||AUSTIN|TX|78720|TOM'S WAY INC.|MACHINIST|2015-06-22|250|1 108 | |C00580100|IND|MCNERNEY|MICHAEL||SAN PEDRO|CA|90731|MCNERNEY'S, INC|FUNERAL SERVICE PROVIDER|2015-06-18|500|1 109 | |C00580100|IND|MCPHERSON|EDWARD||LOS ANGELES|CA|90057|SELF-EMPLOYED|PHYSICIAN|2015-06-21|250|1 110 | |C00580100|IND|MORE|MICHAEL||MARLBOROUGH|MA|01752|TJX|INFRASTRUCTURE ENGINEER III|2015-06-25|350|1 111 | |C00580100|IND|PERSSON|ALFRED||HEMET|CA|92545|CAB CHRISTIAN INC|CEO|2015-06-16|250|1 112 | 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SQLite II: Aggregate Functions 2 | 3 | 1. Open Firefox and click on the menu icon: 4 | 5 | !./images/ff-menu.png! 6 | 7 | Use the "customize" option to find the SQLite Manager Plugin: 8 | 9 | !./images/customize-menu.png! 10 | 11 |
12 | 13 | 2. Create a new database called contributions_candidates 14 | 15 | !./images/new_database.png! 16 | 17 |
18 | 19 | 3. Use a CREATE TABLE statement to create a new contributions table: 20 | 21 |
22 | 23 | 

 24 | CREATE TABLE "contributions" (
 25 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 26 |     "committee_id" VARCHAR, 
 27 |     "entity_type" VARCHAR, 
 28 |     "last_name" VARCHAR, 
 29 |     "first_name" VARCHAR, 
 30 |     "middle_name" VARCHAR, 
 31 |     "city" VARCHAR, 
 32 |     "state" VARCHAR, 
 33 |     "zip_code" VARCHAR, 
 34 |     "employer" VARCHAR, 
 35 |     "occupation" VARCHAR, 
 36 |     "date" DATETIME, 
 37 |     "amount" INTEGER,
 38 |     "candidate_id" INTEGER NOT NULL,
 39 |     FOREIGN KEY(candidate_id) REFERENCES candidates(id)
 40 | );
 41 | 
 42 |
43 | 44 | 4. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import contributions.txt: 45 | 46 |
47 | 48 | h3. Aggregate Functions (count, max, min, sum, avg) 49 | 50 | 5. Count the number of contributors from California: 51 | 
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
52 | 53 | 6. Select the minimum and maximum contributions: 54 | 
SELECT MAX(amount) FROM contributions;
55 | 
SELECT MIN(amount) FROM contributions;
56 | 57 | 7. Add up the contributions from Georgia: 58 | 
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
59 | 60 | 8. Find the average contribution: 61 | 
SELECT AVG(amount) FROM contributions;
62 | 63 | Use GROUP BY to create subtotals 64 | 65 | 9. Find the total amount of contributions per state: 66 | 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
67 | 68 | 10. Find the total amount of contributions per city and state: 69 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
70 | Now order cities by their total contributions: 71 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
72 | 73 | h2. Subqueries and Joins 74 | 75 | 11. Use a CREATE TABLE statement to create a new table for candidate data: 76 | 
 77 | 
 78 | CREATE TABLE "candidates" (
 79 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 80 |     "committee_id" VARCHAR, 
 81 |     "first_name" VARCHAR, 
 82 |     "last_name" VARCHAR, 
 83 |     "middle_name" VARCHAR, 
 84 |     "party" VARCHAR
 85 | );
 86 | 
 87 |
88 | 89 |
90 | 91 | 12. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import candidates.txt 92 | 93 | Remember to select the pipe delimiter ("|") and to check the box indicating that the first row contains headers. 94 | 95 | 96 | 97 | h3. Using Subqueries 98 | 99 | 13. Find out the maxiumum contribution amount in the data set and see which contributors have given this amount. 100 | 101 | First, determine the maximum amount contriubuted: 102 | SELECT MAX(amount) FROM contributions; 103 | 104 | Then SELECT all fields for any contribution at that amount . . . but how? 105 | 106 | Like this? 107 | SELECT * FROM contributions WHERE amount = MAX(amount); 108 | 109 | Nope. 110 | 111 | Hardcode the amount? 112 | 
SELECT * FROM contributions WHERE amount = 5400;
113 | 114 | But what if next month the max changes? How can we make this dynamic? 115 | 116 | Subqueries to the rescue: 117 | 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
118 | 119 |
120 | 121 | 122 | 14. Write a query that gets the sum amount of the top 20 contributions 123 | 124 | 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
125 | 126 |
127 | 128 | 129 | h3. Using a subquery on another table 130 | 131 | 15. Find all of the contributions to Hillary Clinton 132 | 133 | 
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');
134 | 135 |
136 | 137 | 138 | h3. Joins: Queries across tables 139 | 140 | 16. It is possible to do an *implicit* join simply by defining the relationship between the two tables in the WHERE clause: 141 | 142 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
143 | 144 |
145 | 146 | 17. Use table name aliases to save typing: 147 | 148 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
149 | 150 | This query returns the same results as the one above, but it saves some typing by making "a" an alias for "contributions" and "b" an alias for "candidates." The alias can use any valid table name you 151 | like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand. 152 | 153 |
154 | 155 | h4. Explicit JOIN syntax 156 | 157 | 18. Rewrite the above query using explicit JOIN syntax: 158 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
159 | 160 |
161 | 162 | 19. Use aliases work with JOIN syntax: 163 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
164 | 165 |
166 | 167 | 20. Get the number of contributions for each candidate 168 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
169 | 170 | Our list of candidates seems to be coming up short. Let's check it: 171 | 172 | 
SELECT DISTINCT id, last_name FROM candidates;
173 | 174 | Definitely missing candidates. What happened? 175 | 176 | Performing the JOIN returns the same results as the query with this clause: WHERE contributions.candidate_id = candidates.id. 177 | 178 | What if a candidate has no contributions? Then that candidate is not returned by the query. 179 | 180 | The JOIN acts just like the WHERE clause and filters out any rows that don't match the condition defined. 181 | 182 | Joins that return only rows in which there is a match in both tables are known as *INNER JOINs*. This is often exactly the behavior you want from the join (ignore any rows from either table that don't relate to a row in the other table). So by default, the JOIN keyword executes an INNER JOIN. You can also explicitly request an INNER JOIN, just to make things clearer: 183 | 184 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a INNER JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
185 | 186 | The results will be the same. 187 |
188 | 189 | 190 | h3. OUTER JOINS 191 | 192 | * A LEFT OUTER JOIN includes all rows from the table on the left side of the statement and only matching rows from the table on the right side of the statement. 193 | * A RIGHT OUTER JOIN includes all rows from the table on the right side of the statement and only matching rows from the left side of the statement. 194 | * A FULL OUTER JOIN includes all rows from both tables. 195 | 196 | Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well. 197 | 198 | 15. Get the number of contributions for each candidate, including those with no contributions: 199 | 

200 | SELECT candidates.last_name, count(contributions.id)
201 | FROM candidates LEFT OUTER JOIN contributions 
202 | ON candidates.id = contributions.candidate_id 
203 | GROUP BY candidates.id, candidates.last_name 
204 | ORDER BY count(contributions.id);
205 |
206 | 207 | (Note: Aliases would work here as well.) 208 | 209 | -------------------------------------------------------------------------------- /NICAR2015/candidates.txt: -------------------------------------------------------------------------------- 1 | candidate_id|candidate_name|candidate_party|election_year|office_state|candidate_office|candidate_office_district|candidate_ici|candidate_status|candidate_principle_campaign_committee|candidate_street1|candidate_street2|candidate_city|candidate_state|candidate_zip 2 | P00003608|CAIN, HERMAN|REP|2012|US|P|00|C|P|C00496067|PO BOX 2158||STOCKBRIDGE|GA|30281 3 | P20002721|SANTORUM, RICHARD J.|REP|2012|US|P|00|C|C|C00496034|P.O. BOX 37||VERONA|PA|15147 4 | P20003281|PERRY, JAMES R (RICK)|REP|2012|US|P|00|C|P|C00500587|OFFICE OF THE GOVERNOR-ST INS BLDG|1100 SAN JACINTO|AUSTIN|TX|78701 5 | P80000748|PAUL, RON|REP|2012|US|P|00|C|C|C00495820|220 BLACKSTOCK LANE||LAKE JACKSON|TX|77566 6 | P80003338|OBAMA, BARACK|DEM|2012|US|P|00|I|C|C00431445|PO BOX 8102||CHICAGO|IL|60680 7 | P80003353|ROMNEY, MITT / RYAN, PAUL D. |REP|2012|US|P|00|C|C|C00431171|585 COMMERCIAL ST.||BOSTON|MA|02109 8 | -------------------------------------------------------------------------------- /NICAR2015/committees.txt: -------------------------------------------------------------------------------- 1 | commmittee_id|committee_name|treasurer_name|committee_state1|committee_state2|committee_city|committee_state|committee_zip|committee_dsgn|committee_type|committee_party_affiliation|committee_filing_freq|org_type|connected_org_nm|candidate_id 2 | C00431171|ROMNEY FOR PRESIDENT INC.|CRATE, DARRELL|585 COMMERCIAL ST.||BOSTON|MA|02109|P|P|REP|M||ROMNEY VICTORY INC.|P80003353 3 | C00431445|OBAMA FOR AMERICA|NESBITT, MARTIN H|PO BOX 8102||CHICAGO|IL|60680|P|P|DEM|M||OBAMA VICTORY FUND 2012|P80003338 4 | C00495820|RON PAUL 2012 PRESIDENTIAL CAMPAIGN COMMITTEE INC.|PYEATT, LORI|845 W PLANTATION DR||CLUTE|TX|77531|P|P|REP|M|||P80000748 5 | C00496034|RICK SANTORUM FOR PRESIDENT INC.|MAENZA, NADINE|PO BOX 37||VERONA|PA|15147|P|P|REP|Q|||P20002721 6 | C00496067|FRIENDS OF HERMAN CAIN INC|MARK J BLOCK|PO BOX 2158||STOCKBRIDGE|GA|30281|P|P|REP|Q|||P00003608 7 | C00500587|RICKPERRY.ORG INC|PURPURA, SALVATORE MR.|228 S WASHINGTON STREET|STE 115|ALEXANDRIA|VA|22314|P|P|REP|Q|||P20003281 8 | -------------------------------------------------------------------------------- /NICAR2015/images/create_contributions_table.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2015/images/create_contributions_table.png -------------------------------------------------------------------------------- /NICAR2015/part1_steps.textile: -------------------------------------------------------------------------------- 1 | h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 2 | 3 | 1. Open Firefox and find the SQLite Manager Plugin in the tools menu: 4 | 5 | !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/sqlite_manager_ff.png! 6 | 7 | 2. Create a new database called "campaign_finance": 8 | !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! 9 | 10 | 3. Create a new table called "contributions" (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_table.png!) and give it the following field definitions: 11 | !https://github.com/tthibo/SQL-Tutorial/raw/master/NICAR2015/images/create_contributions_table.png! 12 | 13 | 4. Insert a row of data: 14 | Use the "Execute SQL" tab (it's in the second menu row, under the icons): !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/execute_sql.png! 15 | 16 | 

 17 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00494740','IND','BUFFETT','WARREN','OMAHA','Nebraska','68131','BERKSHIRE HATHAWAY/CHAIRMAN','CHAIRMAN','2500');
 18 |
19 | 20 | 5. Insert three more rows (one at a time or all at once) 21 | 

 22 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00494740','IND','WINFREY','OPRAH','CHICAGO','IL','60607','HARPO INC.','CHAIRMAN/CEO',500);
 23 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount)  VALUES ('C00494740','IND','PRITZKER','PENNY S','CHICAGO','IL','60614','HYATT','EXEC',2500);
 24 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2500');
 25 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','587');
 26 |
27 | 28 | 29 | 6. Select everything from the contributions table: 30 | 
SELECT * FROM contributions;
31 | 32 | 7. Choose some fields to select: 33 | 
SELECT city, state FROM contributions;
34 | 35 | 8. Get a distinct set of cities and states: 36 | 
SELECT DISTINCT city, state FROM contributions;
37 | 38 | h3. THE WHERE CLAUSE 39 | 40 | 9. Add some conditions to the SELECT: 41 | 
SELECT * from contributions WHERE state='IL';
42 | Now try playing around with some of the operators: 43 | 44 | |*operator*|*description*| 45 | |=|Equal| 46 | |<>|Not equal*| 47 | |>|Greater than| 48 | |<|Less than| 49 | |>=|Greater than or equal| 50 | |<=|Less than or equal| 51 | 52 | Here are some examples: 53 | 
SELECT * from contributions WHERE amount > 500;
54 | 
SELECT * from contributions WHERE amount >= 500;
55 | 
SELECT last_name from contributions WHERE state <> 'IL';
56 | 57 | 10. Combine conditions with AND or OR: 58 | 59 | Find only the big spenders from Illinois 60 | 
SELECT * FROM contributions WHERE state = 'IL' AND amount > 2000;
61 | 62 | Find any big spender and anyone from Illinois 63 | 
SELECT * FROM contributions WHERE state = 'IL' OR amount > 2000;
64 | 65 | 11. But watch out for operator precedence 66 | 67 | Looking for big spenders from Nevada and Chicago 68 | 
SELECT * FROM contributions WHERE city = 'CHICAGO' OR state = 'NV' AND amount > 2000;
69 | 70 | How did Oprah sneak in there? 71 | 72 | Try that again: 73 | 
SELECT * FROM contributions WHERE (city = 'CHICAGO' OR state = 'NV') AND amount > 2000;
74 | 75 | h3. Sorting the results 76 | 77 | 12. Use ORDER BY to sort: 78 | 
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount;
79 | And reverse the order: 80 | 
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount DESC;
81 | And order by more than one column: 82 | 
SELECT last_name, state, amount FROM contributions ORDER BY state, amount DESC;
83 | 84 | h3. Limiting the number of rows returned 85 | 86 | 13. Return only the top two contributions: 87 | 
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
88 | 89 | 90 | h3. Changing values with UPDATE 91 | 92 | 14. Check the list of state names again: 93 | 
SELECT state FROM contributions;
94 | 95 | Let's correct Nebraska: 96 | 
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
97 | 98 | And check again: 99 | 
SELECT DISTINCT state FROM contributions;
100 | 101 | h3. DELETING rows 102 | 103 | 15. Let's get rid of a row: 104 | 
SELECT * FROM contributions WHERE id = 1;
105 | 
DELETE FROM contributions WHERE id = 1;
106 | 107 | h3. Importing data from a flat file 108 | 109 | 14. Import a delimited file: 110 | a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2015/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2015/contributions.txt] 111 | b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). 112 | 113 | c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). 114 | 115 | * Using the **Select File** button, browse for the **contributions.txt** file you just saved. 116 | * Check the "First row contains column names" check box. 117 | * Select **Pipe(|)** for "Fields separated by" 118 | * For everything else, you can keep the default selections. 119 | * You should have 1006 rows of data. 120 | 121 | 122 | Creative Commons License
A Gentle Introduction to SQL Using SQLite by Troy Thibodeaux is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. -------------------------------------------------------------------------------- /NICAR2015/part2_steps.textile: -------------------------------------------------------------------------------- 1 | h2. An Introduction to Grouping and Summing Using SQLite (Just the Steps) 2 | 3 | h3. Review: CREATE, INSERT, SELECT and WHERE 4 | 5 | 1. Create a new database called campaign_finance (SQLite manager will save the file as campaign_finance.sqlite). 6 | 7 | 2. Create a new table called "contributions": 8 | 
CREATE TABLE "contributions" ("id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL, "committee_id" VARCHAR,
  9 | "entity_type" VARCHAR, "last_name" VARCHAR, "first_name" VARCHAR, "city" VARCHAR, "state" VARCHAR, "zip_code" VARCHAR,
 10 | "employer" VARCHAR, "occupation" VARCHAR, "amount" INTEGER)
11 | 12 | 3. Add a few rows of data: 13 | 

 14 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00494740','IND','WINFREY','OPRAH','CHICAGO','IL','60607','HARPO INC.','CHAIRMAN/CEO',500);
 15 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount)  VALUES ('C00494740','IND','PRITZKER','PENNY S','CHICAGO','IL','60614','HYATT','EXEC',2500);
 16 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2500');
 17 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','587');
 18 |
19 | 20 | 4. Import a data file (contributions.txt) 21 | 22 | 5. Write a query to select all fields from contributions of at least $500 and no more than $1000. 23 | 24 | h3. Using BETWEEN to find values in a range 25 | 26 | 6. 
SELECT * FROM contributions WHERE amount BETWEEN 500 AND 1000;
27 | 28 | h3. Using IN to choose from a list 29 | 30 | 7. Select all contributions from Alabama, Georgia or Florida: 31 | 
SELECT * FROM contributions WHERE state IN ('AL', 'GA', 'FL');
32 | 33 | 8. Select all contributions from some state other than California, Oregon or Arizona: 34 | 
SELECT * FROM contributions WHERE state NOT IN ('CA', 'OR', 'AZ');
35 | 36 | h3. Wildcard matching with LIKE 37 | 38 | 9. Scroll through the New York ZIP codes to see the issue: 39 | 
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
40 | 
SELECT zip_code FROM contributions WHERE zip_code = '10017';
41 | 42 | Use % to match any number of characters. 43 | 
SELECT zip_code FROM contributions WHERE zip_code LIKE '10017%';
44 | 45 | 10. Find all contributions where the middle initial is included in the first_name field: 46 | Use _ to match exactly one character. 47 | 
SELECT * FROM contributions WHERE first_name LIKE '% _.%';
48 | 49 | h3. Dealing with NULL values 50 | 51 | Another data problem: 52 | 
SELECT first_name, last_name FROM contributions WHERE state = 'NH';
53 | 54 | 11. How do we find any rows missing the last_name value? 55 | NOTE: "=" won't work. 56 | 
SELECT * FROM contributions WHERE last_name IS NULL;
57 | 58 | 12. How do we find any New Hampshire contribution by someone whose last name isn't "BAKER": 59 | 60 | 
SELECT * FROM contributions WHERE state = 'NH';
61 | 62 | 
SELECT * FROM contributions WHERE state = 'NH' AND last_name <> 'BAKER';
63 | 64 | 
SELECT * FROM contributions WHERE state = 'NH' AND (last_name <> 'BAKER' OR last_name IS NULL);
65 | 66 | 13. Grab only rows from NH where there is a value for last_name: 67 | 
SELECT * FROM contributions WHERE state = 'NH' AND last_name IS NOT NULL;
68 | 69 | h3. Manipulating text with string functions 70 | 71 | 14. Use TRIM() to get rid of leading and trailing whitespace 72 | a. Change Oprah's row to introduce whitepace around the state name: 73 | 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'WINFREY';
74 | b. Now check to see that you've created a new state: 75 | 
SELECT DISTINCT state FROM contributions ORDER BY state;
76 | c. Use TRIM() to set things right: 77 | 
UPDATE contributions SET state = TRIM(state);
78 | 79 | 15. Use UPPER() or LOWER() to handle inconsistent capitalization 80 | 
SELECT city, state FROM contributions WHERE state = 'AL' ORDER BY city, state;
81 | 
SELECT * FROM contributions WHERE state = 'AL' AND city = 'BIRMINGHAM';
82 | 
SELECT * from contributions WHERE state = 'AL' AND UPPER(city) = 'BIRMINGHAM';
83 | NOTE: case sensitivity is system-dependent. In SQLite, LIKE is not case sensitive. In MySQL, "=" is not case sensitive. 84 | It's safer to be explicit about case so your SQL is portable. 85 | 86 | 16. Use SUBSTR() to get a substring 87 | Let's revisit the ZIP+4 problem: 88 | 
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
89 | 90 | Now let's return just the 5-digit ZIPs: 91 | 
SELECT SUBSTR(zip_code, 1, 5) FROM contributions;
92 | 93 | 94 | h3. Aggregate Functions 95 | 96 | 17. Count the number of contributions from California: 97 | 
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
98 | 99 | 18. Find out how many zip codes there are in the table: 100 | 
SELECT COUNT(DISTINCT zip_code)  FROM contributions;
101 | 
SELECT COUNT(DISTINCT state)  FROM contributions;
102 | 103 | 19. Select the minimum and maximum contributions: 104 | 
SELECT MAX(amount) FROM contributions;
105 | 
SELECT MIN(amount) FROM contributions;
106 | 107 | 20. Find the average contribution: 108 | 
SELECT AVG(amount) FROM contributions;
109 | 110 | 21. Add up the contributions from Georgia: 111 | 
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
112 | 113 | 114 | h3. Grouping with GROUP BY 115 | 116 | 22. Find the total amount of contributions per state: 117 | 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
118 | 119 | 23. Find the total amount of contributions per city and state: 120 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
121 | Now order cities by their total contributions: 122 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
123 | 124 | 125 | h3. Using Subqueries 126 | 127 | 24. A failed attempt to get all contributions who gave the maximum: 128 | 
SELECT * FROM contributions WHERE amount = MAX(amount);
129 | 130 | 25. Doing it right with a subquery 131 | 132 | What we want is: 133 | 
SELECT MAX(amount) FROM contributions;
-- The result is 5000 134 | And then, using the result of that query: 135 | 
SELECT * FROM contributions WHERE amount = 5000;
136 | 137 | Combine them with a subquery: 138 | 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
139 | 140 | 26. Use a subquery to total the amounts from the top 20 contributions 141 | 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
142 | 143 | 144 | h3. Grouping with Group By 145 | 146 | 27. Get total amount contributed by state: 147 | 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
148 | 149 | 28. Or by city (grouping by two columns): 150 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
151 | 152 | 29. And easily see which cities gave the most (using ORDER BY): 153 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
154 | 155 | 30. Be wary of any non-aggregate fields in the SELECT clause that aren't in the GROUP BY: 156 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY state ORDER BY SUM(amount) DESC;
157 | 158 | h3. Filtering by aggregate values using HAVING 159 | 160 | 31. A failed attempt to return totals for cities where at least $3,000 has been contributed: 161 | 
SELECT city, state, SUM(amount) FROM contributions WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
162 | 163 | 32. And the way that works: 164 | 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state HAVING SUM(amount) >= 10000 ORDER BY SUM(amount) DESC;
165 | 166 | 33. What's the difference betwen WHERE and HAVING? 167 | a. A simple WHERE: 168 | 
SELECT city, state, amount FROM contributions WHERE amount >= 2300;
169 | b. Where with a GROUP BY: 170 | 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state;
171 | c. Now limiting those cities by the total contributions: 172 | 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 10000;
173 | 174 | 175 | h3. Is this data normal? 176 | 177 | * If I wanted to find all contributions to a given candidate, what query would I use? 178 | * If I wanted to get the candidate's name or the committee treasurer's name, what would I need to do? Why? 179 | 180 | 181 | Creative Commons License
A Gentle Introduction to SQL Using SQLite by Troy Thibodeaux is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. 182 | 183 | -------------------------------------------------------------------------------- /NICAR2016/part1/data/.#famous_donors.txt: -------------------------------------------------------------------------------- 1 | tthibodeaux@CTCIDGMBTEMP.local.24816 -------------------------------------------------------------------------------- /NICAR2016/part1/data/contributions.txt: -------------------------------------------------------------------------------- 1 | id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount 2 | |C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-14|200 3 | |C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-28|250 4 | |C00573519|IND|STANFIELD|AMY||FRANKLIN|TN|370675672|SELF-EMPLOYED|ARTIST|2015-03-31|250 5 | |C00573519|IND|STARK|CHERYL|ANN|ENGLEWOOD|FL|342245434|PATHWAY LIGHTING PRODUCTS INC.|MARKETING RESEARCH|2015-03-11|500 6 | |C00573519|IND|STARK|FRED||ENGLEWOOD|FL|342245434|PATHWAY LIGHTING PRODUCTS INC.|CEO|2015-03-19|1000 7 | |C00573519|IND|STEBBENS|KIM||EVERETT|WA|982011557|WAYA GROUP INC|EXECUTIVE|2015-03-14|250 8 | |C00573519|IND|STEGEMANN|BELINDA||CLINT|TX|798365308|RETIRED|RETIRED|2015-03-28|500 9 | |C00573519|IND|STEIGERWALD|DONALD||ESCONDIDO|CA|920263942|STEIGERWALD DOUGHERTY INC|BUILDING CONTRACTOR|2015-03-04|250 10 | |C00573519|IND|STEINKAMP|JEFFREY||ROCHESTER|VT|057670098|RETIRED|RETIRED|2015-03-31|1500 11 | |C00573519|IND|STENGEL|LINDA||SIOUX FALLS|SD|571058110|RETIRED|RETIRED|2015-03-30|250 12 | |C00573519|IND|STENSETHER|BARBARA|L|TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500 13 | |C00573519|IND|STENSETHER|JOHN||TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500 14 | |C00573519|IND|STEWARDSON|MELINDA||BUFFALO GAP|TX|795082140|WYLIE INDEPENDENT SCHOOL DISTRICT|READING TEACHER|2015-03-31|250 15 | |C00573519|IND|STOCKSTILL|JAMES||PICAYUNE|MS|394665552|RETIRED|RETIRED|2015-03-04|250 16 | |C00573519|IND|STODDARD|CHERYL||FREDERICKSBURG|VA|224052180|ANTHEM|REGIONAL MANAGER|2015-03-31|250 17 | |C00573519|IND|STOKER|RICHARD||MIAMI BEACH|FL|331417401|RETIRED|RETIRED|2015-03-24|300 18 | |C00573519|IND|THORNTON|JOE||DALLAS|TX|752254816|HFF|REAL ESTATE CAPITAL MARKETS|2015-03-05|2700 19 | |C00573519|IND|TIMBLE|JAMES||FRANKLIN GROVE|IL|610319555|RETIRED|RETIRED|2015-03-28|250 20 | |C00573519|IND|TOBB|RONALD||BROOKEVILLE|MD|208333208|BOWLES FLUIDICS|OPERATIONS VP|2015-03-03|250 21 | |C00573519|IND|TOLLAKSON|DARLENE|M|SOUTH WAYNE|WI|535879761|||2015-03-30|400 22 | |C00573519|IND|TOMANN|DON||BUFFALO|MN|553134253|UMC INC.|BUSINESS OWNER/PROFESSIONAL|2015-03-03|250 23 | |C00573519|IND|TORRENCE|DARREN||PENSACOLA|FL|325075234|SELF-EMPLOYED|OWNER|2015-03-18|250 24 | |C00573519|IND|TORRE|THOMAS||CUMMING|GA|300411205|AT&T|ENGINEER|2015-03-23|250 25 | |C00573519|IND|TRAN|VINCENT||CRESCENT CITY|CA|955319576|STATE OF CALIFORNIA|DENTIST|2015-03-04|1000 26 | |C00574624|IND|CHOATE|CHARLIE||BURLESON|TX|760282217|SELF EMPLOYED|SMALL BUSINESS OWNER|2015-03-31|250 27 | |C00574624|IND|CHOR|MARLENE||ANACONDA|MT|597112904|||2015-03-31|500 28 | |C00574624|IND|MACDONALD|LENNA|R|DANIEL ISLAND|SC|294927523|BVCC LLC|ENTREPRENEUR|2015-03-25|1000 29 | |C00574624|IND|MACH|STEVEN|P|HOUSTON|TX|772190630|MACH INDUSTRIAL GROUP LP|VP FINANCE|2015-03-23|2700 30 | |C00574624|IND|MACKEY|WILLIAM||MIAMI|FL|331566023|||2015-03-31|500 31 | |C00574624|IND|MADDOX|MATTHEW|MATT|HOUSTON|TX|770083017|BROTHERS ALVARADO; 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APPRAISER|2015-04-13|500 120 | |C00575795|IND|ALFERNESS|JOY||SAN FRANCISCO|CA|941072734|NOT EMPLOYED|HUMANITARIAN|2015-05-02|1700 121 | |C00575795|IND|FLYNN|JOHN||VIENNA|VA|221816131|N/A|RETIRED|2015-05-14|500 122 | |C00575795|IND|KASSAN|ALEX||NEW YORK|NY|10013|ANCHORAGE MEDIA GROUP|PRODUCTION|2015-04-28|2700 123 | |C00575795|IND|SENATORE|BRAD||BOSTON|MA|021162671|SELF-EMPLOYED|FASHION DESIGN|2015-06-30|1000 124 | |C00575795|IND|HELLER TRIPENY|MARY||CORNING|NY|148309481|SELF-EMPLOYED||2015-05-08|2700 125 | |C00575795|IND|FINKEL|JUDITH||HOUSTON|TX|770245404|N/A|RETIRED|2015-06-01|2700 126 | |C00575795|IND|FORD|ARLENE||BALTIMORE|MD|212101526|N/A|RETIRED|2015-06-19|300 127 | |C00575795|IND|NEWMAN|MELISSA||ALEXANDRIA|VA|223142414|CENTURYLINK|ATTORNEY|2015-04-20|1000 128 | |C00575795|IND|CANTU|ALONZO||MCALLEN|TX|785042202|CANTU CONSTRUCTION & DEVELOPMENT|OWNER/DEVELOPER|2015-06-30|2700 129 | |C00575795|IND|HARRIS|MAYA||NEW YORK|NY|100366812|HILLARY FOR AMERICA|SENIOR POLICY ADVISOR|2015-06-30|2700 130 | |C00575795|IND|OTTINGER|BARBARA||PASADENA|CA|911051342|||2015-05-13|250 131 | |C00575795|IND|HAIRSTON|JAMES||NEWARK|NJ|071121708|N/A|RETIRED|2015-04-30|225 132 | |C00575795|IND|ELLISON|CARL||NEW YORK|NY|101281248|N/A|RETIRED|2015-06-23|2700 133 | |C00575795|IND|MARDIROSSIAN|SHANT||KATONAH|NY|105362919|KOHLBERG & CO., LLC|PARTNER AND COO|2015-06-28|2700 134 | |C00575795|IND|RATNER|TAWNY||CLEVELAND HEIGHTS|OH|441063138|||2015-04-26|2700 135 | |C00575795|IND|LINDEN|MARTHA||ANCHORAGE|AK|995073970|OWEN R. BELL, MD, APC|CERTIFIED NURSE MIDWIFE|2015-04-13|250 136 | |C00581876|IND|DEMLER|JOHN||NORTHFIELD|IL|600933117|RETIRED|SALES|2015-09-29|500 137 | |C00581876|IND|HUMPHREY|GORDON||CHICHESTER|NH|032586102|RETIRED|RETIRED|2015-09-29|2700 138 | |C00581876|IND|PELLEGRINO|BOB||CINCINNATI|OH|452433220|RETIRED|INVESTOR|2015-09-29|250 139 | |C00581876|IND|BOND|JAMES|D|FARGO|ND|581047058|COLLINS & COMPANY|EXECUTIVE|2015-09-29|250 140 | |C00581876|IND|PALAFOUTAS|JUDITH||ALEXANDRIA|VA|223144761|GEORGETOWN UNIVERSITY|RESEARCH DIRECTOR|2015-09-29|1000 141 | |C00581876|IND|WINSLOW|CLARK||BELVEDERE TIBURON|CA|949202426|WINSLOW CAPITAL|INVESTMENT MGMT|2015-09-29|1000 142 | |C00581876|IND|YOUNG|STEWART||WASHINGTON|DC|200024489|CORLEY CONSULTING|LOBBYIST|2015-09-29|250 143 | |C00581876|IND|BATTOCLETTI|DAVID||COLUMBUS|OH|432157513|ICE MILLER WHITEBOARD|LOBBYIST|2015-09-29|1000 144 | |C00581876|IND|COLEMAN|GEORGE||FAIRFIELD|CT|068245649|CREDIT SUISSE|BANKER|2015-09-29|500 145 | |C00581876|IND|SWALDO|THEODORE||NORTH CANTON|OH|447208807|RETIRED|RETIRED|2015-09-29|250 146 | |C00581876|IND|OSTERTHALER|ROBERT||VIENNA|VA|221821368|RETIRED|CEO|2015-09-29|900 147 | |C00581876|IND|BERGAN|PHILIP||CHARLESTON|SC|294012403|RETIRED|RETIRED|2015-09-29|500 148 | |C00581876|IND|DUBRAVCIC|STEPHEN||BRONWOOD|GA|398260083|DARKOTECH, INC|MANAGER|2015-08-07|250 149 | |C00581876|IND|BRISTOW|JULIE||CINCINNATI|OH|452083332|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-07|2700 150 | |C00581876|IND|WASSERSTROM|ALAN||COLUMBUS|OH|432072167|WASSERSTROM COMPANIES|CEO|2015-08-07|2700 151 | |C00581876|IND|SANDRIDGE|RHONDA|L|WESTFIELD CENTER|OH|442519801|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-07|2700 152 | |C00581876|IND|WILSON|RONALD||SOLON|OH|441395077|GENERAL ELECTRIC|GENERAL MANAGER|2015-08-07|500 153 | |C00581876|IND|BRISTOW|WILLIAM|S|CINCINNATI|OH|452083332|JEFF WYLER AUTOMATIVE FAMILY|PRESIDENT|2015-08-07|2700 154 | |C00581876|IND|FETTER|STEVEN||PORT TOWNSEND|WA|983683058|SELF-EMPLOYED|UTILITY CONSULTANT|2015-08-07|2700 155 | |C00581876|IND|BECHTOLD|TIMOTHY||COLUMBUS|OH|432156108|VORYS SATER|LOBBYIST|2015-08-07|500 156 | |C00581876|IND|STEWART|JENNIFER||NEW HOPE|PA|189389246|STUDENT|STUDENT|2015-08-07|250 157 | |C00581876|IND|ELSANT|MARTIN||WOODMERE|NY|115982909|NORTH SHORE HEALTH SYSTEM|RADIOLOGIST|2015-08-07|200 158 | |C00581876|IND|THOMPSON|LUCINA||DALLAS|TX|752303038|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-17|2700 159 | |C00581876|IND|EARL|SUSAN||COLUMBUS|OH|432061275|CIVIC VOLUNTEER|CIVIC VOLUNTEER|2015-08-17|250 160 | |C00581876|IND|MAUER|VINCENT||CINCINNATI|OH|452443604|BROCK BROWN COD|LAWYER|2015-08-18|250 161 | |C00580399|IND|COHEN|LARRY||NY|NY|100143744|SEVEN BRIDGES ADVISORS|FINANCIAL ADVISOR|2015-08-13|2700 162 | |C00580399|IND|COHEN|STEVE||GREENWICH|CT|068360142|POINT72 ASSET MANAGEMENT|CHAIRMAN/CEO|2015-09-03|859 163 | |C00580399|IND|COHEN|STEVE||GREENWICH|CT|068360142|POINT72 ASSET MANAGEMENT|CHAIRMAN/CEO|2015-08-15|1840 164 | |C00580399|IND|GUREGHIAN|VAHAN|H|GLADWYNE|PA|190351507|CSMI LLC|FOUNDER AND CEO|2015-09-30|2700 165 | |C00580399|IND|PJETERNIKAJ|LILA||SADDLE RIVER|NJ|074583012|||2015-08-04|1350 166 | |C00580399|IND|CONSIDINE|THOMAS||SPRING LAKE|NJ|077622369|GRAVILAUR SOLUTIONS LLC|CONSULTANT|2015-09-29|2700 167 | |C00580399|IND|CONSORTI|JASON||MONROE|NJ|088312242|CSC|IT MANAGER|2015-07-01|500 168 | |C00580399|IND|CONSTABLE|RICHARD||SOUTH ORANGE|NJ|070791141|US ATTORNEY|LAWYER/LEGAL|2015-09-09|1000 169 | |C00580399|IND|CONTI|GINA||NEW PROVIDENCE|NJ|079742117|HOMEMAKER|HOMEMAKER|2015-09-30|2500 170 | |C00580399|IND|CONTI|KURT|G|NEW PROVIDENCE|NJ|079742117|CONTI GROUP|PRESIDENT/CEO|2015-09-30|2500 171 | |C00580399|IND|CONWAY|ARTHUR|W|MAHWAH|NJ|074302040|DIALAMERICA MARKETING INC.|PRESIDENT & CEO|2015-07-16|2700 172 | |C00580399|IND|GIBBONS|MARY|M|BASKING RIDGE|NJ|079204107|ELBERON DEVELOPMENT GROUP ST. CLAIRE'|ADMINISTRATOR/SOCIAL WORKER|2015-08-20|1000 173 | |C00580399|IND|GIBLIN|BRENDON|P|SOUTHBOROUGH|MA|017721706|BRENDON PROPERTIES|DEVELOPER REAL ESTATE|2015-09-08|500 174 | |C00580399|IND|GIDWITZ|CHRISTINA||CHICAGO|IL|606065829|HOUSEWIFE|HOUSEWIFE|2015-09-27|2700 175 | |C00580399|IND|GILFILLAN|CHRISTINE|C|MORRISTOWN|NJ|079607355|THE MCJ AMELIOR FOUNDATION|PRESIDENT|2015-07-02|2700 176 | |C00580399|IND|GILFILLAN|MICHAEL|T|MADISON|NJ|079402751|BRICK CITY SPORTS CAPITAL LLC|INVESTMENTS|2015-07-02|2700 177 | |C00580399|IND|GILMAN|J|PAUL|MENDHAM|NJ|079451205|COVANTA ENERGY|SENIOR VICE PRESIDENT AND CHIEF SUSTAI|2015-07-31|2700 178 | |C00580399|IND|GILMAN|THOMAS||SCOTTSDALE|AZ|852623818|DAIMLERCHRYSLER FINANCIAL SERVICES AME|CEO|2015-07-27|2700 179 | |C00580399|IND|GILMORE|THOMAS|P|ANDOVER|NJ|078214127|GILMORE ELECTRIC INC.|ELECTRICIAN|2015-07-24|2700 180 | |C00580399|IND|CONNORS|CHARLES|T|WESTFIELD|NJ|070902902|H&M INTERNATIONAL TRANSPORTANTION|PRESIDENT & CHIEF OPERATING OFFICER|2015-07-27|2700 181 | |C00580399|IND|CONNORS|CHARLES|T|WESTFIELD|NJ|070902902|H&M INTERNATIONAL TRANSPORTANTION|PRESIDENT & CHIEF OPERATING OFFICER|2015-07-27|2700 182 | |C00580399|IND|CONSIGLIO|THOMAS||RAMSEY|NJ|074461346|RESOURCE REALTY|SALES|2015-08-03|2700 183 | |C00580399|IND|COLE|BRIAN||OAKLAND|NJ|074362350|COLE MEDICAL INC|VICE PRESIDENT|2015-07-01|2700 184 | |C00579458|IND|VILLARI|DAVID|J|FORT LAUDERDALE|FL|333061906|GIG INS GROUP|BUSINESS OWNER|2015-11-30|250 185 | |C00579458|IND|VIEIRA|SEAN||FAIRFAX|VA|220336226|SJV LLC|PRESIDENT|2015-11-29|1000 186 | |C00579458|IND|VIJAYANAGAR|RAGHAVENDRA||TAMPA|FL|336063573|R VIJAYANAGAR M.D. P.A.|SURGEON|2015-11-20|2000 187 | |C00579458|IND|VILLARREAL|AARON||MCALLEN|TX|785044405|||2015-10-30|2700 188 | |C00579458|IND|VILLAMIZAR|JOHN||ROYAL PALM BEACH|FL|334116107|ADVANCED VISION|OPTICIAN|2015-12-29|375 189 | |C00579458|IND|WARE|BLAKE|BEAKLEY|AMARILLO|TX|791092331|HOMEMAKER|HOMEMAKER|2015-11-19|2000 190 | |C00579458|IND|WARD|JIM||PHOENIX|AZ|850162363|THE PHOENIX SYMPHONY|CEO|2015-10-14|1000 191 | |C00579458|IND|WARD|JOHN|M|DALLAS|TX|752047849|RETIRED|RETIRED|2015-11-09|1000 192 | |C00579458|IND|WARE|PATRICK|O'NEILL|AMARILLO|TX|791092331|STUDENT|STUDENT|2015-11-19|4000 193 | |C00579458|IND|WARE|PATRICK|O'NEILL|AMARILLO|TX|791092331|STUDENT|STUDENT|2015-11-19|-2000 194 | |C00579458|IND|WAREHAM|KRISTINA||LAKE HIAWATHA|NJ|070342209|PRECISION CASTPARTS CORP|ENGINEER|2015-12-31|1000 195 | |C00579458|IND|ANDREWS|BARRY|G|DALLAS|TX|752052805|ANDREWS DISTRIBUTING|EXECUTIVE|2015-12-15|-2700 196 | |C00579458|IND|ANDREWS|BARRY|G|DALLAS|TX|752052805|ANDREWS DISTRIBUTING|EXECUTIVE|2015-12-15|2700 197 | |C00579458|IND|MANNING|RALPH|F|MARS|PA|160462633|TUCKER ARENSBERG PC|ATTORNEY|2015-11-10|1000 198 | |C00579458|IND|MANNING|RAMON||HOUSTON|TX|770211611|PARTNERS ENERGY|INVESTOR|2015-10-28|250 199 | |C00579458|IND|MANNING|SAM|BLAGDEN|DALLAS|TX|752195220|SELF-EMPLOYED|INVESTMENTS|2015-11-10|500 200 | |C00579458|IND|MANRIQUE|FRANCISCO||DORAL|FL|331783505|||2015-10-20|500 201 | -------------------------------------------------------------------------------- /NICAR2016/part1/data/famous_donors.txt: -------------------------------------------------------------------------------- 1 | C00575795|A|Q2|P|201509039001608655|15|IND|BUFFETT, WARREN|OMAHA|NE|681313311|BERKSHIRE HATHAWAY|CHAIRMAN|04152015|2700||C51337|1024052|||4090920151249626621 2 | C00577130|N|Q2|P|201507159000153383|15E|IND|RUFFALO, MARK|NEW YORK|NY|100031526|SELF-EMPLOYED|ACTOR|06022015|250|C00401224|VPF7BDV8BV8|1015044||* EARMARKED CONTRIBUTION: SEE BELOW|4071620151247172334 3 | C00458844|A|Q2|G|201510159003030910|22Y|IND|ADELSON, SHELDON|LAS VEGAS|NV|89145|||06122015|2600||GENREF0042|1029436|||4102120151256390199 4 | -------------------------------------------------------------------------------- /NICAR2016/part1/data/scripts/utility/Gemfile: -------------------------------------------------------------------------------- 1 | source 'https://rubygems.org' 2 | 3 | gem 'people' -------------------------------------------------------------------------------- /NICAR2016/part1/data/scripts/utility/filter_contribs.rb: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env ruby 2 | 3 | require 'csv' 4 | 5 | committee_ids = [] 6 | CSV.foreach('candidates.txt', :headers => true, :col_sep => '|', :quote_char => "\x00") do |row| 7 | committee_ids << row[0] 8 | end 9 | 10 | skips = ['C00577981', 'C00578757', 'C00577312', 'C00575449', 'C00578658'] 11 | used = committee_ids - skips 12 | 13 | included = {} 14 | used.each {|id| included[id] = 0} 15 | 16 | CSV.foreach('itcont.txt', :col_sep => '|', :quote_char => "\x00") do |row| 17 | #skip if not a contrib 18 | next if row[5] != '15' 19 | committee_id = row[0] 20 | if used.include? committee_id 21 | if included[committee_id] < 25 22 | puts row.to_csv(:col_sep => '|') 23 | included[committee_id] += 1 24 | else 25 | used.delete committee_id 26 | break if used.length == 0 27 | end 28 | end 29 | end 30 | 31 | 32 | exit 33 | ##### 34 | ## Check the number of contributors per candidate 35 | ##### 36 | committee_counts = {} 37 | committee_ids.each do |id| 38 | committee_counts[id] = 0 39 | end 40 | 41 | 42 | committee_ids.each do |id| 43 | CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| 44 | committee_counts[id] += 1 if row[0] == id 45 | end 46 | end 47 | 48 | puts committee_counts 49 | -------------------------------------------------------------------------------- /NICAR2016/part1/data/scripts/utility/fix_names.rb: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env ruby 2 | 3 | require 'csv' 4 | require 'people' 5 | 6 | np = People::NameParser.new(:case_mode => 'upper') 7 | CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| 8 | no_title = row[7].gsub(/ (MR.|MRS.|DR.|MS.)$/, '') 9 | name = np.parse(no_title) 10 | next if name[:last].empty? 11 | date_parts = row[13].match(/(\d\d)(\d\d)(\d\d\d\d)/) 12 | date_string = "#{date_parts[3]}-#{date_parts[1]}-#{date_parts[2]}" 13 | puts [name[:last], name[:first], name[:middle], row[8], row[9], row[10], row[11], row[12], date_string, row[14], row[0]].to_csv(:col_sep => '|', :quote_char => "\x00") 14 | end 15 | -------------------------------------------------------------------------------- /NICAR2016/part1/images/create_contributions_table.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/create_contributions_table.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/customize-menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/customize-menu.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/execute_sql.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/execute_sql.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/ff-menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/ff-menu.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/import.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/import.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/import_icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/import_icon.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/new_database.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/new_database.png -------------------------------------------------------------------------------- /NICAR2016/part1/images/new_table.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part1/images/new_table.png -------------------------------------------------------------------------------- /NICAR2016/part1/part1_steps.html: -------------------------------------------------------------------------------- 1 |

A Quick Introduction to SQL Using SQLite (Just the Steps)

2 |

1. Open Firefox and click on the menu icon:

3 |

4 |

Use the "customize" option to find the SQLite Manager Plugin:

5 |

6 |

You can add it to your menu by dragging or right-click to add it to the toolbar.

7 |
8 |

2. Create a new database called campaign_finance:

9 |

10 |
11 |

3. Create a new table called contributions () and give it the following field definitions:

12 |

13 |
14 |

Inserting data manually (Don't worry, you won't do this often.)

15 |

4. Insert a row of data.

16 |

Use the "Execute SQL" tab (it's in the second menu row, under the icons)
17 |

18 |

And paste or type the following statement:

19 | 
INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','BUFFETT','WARREN','','OMAHA','Nebraska','68131','BERKSHIRE HATHAWAY','CHAIRMAN','2015-04-15',2700);
20 |
21 |

5. Insert three more rows (one at a time or all at once)

22 | 
INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00577130','IND','RUFFALO','MARK','','NEW YORK','NY','|100031526','SELF-EMPLOYED','ACTOR','2015-06-02',250);
 23 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00458844','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89145','','','2015-06-12',2600);
 24 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00579458','IND','MANNING','PEYTON MR.','','CLEVELAND','OH','441141717','DENVER BRONCOS','PROFESSIONAL ATHLETE','2015-08-04',2700);
 25 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','COPPOLA','SOFIA','','NEW YORK','NY','100130778','SELF-EMPLOYED','DIRECTOR/WRITER/ACTOR','2016-01-22',2700);
26 |
27 |

The SELECT statement

28 |

6. Select everything from the contributions table:

29 | 
SELECT * FROM contributions;
30 |

Note the wildcard * character.

31 |
32 |

7. Choose some fields to select:

33 | 
SELECT city, state FROM contributions;
34 |

Note the duplicates; why are they there?

35 |
36 |

8. Get a distinct set of cities and states:

37 | 
SELECT DISTINCT city, state FROM contributions;
38 |
39 |

THE WHERE CLAUSE

40 |

9. Add some conditions to the SELECT:

41 | 
SELECT * from contributions WHERE state='NY';
42 |

Now try playing around with some of the operators:

43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 |
operatordescription
=Equal
<>Not equal*
>Greater than
<Less than
>=Greater than or equal
<=Less than or equal
75 |

Here are some examples:

76 | 
SELECT * from contributions WHERE amount > 500;
77 | 
SELECT * from contributions WHERE amount >= 500;
78 | 
SELECT last_name from contributions WHERE state <> 'NY';
79 |
80 |

10. Combine conditions with AND or OR:

81 |

Find only the big spenders from New York

82 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
83 |

Find any big spender and anyone from New York

84 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
85 |
86 |

11. But watch out for operator precedence

87 |

Looking for big spenders from Las Vegas and New York City

88 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
89 |

How did Mark Ruffalo sneak in there?

90 |

Try that again:

91 | 
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
92 |
93 |

Pick One: Using BETWEEN and IN (NOT IN)

94 |

11. Find all contributions between $500 and $1000

95 | 
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
96 |

(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.)

97 |
98 |

12. Find all contributors from a list of states:

99 | 
SELECT * from contributions WHERE state IN ('OH', 'NY');
100 |

(Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY'
101 | But the IN syntax makes things much clearer, and it's easier to write.)

102 |
103 |

13. Use NOT IN to find results where a value is not included in the given set:

104 | 
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
105 |
106 |

Sorting the results

107 |

14. Use ORDER BY to sort:

108 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
109 |

And reverse the order:

110 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
111 |

And order by more than one column:

112 | 
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
113 |
114 |

Limiting the number of rows returned

115 |

15. Return only the top two contributions:

116 | 
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
117 |
118 |

Changing values with UPDATE

119 |

16. Check the list of state names again:

120 | 
SELECT state FROM contributions;
121 |

Let's correct Nebraska:

122 | 
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
123 |

And check again:

124 | 
SELECT DISTINCT state FROM contributions;
125 |
126 |

DELETING rows

127 |

17. Let's get rid of a row:

128 | 
SELECT * FROM contributions WHERE id = 1;
129 | 
DELETE FROM contributions WHERE id = 1;
130 |
131 |

Importing data from a flat file

132 |

18. Import a delimited file:
133 | a. Go to https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt (Or grab it from the conference computer.)

134 |

b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place).

135 |

c. Click the SQLite Manager Import Wizard icon ().

136 | 143 | -------------------------------------------------------------------------------- /NICAR2016/part1/part1_steps.textile: -------------------------------------------------------------------------------- 1 | h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 2 | 3 | 1. Open Firefox and click on the menu icon: 4 | 5 | !./images/ff-menu.png! 6 | 7 | Use the "customize" option to find the SQLite Manager Plugin: 8 | 9 | !./images/customize-menu.png! 10 | 11 | You can add it to your menu by dragging or right-click to add it to the toolbar. 12 | 13 |
14 | 15 | 2. Create a new database called campaign_finance: 16 | 17 | !./images/new_database.png! 18 | 19 |
20 | 21 | 3. Create a new table called contributions (!./images/new_table.png!) and give it the following field definitions: 22 | 23 | !./images/create_contributions_table.png! 24 | 25 |
26 | 27 | h3. Inserting data manually (Don't worry, you won't do this often.) 28 | 29 | 4. Insert a row of data. 30 | 31 | Use the "Execute SQL" tab (it's in the second menu row, under the icons) 32 | !./images/execute_sql.png! 33 | 34 | And paste or type the following statement: 35 | 

 36 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','BUFFETT','WARREN','','OMAHA','Nebraska','68131','BERKSHIRE HATHAWAY','CHAIRMAN','2015-04-15',2700);
 37 |
38 | 39 |
40 | 41 | 5. Insert three more rows (one at a time or all at once) 42 | 

 43 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00577130','IND','RUFFALO','MARK','','NEW YORK','NY','|100031526','SELF-EMPLOYED','ACTOR','2015-06-02',250);
 44 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00458844','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89145','','','2015-06-12',2600);
 45 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00579458','IND','MANNING','PEYTON MR.','','CLEVELAND','OH','441141717','DENVER BRONCOS','PROFESSIONAL ATHLETE','2015-08-04',2700);
 46 | INSERT INTO contributions (committee_id, entity_type, last_name, first_name, middle_name,city, state, zip_code, employer, occupation, date, amount) VALUES ('C00575795','IND','COPPOLA','SOFIA','','NEW YORK','NY','100130778','SELF-EMPLOYED','DIRECTOR/WRITER/ACTOR','2016-01-22',2700);
 47 |
48 | 49 | 50 |
51 | 52 | h3. The SELECT statement 53 | 54 | 6. Select everything from the contributions table: 55 | 56 | 
SELECT * FROM contributions;
57 | 58 | Note the wildcard * character. 59 | 60 |
61 | 62 | 7. Choose some fields to select: 63 | 64 | 
SELECT city, state FROM contributions;
65 | 66 | Note the duplicates; why are they there? 67 | 68 |
69 | 70 | 8. Get a distinct set of cities and states: 71 | 72 | 
SELECT DISTINCT city, state FROM contributions;
73 | 74 |
75 | 76 | h3. THE WHERE CLAUSE 77 | 78 | 9. Add some conditions to the SELECT: 79 | 
SELECT * from contributions WHERE state='NY';
80 | Now try playing around with some of the operators: 81 | 82 | |*operator*|*description*| 83 | |=|Equal| 84 | |<>|Not equal*| 85 | |>|Greater than| 86 | |<|Less than| 87 | |>=|Greater than or equal| 88 | |<=|Less than or equal| 89 | 90 | Here are some examples: 91 | 
SELECT * from contributions WHERE amount > 500;
92 | 
SELECT * from contributions WHERE amount >= 500;
93 | 
SELECT last_name from contributions WHERE state <> 'NY';
94 | 95 |
96 | 97 | 10. Combine conditions with AND or OR: 98 | 99 | Find only the big spenders from New York 100 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
101 | 102 | Find any big spender and anyone from New York 103 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
104 | 105 |
106 | 107 | 11. But watch out for operator precedence 108 | 109 | Looking for big spenders from Las Vegas and New York City 110 | 
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
111 | 112 | How did Mark Ruffalo sneak in there? 113 | 114 | Try that again: 115 | 
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
116 | 117 |
118 | 119 | h3. Pick One: Using BETWEEN and IN (NOT IN) 120 | 121 | 11. Find all contributions between $500 and $1000 122 | 
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
123 | (Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) 124 | 125 |
126 | 127 | 12. Find all contributors from a list of states: 128 | 
SELECT * from contributions WHERE state IN ('OH', 'NY');
129 | 130 | (Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY' 131 | But the IN syntax makes things much clearer, and it's easier to write.) 132 | 133 |
134 | 135 | 13. Use NOT IN to find results where a value is not included in the given set: 136 | 
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
137 | 138 | 139 |
140 | 141 | 142 | h3. Sorting the results 143 | 144 | 14. Use ORDER BY to sort: 145 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
146 | And reverse the order: 147 | 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
148 | And order by more than one column: 149 | 
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
150 | 151 |
152 | 153 | h3. Limiting the number of rows returned 154 | 155 | 15. Return only the top two contributions: 156 | 
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
157 | 158 |
159 | 160 | h3. Changing values with UPDATE 161 | 162 | 16. Check the list of state names again: 163 | 
SELECT state FROM contributions;
164 | 165 | Let's correct Nebraska: 166 | 
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
167 | 168 | And check again: 169 | 
SELECT DISTINCT state FROM contributions;
170 | 171 | 172 |
173 | 174 | h3. DELETING rows 175 | 176 | 17. Let's get rid of a row: 177 | 
SELECT * FROM contributions WHERE id = 1;
178 | 
DELETE FROM contributions WHERE id = 1;
179 | 180 | 181 |
182 | 183 | 184 | 185 | h3. Importing data from a flat file 186 | 187 | 18. Import a delimited file: 188 | a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/part1/data/contributions.txt] (Or grab it from the conference computer.) 189 | 190 | b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). 191 | 192 | c. Click the SQLite Manager Import Wizard icon (!./images/import_icon.png!). 193 | 194 | * Using the **Select File** button, browse for the **contributions.txt** file you just saved. 195 | * Check the "First row contains column names" check box. 196 | * Select **Pipe(|)** for "Fields separated by" 197 | * For everything else, you can keep the default selections. 198 | * You should have 204 rows of data. 199 | 200 | -------------------------------------------------------------------------------- /NICAR2016/part3/data/candidates.txt: -------------------------------------------------------------------------------- 1 | id|committee_id|last_name|first_name|middle_name|party 2 | 1|C00580100|TRUMP|DONALD|J|REP 3 | 2|C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP 4 | 3|C00458844|RUBIO|MARCO||REP 5 | 4|C00581876|KASICH|JOHN|R|REP 6 | 5|C00573519|CARSON|BENJAMIN|S|REP 7 | 6|C00577130|SANDERS|BERNARD||DEM 8 | 7|C00575795|CLINTON|HILLARY|RODHAM|DEM 9 | 8|C00578658|O'MALLEY|MARTIN|JOSEPH|DEM 10 | 9|C00579458|BUSH|JEB||REP 11 | 10|C00580399|CHRISTIE|CHRISTOPHER|J|REP 12 | 11|C00575449|PAUL|RAND||REP 13 | 12|C00577312|FIORINA|CARLY||REP 14 | 13|C00578757|GRAHAM|LINDSEY|O|REP 15 | 14|C00577981|HUCKABEE|MIKE||REP 16 | -------------------------------------------------------------------------------- /NICAR2016/part3/images/ff-menu.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part3/images/ff-menu.png -------------------------------------------------------------------------------- /NICAR2016/part3/images/import_icon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part3/images/import_icon.png -------------------------------------------------------------------------------- /NICAR2016/part3/images/new_database.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/NICAR2016/part3/images/new_database.png -------------------------------------------------------------------------------- /NICAR2016/part3/part3_steps.html: -------------------------------------------------------------------------------- 1 |

SQLite III: Subqueries and Joins in SQLite

2 |

1. Open Firefox and the SQLite Manager Plugin
3 |

4 |
5 |

2. Create a new database called contributions_candidates

6 |

7 |
8 |

3. Use a CREATE TABLE statement to create a new table for candidate data:

9 | 

 10 | CREATE TABLE "candidates" (
 11 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 12 |     "committee_id" VARCHAR, 
 13 |     "first_name" VARCHAR, 
 14 |     "last_name" VARCHAR, 
 15 |     "middle_name" VARCHAR, 
 16 |     "party" VARCHAR
 17 | );
 18 |
19 |
20 |

4. Use the SQLite Manager Import Wizard () to import candidates.txt

21 |

Remember to select the pipe delimiter ("|") and to check the box indicating that the first row contains headers.

22 | 23 |

5. Use a CREATE TABLE statement to create a new contributions table:

24 | 
CREATE TABLE "contributions" (
 25 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 26 |     "committee_id" VARCHAR, 
 27 |     "entity_type" VARCHAR, 
 28 |     "last_name" VARCHAR, 
 29 |     "first_name" VARCHAR, 
 30 |     "middle_name" VARCHAR, 
 31 |     "city" VARCHAR, 
 32 |     "state" VARCHAR, 
 33 |     "zip_code" VARCHAR, 
 34 |     "employer" VARCHAR, 
 35 |     "occupation" VARCHAR, 
 36 |     "date" DATETIME, 
 37 |     "amount" INTEGER,
 38 |     "candidate_id" INTEGER NOT NULL,
 39 |     FOREIGN KEY(candidate_id) REFERENCES candidates(id)
 40 | );
 41 |
42 |

6. Use the SQLite Manager Import Wizard () to import contributions.txt:

43 |
44 |

Using Subqueries

45 |

7. Find out the maxiumum contribution amount in the data set and see which contributors have given this amount.

46 |

First, determine the maximum amount contriubuted:
47 | SELECT MAX (amount) FROM contributions;

48 |

Then SELECT all fields for any contribution at that amount . . . but how?

49 |

Like this?
50 | SELECT * FROM contributions WHERE amount = MAX (amount);

51 |

Nope.

52 |

Hardcode the amount?

53 | 
SELECT * FROM contributions WHERE amount = 5400;
54 |

But what if next month the max changes? How can we make this dynamic?

55 |

Subqueries to the rescue:

56 | 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
57 |
58 |

8. Write a query that gets the sum amount of the top 20 contributions

59 | 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
60 |
61 |

Using a subquery on another table

62 |

9. Find all of the contributions to Hillary Clinton

63 | 
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');
64 |
65 |

Joins: Queries across tables

66 |

10. It is possible to do an implicit join simply by defining the relationship between the two tables in the WHERE clause:

67 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
68 |
69 |

11. Use table name aliases to save typing:

70 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
71 |

This query returns the same results as the one above, but it saves some typing by making "a" an alias for "contributions" and "b" an alias for "candidates." The alias can use any valid table name you
72 | like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand.

73 |
74 |

Explicit JOIN syntax

75 |

12. Rewrite the above query using explicit JOIN syntax:

76 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
77 |
78 |

13. Use aliases work with JOIN syntax:

79 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
80 |
81 |

14. Get the number of contributions for each candidate

82 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
83 |

Our list of candidates seems to be coming up short. Let's check it:

84 | 
SELECT DISTINCT id, last_name FROM candidates;
85 |

Definitely missing candidates. What happened?

86 |

Performing the JOIN returns the same results as the query with this clause: WHERE contributions.candidate_id = candidates.id.

87 |

What if a candidate has no contributions? Then that candidate is not returned by the query.

88 |

The JOIN acts just like the WHERE clause and filters out any rows that don't match the condition defined.

89 |

Joins that return only rows in which there is a match in both tables are known as INNER JOINs. This is often exactly the behavior you want from the join (ignore any rows from either table that don't relate to a row in the other table). So by default, the JOIN keyword executes an INNER JOIN. You can also explicitly request an INNER JOIN, just to make things clearer:

90 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a INNER JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
91 |

The results will be the same.

92 |
93 |

OUTER JOINS

94 | 99 |

Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well.

100 |

15. Get the number of contributions for each candidate, including those with no contributions:

101 | 
SELECT candidates.last_name, count(contributions.id)
102 | FROM candidates LEFT OUTER JOIN contributions 
103 | ON candidates.id = contributions.candidate_id 
104 | GROUP BY candidates.id, candidates.last_name 
105 | ORDER BY count(contributions.id);
106 |

(Note: Aliases would work here as well.)

107 | -------------------------------------------------------------------------------- /NICAR2016/part3/part3_steps.textile: -------------------------------------------------------------------------------- 1 | h2. SQLite III: Subqueries and Joins in SQLite 2 | 3 | 1. Open Firefox and the SQLite Manager Plugin 4 | !./images/ff-menu.png! 5 | 6 |
7 | 8 | 2. Create a new database called contributions_candidates 9 | 10 | !./images/new_database.png! 11 | 12 |
13 | 14 | 3. Use a CREATE TABLE statement to create a new table for candidate data: 15 | 
 16 | 
 17 | CREATE TABLE "candidates" (
 18 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 19 |     "committee_id" VARCHAR, 
 20 |     "first_name" VARCHAR, 
 21 |     "last_name" VARCHAR, 
 22 |     "middle_name" VARCHAR, 
 23 |     "party" VARCHAR
 24 | );
 25 | 
 26 |
27 | 28 |
29 | 30 | 4. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import candidates.txt 31 | 32 | Remember to select the pipe delimiter ("|") and to check the box indicating that the first row contains headers. 33 | 34 | 35 | 36 | 5. Use a CREATE TABLE statement to create a new contributions table: 37 | 38 | 

 39 | CREATE TABLE "contributions" (
 40 |     "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
 41 |     "committee_id" VARCHAR, 
 42 |     "entity_type" VARCHAR, 
 43 |     "last_name" VARCHAR, 
 44 |     "first_name" VARCHAR, 
 45 |     "middle_name" VARCHAR, 
 46 |     "city" VARCHAR, 
 47 |     "state" VARCHAR, 
 48 |     "zip_code" VARCHAR, 
 49 |     "employer" VARCHAR, 
 50 |     "occupation" VARCHAR, 
 51 |     "date" DATETIME, 
 52 |     "amount" INTEGER,
 53 |     "candidate_id" INTEGER NOT NULL,
 54 |     FOREIGN KEY(candidate_id) REFERENCES candidates(id)
 55 | );
 56 | 
 57 |
58 | 59 | 60 | 6. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import contributions.txt: 61 | 62 |
63 | 64 | h3. Using Subqueries 65 | 66 | 7. Find out the maxiumum contribution amount in the data set and see which contributors have given this amount. 67 | 68 | First, determine the maximum amount contriubuted: 69 | SELECT MAX(amount) FROM contributions; 70 | 71 | Then SELECT all fields for any contribution at that amount . . . but how? 72 | 73 | Like this? 74 | SELECT * FROM contributions WHERE amount = MAX(amount); 75 | 76 | Nope. 77 | 78 | Hardcode the amount? 79 | 
SELECT * FROM contributions WHERE amount = 5400;
80 | 81 | But what if next month the max changes? How can we make this dynamic? 82 | 83 | Subqueries to the rescue: 84 | 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
85 | 86 |
87 | 88 | 89 | 8. Write a query that gets the sum amount of the top 20 contributions 90 | 91 | 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
92 | 93 |
94 | 95 | 96 | h3. Using a subquery on another table 97 | 98 | 9. Find all of the contributions to Hillary Clinton 99 | 100 | 
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');
101 | 102 |
103 | 104 | 105 | h3. Joins: Queries across tables 106 | 107 | 10. It is possible to do an *implicit* join simply by defining the relationship between the two tables in the WHERE clause: 108 | 109 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
110 | 111 |
112 | 113 | 11. Use table name aliases to save typing: 114 | 115 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
116 | 117 | This query returns the same results as the one above, but it saves some typing by making "a" an alias for "contributions" and "b" an alias for "candidates." The alias can use any valid table name you 118 | like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand. 119 | 120 |
121 | 122 | h4. Explicit JOIN syntax 123 | 124 | 12. Rewrite the above query using explicit JOIN syntax: 125 | 
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
126 | 127 |
128 | 129 | 13. Use aliases work with JOIN syntax: 130 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
131 | 132 |
133 | 134 | 14. Get the number of contributions for each candidate 135 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
136 | 137 | Our list of candidates seems to be coming up short. Let's check it: 138 | 139 | 
SELECT DISTINCT id, last_name FROM candidates;
140 | 141 | Definitely missing candidates. What happened? 142 | 143 | Performing the JOIN returns the same results as the query with this clause: WHERE contributions.candidate_id = candidates.id. 144 | 145 | What if a candidate has no contributions? Then that candidate is not returned by the query. 146 | 147 | The JOIN acts just like the WHERE clause and filters out any rows that don't match the condition defined. 148 | 149 | Joins that return only rows in which there is a match in both tables are known as *INNER JOINs*. This is often exactly the behavior you want from the join (ignore any rows from either table that don't relate to a row in the other table). So by default, the JOIN keyword executes an INNER JOIN. You can also explicitly request an INNER JOIN, just to make things clearer: 150 | 151 | 
SELECT count(a.id), b.id, b.last_name FROM contributions a INNER JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
152 | 153 | The results will be the same. 154 |
155 | 156 | 157 | h3. OUTER JOINS 158 | 159 | * A LEFT OUTER JOIN includes all rows from the table on the left side of the statement and only matching rows from the table on the right side of the statement. 160 | * A RIGHT OUTER JOIN includes all rows from the table on the right side of the statement and only matching rows from the left side of the statement. 161 | * A FULL OUTER JOIN includes all rows from both tables. 162 | 163 | Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well. 164 | 165 | 15. Get the number of contributions for each candidate, including those with no contributions: 166 | 

167 | SELECT candidates.last_name, count(contributions.id)
168 | FROM candidates LEFT OUTER JOIN contributions 
169 | ON candidates.id = contributions.candidate_id 
170 | GROUP BY candidates.id, candidates.last_name 
171 | ORDER BY count(contributions.id);
172 |
173 | 174 | (Note: Aliases would work here as well.) 175 | 176 | -------------------------------------------------------------------------------- /NICAR2016/part3/sql_scripts/create_candidates.sql: -------------------------------------------------------------------------------- 1 | CREATE TABLE "candidates" ( 2 | "id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE , 3 | "committee_id" VARCHAR, 4 | "first_name" VARCHAR, 5 | "last_name" VARCHAR, 6 | "middle_name" VARCHAR, 7 | "party" VARCHAR) 8 | ; -------------------------------------------------------------------------------- /NICAR2016/part3/sql_scripts/create_contributions.sql: -------------------------------------------------------------------------------- 1 | CREATE TABLE "contributions" ( 2 | "id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE , 3 | "committee_id" VARCHAR, 4 | "entity_type" VARCHAR, 5 | "last_name" VARCHAR, 6 | "first_name" VARCHAR, 7 | "middle_name" VARCHAR, 8 | "city" VARCHAR, 9 | "state" VARCHAR, 10 | "zip_code" VARCHAR, 11 | "employer" VARCHAR, 12 | "occupation" VARCHAR, 13 | "date" DATETIME, 14 | "amount" INTEGER, 15 | "candidate_id" INTEGER NOT NULL, 16 | FOREIGN KEY(candidate_id) REFERENCES candidates(id) 17 | ); -------------------------------------------------------------------------------- /quick_steps.textile: -------------------------------------------------------------------------------- 1 | h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 2 | 3 | 1. Open Firefox and find the SQLite Manager Plugin in the tools menu: 4 | 5 | !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/sqlite_manager_ff.png! 6 | 2. Create a new database called "contributions": 7 | !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! 8 | 9 | 3. Create a new table called "contributors" (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_table.png!) and give it the following field definitions: 10 | !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/create_table.png! 11 | 12 | 4. Insert a row of data: 13 | Use the "Execute SQL" tab (it's in the second menu row, under the icons): !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/execute_sql.png! 14 | 15 | 

 16 | INSERT INTO contributors (last_name, first_name, city, state, zip, amount) VALUES ('Buffet', 'Warren', 'Omaha', 'Nebraska', '68101', 1500);
 17 |
18 | 5. Insert three more rows (one at a time or all at once) 19 | 

 20 | INSERT INTO contributors (last_name, first_name, city, state, zip, amount) VALUES ('Winfrey', 'Oprah', 'Chicago', 'IL', '60601', 500);
 21 | INSERT INTO contributors (last_name, first_name, city, state, zip, amount) VALUES ('Chambers', 'Anne Cox', 'Atlanta', 'GA', '30301', 200);
 22 | INSERT INTO contributors (last_name, first_name, city, state, zip, amount) VALUES ('Cathy', 'S. Truett', 'Atlanta', 'GA', '30301', 1200);
 23 |
24 | 25 | 6. Select everything from the contributors table: 26 | 
SELECT * FROM contributors;
27 | 28 | 7. Choose some fields to select: 29 | 
SELECT city, state FROM contributors;
30 | 31 | 8. Get a distinct set of cities and states: 32 | 
SELECT DISTINCT city, state FROM contributors;
33 | 34 | 9. Add some conditions to the SELECT: 35 | 
SELECT * from contributors WHERE state='GA';
36 | Now try playing around with some of the operators: 37 | 38 | |*operator*|*description*| 39 | |=|Equal| 40 | |<>|Not equal*| 41 | |>|Greater than| 42 | |<|Less than| 43 | |>=|Greater than or equal| 44 | |<=|Less than or equal| 45 | 46 | Here are some examples: 47 | 
SELECT * from contributors WHERE amount > 1200;
48 | 
SELECT * from contributors WHERE amount >= 1200;
49 | 
SELECT last_name from contributors WHERE state <> 'GA';
50 | 51 | 10. Combine conditions with AND or OR: 52 | 
SELECT * FROM contributors WHERE state = 'GA' AND amount > 1000;
53 | 
SELECT * FROM contributors WHERE state = 'GA' OR amount > 1000;
54 | 55 | 11. But watch out for operator precedence: 56 | 
SELECT * FROM contributors WHERE city = 'Chicago' OR state = 'GA' AND amount > 1000;
57 | How did Oprah sneak in there? 58 | Try that again: 59 | 
SELECT * FROM contributors WHERE (city = 'Chicago' OR state = 'GA') AND amount > 1000;
60 | 61 | 12. Order the results: 62 | 
SELECT last_name, amount FROM contributors WHERE amount > 200 ORDER BY amount;
63 | And reverse the order: 64 | 
SELECT last_name, amount FROM contributors WHERE amount > 200 ORDER BY amount DESC;
65 | And order by more than one column: 66 | 
SELECT last_name, state, amount FROM contributors ORDER BY state, amount DESC;
67 | 68 | 13. Return only the top two contributors: 69 | 
SELECT * FROM CONTRIBUTORS ORDER BY amount DESC LIMIT 2;
70 | 71 | 72 | h3. Importing data from a flat file 73 | 74 | 14. Import a delimited file: 75 | a. Go to ["https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributors.txt":https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributors.txt] 76 | 77 | b. Save the file as "contributors.txt" somewhere you can find it (your desktop is a good place). 78 | 79 | c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). 80 | 81 | * Using the **Select File** button, browse for the **contributors.txt** file you just saved. 82 | * Check the "First row contains column names" check box. 83 | * Select **Pipe(|)** for "Fields separated by" 84 | * For everything else, you can keep the default selections. 85 | * You should have 103 rows of data. 86 | 87 | h3. Aggregate Functions 88 | 89 | 15. Count the number of contributors from California: 90 | 
SELECT COUNT(id) FROM contributors WHERE state = 'CA';
91 | 92 | 16. Select the minimum and maximum contributions: 93 | 
SELECT MAX(amount) FROM contributors;
94 | 
SELECT MIN(amount) FROM contributors;
95 | 96 | 17. Add up the contributions from Georgia: 97 | 
SELECT SUM(amount) FROM contributors WHERE state = 'GA';
98 | 99 | 18. Find the average contribution: 100 | 
SELECT AVG(amount) FROM contributors;
101 | 102 | 19. Find the total amount of contributions per state: 103 | 
SELECT state, SUM(amount) FROM contributors GROUP BY state;
104 | 105 | 106 | 20. Find the total amount of contributions per city and state: 107 | 
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state;
108 | Now order cities by their total contributions: 109 | 
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state ORDER BY SUM(amount) DESC;
110 | 111 | 112 | 113 | Creative Commons License
A Gentle Introduction to SQL Using SQLite by Troy Thibodeaux is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. 114 | -------------------------------------------------------------------------------- /quick_steps2.textile: -------------------------------------------------------------------------------- 1 | h2. Quick Introduction to Data Normalization Using SQLite 2 | 3 | 1. Open Firefox and the SQLite Manager plugin. 4 | 5 | 2. Create a new database called contributors_candidates. 6 | 7 | 3. Create the *candidates* table: 8 | 
CREATE TABLE "candidates" ("id" INTEGER PRIMARY KEY NOT NULL, "first_name" VARCHAR NOT NULL, "last_name" VARCHAR NOT NULL , "middle_name" VARCHAR, "party" VARCHAR NOT NULL )
9 | 10 | 4. Download the text file at "https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/candidates.txt":https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/candidates.txt and import 11 | it using the Import icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). 12 | * Remember to check the "First row contains column names" check box. 13 | * And set the "Fields separated by" value to "Pipe (|)." 14 | 15 | 5. Create the *contributors* table, including a foreign key: 16 | 

17 | 	CREATE TABLE "contributors" (
18 | 	"id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL, 
19 | 	"last_name" VARCHAR, 
20 | 	"first_name" VARCHAR, 
21 | 	"middle_name" VARCHAR, 
22 | 	"street_1" VARCHAR, 
23 | 	"street_2" VARCHAR, 
24 | 	"city" VARCHAR, 
25 | 	"state" VARCHAR, 
26 | 	"zip" VARCHAR, 
27 | 	"amount" INTEGER, 
28 | 	"date" DATETIME, 
29 | 	"candidate_id" INTEGER NOT NULL,
30 | 	FOREIGN KEY(candidate_id) REFERENCES candidates(id)
31 | 	);
32 | 33 | 6. Dowload the text file at "https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributors_with_candidate_id.txt":https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributors_with_candidate_id.txt and import it into the *contributors* table. 34 | 35 | 7. Use a subquery to find all contributors to Barack Obama: 36 | CREATE TABLE "candidates" ("id" INTEGER PRIMARY KEY NOT NULL, "first_name" VARCHAR NOT NULL, "last_name" VARCHAR NOT NULL , "middle_name" VARCHAR, "party" VARCHAR NOT NULL ) 37 | 38 | 8. Use a *WHERE* clause to join two tables: 39 | CREATE TABLE "candidates" ("id" INTEGER PRIMARY KEY NOT NULL, "first_name" VARCHAR NOT NULL, "last_name" VARCHAR NOT NULL , "middle_name" VARCHAR, "party" VARCHAR NOT NULL ) 40 | 41 | 9. Use aliases to reduce typing: 42 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributors a, candidates b WHERE a.candidate_id = b.id;
43 | 44 | 10. Use explicit *join* syntax to join two tables: 45 | 
SELECT contributors.last_name, contributors.first_name, candidates.last_name FROM contributors JOIN candidates ON contributors.candidate_id = candidates.id;
46 | 47 | 11. Use aliases with explicit *join* syntax: 48 | 
SELECT a.last_name, a.first_name, b.last_name FROM contributors a JOIN candidates b ON a.candidate_id = b.id;
49 | 50 | 12. Count the number of contributors per candidate using a join: 51 | 
SELECT count(a.id), b.id, b.last_name FROM contributors a JOIN candidates b ON a.candidate_id = b.id GROUP BY b.id, b.last_name;
52 | Notice that not all candidates are included: 
SELECT DISTINCT id, last_name FROM candidates;
53 | 54 | 13. Rewrite the query to include all candidates, including those with no contributors: 55 | CREATE TABLE "candidates" ("id" INTEGER PRIMARY KEY NOT NULL, "first_name" VARCHAR NOT NULL, "last_name" VARCHAR NOT NULL , "middle_name" VARCHAR, "party" VARCHAR NOT NULL ) 56 | 57 | 14. Use a *self-join* to look for contributions by the same contributor: 58 | 
SELECT a.last_name, a.first_name, a.id as 'A ID', b.id AS 'B ID', a.amount, b.amount FROM contributors a, contributors b where a.last_name = b.last_name AND a.first_name=b.first_name AND a.id < b.id;
59 | -------------------------------------------------------------------------------- /readme.textile: -------------------------------------------------------------------------------- 1 | h2. Note: This version of the tutorial is out of date: the SQLite Manager Firefox plugin referenced is obsolete. 2 | Please check out the newer version of this tutorial being maintained by @zstumoren: 3 | https://a-gentle-introduction-to-sql.readthedocs.io/en/latest/ 4 | 5 | h2. A Gentle Introduction to SQL Using SQLite 6 | 7 | "Part I":https://github.com/tthibo/SQL-Tutorial/blob/master/tutorial_files/part1.textile 8 | "Part II":https://github.com/tthibo/SQL-Tutorial/blob/master/tutorial_files/part2.textile 9 | "Part III":https://github.com/tthibo/SQL-Tutorial/blob/master/tutorial_files/part3.textile 10 | 11 | Part IV TK 12 | 13 | Creative Commons License
A Gentle Introduction to SQL Using SQLite by Troy Thibodeaux is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. 14 | -------------------------------------------------------------------------------- /tutorial_files/candidates.txt: -------------------------------------------------------------------------------- 1 | id|first_name|last_name|middle_name|party 2 | 33|Joseph|Biden||D 3 | 36|Samuel|Brownback||R 4 | 34|Hillary|Clinton|R.|D 5 | 39|Christopher|Dodd|J.|D 6 | 26|John|Edwards||D 7 | 22|Rudolph|Giuliani||R 8 | 24|Mike|Gravel||D 9 | 16|Mike|Huckabee||R 10 | 30|Duncan|Hunter||R 11 | 31|Dennis|Kucinich||D 12 | 37|John|McCain||R 13 | 20|Barack|Obama||D 14 | 32|Ron|Paul||R 15 | 29|Bill|Richardson||D 16 | 35|Mitt|Romney||R 17 | 38|Tom|Tancredo||R 18 | 41|Fred|Thompson|D.|R 19 | -------------------------------------------------------------------------------- /tutorial_files/contributors.txt: -------------------------------------------------------------------------------- 1 | id|last_name|first_name|city|state|zip|amount 2 | |Helen||Washington|DC|200122649|15 3 | |Vespa|Anthony|Franklin Lakes|NJ|074172831|50 4 | |Cohen|Jared|Sandwich|MA|02563|50 5 | |Albrecht|Robert|woodbridge|VA|22192|10 6 | |Vespa|Anthony|Franklin Lakes|NJ|074172831|11 7 | |Varley|Allyson|birmingham|AL|35242|25 8 | |Norton|Billy|Denton|TX|762012519|5 9 | |Barley|Shannon|Hermosa Beach|CA|90254|10 10 | |Lermitte|A.|Bryn Athyn|PA|19009|25 11 | |Letalik|Adam|Beverly Hills|CA|90210|50 12 | |Welch|Mark|Columbia Falls|MT|599120206|2300 13 | |Welch|Sheila|Columbia Falls|MT|599120206|2300 14 | |See|Alvin B.|Southwick|MA|010770444|500 15 | |Taylor|Richard D.|Shreveport|LA|711013114|250 16 | |Trammell|Joel T.|Austin|TX|787351509|2300 17 | |Whitfield|George R.|Seoul Kore|ZZ|11070|1600 18 | |Young|Craig|Kyle|TX|786400099|500 19 | |Golobek|Donald|Wellsboro|PA|169011837|2100 20 | |Golobek|Mona|Wellsboro|PA|169011837|2100 21 | |Cutter|Kerry E.|Sacramento|CA|958643013|2100 22 | |Miller|James R.|Burtonsville|MD|208661043|2100 23 | |Muldoon|Andrew|Westfield|NJ|070903022|2100 24 | |Muldoon|Joanne|Westfield|NJ|070903022|2100 25 | |Howard|Patrick G.|Concord|NH|033022219|2100 26 | |Shudlick|Jon L.|N Ft Myers|FL|33917|500 27 | |Furber|Donna P.|Downey|CA|902402634|2100 28 | |Furber|James E.|Downey|CA|902402634|2100 29 | |Baumgardner|David|Wellington|TX|790954836|1000 30 | |Cowles|Donald K.|Los Angeles|CA|900461410|500 31 | |Young|Robert S.|Rancho Palos Verde|CA|902756342|1000 32 | |Brown|Robert J.|Shawnee Mission|KS|662243709|2100 33 | |Bucciarelli|Joseph C.|Norwalk|CT|068551110|500 34 | |Wrenn|Edgar|Las Vegas|NV|891213950|2100 35 | |Jacknow|Gerald|Austin|TX|787461638|1000 36 | |Low|John|Mountain View|CA|940434237|1000 37 | |Hommel|Jason E.|Penn Valley|CA|959469705|2300 38 | |Hallam|Robert W.|Tracy|CA|953779020|500 39 | |Geisick|Shanna|Penn Valley|CA|959469705|2300 40 | |Teasley|Harry E.|Tampa|FL|336112814|1000 41 | |Hull|Robert P.|Grafton|NH|032403439|2300 42 | |Ham|George E.|Independence|MO|640555135|2100 43 | |Messenger|Andrew L.|San Antonio|FL|335767965|2100 44 | |Messenger|Catherine|San Antonio|FL|335767965|2100 45 | |Dermer|Jeffrey D.|Atlanta|GA|303097662|500 46 | |Que|Simon|San Jose|CA|951173027|1000 47 | |Book|Everard|Englewood|FL|342244503|2100 48 | |Speicher|John M.|Geyserville|CA|954419687|1000 49 | |Coker|Ed|Edwardsville|AL|362610128|2100 50 | |Koszyk|Francis J.|Prospect Heights|IL|600701117|250 51 | |||Rocky Mount|VA|24151|500 52 | |Rodney|James M.|Southfield|MI|480755726|2100 53 | |Borst|Laura Jane|Houston|TX|770421411|1000 54 | |Bailey|Donald T.|Springfield|CO|810731420|2100 55 | |Giannone|Nick F.|Lake Jackson|TX|775661497|250 56 | |Bailey|Lloyd W.|Rocky Mount|NC|278043319|1000 57 | |Hays|Leslie C.|Alamogordo|NM|883110161|1000 58 | |Nigro|Robert S.J.|New Ringgold|PA|179609069|500 59 | |Costin|Brian M.|Schaumburg|IL|60193|250 60 | |Whitfield|George R.|Seoul Kore|ZZ|11070|500 61 | |Harrington|Eric J.|Suffield|CT|060781624|300 62 | |Willmann|Marie E.|Port Lavaca|TX|779795166|1000 63 | |Burden|Steven J|Lutz|FL|335585313|250 64 | |Hamilton|Dirk|Bryant|AR|720223949|500 65 | |Davis|Jeremy S.|Houston|TX|770252267|2300 66 | |McClellan|James M.|Sikeston|MO|63801|2100 67 | |Burrows|Donald|Zephyrhills|FL|335405728|400 68 | |Chang|Chung K.|Santa Clara|CA|950513534|1000 69 | |Whitfield|George R.|Seoul Kore|ZZ|11070|200 70 | |Offerdahl|Richard E.|Incline Village|NV|894527899|2400 71 | |Royalty|James W.|Lake Jackson|TX|775666036|500 72 | |Bullard|Darrell E.|Houston|TX|770195726|250 73 | |Cowan|Daniel F.|Galveston|TX|775515861|250 74 | |Franklin|William J.|Mesa|AZ|852057444|250 75 | |Pearlman|Howard B.|FPO AE|ZZ|096360001|250 76 | |Lengel|Thomas E.|Bastrop|TX|78602|250 77 | |Barth|George R.|Cape Coral|FL|339044462|2000 78 | |Martin|John I.|Graham|TX|764504420|250 79 | |Williams|James|Sebring|FL|338721443|250 80 | |Brueggemann|Bernard|Walton|KY|41094|250 81 | |Hansen|Donald P.|Medina|WA|980395321|250 82 | |Siemens|Abe|Rancho Mirage|CA|922703116|500 83 | |Weymer|Richard A.|George West|TX|780223587|2300 84 | |Weymer|Jean Dea|George West|TX|780223587|2300 85 | |Ellinwood|John C.|Tubac|AZ|856461445|2100 86 | |Solcher|Albert Kennedy|Katy|TX|774941824|500 87 | |Euvall|Arland|Brigham City|UT|843023721|500 88 | |Rosenlund|Jack C.|Ennis|MT|597290891|1000 89 | |Buckman|Robert F.|Elkton|MD|219217617|500 90 | |Sturdevant|William E.|Brazoria|TX|77422|1000 91 | |Scudder|Stuart B.|Lomita|CA|907173213|500 92 | |Green|Adam T.|Salt Lake City|UT|841204092|114 93 | |Reynolds|Lois C.|Bellaire|TX|774013402|1150 94 | |Cole|Joshua David|Sarasota|FL|342381508|1000 95 | |Keyston|David H.|Carmel|CA|939217066|500 96 | |Getz|Clarence D.|Helena|MT|596026535|1000 97 | |Abbott|Robert L.|Greenville|NC|278585416|250 98 | |Antosh|Jim|Shawnee|OK|748010506|250 99 | |Lewis|Donald S.|Virginia Beach|VA|234513838|250 100 | |Oden|Ray P.|Shreveport|LA|711061824|2300 101 | |Verster|Jeanette M.|Miami|FL|331431807|2300 102 | -------------------------------------------------------------------------------- /tutorial_files/contributors_candidates.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/tutorial_files/contributors_candidates.sqlite -------------------------------------------------------------------------------- /tutorial_files/contributors_with_candidate_id.txt: -------------------------------------------------------------------------------- 1 | id|last_name|first_name|middle_name|street_1|street_2|city|state|zip|amount|date|candidate_id 2 | |Agee|Steven||549 Laurel Branch Road||Floyd|VA|24091|500.00|2007-06-30|16 3 | |Ahrens|Don||4034 Rennellwood Way||Pleasanton|CA|94566|250.00|2007-05-16|16 4 | |Ahrens|Don||4034 Rennellwood Way||Pleasanton|CA|94566|50.00|2007-06-18|16 5 | |Ahrens|Don||4034 Rennellwood Way||Pleasanton|CA|94566|100.00|2007-06-21|16 6 | |Akin|Charles||10187 Sugar Creek Road||Bentonville|AR|72712|100.00|2007-06-16|16 7 | |Akin|Mike||181 Baywood Lane||Monticello|AR|71655|1500.00|2007-05-18|16 8 | |Akin|Rebecca||181 Baywood Lane||Monticello|AR|71655|500.00|2007-05-18|16 9 | |Aldridge|Brittni||808 Capitol Square Place, SW||Washington|DC|20024|250.00|2007-06-06|16 10 | |Allen|John D.||1052 Cannon Mill Drive||North Augusta|SC|29860|1000.00|2007-06-11|16 11 | |Allen|John D.||1052 Cannon Mill Drive||North Augusta|SC|29860|1300.00|2007-06-29|16 12 | |Allison|John W.||P.O. Box 1089||Conway|AR|72033|1000.00|2007-05-18|16 13 | |Allison|Rebecca||3206 Summit Court||Little Rock|AR|72227|1000.00|2007-04-25|16 14 | |Allison|Rebecca||3206 Summit Court||Little Rock|AR|72227|200.00|2007-06-12|16 15 | |Altes|R.D.||8600 Moody Road||Fort Smith|AR|72903|2300.00|2007-06-21|16 16 | |Andres|Dale||1160 Glen Oaks Drive||West Des Moines|IA|50266|250.00|2007-06-06|16 17 | |Anthony|John||211 Long Island Drive||Hot Springs|AR|71913|2300.00|2007-06-12|16 18 | |Arbogast|Robert||12900 State Route 56 SE||Mount Sterling|OH|43143|500.00|2007-04-08|16 19 | |Arbogast|Robert||12900 State Route 56 SE||Mount Sterling|OH|43143|100.00|2007-06-22|16 20 | |Ardle|William||412 Dakota Avenue||Springfield|OH|45504|50.00|2007-06-28|16 21 | |Atiq|Omar||7200 S Hazel Street||Pine Bluff|AR|71603|1000.00|2007-05-18|16 22 | |Atiq|Omar||7200 S Hazel Street||Pine Bluff|AR|71603|1000.00|2007-06-27|16 23 | |Baker|David||2550 Adamsbrooke Drive||Conway|AR|72034|2300.00|2007-04-11|16 24 | |Bancroft|David||2934 Broderick Street||San Francisco|CA|94123|250.00|2007-04-24|16 25 | |Banks|Charles||P.O. Box 251310||Little Rock|AR|72225|1000.00|2007-05-14|16 26 | |Barbee|John||516 Kellyridge Drive||Apex|NC|27502|500.00|2007-05-23|16 27 | |Buckler|Steve||24351 Armada Dr||Dana Point|CA|926291306|50.00|2007-07-30|20 28 | |Buckler|Steve||24351 Armada Dr||Dana Point|CA|926291306|25.00|2007-08-16|20 29 | |Buckheit|Bruce||8904 KAREN DR||FAIRFAX|VA|220312731|100.00|2007-09-19|20 30 | |Buckel|Linda||PO Box 683130||Park City|UT|840683130|2300.00|2007-08-14|20 31 | |Buckel|Linda||PO Box 683130||Park City|UT|840683130|-2300.00|2007-08-14|20 32 | |Buckel|Linda||PO Box 683130||Park City|UT|840683130|4600.00|2007-08-14|20 33 | |Buck|Thomas||4206 Terrace Street||Kansas City|MO|64111|100.00|2007-09-25|20 34 | |Buck|Jay|K.|1855 Old Willow Rd Unit 322||Northfield|IL|600932918|200.00|2007-09-12|20 35 | |Buck|Blaine|M|45 Eaton Ave||Camden|ME|048431752|2300.00|2007-09-30|20 36 | |Buck|Barbara||1780 NE 138th St||North Miami|FL|331811316|50.00|2007-09-13|20 37 | |Buck|Barbara||1780 NE 138th St||North Miami|FL|331811316|50.00|2007-07-19|20 38 | |Buchman|Mark M||2530 Lawton Ave||San Luis Obispo|CA|934015622|460.80|2007-07-18|20 39 | |Bucher|Ida|M|1400 Warnall Ave||Los Angeles|CA|900245333|100.00|2007-07-10|20 40 | |Buchanek|Elizabeth||7917 Kentbury Dr||Bethesda|MD|208144615|50.00|2007-09-30|20 41 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|500.00|2007-09-24|20 42 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|-500.00|2007-09-24|20 43 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|500.00|2007-09-24|20 44 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|700.00|2007-08-28|20 45 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|-700.00|2007-08-28|20 46 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|1000.00|2007-08-28|20 47 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|1300.00|2007-08-09|20 48 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|200.00|2007-08-14|20 49 | |Buchanan|John||2025 NW 29th Rd||Boca Raton|FL|334316303|500.00|2007-07-25|20 50 | |Buchanan|John||4635 49th St NW||Washington|DC|200164320|200.09|2007-09-23|20 51 | |Harrison|Ryan||2247 3rd St||La Verne|CA|917504918|25.00|2007-07-26|20 52 | |BYNUM|HERBERT||332 SUNNYSIDE ROAD||TAMPA|FL|336177249|-500.00|2008-03-10|22 53 | |BYINGTON|MARGARET|E.|2633 MIDDLEBORO LANE N.E.||GRAND RAPIDS|MI|495061254|-2300.00|2008-03-03|22 54 | |BYERS|BOB|A.|13170 TELFAIR AVENUE||SYLMAR|CA|913423573|-2300.00|2008-03-07|22 55 | |BYERS|AUDREY||2658 LADBROOK WAY||THOUSAND OAKS|CA|913615073|-200.00|2008-03-07|22 56 | |BUSH|KRYSTIE||P.O. BOX 61046||DENVER|CO|802061046|-2300.00|2008-03-06|22 57 | |BUSH|ERIC||P.O. BOX 61046||DENVER|CO|802061046|-2300.00|2008-03-06|22 58 | |BURTON|SUSAN||9338 DEER CREEK DRIVE||TAMPA|FL|336472286|-2300.00|2008-03-05|22 59 | |BURTON|STEVEN|G.|9938 DEER CREEK DRIVE||TAMPA|FL|33647|-2300.00|2008-03-05|22 60 | |BURTON|GLENN|M.|4404 CHARLESTON COURT||TAMPA|FL|336092620|-2300.00|2008-03-05|22 61 | |BURKHARDT|CRAIG|S.|910 15TH STREET N.W.||WASHINGTON|DC|200052503|-500.00|2008-03-07|22 62 | |BURKHARDT|CRAIG|S.|910 15TH STREET N.W.||WASHINGTON|DC|200052503|-1000.00|2008-03-07|22 63 | |BURKHARDT|BARBARA||910 15TH STREET N.W.||WASHINGTON|DC|200052503|-500.00|2008-03-07|22 64 | |BURKE|SUZANNE|M.|3401 EVANSTON||SEATTLE|WA|981038677|-700.00|2008-03-05|22 65 | |BURKE|GAIL||165 E. 32ND STREET|APARTMENT 9E|NEW YORK|NY|100166014|-2000.00|2008-03-05|22 66 | |BURKE|DONALD|J.|12 LOMPOC||RANCHO SANTA MARGA|CA|926881817|-2300.00|2008-03-11|22 67 | |BURGERT|RONALD|L.|5723 PLUMTREE DRIVE||DALLAS|TX|752524926|-1000.00|2008-03-05|22 68 | |BULL|BARTLE|B.|439 E. 51ST STREET||NEW YORK|NY|100226473|-800.00|2008-03-10|22 69 | |BULL|BARTLE|B.|439 E. 51ST STREET||NEW YORK|NY|100226473|-1000.00|2008-03-10|22 70 | |BUKOWSKI|DANIEL|J.|702 S. WRIGHT STREET||NAPERVILLE|IL|605406736|-100.00|2008-03-10|22 71 | |BUISSON|MARGARET|A.|P.O. BOX 197029||LOUISVILLE|KY|402597029|-200.00|2008-03-11|22 72 | |BUCKLEY|WALTER|W.|1635 COUNTRY ROAD||BETHLEHEM|PA|180155718|-100.00|2008-03-05|22 73 | |BUCKLEY|MARJORIE|B.|1635 COUNTRY ROAD||BETHLEHEM|PA|180155718|-100.00|2008-03-05|22 74 | |BRUNO|JOHN||10136 WINDERMERE CHASE BLVD.||GOTHA|FL|347344707|-2300.00|2008-03-06|22 75 | |BRUNO|IRENE||10136 WINDERMERE CHASE BLVD.||GOTHA|FL|347344707|-2300.00|2008-03-06|22 76 | |BROWN|TIMOTHY|J.|26826 MARLOWE COURT||STEVENSON RANCH|CA|913811020|-2300.00|2008-03-06|22 77 | |Schuff|Bryan||1700 W Sweden Rd||Brockport|NY|14420|-25.00|2008-08-22|32 78 | |Hobbs|James||229 Cherry Lane||White House|TN|37188|-25.00|2008-08-19|32 79 | |Ranganath|Anoop||2507 Willard Drive||Charlottesville|VA|22903|-100.00|2008-04-21|32 80 | |Nystrom|Michael|A|93A Fairmont Street||Arlington|MA|02474|-503.00|2008-04-21|32 81 | |Muse|Nina|Jo|2915 Toro Canyon Rd||Austin|TX|78746|-50.00|2008-04-21|32 82 | |Waddell|James|L.|1823 Spel Lane SW||Rochester|MN|55902|-28.00|2008-04-21|32 83 | |Brucks|William|C.|PO Box 391||Corona del Mar|CA|92625|-150.00|2008-04-21|32 84 | |Kuehn|David||14502 West 93rd Street||Lenexa|KS|66215|-330.00|2008-04-21|32 85 | |Verster|Jeanette|M.|7220 SW 61st St||Miami|FL|331431807|-1000.00|2008-04-21|32 86 | |Uihlein|Richard||1396 N Waukegan Rd||Lake Forest|IL|600451147|-2300.00|2008-04-21|32 87 | |Eskenberry|Robert|P|10960 Gray Cir||Westminster|CO|80020|-223.00|2008-04-21|32 88 | |Froehling|Alan|L.|302 Broadway St||Mount Vernon|IL|628645116|-844.80|2008-04-21|32 89 | |Duryea|Marcia|A.|123 Bayview Ave||Amityville|NY|11701|-299.50|2008-04-21|32 90 | |Perreault|Louise||503 Brockridge Hunt Drive||Hampton|VA|23666|-34.08|2008-04-21|32 91 | |Rozenfeld|Timur||57 Herbert Road||Robbinsville|NJ|08691|-777.95|2008-04-21|32 92 | |Kazor|Christopher|M|707 Spindletree ave||Naperville|IL|60565|-2592.00|2008-04-21|32 93 | |Lehner|Thomas|S.|2701 Star Lane||Wadsworth|OH|44281|-200.00|2008-04-21|32 94 | |Plummer|Joseph||587 Blake Hill Rd||New Hampton|NH|032564424|-24.60|2008-04-21|32 95 | |Raught|Philip|M|4714 Plum Way||Pittsburgh|PA|15201|-1046.00|2008-04-21|32 96 | |Ferrara|Judith|D|1508 Waterford Road||Yardley|PA|19067|-1100.00|2008-04-21|32 97 | |Johnson|Cathleen|E.|1003 Justin Ln Apt 2016||Austin|TX|787572648|-14.76|2008-04-21|32 98 | |Sanford|Bradley||940 Post St #43||San Francisco|CA|94109|-24.53|2008-04-21|32 99 | |Gaarder|Bruce||PO Box 4085||Mountain Home AFB|ID|83648|-261.00|2008-04-21|32 100 | |Choe|Hyeokchan||207 Bridle Way||Fort Lee|NJ|070246302|-39.50|2008-04-21|32 101 | |Jacobs|Richard|G.|14337 Tawya Rd||Apple Valley|CA|923075545|-1000.00|2008-04-21|32 102 | |Aaronson|Rebecca||2000 Village Green Dr Apt 12||Mill Creek|WA|980125787|100.00|2008-02-08|34 103 | |Aarons|Elaine||481 Buck Island Rd Apt 17A|APT 17A|West Yarmouth|MA|026733300|25.00|2008-02-26|34 104 | |Aarons|Elaine||481 Buck Island Rd Apt 17A|APT 17A|West Yarmouth|MA|026733300|70.00|2008-02-25|34 105 | |Aarons|Elaine||481 Buck Island Rd Apt 17A|APT 17A|West Yarmouth|MA|026733300|100.00|2008-02-08|34 106 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|50.00|2008-02-29|34 107 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-29|34 108 | |Aaronson|Rebecca||2000 Village Green Dr Apt 12||Mill Creek|WA|980125787|100.00|2008-02-14|34 109 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-24|34 110 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-22|34 111 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-17|34 112 | |Reid|Elizabeth||73 W Patent Rd|OPHIR FARM NORTH|Bedford Hills|NY|105072222|-350.00|2008-08-28|34 113 | |Reich|Thomas||499 Park Ave||New York|NY|100221240|-2300.00|2008-08-28|34 114 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-08|34 115 | |Aaron|Shirley||101 Cherry Ave||Havana|FL|323331311|100.00|2008-02-03|34 116 | |Aaron|Sharron||1804 E Montgomery St||Broken Arrow|OK|740121840|500.00|2008-02-09|34 117 | |Aaron|Patricia||418 NW 35th St||Oklahoma City|OK|731188602|200.00|2008-02-26|34 118 | |Aaron|Patricia||418 NW 35th St||Oklahoma City|OK|731188602|100.00|2008-02-12|34 119 | |Aaron|Jim||2178 Fairway Cir||Canton|MI|481885097|300.00|2008-02-07|34 120 | |Aaron|Jim||2178 Fairway Cir||Canton|MI|481885097|200.00|2008-02-29|34 121 | |Aaron|Carole||PO Box 1806||Ogunquit|ME|039071806|70.00|2008-02-29|34 122 | |Aaron|Carole||PO Box 1806||Ogunquit|ME|039071806|50.00|2008-02-07|34 123 | |Aaron|Carole||PO Box 1806||Ogunquit|ME|039071806|100.00|2008-02-03|34 124 | |Aaron|Barbara||2298 Pacific Ave # 6||San Francisco|CA|941151435|1000.00|2008-02-11|34 125 | |Aanonsen|Lin||897 Raymond Ave||Saint Paul|MN|551141508|250.00|2008-02-21|34 126 | |Aanonsen|Lin||897 Raymond Ave||Saint Paul|MN|551141508|100.00|2008-02-08|34 127 | |BOURNE|TRAVIS||LAGE KAART 77||BRASSCHATT||02930|-500.00|2008-11-20|35 128 | |SECRIST|BRIAN|L.|3 MULE DEER TRAIL||LITTLETON|CO|801275722|-1000.00|2008-04-07|35 129 | |TOLLESTRUP|TRAVIS|W.|16331 WINECREEK RD.||SAN DIEGO|CA|92127|-1000.00|2008-05-15|35 130 | |ACCORD|DEAN|C.|8813 ROBINSON RIDGE ROAD||LAS VEGAS|NV|891175812|500.00|2007-07-13|35 131 | |ABTS|HENRY||P. 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Changing the database: 2 | 3 | h3. SELECT INTO 4 | 5 | h3. ALTER 6 | 7 | h3. DROP 8 | 9 | h2. Optimizing queries. 10 | 11 | h3. CREATE INDEX 12 | 13 | 14 | Creative Commons License
A Gentle Introduction to SQL Using SQLite by Troy Thibodeaux is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. 15 | -------------------------------------------------------------------------------- /tutorial_files/sql_keywords.rtf: -------------------------------------------------------------------------------- 1 | {\rtf1\ansi\ansicpg1252\cocoartf1038\cocoasubrtf250 2 | {\fonttbl\f0\fmodern\fcharset0 Courier;\f1\froman\fcharset0 Times-Roman;} 3 | {\colortbl;\red255\green255\blue255;} 4 | \margl1440\margr1440\vieww9000\viewh8400\viewkind0 5 | \deftab720 6 | \pard\pardeftab720\sa240\ql\qnatural 7 | 8 | \f0\fs24 \cf0 ADD 9 | \f1 \ 10 | 11 | \f0 ALL 12 | \f1 \ 13 | 14 | \f0 ALTER 15 | \f1 \ 16 | 17 | \f0 AND 18 | \f1 \ 19 | 20 | \f0 AS 21 | \f1 \ 22 | 23 | \f0 ASC 24 | \f1 \ 25 | 26 | \f0 BY 27 | \f1 \ 28 | 29 | \f0 CHECK 30 | \f1 \ 31 | 32 | \f0 COLUMN 33 | \f1 \ 34 | 35 | \f0 CREATE 36 | \f1 \ 37 | 38 | \f0 DATE 39 | \f1 \ 40 | 41 | \f0 DEFAULT 42 | \f1 \ 43 | 44 | \f0 DELETE 45 | \f1 \ 46 | 47 | \f0 DESC 48 | \f1 \ 49 | 50 | \f0 DROP 51 | \f1 \ 52 | 53 | \f0 FOR 54 | \f1 \ 55 | 56 | \f0 FROM 57 | \f1 \ 58 | 59 | \f0 IN 60 | \f1 \ 61 | 62 | \f0 INTO 63 | \f1 \ 64 | 65 | \f0 IS 66 | \f1 \ 67 | 68 | \f0 LIKE 69 | \f1 \ 70 | 71 | \f0 NOT 72 | \f1 \ 73 | 74 | \f0 NULL 75 | \f1 \ 76 | 77 | \f0 ON 78 | \f1 \ 79 | 80 | \f0 OR 81 | \f1 \ 82 | 83 | \f0 ORDER 84 | \f1 \ 85 | 86 | \f0 REVOKE 87 | \f1 \ 88 | 89 | \f0 SELECT 90 | \f1 \ 91 | 92 | \f0 SET 93 | \f1 \ 94 | 95 | \f0 TABLE 96 | \f1 \ 97 | 98 | \f0 THEN 99 | \f1 \ 100 | 101 | \f0 TO 102 | \f1 \ 103 | 104 | \f0 UNIQUE 105 | \f1 \ 106 | 107 | \f0 UPDATE 108 | \f1 \ 109 | 110 | \f0 WITH 111 | \f1 \ 112 | } -------------------------------------------------------------------------------- /tutorial_files/sql_keywords.txt: -------------------------------------------------------------------------------- 1 | SQL Data Statements: 2 | SELECT 3 | INSERT 4 | UPDATE 5 | DELETE 6 | 7 | SQL Schema Statements 8 | CREATE TABLE 9 | CREATE VIEW 10 | DROP TABLE 11 | DROP VIEW 12 | GRANT 13 | REVOKE 14 | ALTER 15 | 16 | SQL-Transaction 17 | COMMIT 18 | ROLLBACK 19 | 20 | Modifiers 21 | WHERE 22 | FROM 23 | DISTINCT 24 | IS NULL (IS NOT NULL) 25 | ORDER BY (ASC, DESC) 26 | GROUP BY 27 | HAVING 28 | AND & OR 29 | JOIN 30 | UNION 31 | IN 32 | INTO 33 | BETWEEN 34 | LIKE 35 | COUNT 36 | MAX 37 | MIN 38 | SUM 39 | LIKE 40 | AVERAGE 41 | JOIN 42 | ALIAS (AS?) 43 | OUTER JOIN 44 | CONCAT 45 | SUBSTRING 46 | TRIM 47 | -------------------------------------------------------------------------------- /tutorial_files/sql_tutorial.txt: -------------------------------------------------------------------------------- 1 | A Gentle Introduction to SQL using SQLite 2 | 3 | SQL or Structured Query language is the language used to communicate with relational databases. What are relational databases? Well, most of the popular database systems you may know, such as MS Access, MySQL or SQLite, are all relational. That is, they all use a relational model, which, it turns out, can be described much like a spreadsheet: 4 | * Data is organized into tables (relations) that represent a collection of similar objects (e.g. contributors). 5 | * The columns of the table represent the attributes that members of the collection share (last name, home address, amount of contribution). 6 | * Each row in the table represents an individual member of the collection (one contributor). 7 | * And the fields in the row represent the attributes of that individual (Smith, 1228 Laurel St., $250). 8 | 9 | Much of the power of a relational database lies in the ability to query these relations, both within a table (give me all contributors who donated at least $500 and who live in Wyoming) and among tables (from the contributors, judges and litigants tables, give me all contributors who donated at least $1000 to Judge Crawford and who also had legal cases over which Judge Crawford presided). SQL is the powerful and rather minimalist language we use to ask such questions of our data in a relational database. How minimalist is SQL? The basic vocabulary comes down to a few main verbs: 10 | 11 | SELECT 12 | INSERT 13 | UPDATE 14 | DELETE 15 | 16 | I imagine you can guess what each of those verbs does, even if you've never written a database query. 17 | To create and change tables in the database, there are a few other verbs to use: 18 | 19 | CREATE 20 | DROP 21 | ALTER 22 | 23 | Those are the keyword verbs for almost everything you need to do. The language also includes a number of modifiers that help specify the action of the verbs, but the core list comes down to a couple dozen words. These basic keywords are common across pretty much all relational databases. A specific database management system (Access, MySQL or SQLite) may add its own extensions to the common keywords, but the lion's share of the work is done with this handful of words, and they're basically the same across database applications. 24 | 25 | By combining these simple keywords, you can create remarkably complex and specific queries. And the basic syntax still reads fairly clearly: 26 | SELECT last_name FROM contributors WHERE state = 'WY'; 27 | 28 | The SQL query above reads pretty much like the English sentence for the same request: "Select the last name from the contributors table for all contributors whose state is WY." If you're using a graphical interface for Access or another database, that interface is simply constructing queries like these behind the scenes. So, why not 29 | take command of your queries and write them yourself? 30 | 31 | So, let's dive in. For this tutorial, we will be using SQLite, a free and open source database manager that's lightweight and portable. 32 | 33 | DO WE HAVE TO INSTALL SINCE WE'RE USING FIREFOX SQLITE MANAGER? 34 | First, we have to install SQLite. 35 | 36 | For Mac: 37 | If you're using a Mac with OS X 10.5 Leopard or newer, do nothing. It's already installed. 38 | Open up your Terminal (here's how), and type 'which sqlite3'. 39 | You should see the path to SQLite 3, which looks something like this: /usr/local/bin/sqlite3. 40 | Now test the SQLite command prompt. Type 'sqlite3'. You should see something like this: 41 | 42 | SQLite version 3.6.22 43 | Enter ".help" for instructions 44 | Enter SQL statements terminated with a ";" 45 | sqlite> 46 | 47 | For PC: 48 | First you will need to download two files from the SQLite web site: 49 | 1) Go to http://www.sqlite.org/download.html 50 | 2) Under the ‘Precompiled Binaries For Windows’ section, download these two files: 51 | 52 | sqlite-3_6_23.zip (214.32 KiB) 53 | A command-line program for accessing and modifing SQLite databases. 54 | See the documentation for additional information. 55 | 56 | sqlitedll-3_6_23.zip (213.17 KiB) 57 | This is a DLL of the SQLite library without the TCL bindings. 58 | The only external dependency is MSVCRT.DLL. 59 | 60 | Download the zip files to C:\Program Files (Technically, you just need to download these files to somewhere your system can find them; any folder defined in the system PATH will do). 61 | 62 | 3) Unzip the files. (If you do not have WinZip or another Zip archive extractor on your system, then you may need to download one, such as Aladdin Expander: http://download.cnet.com/Aladdin-Expander/3000-2250_4-2931968.html.) 63 | 64 | 4) Inside the unzipped sqlite-3_6_23, you should find sqlite3.exe. Click on that file, and you should see a black box pop up with something like 65 | SQLite version 3.6.22 66 | Enter ".help" for instructions 67 | Enter SQL statements terminated with a ";" 68 | sqlite> 69 | 70 | At this point you have done all the configuration and installation you need to do for SQLite. Taxing, I know. 71 | You are looking at a command-line interface for SQLite. You can type all of your SQL commands here, and eventually you may want to. But we're going to use a different interface to get started. If you want to check out the command-line interface, type ".help" and see what's there. When you're ready, go ahead and exit from that prompt 72 | by typing ".exit". Close the Terminal window for Mac or the command console for Windows. 73 | 74 | 75 | Install the SQLite Manager Firefox Plugin 76 | Go to https://addons.mozilla.org/en-US/firefox/addon/5817 and click Add to Firefox. 77 | Click Install Now. 78 | Restart Firefox. 79 | In the Tools menu, you should find the SQLite Manager: [screen shot] 80 | Clicking that item should bring up the manager. It's a simple interface for creating and interacting 81 | with your SQLite databases. 82 | 83 | 84 | Creating the First Database 85 | Mousing over each of the icons at the top of the SQLite Manager tool will show what the icon does. 86 | To create a database, simply click on the icon for "New Database" [screen shot] 87 | Name the database "test" and save it anywhere you like (the desktop will work, or your documents folder). 88 | 89 | Creating a Table 90 | Click the "New Table" icon, and you'll get a form allowing you to create a new table. 91 | To create a table, we have to define the attributes or columns it contains. For each column, we define the 92 | datatype of the data it will contain. Define the columns as follows: [screen shot] 93 | 94 | Name the table "contributors." 95 | The id field will be a unique identifier for each contributor (and therefore will be the "Primary Key" for the row). It will be an integer that will automatically increment with each row we add, and it cannot be null or empty (because we need it as the unique identifier. 96 | 97 | The next three columns will all contain text strings of undetermined lengths (last names, for example, come in all kinds of lengths), so we'll use the VARCHAR or "variable character" datatype. 98 | 99 | For the state and zip columns, if we know that the number of characters will always be the same (for example, 2 for state postal abbreviation), we could use the CHAR character type, rather than VARCHAR. The pros and cons of choosing between the two vary among database management systems. SQLite doesn't really distinguish between the two, so we'll just use VARCHAR to keep things simple. 100 | 101 | By the way, It may seem strange that the zip column uses a VARCHAR datatype, but remember that some zips start with a 0 (00501 is in NY). So, we want to treat this column as a string of text, rather than as a number (which would be 501). 102 | 103 | Click OK, and you will see a dialogue box with the full SQL statement that the manager will execute on your database: 104 | CREATE TABLE "contributors" ("id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL , "last_name" VARCHAR, "first_name" VARCHAR, "city" VARCHAR, "state" VARCHAR, "zip" VARCHAR, "amount" INTEGER) 105 | 106 | The syntax should be clear, since it just reflects the choices we made in the form. Before you click OK, you can edit the SQL in the box. 107 | 108 | SQLite is not at all particular about datatypes, but most other relational databases are, so it's good practice to define the datatypes of a column in a meaningful way. In practice, if we know maximum the length of the string, as in a date or zipcode, we could define it as part of the column definition: VARCHAR(2) or VARCHAR(5). 109 | 110 | 111 | The syntax is "CREATE TABLE" followed by the 112 | 113 | 114 | http://www.firstsql.com/tutor.htm 115 | http://www.sqlite.org/lang_keywords.html 116 | 117 | SQL Data Statements: 118 | SELECT 119 | INSERT 120 | UPDATE 121 | DELETE 122 | 123 | SQL Schema Statements 124 | CREATE TABLE 125 | CREATE VIEW 126 | DROP TABLE 127 | DROP VIEW 128 | GRANT 129 | REVOKE 130 | ALTER 131 | 132 | SQL-Transaction 133 | COMMIT 134 | ROLLBACK 135 | 136 | Modifiers 137 | WHERE 138 | FROM 139 | DISTINCT 140 | IS NULL (IS NOT NULL) 141 | ORDER BY (ASC, DESC) 142 | GROUP BY 143 | HAVING 144 | AND & OR 145 | JOIN 146 | UNION 147 | IN 148 | INTO 149 | BETWEEN 150 | LIKE 151 | COUNT 152 | MAX 153 | MIN 154 | SUM 155 | LIKE 156 | AVERAGE 157 | JOIN 158 | ALIAS (AS?) 159 | OUTER JOIN 160 | CONCAT 161 | SUBSTRING 162 | TRIM 163 | -------------------------------------------------------------------------------- /tutorial_files/sqlite_install.textile: -------------------------------------------------------------------------------- 1 | First, we have to install SQLite. 2 | 3 | For Mac: 4 | If you're using a Mac with OS X 10.5 Leopard or newer, do nothing. It's already installed. 5 | Open up your Terminal (here's how), and type 'which sqlite3'. 6 | You should see the path to SQLite 3, which looks something like this: /usr/local/bin/sqlite3. 7 | Now test the SQLite command prompt. Type 'sqlite3'. You should see something like this: 8 | 9 | SQLite version 3.6.22 10 | Enter ".help" for instructions 11 | Enter SQL statements terminated with a ";" 12 | sqlite> 13 | 14 | For PC: 15 | First you will need to download two files from the SQLite web site: 16 | 1) Go to http://www.sqlite.org/download.html 17 | 2) Under the ‘Precompiled Binaries For Windows’ section, download these two files: 18 | 19 | sqlite-3_6_23.zip (214.32 KiB) 20 | A command-line program for accessing and modifing SQLite databases. 21 | See the documentation for additional information. 22 | 23 | sqlitedll-3_6_23.zip (213.17 KiB) 24 | This is a DLL of the SQLite library without the TCL bindings. 25 | The only external dependency is MSVCRT.DLL. 26 | 27 | Download the zip files to C:\Program Files (Technically, you just need to download these files to somewhere your system can find them; any folder defined in the system PATH will do). 28 | 29 | 3) Unzip the files. (If you do not have WinZip or another Zip archive extractor on your system, then you may need to download one, such as Aladdin Expander: http://download.cnet.com/Aladdin-Expander/3000-2250_4-2931968.html.) 30 | 31 | 4) Inside the unzipped sqlite-3_6_23, you should find sqlite3.exe. Click on that file, and you should see a black box pop up with something like 32 | 
33 | SQLite version 3.6.22
34 | Enter ".help" for instructions
35 | Enter SQL statements terminated with a ";"
36 | sqlite> 
37 |
38 | 39 | At this point you have done all the configuration and installation you need to do for SQLite. Taxing, I know. 40 | You are looking at a command-line interface for SQLite. You can type all of your SQL commands here, and eventually you may want to. But we're going to use a different interface to get started. If you want to check out the command-line interface, type ".help" and see what's there. When you're ready, go ahead and exit from that prompt 41 | by typing ".exit". Close the Terminal window for Mac or the command console for Windows. 42 | 43 | 44 | -------------------------------------------------------------------------------- /tutorial_files/sqlite_keywords.rtf: -------------------------------------------------------------------------------- 1 | {\rtf1\ansi\ansicpg1252\cocoartf1038\cocoasubrtf250 2 | {\fonttbl\f0\froman\fcharset0 Times-Roman;} 3 | {\colortbl;\red255\green255\blue255;} 4 | \margl1440\margr1440\vieww9000\viewh8400\viewkind0 5 | \deftab720 6 | \pard\pardeftab720\ql\qnatural 7 | 8 | \f0\fs24 \cf0 ABORT\ 9 | ACTION\ 10 | ADD\ 11 | AFTER\ 12 | ALL\ 13 | ALTER\ 14 | ANALYZE\ 15 | AND\ 16 | AS\ 17 | ASC\ 18 | ATTACH\ 19 | AUTOINCREMENT\ 20 | BEFORE\ 21 | BEGIN\ 22 | BETWEEN\ 23 | BY\ 24 | CASCADE\ 25 | CASE\ 26 | CAST\ 27 | CHECK\ 28 | COLLATE\ 29 | COLUMN\ 30 | COMMIT\ 31 | CONFLICT\ 32 | CONSTRAINT\ 33 | CREATE\ 34 | CROSS\ 35 | CURRENT_DATE\ 36 | CURRENT_TIME\ 37 | CURRENT_TIMESTAMP\ 38 | DATABASE\ 39 | DEFAULT\ 40 | DEFERRABLE\ 41 | DEFERRED\ 42 | DELETE\ 43 | DESC\ 44 | DETACH\ 45 | DISTINCT\ 46 | DROP\ 47 | EACH\ 48 | ELSE\ 49 | END\ 50 | ESCAPE\ 51 | EXCEPT\ 52 | EXCLUSIVE\ 53 | EXISTS\ 54 | EXPLAIN\ 55 | FAIL\ 56 | FOR\ 57 | FOREIGN\ 58 | FROM\ 59 | FULL\ 60 | GLOB\ 61 | GROUP\ 62 | HAVING\ 63 | IF\ 64 | IGNORE\ 65 | IMMEDIATE\ 66 | IN\ 67 | INDEX\ 68 | INDEXED\ 69 | INITIALLY\ 70 | INNER\ 71 | INSERT\ 72 | INSTEAD\ 73 | INTERSECT\ 74 | INTO\ 75 | IS\ 76 | ISNULL\ 77 | JOIN\ 78 | KEY\ 79 | LEFT\ 80 | LIKE\ 81 | LIMIT\ 82 | MATCH\ 83 | NATURAL\ 84 | NO\ 85 | NOT\ 86 | NOTNULL\ 87 | NULL\ 88 | OF\ 89 | OFFSET\ 90 | ON\ 91 | OR\ 92 | ORDER\ 93 | OUTER\ 94 | PLAN\ 95 | PRAGMA\ 96 | PRIMARY\ 97 | QUERY\ 98 | RAISE\ 99 | REFERENCES\ 100 | REGEXP\ 101 | REINDEX\ 102 | RELEASE\ 103 | RENAME\ 104 | REPLACE\ 105 | RESTRICT\ 106 | RIGHT\ 107 | ROLLBACK\ 108 | ROW\ 109 | SAVEPOINT\ 110 | SELECT\ 111 | SET\ 112 | TABLE\ 113 | TEMP\ 114 | TEMPORARY\ 115 | THEN\ 116 | TO\ 117 | TRANSACTION\ 118 | TRIGGER\ 119 | UNION\ 120 | UNIQUE\ 121 | UPDATE\ 122 | USING\ 123 | VACUUM\ 124 | VALUES\ 125 | VIEW\ 126 | VIRTUAL\ 127 | WHEN\ 128 | WHERE} -------------------------------------------------------------------------------- /tutorial_files/test.sqlite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/tthibo/SQL-Tutorial/0efb78bee64373c0f96bfdcbfbae124dd7d1103e/tutorial_files/test.sqlite --------------------------------------------------------------------------------