From 8e71344096a24c7ffe179bf08f6789a5214d2175 Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Wed, 4 Mar 2015 22:47:47 -0600 Subject: [PATCH 01/25] Adding NICAR2015 files --- NICAR2015/candidates.txt | 7 + NICAR2015/committees.txt | 7 + NICAR2015/contributions.txt | 1004 +++++++++++++++++ .../images/create_contributions_table.png | Bin 0 -> 91946 bytes NICAR2015/part1_steps.textile | 114 ++ 5 files changed, 1132 insertions(+) create mode 100644 NICAR2015/candidates.txt create mode 100644 NICAR2015/committees.txt create mode 100644 NICAR2015/contributions.txt create mode 100644 NICAR2015/images/create_contributions_table.png create mode 100644 NICAR2015/part1_steps.textile diff --git a/NICAR2015/candidates.txt b/NICAR2015/candidates.txt new file mode 100644 index 0000000..7e13a0a --- /dev/null +++ b/NICAR2015/candidates.txt @@ -0,0 +1,7 @@ +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 +P00003608|CAIN, HERMAN|REP|2012|US|P|00|C|P|C00496067|PO BOX 2158||STOCKBRIDGE|GA|30281 +P20002721|SANTORUM, RICHARD J.|REP|2012|US|P|00|C|C|C00496034|P.O. BOX 37||VERONA|PA|15147 +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 +P80000748|PAUL, RON|REP|2012|US|P|00|C|C|C00495820|220 BLACKSTOCK LANE||LAKE JACKSON|TX|77566 +P80003338|OBAMA, BARACK|DEM|2012|US|P|00|I|C|C00431445|PO BOX 8102||CHICAGO|IL|60680 +P80003353|ROMNEY, MITT / RYAN, PAUL D. |REP|2012|US|P|00|C|C|C00431171|585 COMMERCIAL ST.||BOSTON|MA|02109 diff --git a/NICAR2015/committees.txt b/NICAR2015/committees.txt new file mode 100644 index 0000000..8cc00f1 --- /dev/null +++ b/NICAR2015/committees.txt @@ -0,0 +1,7 @@ +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 +C00431171|ROMNEY FOR PRESIDENT INC.|CRATE, DARRELL|585 COMMERCIAL ST.||BOSTON|MA|02109|P|P|REP|M||ROMNEY VICTORY INC.|P80003353 +C00431445|OBAMA FOR AMERICA|NESBITT, MARTIN H|PO BOX 8102||CHICAGO|IL|60680|P|P|DEM|M||OBAMA VICTORY FUND 2012|P80003338 +C00495820|RON PAUL 2012 PRESIDENTIAL CAMPAIGN COMMITTEE INC.|PYEATT, LORI|845 W PLANTATION DR||CLUTE|TX|77531|P|P|REP|M|||P80000748 +C00496034|RICK SANTORUM FOR PRESIDENT INC.|MAENZA, NADINE|PO BOX 37||VERONA|PA|15147|P|P|REP|Q|||P20002721 +C00496067|FRIENDS OF HERMAN CAIN INC|MARK J BLOCK|PO BOX 2158||STOCKBRIDGE|GA|30281|P|P|REP|Q|||P00003608 +C00500587|RICKPERRY.ORG INC|PURPURA, SALVATORE MR.|228 S WASHINGTON STREET|STE 115|ALEXANDRIA|VA|22314|P|P|REP|Q|||P20003281 diff --git a/NICAR2015/contributions.txt b/NICAR2015/contributions.txt new file mode 100644 index 0000000..561e484 --- /dev/null +++ b/NICAR2015/contributions.txt @@ -0,0 +1,1004 @@ +committee_id|entity_type|last_name|first_name|city|state|zip_code|employer|occupation|amount +C00496497|IND|DAYTON|PETER|STUART|FL|34996|PHYSICIANS TO WOMEN P.A.|PHYSICIAN|1000 +C00496497|IND|COHEN|ALAN MR.|ATLANTA|GA|30360|PULL-A-PART L.L.C|EXECUTIVE|-2500 +C00496497|IND|COHEN|GREGG MR.|ATLANTA|GA|30360|PULL-A-PART|EXECUTIVE|5000 +C00496497|IND|DENAULT|GERARD MR.|DANBURY|CT|06810|SAIC|IT CONSULTANT|500 +C00496497|IND|DEWBERRY|BETH W. MRS.|GRAYSON|GA|30017|REALTOR|RETIRED|200 +C00496497|IND|DICKERSON|MICHAEL|ATLANTA|GA|30327|PGI|EXECUTIVE|250 +C00496497|IND|MERRITT|CAROL|SAN ANTONIO|TX|78256|RETIRED|RETIRED|2500 +C00496497|IND|MERRITT|DAVID|ALEXANDRIA|VA|22315|CENTER FOR HEALTH TRANSFORMATION|EVP|2500 +C00496497|IND|MEYERS|CINDY|BEAUMONT|TX|77708|RETIRED|RETIRED|250 +C00496497|IND|MEYERS|SUSAN|DECATUR|GA|30033|SELF|PUBLIC RELATIONS CONSULTANT|500 +C00500587|IND|AESCH|MARK|LIVONIA|NY|14487||INFORMATION REQUESTED PER BEST EFFORTS|2500 +C00500587|IND|AGA|DONNIE|MISSOURI CITY|TX|77459|KELSEY-SEYBOLD CLICIC|PHYSICIAN|250 +C00500587|IND|AGEE|KRISTINE MRS.|HOUSTON|TX|77024|HOMEMAKER|HOMEMAKER|2500 +C00500587|IND|AGEE|MARIE|BIRMINGHAM|AL|35242|HEALTH TALENTS|FUNDRAISER/ADM|250 +C00500587|IND|AGEE|RICHARD B. MR.|HOUSTON|TX|77024|FORTITUDE MANAGEMENT GROUP L.L.C.|OIL & GAS|2500 +C00500587|IND|AGGARWAL|ANIL|HOUSTON|TX|77005|PROFESSIONAL AUDIO VIDEO|BUSINESS|2500 +C00500587|IND|AGRAWAL|SUSHILA MRS.|HOUSTON|TX|77005|PIPING TECHNOLOGY & PRODUCTS INC.|VICE PRESIDENT|2500 +C00500587|IND|AGRAWAL|VIKIE MRS.|SUGAR LAND|TX|77479|NITU ENTERPRISES LP|MANAGING DIRECTOR|2500 +C00500587|IND|AGUILAR|CORINA MRS.|EL PASO|TX|79925|HOMEMAKER|HOMEMAKER|2500 +C00500587|IND|AGUIRRE|LIONEL B. MR.|AUSTIN|TX|78739|SELF EMPLOYED|LOBBYIST|2500 +C00500587|IND|ALKEK|JAMES|HOUSTON|TX|77025||INFORMATION REQUESTED PER BEST EFFORTS|250 +C00500587|IND|ALKHAWAM|AHMAD MR.|ARLINGTON|TX|76006|DAMASS INC.|DIRECTOR|1000 +C00500587|IND|ALLEN|CLAUDE R. MR.|BEAVER|WV|25813|PHILLIPS|SALES MANAGER|5000 +C00500587|IND|ALLEN|CLYDE III|BRENTWOOD|TN|37027|WELLS FARGO ADVISORS|FINANCIAL ADVISOR|250 +C00500587|IND|AHLBORN|TOM|WINDSOR|CA|95492|AHLBORN STRUCTURAL STEEL INC|CONTRACTOR|250 +C00500587|IND|AINSWORTH|GEORGE W. MR.|COPPELL|TX|75019|ORACLE RESOURCES|EXECUTIVE VICE PRESIDENT|2500 +C00500587|IND|ALBEANU|DAVID|CHINO HILLS|CA|91709|EE NET SUPPLY INC|PRESIDENT|500 +C00500587|IND|ALEXANDER|CAROLYN J. DR.|LOS ANGELES|CA|90049|CEDARS SINAI HOSPITAL|MEDICAL DOCTOR|500 +C00500587|IND|ALLEN|DIANE|CEDAR RAPIDS|IA|52403|SELF EMPLOYED|MASSAGE THERAPY|250 +C00500587|IND|ALLEN|HAROLD MR.|EL PASO|TX|79922|M.M.I.|PRESIDENT|2000 +C00496067|IND|MILLER|MICHAEL|BETHESDA|MD|20816|SELF-EMPLOYED|INVESTMENTS|200 +C00496067|IND|KNIGHT|WALTER|TROY|MI|48083|KNIGHT & FIRTH PC|ATTORNEY|1000 +C00496067|IND|SHEPARD|THEO|MURFREESBORO|TN|37130|SELF|MD|250 +C00496067|IND|REEVES|EARL|KEMPNER|TX|76539|INFORMATION REQUESTED|RURAL ROUTE POSTAL CARRIER|250 +C00496067|IND|CHICK|TERRY|MARANA|AZ|85658|RETIRED|RETIRED|250 +C00496067|IND|WHITAKER|COLIE III|MABLETON|GA|30126||INFORMATION REQUESTED|2500 +C00496067|IND|BARNETT|FLORENCE|ALPHARETTA|GA|30004|SELF EMPLOYED|PHYSICIAN|500 +C00496067|IND|WEISS|MARYELLEN|HINSDALE|IL|60521||INFORMATION REQUESTED|250 +C00496067|IND|GIL|CHRISTIAN|PATTERSON|LA|70392|SELF|MANAGER|250 +C00496067|IND|EBY|RICHARD|TULSA|OK|74104|STAGHORN ENERGY LLC|LANDMAN|500 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UNIVERSITY|RESEARCHER|500 +C00431445|IND|MOORE|CHARLOTTE|ESSEX|MA|01929|SELF-EMPLOYED|ARCHITECT|500 +C00431445|IND|BAKKER|WILLEM|HUME|IL|61932|NOT EMPLOYED|TEACHER|200 +C00431445|IND|DEMONT|JUDITH LYNN|LEXINGTON|MA|02421||INFORMATION REQUESTED|200 +C00431445|IND|FULMER|INGRID|COLUMBIA|SC|29201|NOT EMPLOYED|NOT EMPLOYED|250 +C00431445|IND|WATERSTON|SAM|GARRISON|NY|10524|NEW YORK PUBLIC THEATER|ACTOR|2500 +C00431445|IND|HAEMMEL|MABEL D|CHAPEL HILL|NC|27514|NOT EMPLOYED|RETIRED|300 +C00431445|IND|REKUC|MARYANN|BROOKLYN|NY|11211|PFIZER INC|LAWYER|1000 +C00431445|IND|HERSHEWE|ALISON|JOPLIN|MO|64804|THE HERSHEWE LAW FIRM, PC|ATTORNEY|500 +C00431445|IND|ZEORNES|WAYNE|HOUSTON|TX|77066|RETIRED|GEOPHYSICIST|250 +C00431445|IND|MC EVOY|CONNIE|BOYNTON BEACH|FL|33472|RETIRED|RETIRED|200 +C00431445|IND|CALDWELL|HARRY C.|SCOTTVILLE|MI|49454|RETIRED|RETIRED|200 +C00431445|IND|LENTINI|JOHN|BIG PINE KEY|FL|33043|SELF-EMPLOYED|FIRE INVESTIGATION CONSULTANT|250 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BERCK, LLC|ATTORNEY|250 +C00431445|IND|HAMILTON|MARION|BANGOR|ME|04401||INFORMATION REQUESTED|212 +C00431445|IND|BENSON|BRETT|SAINT PAUL|MN|55105|ST. JUDE MEDICAL|CUSTOMER SERVICE DIRECTOR|500 +C00431445|IND|WHITE|ALEXANDER|BYFIELD|MA|01922|NOT EMPLOYED|RETIRED|2000 +C00431445|IND|APPLEYARD|VERA|CULVER CITY|CA|90232|ELEMENTS BEHAVIORAL HEALTH|VICE PRESIDENT, INTERNET MARKETING|500 +C00431445|IND|MASTROMATTEO|GIOVANNI|PFLUGERVILLE|TX|78660|INFORMATION REQUESTED|INFORMATION SECURITY ANALYST|200 +C00431445|IND|ELLMAN|STEVEN J|NEW YORK|NY|10024|SELF-EMPLOYED|PSYCHOLOGIST|250 +C00431445|IND|RIDDELL|JEAN|PARKER|CO|80134|RETIRED|RETIRED|390 +C00431445|IND|STAVISKY|KATHERINE|WALTHAM|MA|02451|DAVID BEAL INTERIORS|BOOKKEEPER|250 +C00431445|IND|JONES|KRISTI|SEATTLE|WA|98136|VIRTUOSO|PRESIDENT|500 +C00431445|IND|BALL|ANDREA|LONG BEACH|CA|90807|CA STATE UNIV OFFICE OF CHANCELLOR|PROGRAM DIRECTOR|500 +C00431445|IND|MEYER|LAURA|BURLINGTON|VT|05495|HOMEMAKER|HOMEMAKER|722 +C00431445|IND|WILDER|JOANNE ROSS|PITTSBURGH|PA|15219|WILDER AND MAHOOD PC|LAWYER|1000 +C00431445|IND|STRONG|EDWARD|NEW YORK|NY|10024|DODGER THEATRICALS LTD|THEATRICAL PRODUCER|250 +C00431445|IND|TROTTER|RHONDA|LOS ANGELES|CA|90008|KAYE SCHOLER LLP|ATTORNEY|500 +C00431445|IND|DYKERS|DOROTHY ANNE|SILVER SPRING|MD|20910|SELF-EMPLOYED|THERAPIST|250 +C00431445|IND|BENECKI|SHIRLEY|SEATTLE|WA|98117||INFORMATION REQUESTED|200 +C00431445|IND|DORIAN|PETER|FRESNO|CA|93711|SELF-EMPLOYED|AUDIOLOGIST|200 +C00431445|IND|MAGNESON|PAMELA|CRESSEY|CA|95312|MAGNESON DAIRY, INC.|SECRETARY|250 +C00431445|IND|ZIMBEROFF|RAFAEL|SEATTLE|WA|98122|SELF-EMPLOYED|BUSINESS OWNER|250 +C00431445|IND|CHABOT|BRIAN F|ITHACA|NY|14850|CORNELL UNIVERSITY|PROFESSOR|250 +C00431445|IND|UNDERWOOD|ALICIA|NEW CANAAN|CT|06840|AC UNDERWOOD ASSOC, LLC|PHARMACIST|1000 +C00431445|IND|MILLER|ROBERT|EAST BRIDGEWATER|MA|02333|GENZYME CORPORATION|VICE PRESIDENT|300 +C00431445|IND|BRODEN|NANCY|SAN FRANCISCO|CA|94107|TWITTER, INC.|DESIGNER|250 +C00431445|IND|TIELBORG|MICHAEL|CHAPEL HILL|NC|27516|UNC - CHAPEL HILL|PHYSICIAN|250 +C00431445|IND|ALLAN|MARY S|CHICAGO|IL|60637|RETIRED|RETIRED|200 +C00431445|IND|HEALY|MARY C.|HILLSDALE|NJ|07642|HOME BOX OFFICE INC|CABLE TELEVISION EXECUTIVE|250 +C00431445|IND|YOUSDEF|ROGER|MARBLE FALLS|TX|78654|YOUSSEF DIAMOND MINING COMPANY|GEOLOGIST|500 +C00431445|IND|CALANTONI|VICTORIA L.|EASTON|PA|18045|SELF-EMPLOYED|BUILDER DEVELOPER|500 +C00431445|IND|VAIL|CHARLES D.|CENTENNIAL|CO|80121|RETIRED|RETIRED|250 +C00431445|IND|COPELAN|JANE GAIL|SANTA BARBARA|CA|93103|SELF-EMPLOYED|INVESTOR|300 +C00431445|IND|MALAK|GERALDIN|MIDDLETON|WI|53562|NOT EMPLOYED|RETIRED|200 +C00431445|IND|VAN WYK|SUZANNE|TALLAHASSEE|FL|32312|BRYANT, MILLER & OLIVE|ATTORNEY|250 +C00431445|IND|BING|JOANNE|NEW YORK|NY|10028|RETIRED|RETIRED|500 +C00431445|IND|CLARKE|DEIDRE|LITTLETON|CO|80120|SELF-EMPLOYED|VOLUNTEER|200 +C00431445|IND|ADAMS|JUNE ANN|COLORADO SPRINGS|CO|80906|RETIRED|RETIRED|250 +C00431445|IND|ROWLAND|MARILYN|CHARLOTTE|NC|28211|RETIRED|RN|5000 +C00431445|IND|POLANER|MELISSA|MONTCLAIR|NJ|07042|SELF-EMPLOYED|LAWYER|1000 +C00431445|IND|NELSON|BRUCE|SILVER CITY|IA|51571|L-3 COMMUNICATIONS INTERGRATED SYSTEMS|EXECUTIVE|200 +C00431445|IND|DONNELLY|ANGELA|DENVER|CO|80218|NOT EMPLOYED|RETIRED|250 +C00431445|IND|HART|ROBERT|PONTE VEDRA BEACH|FL|32082|RETIRED|RETIRED|500 +C00431445|IND|SHIVELY|TIMOTHY|ALLEN|TX|75002|RAYTHEON|ELECTRICAL ENGINEER|250 +C00431445|IND|HICKS|KAREN T|BLOOMINGTON|IN|47401|RETIRED|RETIRED|250 +C00431445|IND|GAFARIAN|KATHLEEN|SPRINGFIELD|MO|65804|ST. 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JR.|BERKELEY|CA|94710|SAN FRANCISCO SYMPHONY ORCHESTRA|MUSICIAN|300 +C00431445|IND|LENTZ|ERIC|RENO|NV|89502|CARDMI CENTRIX|ENGINEER|212 +C00431445|IND|BERGGREN|SONJA G|LONG BEACH|CA|90803|PANNDORA PRODUCTIONS|PRODUCER/ACTOR|500 +C00431445|IND|CRIBARO|CHRISTOPHER D.|CHICAGO|IL|60656|RETIRED|RETIRED|750 +C00431445|IND|SALINAS|ANA MARTA|EMERYVILLE|CA|94608|SELF-EMPLOYED|MD|250 +C00431445|IND|LAWSON|KELLI R R|BETHESDA|MD|20817|K4 AND CO LLC|CEO|1000 +C00431445|IND|HAVRILLA|MARSHA|BARDONIA|NY|10954|COUNTY ANIMAL HOSPITAL|CLERGY|200 +C00431445|IND|ROSINSKI|JOHN|ORLANDO|FL|32814|CISCO|CONSULTANT|2500 +C00431445|IND|FLETCHER|JAMES|ANGELS CAMP|CA|95222|RETIRED|RETIRED|250 +C00431445|IND|GREENE|SPENCER|PALO ALTO|CA|94301|JUNIPER NETWORKS|MANAGEMENT|500 +C00431445|IND|GREEN|WILLIAM|SHORELINE|WA|98177|RETIRED|PHYSICIAN|250 +C00431445|IND|JONES|ALVINA|BELTSVILLE|MD|20705||INFORMATION REQUESTED|300 +C00431445|IND|SCHMID|THEODORA|TAMPA|FL|33611|HILLSBOROUGH COUNTY SCHOOLS|COUNSELOR|250 +C00431445|IND|HOVANEC|ANDREW|COLUMBIA|MD|21045|MICROS SYSTEMS INC|SOFTWARE TEST SPECIALIST|250 +C00431445|IND|FOSSO|CONSTANCE|GLENDALE|CA|91201|THOMSON-ELITE|TEST ENGINEER|200 +C00431445|IND|KNUDSEN|HARRY J.|MUSKEGON|MI|49445|SELF-EMPLOYED|ATTORNEY|200 +C00431445|IND|RUDKIN|JOHN|WINSTON SALEM|NC|27103||INFORMATION REQUESTED|212 +C00431445|IND|EMETT|MARY ANN|NEWPORT BEACH|CA|92661|SELF-EMPLOYED|ARTIST|2500 +C00431445|IND|REESE|ELIZABETH S.|NEW YORK|NY|10021|RETIRED|RETIRED|250 +C00431445|IND|HOLLAND|LAWRENCE|BAINBRIDGE ISLAND|WA|98110|BAINBRIDGE ISLAND SCHOOL DISTRICT|TEACHER|250 +C00431445|IND|KHAIRANDISH|MOHI|WESTCHESTER|CA|90045|AWR|ENGINEER|200 +C00431445|IND|FROH|EDWARD J.|OAKLAND|MI|48363|COMAN INC|PROJECT ENGINEER|550 +C00431445|IND|CANTWELL|BRIAN|PALO ALTO|CA|94303|STANFORD UNIVERSITY|PROFESSOR|500 +C00431445|IND|MARCH|SUSAN M.|ENGLEWOOD|NJ|07631|RETIRED|RETIRED|250 +C00431445|IND|O'BRIEN|KATHLEEN ANN|MINNEAPOLIS|MN|55406|UNIVERSITY OF MINNESOTA|ADMINISTRATOR|500 +C00431445|IND|BICKMORE|BEVERLEE|LOS ANGELES|CA|90042||INFORMATION REQUESTED|200 +C00431445|IND|FIELDS|WILLIAM|PARMA|ID|83660|RETIRED|RETIRED|250 +C00431445|IND|MEDORE|MARK|OAKLAND PARK|FL|33334|SELF-EMPLOYED|INVESTOR|1000 +C00431445|IND|HASKINS|MARK|PHILADELPHIA|PA|19119|UNIVERSITY OF PENNSYLVANIA|PROFESSOR|500 +C00431445|IND|BUENZ|JOHN B.|CHICAGO|IL|60614|RETIRED|RETIRED|250 +C00431445|IND|JOHNSON|MARIE A.|HOUSTON|TX|77028||INFORMATION REQUESTED|250 +C00431445|IND|PERRY|CARMELLA|GOODYEAR|AZ|85395|RETIRED|RETIRED|1000 +C00431445|IND|BRIZEL|HERBERT|HOLLYWOOD|FL|33021|RETIRED|PHYSICIAN|250 +C00431445|IND|HANKINS|JACK|HUNTINGTON BEACH|CA|92648|SELF-EMPLOYED|SELF-EMPLOYED|250 +C00431445|IND|PERKINS|WALTER|SOUTH RIDING|VA|20152|SELF-EMPLOYED|INVENTOR|2500 +C00431445|IND|FORTE|FREDERICK|PRESCOTT|AZ|86305|RETIRED|RETIRED|500 +C00431445|IND|POTTER|JAMES|SANTA FE|NM|87508|STUDENT|STUDENT|1000 +C00431445|IND|VANDEVENTER|JANE|NASHVILLE|TN|37221|RETIRED|ADMINISTRATIVE LAW JUDGE|500 +C00431445|IND|TAYLOR|TOM|SALT LAKE CITY|UT|84103|SELF-EMPLOYED|SELF-EMPLOYED|500 +C00431445|IND|SHEVELOW|RICHARD|ESTERO|FL|33928|PICA SALES & ENGINEERING|SMALL BUSINESS OWNER/ENGINEER|500 +C00431445|IND|LAPOINTE|MARGOT|ROYAL OAK|MI|48067|HENRY FORD HEALTH SYSTEM|VICE PRESIDENT FOR RESEARCH|250 +C00431445|IND|MCGUIRE|KRISTINE|LARAMIE|WY|82070|RETIRED|RETIRED|250 +C00431445|IND|RIDAY|BARRIE|EMPIRE|MI|49630|RETIRED|RETIRED|250 +C00431445|IND|GLASCO|ANITA|MARINA DL REY|CA|90292|RETIRED|RETIRED|200 +C00431445|IND|MOOS|JULIAN CLIFFORD|HIGHLAND PARK|IL|60035|EFBC|EXECUTIVE|1000 +C00431445|IND|SONNE|CHRISTIAN|SAN FRANCISCO|CA|94117|KAISER PERMANENTE|PHYSICIAN|500 +C00431445|IND|VADEN|JEFF|MISSOURI CITY|TX|77459|DOJ/USAO|ATTORNEY|250 +C00431445|IND|ANDERSON JR.|ROBERT|SARASOTA|FL|34242|SELF-EMPLOYED|REAL ESTATE|500 +C00431445|IND|PARKER|NANCY|MORRISON|CO|80465|U.S. BUREAU OF RECLAMATION|ENGINEER|500 +C00431445|IND|HOLTZER|ALFRED|UNIVERSITY CY|MO|63130|NOT EMPLOYED|RETIRED|250 +C00431445|IND|ANGELL|JEAN B|NEW YORK|NY|10023|RETIRED|RETIRED|500 +C00431445|IND|FROMSTEIN|ELIZABETH|WINNETKA|IL|60093|HOMEMAKER|HOMEMAKER|250 +C00431445|IND|SIMPSON|MELODY A.|CHARLESTON|WV|25314|BOWLES MCDAVID ET AL. LAW FIRM|ATTORNEY|1500 +C00431445|IND|PAYTON|BENJAMIN F.|ESTERO|FL|33928|TUSKECER UNIV|PRESIDENT EMERITUS|1000 +C00431445|IND|EARLE|ELIZA|WINNETKA|IL|60093|RETIRED|RETIRED|500 +C00431445|IND|SHABAHANG|MOHSEN|LEWISBURG|PA|17837|SELF-EMPLOYED|PHYSICIAN|300 +C00431445|IND|DAVID|MERYL M|LOS ANGELES|CA|90036|RETIRED|RETIRED|300 +C00431445|IND|THEODORE|FOTIS|GREECE|||RETIRED|RETIRED|250 +C00431445|IND|BRAUN|JEAN|UNIONTOWN|PA|15401||INFORMATION REQUESTED|250 +C00431445|IND|HOLMER|STEVE|WASHINGTON|DC|20010|AMERICAN BIRD CONSERVANCY|ENVIRONMENTALIST|500 +C00431445|IND|MATTHAEI|GEORGE L|SANTA BARBARA|CA|93105|SUPERCONDUCTOR TECHNOLOGIES INC|RETIRED|2000 +C00431445|IND|LANDFAIR|STANLEY|SAN FRANCISCO|CA|94111|MCKENNA LONG & ALDRIDGE|ATTORNEY|500 +C00431445|IND|CLANCY|ROBERT|BALD HEAD ISL|NC|28461|SPACE SCIENCE INSTITUTE|SCIENTIST|200 +C00431445|IND|MCENIRY|COURTNEY J|TUCSON|AZ|85718|HOMEMAKER|HOMEMAKER|500 +C00431445|IND|WATSON|WILLIAM|FRIDAY HARBOR|WA|98250|WILLIAM WATSON|SOFTWARE ENGINEER CONTRACTOR|250 +C00431445|IND|ROBINSON|LULA M|SPRINGFIELD GARDEN|NY|11413||INFORMATION REQUESTED|200 +C00431445|IND|ABERGER|KATE|HILLSBOROUGH|NJ|08844|KATE ABERGER|PHYSICIAN|1000 +C00431445|IND|COHEN|JANET|NEW YORK|NY|10023|SELF-EMPLOYED|ARTIST|1000 +C00431445|IND|HART|NANCY|SPRINGFIELD|OR|97477|LANE COMMUNITY COLLEGE|DEAN|500 +C00431445|IND|CRAWSHAW|CANDACE A|SARASOTA|FL|34236|NOT EMPLOYED|RETIRED|2000 +C00431445|IND|SINATRA|NANCY SR|BEVERLY HILLS|CA|90210|SELF-EMPLOYED|ENTERTAINER|1000 +C00431445|IND|D'ERASMO|ROCCO|COLLEGE POINT|NY|11356|NEW YORK DIGITAL PRINT CENTER, INC|OWNER|250 +C00431445|IND|NELSON|MADELINE ANN|RESTON|VA|20191|ICF INTERNATIONAL|POLICY ANALYST|500 +C00431445|IND|KIMBROUGH|LINDA E.|CHICAGO|IL|60610|SELF-EMPLOYED|ACTRESS|250 +C00431445|IND|MAUN|RHONDA|PARADISE VALLEY|AZ|85253|CONSORTIA CONSULTING.COM|CEO|250 +C00431445|IND|HOLLAND|MARY JEAN|BROOKLYN|NY|11231|RETIRED|RETIRED|200 +C00431445|IND|WELCH|NANCY|SANTA CLARA|CA|95054|RETIRED|RETIRED|300 +C00431445|IND|MILLER|LAURA SR.|SANTA FE|TX|77510|UNIVERSITY OF TEXAS MEDICAL BRANCH|SYSTEMS ANALYST|250 +C00431445|IND|GLICKMAN|BARBARA|ARDMORE|PA|19003|NOT EMPLOYED|NOT EMPLOYED|500 +C00431445|IND|BERNHARDT|MARY JO|EVANSVILLE|WI|53536|WHITEWATER USD|PRINCIPAL|250 +C00431445|IND|CRATSENBURG|MARK|ORINDA|CA|94563|AERIS COMMUNICATIONS|SALES|500 +C00431445|IND|GROVER|FRED|DENVER|CO|80209|UNIVERSITY OF COLORADO|SURGEON|500 +C00431445|IND|MAHMOOD|TAHA|WEST VALLEY|UT|84128|MARATHON MAINTENANCE INC|OWNER|250 +C00431445|IND|RIGBY|DICK|CAPE MAY|NJ|08204|THE WATERFRONT CENTER|PLANNER|250 +C00431445|IND|LEE|STEVEN C|AUSTIN|TX|78701|SELF-EMPLOYED|ATTORNEY|250 +C00431445|IND|BENTLEY|JOHN|PORTLAND|OR|97205||INFORMATION REQUESTED|200 +C00431445|IND|SULLIVAN|PEGGY J|MATTESON|IL|60443|FEDERAL SIGNAL|SOLDES|250 +C00431445|IND|REDMOND|WILLIAM|MONTVILLE|NJ|07045|GENERAL CHEMICAL CORPORATION|CHIEF EXECUTIVE OFFICER & PRESIDENT|1000 +C00431445|IND|CHURCH|VIVIAN E.|CHICAGO|IL|60637|RETIRED|RETIRED|300 +C00431445|IND|ANDERSEN|MICHAEL|CAPTAIN COOK|HI|96704||INFORMATION REQUESTED|200 +C00431445|IND|WARREN|CLARENCE|CINCINNATI|OH|45249|32 AUTOMOTIVE|PRESIDENT & CEO|250 +C00431445|IND|KREGER|THERESA H.|EASTPOINTE|MI|48021|RETIRED|RETIRED|200 +C00431445|IND|SPAHR|JOHN|JACKSON|WY|83001|TETON NEWMEDIA, INC|CHAIRPERSON & CEO|250 +C00431445|IND|COLE|KRISTEN|NORTHAMPTON|MA|01060|SMITH COLLEGE|DIRECTORY|250 +C00431445|IND|WELLBERY|DAVID|CHICAGO|IL|60615|UNIVERSITY OF CHICAGO|PROFESSOR|500 +C00431445|IND|HOLLINGSHEAD|IRVING|NEWTOWN|PA|18940||INFORMATION REQUESTED|500 +C00431445|IND|MURTAJ|ZAHRIE|WESTLAND|MI|48185|MALARKEYS|COOK|210 +C00431445|IND|SABOURIN|TOM J.|MILWAUKEE|WI|53211|GENERAL ELECTRIC|ENGINEER|250 +C00431445|IND|CONNER|VERDIA Y|CLEVELAND|OH|44105|NORTH COAST DISTRICT OF THE UMC|PASTOR & DISTRICT ASSOCIATE|300 +C00431445|IND|LAI|CHAK|TROY|MI|48083|MICROSOFT CORPORATION|TECHNICAL ACCOUNT MANAGER|2500 +C00431445|IND|MALONEY|J. MICHAEL|ENCINITAS|CA|92024|RETIRED|RETIRED|1000 +C00431445|IND|JONGCO|BIENVENIDO|SOUTH ORANGE|NJ|07079|PEDIATRIC SURGICAL GROUP, LLC|PHYSICIAN|500 +C00431445|IND|FISCHER|PAULINE|SHERMAN OAKS|CA|91401|NETFLIX|CONTENT ACQUISITION|250 +C00431445|IND|JEAN-CHARLES|GUTTERIDGE|WINTER SPRINGS|FL|32708|SELF-EMPLOYED|HEALTHCARE|250 +C00431445|IND|JEPPESEN|CARL R|SLIDELL|LA|70461|RETIRED|RETIRED|212 +C00431445|IND|GERSHMAN|RUSSELL|LINCOLN|MA|01773|CALIPER LIFE SCIENCES|ENGINEER|500 +C00431445|IND|FLEISCHER|ROBERT|KENSINGTON|MD|20895|SMITHSONIAN INSTITUTION|GENETICIST|250 +C00431445|IND|CHAN|TERESA|MONTVILLE|NJ|07045|SELF-EMPLOYED|REALTOR|1000 +C00431445|IND|PORTERFIELD|ELIZABETH|DALLAS|TX|75208|CBRE|PROJECT MANAGER|250 +C00431445|IND|EDWARDS|GENELLE|AUSTIN|TX|78709|EMPOWERMENT OPTIONS|CEO|1000 +C00431445|IND|ROMAN|ROBERT|NORFOLK|VA|23504|DECORUM|OWNER|250 +C00431445|IND|JENNINGS|CYNTHIA|LOS ANGELES|CA|90024|SELF-EMPLOYED|PROPERTY MANAGEMENT|300 +C00431445|IND|GAREL|JOHN|VIRGINIA BEACH|VA|23451|ENVEST HOLDINGS, LLC|FINANCIAL MANAGER|500 +C00431445|IND|MCMULLIN|KAREN WATSON|ENNIS|MT|59729|SELF-EMPLOYED|ATTORNEY|250 +C00431445|IND|FINK|DALE|GREENBELT|MD|20770|NASA|PHYSICIST|1000 +C00431445|IND|ACKIL|ANN|CANTON|MA|02021|COMMONWEALTH OF MA|LAWYER|200 +C00431445|IND|SALINAS|JIM|DALLAS|TX|75208|RETIRED|RETIRED|220 +C00431445|IND|STOUT|BARBARA R|WASHINGTON|DC|20008|RETIRED|RETIRED|200 +C00431445|IND|SIEHL|CAREN J.|PHOENIX|AZ|85028|THUNDERBIRD|PROFESSOR|200 +C00431445|IND|HANSEN|BEN B|ANN ARBOR|MI|48104|UNIVERSITY OF MICHIGAN|PROFESSOR|250 +C00431445|IND|ASHFORTH|MARGARET|CONCORD|MA|01742|MAD SCIENCE OF GREATER BOSTON|TEACHER|300 +C00431445|IND|ROSENTHAL|NAN|NEW YORK|NY|10021|NOT EMPLOYED|RETIRED|250 +C00431445|IND|BARTHOLOMEW|FELIX C|FAIRFAX|VA|22033|SELF-EMPLOYED|CONSULTANT|250 +C00431445|IND|WYAND|JEFFREY A.|BETHESDA|MD|20814|LAYDIG VOIT & MAYER, LTD.|PATENT ATTORNEY|500 +C00431445|IND|TIERMAN|ANDREW JAY|SAGINAW|MI|48603|SAGINAW VALLEY STATE UNIVERSITY|LECTURER|200 +C00431445|IND|THOMPSON|KENNETH|BOXFORD|MA|01921|KLT ASSOCIATES, INC.|SOFTWARE ENGINEER|500 +C00431445|IND|PAULRAJ|AROGYASWAMI|STANFORD|CA|94305|STANFORD UNIVERSITY|PROFESSOR|250 +C00431445|IND|WERNER|ROBIN|SEATTLE|WA|98103|NOT EMPLOYED|NOT EMPLOYED|2500 +C00431445|IND|CUMMINGS|CAREY|ALBANY|NY|12203|RETIRED|COMPUTER CONSULTANT|1000 +C00431445|IND|FAIRBANKS|TRACEY|STRATFORD|CT|06614||INFORMATION REQUESTED|200 +C00431445|IND|RICHMAN|KAY|BETHESDA|MD|20814|RETIRED|ATTORNEY|500 +C00431445|IND|COX|JEROME|SAINT LOUIS|MO|63122|WASHINGTON UNIVERSITY|SENIOR PROFESSOR|200 +C00431445|IND|DOUGLASS|PAUL|CHEVY CHASE|MD|20815||INFORMATION REQUESTED|250 +C00431445|IND|DICKIE|RUTHANN|ANCHORAGE|AK|99501|RETIRED|RETIRED|200 +C00431445|IND|SINGER|BARB|TIJERAS|NM|87059|RETIRED|RETIRED|250 +C00431445|IND|THOMPSON|PRESTON|SEATTLE|WA|98125|QUIXOTE ASSOCIATES|CONSULTANT|250 +C00431445|IND|BEYER|CARL|SAN DIEGO|CA|92128|NATIONAL UNIVERSITY|ADMINISTRATOR|500 +C00431445|IND|WINNACKER|ELIZABETH|COLUMBIA|MO|65203|HOMEMAKER|HOMEMAKER|280 +C00431445|IND|PLETNIKOFF|LORNA|ANCHORAGE|AK|99517|STATE OF ALASKA|PUBLIC HEALTH NURSE|200 +C00431445|IND|ALBERT|ELISABETH M.|NEW YORK|NY|10025||INFORMATION REQUESTED|250 +C00431445|IND|WILLIAMS|ELIZABETH S. S|CHARLESTON|SC|29412|RETIRED|RETIRED|200 +C00431445|IND|SCHIESZLER|LAVERNE|BURR RIDGE|IL|60527|HOMEMAKER|HOMEMAKER|500 +C00431445|IND|NEWKIRK|DAVID|SHAWNEE|KS|66217|SWISS RE AMERICA HOLDING CORPORATION|ATTORNEY|1000 +C00431445|IND|TEDLA|LEAH|POTOMAC|MD|20854|SELF-EMPLOYED|AFFORDABLE HSG. MTG. BANKER|250 +C00431445|IND|DANIELS|PAM|SAN CARLOS|CA|94070|REFUSED|REFUSED|250 +C00431445|IND|ALTEON|RENANDE|BOSTON|MA|02124|THE 21TH CENTURY I.D. CARD|CEO|205 +C00431445|IND|NAVAS|MARY ELLEN|SANDY|UT|84092|RETIRED|RETIRED|1000 +C00431445|IND|BORRERO|MARIA|SILVER SPRING|MD|20904|INTER-AMERICAN DEVELOPMENT BANK|ETHICS OFFICER|1000 +C00431445|IND|ASSELSTINE|GORDON B.|MINNEAPOLIS|MN|55416|RETIRED|RETIRED|500 +C00431445|IND|WRIGHT|GAIL|FORT WORTH|TX|76109|RETIRED|RETIRED|250 +C00431445|IND|DIPEGO|GERALD F.|SANTA YNEZ|CA|93460|SELF-EMPLOYED|WRITER|750 +C00431445|IND|ABELL|JEFFREY M.|NORTHRIDGE|CA|91325|SARAH LEONARD FINE JEWELERS, INC.|JEWELER/BUSINESSMAN|250 +C00431445|IND|LEHMBECKER|D. J.|MANSON|WA|98831|VIU DU LAC WINERY|CEO GRUNT|200 +C00431445|IND|FOXX-ORENSTEIN|AMY|PARADISE VALLEY|AZ|85253|MAYO|PHYSICIAN|500 +C00431445|IND|VAN HOESEN|DAVID M|ORINDA|CA|94563|RETIRED|RETIRED|250 +C00431445|IND|DONOIAN|STEVE P.|DAVIDSONVILLE|MD|21035|SONCO WORLDWIDE, INC|CFO|1000 +C00431445|IND|HARTLEY|BRODES JR|CUTLER BAY|FL|33157|CHI|HEALTHCARE|1000 +C00431445|IND|LONGMATE|JOHN|BREMERTON|WA|98312||INFORMATION REQUESTED|200 +C00431445|IND|ALLEYNE|ESMOND|AUBURN|KS|66402|RETIRED|RETIRED|500 +C00431445|IND|MEGHJI|MOHSIN Y|OLD WESTBURY|NY|11568|INFORMATION REQUESTED|MANAGING DIRECTOR|1000 +C00431445|IND|GRAY|TIMOTHY|BERKELEY|CA|94707|AUTODESK|MARKETING|500 +C00431445|IND|SHORTALL|BRIAN|CHICAGO|IL|60605|NOT EMPLOYED|SALES & MARKETING|375 +C00431445|IND|ESPARZA|IRMA|ORANGE|CA|92869|RETIRED|TEACHER|500 +C00431445|IND|MOON|MILDRED|THOUSAND OAKS|CA|91362|WELLPOINT|STAFF|375 +C00431445|IND|MEYER|PAMELA|WAUWATOSA|WI|53226|RETIRED|RETIRED|1000 +C00431445|IND|GORTON|JEAN|BELLINGHAM|WA|98229|RETIRED|RETIRED|250 +C00431445|IND|KELLEY|JAMES|FALLS CHURCH|VA|22046|RETIRED|RETIRED|350 +C00431445|IND|DAVIDSON|ROBERT C JR|PASADENA|CA|91106|NOT EMPLOYED|RETIRED|2500 +C00431445|IND|ORDWAY|KATHRYN|BIGFORK|MT|59911|NOT EMPLOYED|RETIRED|500 +C00431445|IND|SWORDS|JULIA|GEORGETOWN|KY|40324|SELF-EMPLOYED|WRITER|500 +C00431445|IND|HEFLIN|CLARE E|ANNAPOLIS|MD|21409|CLARE E HEFLIN|PATENT EXAMINER|250 +C00431445|IND|CUTLER|KRISTIN A.|PLYMOUTH|MN|55441|WELLS FARGO & CO.|ATTORNEY|1000 +C00431445|IND|ASALI|NAILA|WASHINGTON|DC|20007|NOT EMPLOYED|RETIRED|500 +C00431445|IND|CRAWFORD|KAYE|PALO ALTO|CA|94303|ILM|CONSULTANT|250 +C00431445|IND|SNOW|ROSLYN|NEWPORT BEACH|CA|92663|RETIRED|RETIRED|250 +C00431445|IND|GEBHART|DAVID|DALLAS|TX|75219|GLOBAL VIEWS|WHOLESALE/IMPORTER|2500 +C00431445|IND|MARGOLSKEE|DANIEL|NEW YORK|NY|10025|STUDENT|STUDENT|500 +C00431445|IND|HALL|ROY|MOUNT JULIET|TN|37122|HALL CONSULTING GROUP|OWNER|250 +C00431445|IND|DICKINSON|RICHARD|SALEM|OR|97305|RETIRED|RETIRED|300 +C00431445|IND|WILLIAMS|SHARON W|ELMIRA|NY|14904||INFORMATION REQUESTED|215 +C00431445|IND|REINHARDT|WILLIAM PARKER|SEATTLE|WA|98121|UNIVERSITY OF WASHINGTON|PROFESSOR OF CHEMISTRY|250 +C00431445|IND|KAUFMAN|ALLAN|TEANECK|NJ|07666|FTI CONSULTING|ACTUARY|500 +C00431445|IND|WRIGHT|JD|JOLIET|IL|60435|SELF-EMPLOYED|PHYSICIAN|380 +C00431445|IND|COHEN|NATHAN|SAINT LOUIS|MO|63130|SELF-EMPLOYED|LAWYER|300 +C00431445|IND|LYNCH|THOMAS|NEWTOWN|PA|18940|TYCO ELECTRONICS LTD|CEO|2500 +C00431445|IND|WILLIAMS|LEIGHTON|NEW YORK|NY|10039|DEPARTMENT OF EDUCATION|TEACHER|210 +C00431445|IND|O'NEILL|CATHY|SANTA MONICA|CA|90402|NOT EMPLOYED|NOT EMPLOYED|1000 +C00431445|IND|PRICE|VIRGINIA N.|RALEIGH|NC|27608|NC DEPT. OF CORRECTION|RETIRED|200 +C00431445|IND|ANDERSON|WINTHROP|SAN DIEGO|CA|92103|SELF-EMPLOYED|LAWYER|250 +C00431445|IND|HARMAN|KATHLEEN|ROSEDALE|MD|21237|RETIRED|RETIRED|250 +C00431445|IND|POLK|ANN|SPARKS|MD|21152|NOT EMPLOYED|REGISTERED NURSE|200 +C00431445|IND|PINSKI|GREGORY|GREAT FALLS|MT|59404|CONNER & PINSKI, PLLP|ATTORNEY|1000 +C00431445|IND|BILLINGTON|CHARLES|SAINT PAUL|MN|55105|US DEPT OF VETERANS AFFAIRS|PHYSICIAN|250 +C00431445|IND|DANIELS|THEODORE|SEATTLE|WA|98194|NOT EMPLOYED|RETIRED|1000 +C00431445|IND|MERRILEES|EDWARD J|LAGUNA BEACH|CA|92651|RETIRED|RETIRED|200 +C00431445|IND|KRANTZ|SHELDON|WASHINGTON|DC|20016|UNIVERSITY OF MARYLAND|OCCUPATIONAL THERAPIST|1000 +C00431445|IND|JOYCE|JAMES|WAKEFIELD|NE|03872|MEBA|ENGINEER|300 +C00431445|IND|REINER|CARL|BEVERLY HILLS|CA|90210|SELF-EMPLOYED|ACTOR|300 +C00431445|IND|LEVISETTI|EMILE|SANTA MONICA|CA|90402|BUSHWACKER PRODUCTIONS|TV DIRECTOR|250 +C00431445|IND|POWERS|KATHLEEN|BRENTWOOD|TN|37027|METRO NASHVILLE HEALTH DEPT|RN|250 +C00431445|IND|MEURS|ARNOLD|CLINTON|CT|06413|RETIRED|RETIRED|250 +C00431445|IND|PAGET|JOE A|BLACKSBURG|VA|24060|JOE A. PAGET, JR. DDS LTD|DENTIST|500 +C00431445|IND|LEPPLA|STEPHEN|BETHESDA|MD|20817|NATIONAL INSTITUTE OF HEALTH|CHEMIST|250 +C00431445|IND|HALL|DIEDRE|FORT WAYNE|IN|46807|HOMEMAKER|HOMEMAKER|212 +C00431445|IND|FELLER|IRWIN|STATE COLLEGE|PA|16803|RETIRED|PROFESSOR EMERITUS|200 +C00431445|IND|MULLEN|AMY DUPONT|SAN FRANCISCO|CA|94114|RETIRED|RETIRED|1000 +C00431445|IND|BECKMAN|PEGGY|CORALVILLE|IA|52241|RETIRED|TEACHER|250 +C00431445|IND|WOLFGANG|ANNE|LENOX|MA|01240|RETIRED|RETIRED|200 +C00431445|IND|SHERBROOKE|ALAN|SEATTLE|WA|98103|GARVEY SCHUBERT BAUE|LAWYER|500 +C00431445|IND|STORRAR|ANN|MOSCOW|ID|83843|NEZ PERCE TRIBE|BIOLOGIST|250 +C00431445|IND|O'DONNELL|AMBROSE|NEW HOPE|PA|18938|SELF-EMPLOYED|INTERIOR DESIGNER/STUDENT|500 +C00431445|IND|WIERSBA|RICHARD|SAN FRANCISCO|CA|94109|RETIRED|RETIRED|1000 +C00431445|IND|STEVENS|ANNA|CONCORD|MA|01742|NOT EMPLOYED|PHYSICIAN|200 +C00431445|IND|LEATHERBEE|WILLIAM|PHILADELPHIA|PA|19107|SELF-EMPLOYED|ARCHITECT|250 +C00431445|IND|OTTEMAN|SCOTT|WASHINGTON|DC|20016|INSIDE WASHINGTON PUBLISHERS|JOURNALISM|500 +C00431445|IND|GIBSON|REBECCA|MARLBOROUGH|NH|03455|TUFTS UNIVERSITY|LECTURER|1000 +C00431445|IND|YANTIS|BARBARA|HOUSTON|TX|77027|SHELL GLOBAL SOLUTIONS, INC US|GEOPHYSICIST|250 +C00431445|IND|KEMPTON|MELINDA|CHICAGO|IL|60661|DAWN FOOD PRODUCTS|DIRECTOR, R&D|500 +C00431445|IND|MOORE|NANCY|FORT WORTH|TX|76107|RETIRED|RETIRED|500 +C00431445|IND|WILDER|KEVEN C|CHICAGO|IL|60622|WILDER INC.|CONSULTANT|200 +C00431445|IND|RONA|ELIZABETH|STUDIO CITY|CA|90049|SELF-EMPLOYED|MARRIAGE AND FAMILY THERAPIST|500 +C00431445|IND|ENGRAM|DIANNE|NAPERVILLE|IL|60540|FERMI NATIONAL LAB|HR MANAGER|300 +C00431445|IND|PATEL|ALPESH|SAN FRANCISCO|CA|94110|AB SCIEX|PORTFOLIO MANAGER|2500 +C00431445|IND|LAPINE|JENNIFER W.|STAMFORD|CT|06903|RETIRED|EDUCATOR|500 +C00431445|IND|FORDNEY|DIANE|TUCSON|AZ|85718|RETIRED|RETIRED|213 +C00431445|IND|DAWSON-CRAIN|DEBORAH|LOUISVILLE|KY|40207|BAPTIST HOSPITAL EAST|BEHAVIORAL HEALTH THERAPIST|200 +C00431445|IND|MACLAURY|MICHAEL R.|REXFORD|NY|12148|RETIRED|RETIRED|250 +C00431445|IND|JOHNSON|JOE|STAFFORD|VA|22554|RETIRED|RETIRED|250 +C00431445|IND|CASDEN|MARY|NEWTOWN|CT|06470|SELF-EMPLOYED|LAWYER|250 +C00431445|IND|RUBIN|GAIL P.|FOREST HILLS|NY|11375||INFORMATION REQUESTED|250 +C00431445|IND|BURMEISTER|CAROL|ROCKFORD|IL|61107|RETIRED|RETIRED|250 +C00431445|IND|MEYER|RICHARD J|NEWPORT BEACH|CA|92659|SELF-EMPLOYED|FINANCIAL MIT|2500 +C00431445|IND|KROMHOLZ|ALAN J|WHITEWATER|WI|53190|RETIRED|RETIRED|400 +C00431445|IND|MIROFF|FRANKLIN|INDIANAPOLIS|IN|46240|SELF-EMPLOYED|ATTORNEY|500 +C00431445|IND|PETERSON|PATRICIA D MD|SAN DIEGO|CA|92103|RETIRED|RETIRED|500 +C00431445|IND|LOVELL|JUNE W|VENTURA|CA|93003|RETIRED|RETIRED|500 +C00431445|IND|FINNEMORE|DOUGLAS K|AMES|IA|50010|IOWA STATE UNIVERSITY|RETIRED|500 +C00431445|IND|MCALLISTER|W. A.|SAINT LOUIS|MO|63124|RETIRED|RETIRED|200 +C00431445|IND|GARNICH|GOLDIE|SEBRING|FL|33876|NOT EMPLOYED|RETIRED|250 +C00431445|IND|BRADBURY|OLIGON B.|CHICAGO|IL|60619||INFORMATION REQUESTED|250 +C00431445|IND|COX|JOHN P|AMAZONIA|MO|64421|RETIRED|RETIRED|250 +C00431445|IND|DRUCKREY|INGE H|CHESHIRE|CT|06410|RETIRED|RETIRED|400 +C00431445|IND|ANDERSON|SHARON|NEW PRAGUE|MN|56071|RETIRED|RETIRED|300 +C00431445|IND|TIPIRNENI|HIRAL V|GLENDALE|AZ|85308|HIRAL V. TIPIRNENI|PHYSICIAN|200 +C00431445|IND|VOORDECKERS|ANNA M.|EAST WINDSOR|NJ|08520|RUTGERS UNIV|TECHNICIAN|250 +C00431445|IND|BRADSHAW|SYBLE J|CANYON COUNTRY|CA|91351|RETIRED|RETIRED|250 +C00431445|IND|BRITZ|NANCY|CAPE ELIZABETH|ME|04107|RETIRED|RETIRED|300 +C00431445|IND|LEWIS|JUDITH A.|CHICAGO|IL|60637|RETIRED|RETIRED|295 +C00431445|IND|KING|OSCAR|LATHRUP VILLAGE|MI|48076|NORTHWEST UNITY CHURCH|SR. PASTOR|200 +C00431445|IND|BLICKENSTAFF|RITA|SAN DIEGO|CA|92116|BLESSED SACRAMENT PARISH SCHOOL|TEACHER|1500 +C00431445|IND|BRYAN|CAROLE|MARIETTA|GA|30068|RETIRED|RETIRED|250 +C00431445|IND|BERRY|ELIZABETH|SUMMIT|NJ|07901|THE CHATHAM CLUB|FRONT DESK STAFF|500 +C00431445|IND|BACON|ELLEN|DURHAM|NC|27707|NORTH CAROLINA CENTRAL UNIVERSITY|PROFESSOR|250 +C00431445|IND|MATTHEWS|ANN-MARIE|THE VILLAGES|FL|32162|RETIRED|RETIRED|500 +C00431445|IND|WALKER|SANDRA R.|SCOTT DEPOT|WV|25560|HOMEMAKER|HOMEMAKER|500 +C00431445|IND|BEAK|PETER|URBANA|IL|61802|RETIRED|TEACHER|500 +C00431445|IND|NYAGGAH|LYNETTE|YORBA LINDA|CA|92885|RIO HONDO COLLEGE|PROFESSOR|250 +C00431445|IND|JESS|MARK|KANSAS CITY|MO|64102|EMPLOYEE RIGHTS LAW FIRM|LAWYER|500 +C00431445|IND|SHELTON|PENNY|ATHENS|OH|45701|HOLZER CLINIC|PHYSICIAN|250 +C00431445|IND|SELKIN|CARL|PASADENA|CA|91104|RETIRED|RETIRED|250 +C00431445|IND|WASHINGTON|DONALD|JAMAICA|NY|11434|RETIRED|RETIRED|300 +C00431445|IND|HAMILTON|STEVEN|BETHESDA|MD|20814|HAMILTON, ALTMAN, CO|ATTORNEY|1000 +C00431445|IND|WEBB|PETER|HONOLULU|HI|96816|IOLANI SCHOOL|TEACHER|250 +C00431445|IND|ROBLES|OSCAR|AUSTIN|TX|78757|STATE OF TEXAS|SYSTEMS ANALYST|500 +C00431445|IND|CARTER|WANDA|UPPER MARLBORO|MD|20774|UNITED NATIONS|ATTORNEY|200 +C00431445|IND|AUSTIN|REGINA|PHILADELPHIA|PA|19106|UNIVERSITY OF PA|PROFESSOR|1000 +C00431445|IND|BRINKLEY|RUTH|TUCSON|AZ|85740|CARONDELET HEALTH NETWORK|HEALTH CARE EXECUTIVE|250 +C00431445|IND|RYAN|PATRICIA|PLANDOME|NY|11030|SELF-EMPLOYED|PHYSICIAN|250 +C00431445|IND|TRIPP|ZACHARY|WASHINGTON|DC|20001|KING & SPALDING LLP|ATTORNEY|1000 +C00431445|IND|GLASSING|CHARLES R|PLYMOUTH|MN|55447|CHARLES R. GLASSING|REAL ESTATE APPRAISER|500 +C00431445|IND|ADAMS|JUNE ANN|COLORADO SPRINGS|CO|80906|RETIRED|RETIRED|250 +C00431445|IND|ALLEYNE|ESMOND|AUBURN|KS|66402|RETIRED|RETIRED|250 +C00431445|IND|MORRISSEY|STEVE|SEATTLE|WA|98119|SUSMAN GODFREY LLP|LAWYER|375 +C00431445|IND|OLIFER|ANDREI|DECATUR|GA|30033|EMORY UNIVERSITY|RESEARCHER|5000 +C00431445|IND|DUNWELL|ROGER|MIAMI|FL|33176|RETIRED|HOTELIER|250 +C00431445|IND|TSE|JEFFREY|SAN ANSELMO|CA|94960|SELF-EMPLOYED|PHOTOGRAPHER|250 +C00431445|IND|BRADY|JOHN|LOS ANGELES|CA|90039|SELF-EMPLOYED|ACTOR|500 +C00431445|IND|NEWELL|PETER|CHICAGO|IL|60611|RETIRED|RETIRED|250 +C00431445|IND|ROPER|WARD|SAMMAMISH|WA|98074|MICROSOFT|PROGRAM MANAGER|250 +C00431445|IND|MERCURO|LINDA|ARLINGTON|VA|22209|RETIRED|RETIRED|1000 +C00431445|IND|NIXON|CYNTHIA E|NEW YORK|NY|10024|SELF-EMPLOYED|ACTOR|500 +C00431445|IND|WATSON|CASEY|SAN MATEO|CA|94402|STUDENT|STUDENT|200 +C00431445|IND|CASCINO|MIKE P P|CHICAGO|IL|60613|CASCINO VAUGHAN|ATTORNEY|500 +C00431445|IND|SPATT|ROBERT E|NEW YORK|NY|10021|SIMPSON THACHER & BARTLETT|ATTORNEY|500 +C00431445|IND|DEITCH|SANDE|SANTA FE|NM|87501|SELF-EMPLOYED|ART|500 +C00431445|IND|ELLIS|DOUGLAS W.|ROCHESTER|MA|02770|THE SHAW GROUP|ENGINEER|200 +C00431445|IND|HANSEN|BRENDA|BETHESDA|MD|20814|SELF-EMPLOYED|MEDIATOR|250 +C00431445|IND|SIEVERS|GERALD|NEW YORK|NY|10010|DRY OPERA COMPANY|DIRECTOR|250 +C00431445|IND|GANGULI|MARY|PITTSBURGH|PA|15208|UNIVERSITY OF PITTSBURGH|PROFESSOR|500 +C00431445|IND|BOYD|ROBERT|LOS ANGELES|CA|90064|UNIVERSITY OF CALIFORNIA|PROFESSOR|500 +C00431445|IND|TESORO|CLAUDIA|PHILADELPHIA|PA|19119|OFFICE OF ATTORNEY GENERAL|ATTORNEY|500 +C00431445|IND|ENGELSMAN|RALPH|MORRISTOWN|NJ|07960|RETIRED|RETIRED|240 +C00431445|IND|BROOKS|PHILLIP A|BEVERLY HILLS|MI|48025|NEW ST. PAUL TABERNACLE|PASTOR|500 +C00431445|IND|HAYDON|THELMA G|BOULDER|CO|80303|RETIRED|RETIRED|250 +C00431445|IND|RIZZARDI|BRENNA|DECATUR|GA|30030|SELF-EMPLOYED|CUSTOM CLOTHIER|1500 +C00431445|IND|ROSS|P. 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MEDICAL CENTER|NURSE|225 +C00431445|IND|GRANT|SCOTT|SUNNYVALE|CA|94086|APPLE INC.|SOFTWARE ENGINEER|500 +C00431445|IND|DALTON|ELLEN|EVANSTON|IL|60201|HEALTH CARE SERVICE|MANAGEMENT INSURANCE|500 +C00431445|IND|WANNEN|DAVID|BRONX|NY|10463|NEW YORK GILBERT & SULLIVAN PLAYERS|MANAGING DIRECTOR FOR NON-PROFIT ORGAN|250 +C00431445|IND|SIPE|RICHARD|SEATTLE|WA|98112|RETIRED|RETIRED|250 +C00431445|IND|KAY|STEPHEN|ENCINO|CA|91436|HOGAN LOVELLS|ATTORNEY|250 +C00431445|IND|THOMAS|MARION|EAST SETAUKET|NY|11733|RETIRED|RETIRED|1000 +C00431445|IND|KOSS|ANDREA|KEY BISCAYNE|FL|33149|NOT EMPLOYED|RETIRED|500 +C00431445|IND|BAKER|BETSY B|FRANCONIA|NH|03580|VERMONT LAW SCHOOL|PROFESSOR|212 +C00431445|IND|VAHLE|VAN|BROOKLYN|NY|11201|AVON|INTERNET SALES|250 +C00431445|IND|CLARE|SUSAN|WELLESLEY|MA|02482|SELF-EMPLOYED|CONSULTANT|500 +C00431445|IND|GINZBERG|DAVID|NEW YORK|NY|10021|INFORMATION REQUESTED|FARMS|500 +C00431445|IND|MYERS|ALLEN|BERKELEY|CA|94703|NTT AMERICA|ENGINEER|300 +C00431445|IND|GUMBS|LAUNCELOT|SOUTHAMPTON|NY|11969|SELF-EMPLOYED|RETAIL|500 +C00431445|IND|HENRY|ERIK C.|KNOXVILLE|TN|37919|BUSH BROTHERS & COMPANY|SALES|250 +C00431445|IND|CHIERICHETTI|DAVID|LOS ANGELES|CA|90029||INFORMATION REQUESTED|212 +C00431445|IND|SNELL|JOHN|BROOKLYN|WI|53521|SELF-EMPLOYED|REGISTERED NURSE|250 +C00431445|IND|TYO|JOSEPH|DUPONT|WA|98327|PUGET SOUND EDUCATIONAL SERVICE DISTRI|SCHOOL ADMINISTRATOR|500 +C00431445|IND|CHRISTENSON|MARY|CHANHASSEN|MN|55317|JOHNSON & CONDON, P.A.|ATTORNEY|500 +C00431445|IND|SOTTILE|JAMES|CHEVY CHASE|MD|20815|ZUCKERMAN SPAEDER LLP|LAW|500 +C00431445|IND|GADSDEN|FRANK E.|CHARLOTTE|NC|28216|RETIRED|RETIRED|250 +C00431445|IND|FUQUA|MARY E|CLEVELAND|OH|44115|HOMEMAKER|HOMEMAKER|-200 +C00431445|IND|WELLMAN|SUSAN|ERIE|PA|16512|RETIRED|TEACHER|250 +C00431445|IND|SACHS|FRED|ROANOKE|VA|24018|RETIRED|RETIRED|500 +C00431445|IND|ARMANTROUT|LEON|REDLANDS|CA|92373|SELF-EMPLOYED|ARCHITECT|200 +C00431445|IND|BATTLE|DAVID|ASHEVILLE|NC|28801|SHAW CONSTRUCTORS, INC|COST ANALYST|500 +C00431445|IND|GROVE|ELIZABETH|CHICAGO|IL|60615|UNIVERSITY OF CHICAGO|SCIENTIST|250 +C00431445|IND|STRAUSMAN|GEORGE|GREAT NECK|NY|11024|SELF-EMPLOYED|BUILDER|250 +C00431445|IND|SLETTEN|PHYLLIS S|WOODSIDE|CA|94062|RETIRED|RETIRED|500 +C00431445|IND|MCCLINTON|JOANN|ATLANTA|GA|30317|RETIRED|RETIRED|200 +C00431445|IND|REIBER|DAN J|OBERLIN|OH|44074|RETIRED|RETIRED|200 +C00431445|IND|OGDEN|POLLY|SAINT HELENA|CA|94574|SELF-EMPLOYED|DESIGN/CONSTRUCTION|500 +C00431445|IND|EDWARDS|GERALDINE|EAST SETAUKET|NY|117331838|RETIRED|RETIRED|465 +C00431445|IND|PFISTER|SIGRUN|LINCOLN|NE|685059401|RETIRED|RETIRED|1500 +C00495820|IND||DANN|LITTLETON|NH|03561|NONE|RETIRED|201 +C00431445|IND|RICHARDSON|MAURA|ENCINO|CA|914363447|||1000 +C00431445|IND|ANCKER|SUSAN WEIR|LINCOLN|NM|883380201|SELF-EMPLOYED|TEACHER|1000 +C00495820|IND|SIGNORINO|LOU S.|WEST MILFORD|NJ|07480|SELF|GENERAL CONTRACTOR|201 +C00495820|IND|CRABBS|MURIEL A MR.|HANOVER|PA|17331|CRABBS & CRABBS|LAWYER|250 +C00495820|IND|ROE|JOSHUA P MR.|PASCO|WA|99301|KADLEC|HEALTH CARE|250 +C00495820|IND|PIERONE|JOSEPH V MR.|BELVIDERE|NJ|07823|CENTER FOR HUMANISTIC CHANGE|REQUESTED|350 +C00495820|IND|PARIS|GREGORY|ASHBURN|VA|20147|FREE LANCE CONTRACTOR|WRITER|201 +C00495820|IND|PRUCHA|BRETT A.|LEONARDTOWN|MD|20650|BOOZ ALLEN HAMILTON|WEB DEVELOPER|301 +C00495820|IND|SIMS|WILLIAM H.|COLUMBUS|OH|43214|NONE|RETIRED|400 +C00495820|IND|HATHAWAY|JOHN F. 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diff --git a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile new file mode 100644 index 0000000..48e4d45 --- /dev/null +++ b/NICAR2015/part1_steps.textile @@ -0,0 +1,114 @@ +h2. A Quick Introduction to SQL Using SQLite (Just the Steps) + +1. Open Firefox and find the SQLite Manager Plugin in the tools menu: + +!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/sqlite_manager_ff.png! +2. Create a new database called "campaign_finance": +!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! + +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: +!https://github.com/tthibo/SQL-Tutorial/raw/master/NICAR2015/images/create_contributions_table.png! + +4. Insert a row of data: +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! + +


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

+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',250);
+INSERT INTO contributions (last_name, first_name, city, state, zip, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2500');
+INSERT INTO contributions (last_name, first_name, city, state, zip, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','587');
+
+ +6. Select everything from the contributions table: +
SELECT * FROM contributions;
+ +7. Choose some fields to select: +
SELECT city, state FROM contributions;
+ +8. Get a distinct set of cities and states: +
SELECT DISTINCT city, state FROM contributions;
+ +9. Add some conditions to the SELECT: +
SELECT * from contributions WHERE state='GA';
+Now try playing around with some of the operators: + +|*operator*|*description*| +|=|Equal| +|<>|Not equal*| +|>|Greater than| +|<|Less than| +|>=|Greater than or equal| +|<=|Less than or equal| + +Here are some examples: +
SELECT * from contributions WHERE amount > 1200;
+
SELECT * from contributions WHERE amount >= 1200;
+
SELECT last_name from contributions WHERE state <> 'GA';
+ +10. Combine conditions with AND or OR: +
SELECT * FROM contributions WHERE state = 'GA' AND amount > 1000;
+
SELECT * FROM contributions WHERE state = 'GA' OR amount > 1000;
+ +11. But watch out for operator precedence: +
SELECT * FROM contributions WHERE city = 'Chicago' OR state = 'GA' AND amount > 1000;
+How did Oprah sneak in there? +Try that again: +
SELECT * FROM contributions WHERE (city = 'Chicago' OR state = 'GA') AND amount > 1000;
+ +12. Order the results: +
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount;
+And reverse the order: +
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount DESC;
+And order by more than one column: +
SELECT last_name, state, amount FROM contributions ORDER BY state, amount DESC;
+ +13. Return only the top two contributions: +
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+ + +h3. Importing data from a flat file + +14. Import a delimited file: +a. Go to ["https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributions.txt":https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributions.txt] + +b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). + +c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). + +* Using the **Select File** button, browse for the **contributions.txt** file you just saved. +* Check the "First row contains column names" check box. +* Select **Pipe(|)** for "Fields separated by" +* For everything else, you can keep the default selections. +* You should have 103 rows of data. + +h3. Aggregate Functions + +15. Count the number of contributions from California: +
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
+ +16. Select the minimum and maximum contributions: +
SELECT MAX(amount) FROM contributions;
+
SELECT MIN(amount) FROM contributions;
+ +17. Add up the contributions from Georgia: +
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
+ +18. Find the average contribution: +
SELECT AVG(amount) FROM contributions;
+ +19. Find the total amount of contributions per state: +
SELECT state, SUM(amount) FROM contributions GROUP BY state;
+ + +20. Find the total amount of contributions per city and state: +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
+Now order cities by their total contributions: +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
+ + + +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. From 0252959a3dab5cd719199efc954c6ea63d63275b Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Thu, 5 Mar 2015 16:02:41 -0600 Subject: [PATCH 02/25] Updating steps files for 2 hour structure --- NICAR2015/part1_steps.textile | 93 +++++++++++++----------- NICAR2015/part2_steps.textile | 130 ++++++++++++++++++++++++++++++++++ 2 files changed, 180 insertions(+), 43 deletions(-) create mode 100644 NICAR2015/part2_steps.textile diff --git a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile index 48e4d45..18a7766 100644 --- a/NICAR2015/part1_steps.textile +++ b/NICAR2015/part1_steps.textile @@ -3,6 +3,7 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 1. Open Firefox and find the SQLite Manager Plugin in the tools menu: !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/sqlite_manager_ff.png! + 2. Create a new database called "campaign_finance": !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! @@ -13,14 +14,15 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 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! 

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

-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',250);
-INSERT INTO contributions (last_name, first_name, city, state, zip, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2500');
-INSERT INTO contributions (last_name, first_name, city, state, zip, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','587');
+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);
+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);
+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');
+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');
6. Select everything from the contributions table: @@ -32,8 +34,10 @@ INSERT INTO contributions (last_name, first_name, city, state, zip, amount) VALU 8. Get a distinct set of cities and states: 
SELECT DISTINCT city, state FROM contributions;
+h3. THE WHERE CLAUSE + 9. Add some conditions to the SELECT: -
SELECT * from contributions WHERE state='GA';
+
SELECT * from contributions WHERE state='IL';
Now try playing around with some of the operators: |*operator*|*description*| @@ -45,36 +49,64 @@ Now try playing around with some of the operators: |<=|Less than or equal| Here are some examples: -
SELECT * from contributions WHERE amount > 1200;
-
SELECT * from contributions WHERE amount >= 1200;
-
SELECT last_name from contributions WHERE state <> 'GA';
+
SELECT * from contributions WHERE amount > 500;
+
SELECT * from contributions WHERE amount >= 500;
+
SELECT last_name from contributions WHERE state <> 'IL';
10. Combine conditions with AND or OR: -
SELECT * FROM contributions WHERE state = 'GA' AND amount > 1000;
-
SELECT * FROM contributions WHERE state = 'GA' OR amount > 1000;
-11. But watch out for operator precedence: -
SELECT * FROM contributions WHERE city = 'Chicago' OR state = 'GA' AND amount > 1000;
+Find only the big spenders from Illinois +
SELECT * FROM contributions WHERE state = 'IL' AND amount > 2000;
+ +Find any big spender and anyone from Illinois +
SELECT * FROM contributions WHERE state = 'IL' OR amount > 2000;
+ +11. But watch out for operator precedence + +Looking for big spenders from Nevada and Chicago +
SELECT * FROM contributions WHERE city = 'Chicago' OR state = 'NV' AND amount > 2000;
+ How did Oprah sneak in there? + Try that again: -
SELECT * FROM contributions WHERE (city = 'Chicago' OR state = 'GA') AND amount > 1000;
+
SELECT * FROM contributions WHERE (city = 'Chicago' OR state = 'NV') AND amount > 2000;
+ +h3. Sorting the results -12. Order the results: +12. Use ORDER BY to sort: 
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount;
And reverse the order: 
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount DESC;
And order by more than one column: 
SELECT last_name, state, amount FROM contributions ORDER BY state, amount DESC;
+h3. Limiting the number of rows returned + 13. Return only the top two contributions: 
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+h3. Changing values with UPDATE + +14. Check the list of state names again: +SELECT state FROM contributors; + +Let's correct Nebraska: +
UPDATE contributors SET state = 'NE' WHERE state = 'Nebraska';
+ +And check again: +
SELECT DISTINCT state FROM contributors;
+ +h3. DELETING rows + +15. Let's get rid of a row: +
SELECT * FROM contributors WHERE id = 1;
+
DELETE FROM contributors WHERE id = 1;
+ h3. Importing data from a flat file 14. Import a delimited file: -a. Go to ["https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributions.txt":https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/contributions.txt] - +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] b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). @@ -83,32 +115,7 @@ c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-T * Check the "First row contains column names" check box. * Select **Pipe(|)** for "Fields separated by" * For everything else, you can keep the default selections. -* You should have 103 rows of data. - -h3. Aggregate Functions - -15. Count the number of contributions from California: -
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
- -16. Select the minimum and maximum contributions: -
SELECT MAX(amount) FROM contributions;
-
SELECT MIN(amount) FROM contributions;
- -17. Add up the contributions from Georgia: -
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
- -18. Find the average contribution: -
SELECT AVG(amount) FROM contributions;
- -19. Find the total amount of contributions per state: -
SELECT state, SUM(amount) FROM contributions GROUP BY state;
- - -20. Find the total amount of contributions per city and state: -
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
-Now order cities by their total contributions: -
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
- +* You should have 1006 rows of data. -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. +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. \ No newline at end of file diff --git a/NICAR2015/part2_steps.textile b/NICAR2015/part2_steps.textile new file mode 100644 index 0000000..9067b30 --- /dev/null +++ b/NICAR2015/part2_steps.textile @@ -0,0 +1,130 @@ +h2. An Introduction to Grouping and Summing Using SQLite (Just the Steps) + +h3. Opening an existing SQLite database + +h3. Review: CREATE TABLE + +h3. Review: SELECT and WHERE + +h3. Using BETWEEN to find values in a range +
SELECT * FROM contributions WHERE amount BETWEEN 500 AND 1000;
+Note: equivalent is 
SELECT * FROM contributions WHERE amount >= 500 AND amount <= 1000;
+ +h3. Using IN to choose from a list +
SELECT * FROM contributions WHERE state IN ('AL', 'GA', 'FL');
+
SELECT * FROM contributions WHERE state NOT IN ('CA', 'OR', 'AZ');
+ +h3. Wildcard matching with LIKE +1. Use % to match any number of characters. +
SELECT zip_code FROM contributions WHERE zip_code LIKE '77566%';
+ +2. Use _ to match exactly one character. +
SELECT * FROM contributions WHERE first_name LIKE '% _.';
+ +h3. Dealing with NULL values + +1. Using IS NULL +2. Using IS NOT NULL + +h3. Manipulating text with string functions + +1. Use SUBSTR() to get a substring + +2. Use TRIM() to get rid of leading and trailing whitespace + a. Change Oprah's row to introduce whitepace around the state name: + 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'Winfrey';
+ b. Now check to see that you've created a new state: + 
SELECT DISTINCT state FROM contributions;
+ c. Use TRIM() to set things right: + 
UPDATE contributions SET state = TRIM(state);
+ +3. Use UPPER() or LOWER() to handle inconsistent capitalization +
SELECT * FROM contributions WHERE state = 'AL' AND city = 'Birmingham';
+
SELECT * FROM contributions WHERE state = 'AL';
+
SELECT * from contributions WHERE UPPER(city) = 'BIRMINGHAM';
+NOTE: case sensitivity is system-dependent. In SQLite, LIKE is not case sensitive. In MySQL, "=" is not case sensitive. +It's safer to be explicit about case so your SQL is portable. + +4. Use || to concatenate strings +
SELECT city || ',' || state FROM contributions ORDER BY state, city;
+ + +h3. Aggregate Functions + +14. Count the number of contributions from California: +
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
+ +15. Find out how many zip codes there are in the table: +SELECT COUNT(DISTINCT zip_code) FROM contributions; + +16. Select the minimum and maximum contributions: +
SELECT MAX(amount) FROM contributions;
+
SELECT MIN(amount) FROM contributions;
+ +17. Add up the contributions from Georgia: +
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
+ +18. Find the average contribution: +
SELECT AVG(amount) FROM contributions;
+ +19. Find the total amount of contributions per state: +
SELECT state, SUM(amount) FROM contributions GROUP BY state;
+ +20. Find the total amount of contributions per city and state: +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
+Now order cities by their total contributions: +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
+ + +h3. Using Subqueries + +21. A failed attempt to get all contributors who gave the maximum: +
SELECT * FROM contributions WHERE amount = MAX(amount);
+ +22. Doing it right with a subquery + +What we want is: +
SELECT MAX(amount) FROM contributions;
-- The result is 5000 +And then, using the result of that query: +
SELECT * FROM contributions WHERE amount = 5000;
+ +Combine them with a subquery: +
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
+ +22. Use a subquery to total the amounts from the top 20 contributions +
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
+ +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. + + +h3. Grouping with Group By + +23. Get total amount contributed by state: +
SELECT state, SUM(amount) FROM contributions GROUP BY state;
+ +24. Or by city (grouping by two columns): +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
+ +25. And easily see which cities gave the most (using ORDER BY): +
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
+ +26. Be wary of any non-aggregate fields in the SELECT clause that aren't in the GROUP BY: +
SELECT city, state, SUM(amount) FROM contributions GROUP BY state ORDER BY SUM(amount) DESC;
+ +h3. Filtering by aggregate values using HAVING + +27. A failed attempt to return totals for cities where at least $3,000 has been contributed: +
SELECT city, state, SUM(amount) FROM contributors WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
+ +28. And the way that works: +
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state HAVING SUM(amount) >= 3000 ORDER BY SUM(amount) DESC;
+ +29. What's the difference betwen WHERE and HAVING? + a. A simple WHERE: + 
SELECT city, state, amount FROM contributors WHERE amount >= 2300;
+ b. Where with a GROUP BY: + 
SELECT city, state, SUM(amount) FROM contributors WHERE amount >= 2300 GROUP BY city, state;
+ c. Now limiting those cities by the total contributions: + 
SELECT city, state, SUM(amount) FROM contributors WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 4000;
+ + From ae4fdc288a1e5cb5c864a95149017518c346679a Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Fri, 6 Mar 2015 15:11:43 -0600 Subject: [PATCH 03/25] Adding steps to walkthrough --- .../images/create_contributions_table.png | Bin 91946 -> 97459 bytes NICAR2015/part1_steps.textile | 1 + NICAR2015/part2_steps.textile | 89 +++++++++++++----- 3 files changed, 64 insertions(+), 26 deletions(-) diff --git a/NICAR2015/images/create_contributions_table.png b/NICAR2015/images/create_contributions_table.png index 55878a75c4f8480df034cb4bd9aec3688306995f..e8a758409b8961c42a50a270f9bf7ebc9b937ce7 100644 GIT binary patch literal 97459 zcmdqHgL9=%^9OojZEV|iHnweTvhl_`v2A-}+uCGfY;4=Mo!sn;@9(X;|G=$#s!r88 zJ>4^ndFGk^bcZR*OTfe8zybgOcqvIyWdH#5>GR734f;t*_`0J40AR_Ri-;&niHHy> zI@p?;TbTd=nlP1GF1+d&FP#&tV*{?I?PhgmOCH8ClJiDUlnMvb;T0{9MH04>c1Smc+)jEacP*~SiCqEZwJT`1xDj=Ga`?>lyYiLtV4-@pkD zCDR23JTYv7qh5QN`%jRxt4J#oL@nGEc8E z@?D)Z&CL_~Y3u7CK39x7o05;;KeZ7Jb?Mn3`!GYrK>>Sfbc60qCXO{UMXa`(v^DA8 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a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile index 18a7766..d38da97 100644 --- a/NICAR2015/part1_steps.textile +++ b/NICAR2015/part1_steps.textile @@ -25,6 +25,7 @@ INSERT INTO contributions (committee_id, entity_type, last_name, first_name, cit 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'); + 6. Select everything from the contributions table: 

SELECT * FROM contributions;
diff --git a/NICAR2015/part2_steps.textile b/NICAR2015/part2_steps.textile index 9067b30..14c5935 100644 --- a/NICAR2015/part2_steps.textile +++ b/NICAR2015/part2_steps.textile @@ -1,36 +1,73 @@ h2. An Introduction to Grouping and Summing Using SQLite (Just the Steps) -h3. Opening an existing SQLite database +h3. Review: CREATE and INSERT statements -h3. Review: CREATE TABLE +1. Create a new database called campaign_finance (SQLite manager will save the file as campaign_finance.sqlite). + +2. Create a new table called "contributions": +
CREATE TABLE "contributions" ("id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL , "committee_id" VARCHAR, "entity_type" VARCHAR, "last_name" VARCHAR, "first_name" VARCHAR, "city" VARCHAR, "state" VARCHAR, "zip_code" VARCHAR, "employer" VARCHAR, "occupation" VARCHAR, "amount" INTEGER)
+ +3. Add a few rows of data: +

+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);
+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);
+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');
+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');
+
h3. Review: SELECT and WHERE +4. Write a query to select all fields from contributions greater than $500 and less than $1000. + h3. Using BETWEEN to find values in a range 
SELECT * FROM contributions WHERE amount BETWEEN 500 AND 1000;
Note: equivalent is 
SELECT * FROM contributions WHERE amount >= 500 AND amount <= 1000;
h3. Using IN to choose from a list + +1. Select all contributions from Alabama, Georgia or Florida: 
SELECT * FROM contributions WHERE state IN ('AL', 'GA', 'FL');
+ +2. Select all contributions from some state other than California, Oregon or Arizona: 
SELECT * FROM contributions WHERE state NOT IN ('CA', 'OR', 'AZ');
h3. Wildcard matching with LIKE -1. Use % to match any number of characters. -
SELECT zip_code FROM contributions WHERE zip_code LIKE '77566%';
-2. Use _ to match exactly one character. +3. Scroll through the Zip codes to see the issue: +
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
+
SELECT zip_code FROM contributions WHERE zip_code = '10017';
+ +Use % to match any number of characters. +
SELECT zip_code FROM contributions WHERE zip_code LIKE '10017%';
+ +5. Find all contributions where the middle initial is included in the first_name field: +Use _ to match exactly one character. 
SELECT * FROM contributions WHERE first_name LIKE '% _.';
h3. Dealing with NULL values -1. Using IS NULL -2. Using IS NOT NULL +Another data problem: +
SELECT first_name, last_name FROM contributions WHERE state = 'NH';
+ +6. How do we find any rows missing the last_name value? +NOTE: "=" won't work. +
SELECT * FROM contributions WHERE last_name IS NULL;
+ +7. How do we find any New Hampshire contribution by someone whose last name isn't "BAKER": + +
SELECT * FROM contributions WHERE state = 'NH';
+ +
SELECT * FROM contributors WHERE state = 'VA' AND last_name <> 'BAKER';
+ +
SELECT * FROM contributors WHERE state = 'VA' AND last_name <> 'BAKER' AND last_name IS NOT NULL;
h3. Manipulating text with string functions -1. Use SUBSTR() to get a substring +8. Use SUBSTR() to get a substring +
SELECT zip_code FROM contributions WHERE SUBSTR(zip_code, 1, 5) = '10017';
+
SELECT SUBSTR(zip_code, 1, 5) FROM contributions;
-2. Use TRIM() to get rid of leading and trailing whitespace +9. Use TRIM() to get rid of leading and trailing whitespace a. Change Oprah's row to introduce whitepace around the state name: 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'Winfrey';
b. Now check to see that you've created a new state: @@ -38,39 +75,39 @@ h3. Manipulating text with string functions c. Use TRIM() to set things right: 
UPDATE contributions SET state = TRIM(state);
-3. Use UPPER() or LOWER() to handle inconsistent capitalization +10. Use UPPER() or LOWER() to handle inconsistent capitalization 
SELECT * FROM contributions WHERE state = 'AL' AND city = 'Birmingham';
SELECT * FROM contributions WHERE state = 'AL';
SELECT * from contributions WHERE UPPER(city) = 'BIRMINGHAM';
NOTE: case sensitivity is system-dependent. In SQLite, LIKE is not case sensitive. In MySQL, "=" is not case sensitive. It's safer to be explicit about case so your SQL is portable. -4. Use || to concatenate strings +11. Use || to concatenate strings 
SELECT city || ',' || state FROM contributions ORDER BY state, city;
h3. Aggregate Functions -14. Count the number of contributions from California: +12. Count the number of contributions from California: 
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
-15. Find out how many zip codes there are in the table: +13. Find out how many zip codes there are in the table: SELECT COUNT(DISTINCT zip_code) FROM contributions; -16. Select the minimum and maximum contributions: +14. Select the minimum and maximum contributions: 
SELECT MAX(amount) FROM contributions;
SELECT MIN(amount) FROM contributions;
-17. Add up the contributions from Georgia: +15. Add up the contributions from Georgia: 
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
-18. Find the average contribution: +16. Find the average contribution: 
SELECT AVG(amount) FROM contributions;
-19. Find the total amount of contributions per state: +17. Find the total amount of contributions per state: 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
-20. Find the total amount of contributions per city and state: +18. Find the total amount of contributions per city and state: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
Now order cities by their total contributions: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
@@ -78,10 +115,10 @@ Now order cities by their total contributions: h3. Using Subqueries -21. A failed attempt to get all contributors who gave the maximum: +19. A failed attempt to get all contributors who gave the maximum: 
SELECT * FROM contributions WHERE amount = MAX(amount);
-22. Doing it right with a subquery +20. Doing it right with a subquery What we want is: 
SELECT MAX(amount) FROM contributions;
-- The result is 5000 @@ -91,7 +128,7 @@ And then, using the result of that query: Combine them with a subquery: 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
-22. Use a subquery to total the amounts from the top 20 contributions +21. Use a subquery to total the amounts from the top 20 contributions 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
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. @@ -114,17 +151,17 @@ h3. Grouping with Group By h3. Filtering by aggregate values using HAVING 27. A failed attempt to return totals for cities where at least $3,000 has been contributed: -
SELECT city, state, SUM(amount) FROM contributors WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
+
SELECT city, state, SUM(amount) FROM contributions WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
28. And the way that works: -
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state HAVING SUM(amount) >= 3000 ORDER BY SUM(amount) DESC;
+
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state HAVING SUM(amount) >= 3000 ORDER BY SUM(amount) DESC;
29. What's the difference betwen WHERE and HAVING? a. A simple WHERE: - 
SELECT city, state, amount FROM contributors WHERE amount >= 2300;
+ 
SELECT city, state, amount FROM contributions WHERE amount >= 2300;
b. Where with a GROUP BY: - 
SELECT city, state, SUM(amount) FROM contributors WHERE amount >= 2300 GROUP BY city, state;
+ 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state;
c. Now limiting those cities by the total contributions: - 
SELECT city, state, SUM(amount) FROM contributors WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 4000;
+ 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 4000;
From 1ff2ad15f1b3fa925cfde0c1972555db212cb3c0 Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Fri, 6 Mar 2015 22:50:55 -0600 Subject: [PATCH 04/25] Correct case for CHICAGO --- NICAR2015/part1_steps.textile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile index d38da97..f961f8c 100644 --- a/NICAR2015/part1_steps.textile +++ b/NICAR2015/part1_steps.textile @@ -65,12 +65,12 @@ Find any big spender and anyone from Illinois 11. But watch out for operator precedence Looking for big spenders from Nevada and Chicago -
SELECT * FROM contributions WHERE city = 'Chicago' OR state = 'NV' AND amount > 2000;
+
SELECT * FROM contributions WHERE city = 'CHICAGO' OR state = 'NV' AND amount > 2000;
How did Oprah sneak in there? Try that again: -
SELECT * FROM contributions WHERE (city = 'Chicago' OR state = 'NV') AND amount > 2000;
+
SELECT * FROM contributions WHERE (city = 'CHICAGO' OR state = 'NV') AND amount > 2000;
h3. Sorting the results From ada136d25d377e08ab0532a648d2f42542279bd5 Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Fri, 6 Mar 2015 22:54:07 -0600 Subject: [PATCH 05/25] Correct typos --- NICAR2015/part1_steps.textile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile index f961f8c..3444079 100644 --- a/NICAR2015/part1_steps.textile +++ b/NICAR2015/part1_steps.textile @@ -90,7 +90,7 @@ h3. Limiting the number of rows returned h3. Changing values with UPDATE 14. Check the list of state names again: -SELECT state FROM contributors; +
SELECT state FROM contributors;
Let's correct Nebraska: 
UPDATE contributors SET state = 'NE' WHERE state = 'Nebraska';
From 94fe855972042c2372a08e18c8f3bb4d5434beaf Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Sat, 7 Mar 2015 07:18:01 -0600 Subject: [PATCH 06/25] Reformat CREATE statement --- NICAR2015/part1_steps.textile | 10 +++++----- NICAR2015/part2_steps.textile | 4 +++- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/NICAR2015/part1_steps.textile b/NICAR2015/part1_steps.textile index 3444079..d7fca08 100644 --- a/NICAR2015/part1_steps.textile +++ b/NICAR2015/part1_steps.textile @@ -90,19 +90,19 @@ h3. Limiting the number of rows returned h3. Changing values with UPDATE 14. Check the list of state names again: -
SELECT state FROM contributors;
+
SELECT state FROM contributions;
Let's correct Nebraska: -
UPDATE contributors SET state = 'NE' WHERE state = 'Nebraska';
+
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
And check again: -
SELECT DISTINCT state FROM contributors;
+
SELECT DISTINCT state FROM contributions;
h3. DELETING rows 15. Let's get rid of a row: -
SELECT * FROM contributors WHERE id = 1;
-
DELETE FROM contributors WHERE id = 1;
+
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
h3. Importing data from a flat file diff --git a/NICAR2015/part2_steps.textile b/NICAR2015/part2_steps.textile index 14c5935..cc85aeb 100644 --- a/NICAR2015/part2_steps.textile +++ b/NICAR2015/part2_steps.textile @@ -5,7 +5,9 @@ h3. Review: CREATE and INSERT statements 1. Create a new database called campaign_finance (SQLite manager will save the file as campaign_finance.sqlite). 2. Create a new table called "contributions": -
CREATE TABLE "contributions" ("id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL , "committee_id" VARCHAR, "entity_type" VARCHAR, "last_name" VARCHAR, "first_name" VARCHAR, "city" VARCHAR, "state" VARCHAR, "zip_code" VARCHAR, "employer" VARCHAR, "occupation" VARCHAR, "amount" INTEGER)
+
CREATE TABLE "contributions" ("id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL, "committee_id" VARCHAR,
+"entity_type" VARCHAR, "last_name" VARCHAR, "first_name" VARCHAR, "city" VARCHAR, "state" VARCHAR, "zip_code" VARCHAR,
+"employer" VARCHAR, "occupation" VARCHAR, "amount" INTEGER)
3. Add a few rows of data: 


From 6957848e208dfc8a6ee526ea5a1c9b1da8c9f0bc Mon Sep 17 00:00:00 2001
From: Troy Thibodeaux 
Date: Sat, 7 Mar 2015 10:59:53 -0600
Subject: [PATCH 07/25] Fixing typos

---
 NICAR2015/part2_steps.textile | 68 ++++++++++++++++++-----------------
 1 file changed, 36 insertions(+), 32 deletions(-)

diff --git a/NICAR2015/part2_steps.textile b/NICAR2015/part2_steps.textile
index cc85aeb..d358989 100644
--- a/NICAR2015/part2_steps.textile
+++ b/NICAR2015/part2_steps.textile
@@ -1,6 +1,6 @@
 h2. An Introduction to Grouping and Summing Using SQLite (Just the Steps)
 
-h3. Review: CREATE and INSERT statements
+h3. Review: CREATE, INSERT, SELECT and WHERE
 
 1. Create a new database called campaign_finance (SQLite manager will save the file as campaign_finance.sqlite).
 
@@ -17,32 +17,32 @@ INSERT INTO contributions (committee_id, entity_type, last_name, first_name, cit
 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');
-h3. Review: SELECT and WHERE +4. Import a data file (contributions.txt) -4. Write a query to select all fields from contributions greater than $500 and less than $1000. +5. Write a query to select all fields from contributions of at least $500 and no more than $1000. h3. Using BETWEEN to find values in a range -
SELECT * FROM contributions WHERE amount BETWEEN 500 AND 1000;
-Note: equivalent is 
SELECT * FROM contributions WHERE amount >= 500 AND amount <= 1000;
+ +6. 
SELECT * FROM contributions WHERE amount BETWEEN 500 AND 1000;
h3. Using IN to choose from a list -1. Select all contributions from Alabama, Georgia or Florida: +7. Select all contributions from Alabama, Georgia or Florida: 
SELECT * FROM contributions WHERE state IN ('AL', 'GA', 'FL');
-2. Select all contributions from some state other than California, Oregon or Arizona: +8. Select all contributions from some state other than California, Oregon or Arizona: 
SELECT * FROM contributions WHERE state NOT IN ('CA', 'OR', 'AZ');
h3. Wildcard matching with LIKE -3. Scroll through the Zip codes to see the issue: +9. Scroll through the Zip codes to see the issue: 
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
SELECT zip_code FROM contributions WHERE zip_code = '10017';
Use % to match any number of characters. 
SELECT zip_code FROM contributions WHERE zip_code LIKE '10017%';
-5. Find all contributions where the middle initial is included in the first_name field: +10. Find all contributions where the middle initial is included in the first_name field: Use _ to match exactly one character. 
SELECT * FROM contributions WHERE first_name LIKE '% _.';
@@ -51,11 +51,11 @@ h3. Dealing with NULL values Another data problem: 
SELECT first_name, last_name FROM contributions WHERE state = 'NH';
-6. How do we find any rows missing the last_name value? +11. How do we find any rows missing the last_name value? NOTE: "=" won't work. 
SELECT * FROM contributions WHERE last_name IS NULL;
-7. How do we find any New Hampshire contribution by someone whose last name isn't "BAKER": +12. How do we find any New Hampshire contribution by someone whose last name isn't "BAKER": 
SELECT * FROM contributions WHERE state = 'NH';
@@ -65,11 +65,11 @@ NOTE: "=" won't work. h3. Manipulating text with string functions -8. Use SUBSTR() to get a substring +13. Use SUBSTR() to get a substring 
SELECT zip_code FROM contributions WHERE SUBSTR(zip_code, 1, 5) = '10017';
SELECT SUBSTR(zip_code, 1, 5) FROM contributions;
-9. Use TRIM() to get rid of leading and trailing whitespace +14. Use TRIM() to get rid of leading and trailing whitespace a. Change Oprah's row to introduce whitepace around the state name: 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'Winfrey';
b. Now check to see that you've created a new state: @@ -77,39 +77,39 @@ h3. Manipulating text with string functions c. Use TRIM() to set things right: 
UPDATE contributions SET state = TRIM(state);
-10. Use UPPER() or LOWER() to handle inconsistent capitalization +15. Use UPPER() or LOWER() to handle inconsistent capitalization 
SELECT * FROM contributions WHERE state = 'AL' AND city = 'Birmingham';
SELECT * FROM contributions WHERE state = 'AL';
SELECT * from contributions WHERE UPPER(city) = 'BIRMINGHAM';
NOTE: case sensitivity is system-dependent. In SQLite, LIKE is not case sensitive. In MySQL, "=" is not case sensitive. It's safer to be explicit about case so your SQL is portable. -11. Use || to concatenate strings +16. Use || to concatenate strings 
SELECT city || ',' || state FROM contributions ORDER BY state, city;
h3. Aggregate Functions -12. Count the number of contributions from California: +17. Count the number of contributions from California: 
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
-13. Find out how many zip codes there are in the table: +18. Find out how many zip codes there are in the table: SELECT COUNT(DISTINCT zip_code) FROM contributions; -14. Select the minimum and maximum contributions: +19. Select the minimum and maximum contributions: 
SELECT MAX(amount) FROM contributions;
SELECT MIN(amount) FROM contributions;
-15. Add up the contributions from Georgia: +20. Add up the contributions from Georgia: 
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
-16. Find the average contribution: +21. Find the average contribution: 
SELECT AVG(amount) FROM contributions;
-17. Find the total amount of contributions per state: +22. Find the total amount of contributions per state: 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
-18. Find the total amount of contributions per city and state: +23. Find the total amount of contributions per city and state: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
Now order cities by their total contributions: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
@@ -117,10 +117,10 @@ Now order cities by their total contributions: h3. Using Subqueries -19. A failed attempt to get all contributors who gave the maximum: +24. A failed attempt to get all contributors who gave the maximum: 
SELECT * FROM contributions WHERE amount = MAX(amount);
-20. Doing it right with a subquery +25. Doing it right with a subquery What we want is: 
SELECT MAX(amount) FROM contributions;
-- The result is 5000 @@ -130,7 +130,7 @@ And then, using the result of that query: Combine them with a subquery: 
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
-21. Use a subquery to total the amounts from the top 20 contributions +26. Use a subquery to total the amounts from the top 20 contributions 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
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. @@ -138,27 +138,27 @@ Combine them with a subquery: h3. Grouping with Group By -23. Get total amount contributed by state: +27. Get total amount contributed by state: 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
-24. Or by city (grouping by two columns): +28. Or by city (grouping by two columns): 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
-25. And easily see which cities gave the most (using ORDER BY): +29. And easily see which cities gave the most (using ORDER BY): 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
-26. Be wary of any non-aggregate fields in the SELECT clause that aren't in the GROUP BY: +30. Be wary of any non-aggregate fields in the SELECT clause that aren't in the GROUP BY: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY state ORDER BY SUM(amount) DESC;
h3. Filtering by aggregate values using HAVING -27. A failed attempt to return totals for cities where at least $3,000 has been contributed: +31. A failed attempt to return totals for cities where at least $3,000 has been contributed: 
SELECT city, state, SUM(amount) FROM contributions WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
-28. And the way that works: +32. And the way that works: 
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state HAVING SUM(amount) >= 3000 ORDER BY SUM(amount) DESC;
-29. What's the difference betwen WHERE and HAVING? +33. What's the difference betwen WHERE and HAVING? a. A simple WHERE: 
SELECT city, state, amount FROM contributions WHERE amount >= 2300;
b. Where with a GROUP BY: @@ -167,3 +167,7 @@ h3. Filtering by aggregate values using HAVING 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 4000;
+h3. Is this data normal? + +* If I wanted to find all contributions to a given candidate, what query would I use? +* If I wanted to get the candidate's name or the committee treasurer's name, what would I need to do? Why? From 73032f4ebcc06d96ed29e2faaf240cc2d0e322e0 Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Sat, 7 Mar 2015 12:13:46 -0600 Subject: [PATCH 08/25] Final edits --- NICAR2015/part2_steps.textile | 57 ++++++++++++++++++++--------------- 1 file changed, 33 insertions(+), 24 deletions(-) diff --git a/NICAR2015/part2_steps.textile b/NICAR2015/part2_steps.textile index d358989..2583098 100644 --- a/NICAR2015/part2_steps.textile +++ b/NICAR2015/part2_steps.textile @@ -35,7 +35,7 @@ h3. Using IN to choose from a list h3. Wildcard matching with LIKE -9. Scroll through the Zip codes to see the issue: +9. Scroll through the New York ZIP codes to see the issue: 
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
SELECT zip_code FROM contributions WHERE zip_code = '10017';
@@ -44,7 +44,7 @@ Use % to match any number of characters. 10. Find all contributions where the middle initial is included in the first_name field: Use _ to match exactly one character. -
SELECT * FROM contributions WHERE first_name LIKE '% _.';
+
SELECT * FROM contributions WHERE first_name LIKE '% _.%';
h3. Dealing with NULL values @@ -59,33 +59,36 @@ NOTE: "=" won't work. 
SELECT * FROM contributions WHERE state = 'NH';
-
SELECT * FROM contributors WHERE state = 'VA' AND last_name <> 'BAKER';
+
SELECT * FROM contributions WHERE state = 'NH' AND last_name <> 'BAKER';
-
SELECT * FROM contributors WHERE state = 'VA' AND last_name <> 'BAKER' AND last_name IS NOT NULL;
+
SELECT * FROM contributions WHERE state = 'NH' AND (last_name <> 'BAKER' OR last_name IS NULL);
-h3. Manipulating text with string functions +13. Grab only rows from NH where there is a value for last_name: +
SELECT * FROM contributions WHERE state = 'NH' AND last_name IS NOT NULL;
-13. Use SUBSTR() to get a substring -
SELECT zip_code FROM contributions WHERE SUBSTR(zip_code, 1, 5) = '10017';
-
SELECT SUBSTR(zip_code, 1, 5) FROM contributions;
+h3. Manipulating text with string functions 14. Use TRIM() to get rid of leading and trailing whitespace a. Change Oprah's row to introduce whitepace around the state name: - 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'Winfrey';
+ 
UPDATE contributions SET state = ' IL ' WHERE last_name = 'WINFREY';
b. Now check to see that you've created a new state: - 
SELECT DISTINCT state FROM contributions;
+ 
SELECT DISTINCT state FROM contributions ORDER BY state;
c. Use TRIM() to set things right: 
UPDATE contributions SET state = TRIM(state);
15. Use UPPER() or LOWER() to handle inconsistent capitalization -
SELECT * FROM contributions WHERE state = 'AL' AND city = 'Birmingham';
-
SELECT * FROM contributions WHERE state = 'AL';
-
SELECT * from contributions WHERE UPPER(city) = 'BIRMINGHAM';
+
SELECT city, state FROM contributions WHERE state = 'AL' ORDER BY city, state;
+
SELECT * FROM contributions WHERE state = 'AL' AND city = 'BIRMINGHAM';
+
SELECT * from contributions WHERE state = 'AL' AND UPPER(city) = 'BIRMINGHAM';
NOTE: case sensitivity is system-dependent. In SQLite, LIKE is not case sensitive. In MySQL, "=" is not case sensitive. It's safer to be explicit about case so your SQL is portable. -16. Use || to concatenate strings -
SELECT city || ',' || state FROM contributions ORDER BY state, city;
+16. Use SUBSTR() to get a substring +Let's revisit the ZIP+4 problem: +
SELECT DISTINCT city, state, zip_code FROM contributions WHERE city = 'NEW YORK' AND state = 'NY' ORDER BY zip_code;
+ +Now let's return just the 5-digit ZIPs: +
SELECT SUBSTR(zip_code, 1, 5) FROM contributions;
h3. Aggregate Functions @@ -94,17 +97,21 @@ h3. Aggregate Functions 
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
18. Find out how many zip codes there are in the table: -SELECT COUNT(DISTINCT zip_code) FROM contributions; +
SELECT COUNT(DISTINCT zip_code)  FROM contributions;
+
SELECT COUNT(DISTINCT state)  FROM contributions;
19. Select the minimum and maximum contributions: 
SELECT MAX(amount) FROM contributions;
SELECT MIN(amount) FROM contributions;
-20. Add up the contributions from Georgia: +20. Find the average contribution: +
SELECT AVG(amount) FROM contributions;
+ +21. Add up the contributions from Georgia: 
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
-21. Find the average contribution: -
SELECT AVG(amount) FROM contributions;
+ +h3. Grouping with GROUP BY 22. Find the total amount of contributions per state: 
SELECT state, SUM(amount) FROM contributions GROUP BY state;
@@ -117,7 +124,7 @@ Now order cities by their total contributions: h3. Using Subqueries -24. A failed attempt to get all contributors who gave the maximum: +24. A failed attempt to get all contributions who gave the maximum: 
SELECT * FROM contributions WHERE amount = MAX(amount);
25. Doing it right with a subquery @@ -133,8 +140,6 @@ Combine them with a subquery: 26. Use a subquery to total the amounts from the top 20 contributions 
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
-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. - h3. Grouping with Group By @@ -156,7 +161,7 @@ h3. Filtering by aggregate values using HAVING 
SELECT city, state, SUM(amount) FROM contributions WHERE SUM(amount) >= 3000  GROUP BY city, state  ORDER BY SUM(amount) DESC;
32. And the way that works: -
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state HAVING SUM(amount) >= 3000 ORDER BY SUM(amount) DESC;
+
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state HAVING SUM(amount) >= 10000 ORDER BY SUM(amount) DESC;
33. What's the difference betwen WHERE and HAVING? a. A simple WHERE: @@ -164,10 +169,14 @@ h3. Filtering by aggregate values using HAVING b. Where with a GROUP BY: 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state;
c. Now limiting those cities by the total contributions: - 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 4000;
+ 
SELECT city, state, SUM(amount) FROM contributions WHERE amount >= 2300 GROUP BY city, state HAVING SUM(amount) > 10000;
h3. Is this data normal? * If I wanted to find all contributions to a given candidate, what query would I use? * If I wanted to get the candidate's name or the committee treasurer's name, what would I need to do? Why? + + +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. + From 31fd1d5781c2814ed805944e686bafd43bd73bd6 Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Mon, 11 May 2015 12:37:07 -0500 Subject: [PATCH 09/25] Update part 3 to make clear that the first example in self-joins refers to two different (hypothetical) tables --- tutorial_files/part3.textile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorial_files/part3.textile b/tutorial_files/part3.textile index 7b18b28..68778d4 100644 --- a/tutorial_files/part3.textile +++ b/tutorial_files/part3.textile @@ -179,8 +179,8 @@ Much better. h3. Know thyself: Self Joins -Occasionally, it's useful to perform a query that joins a table to itself. Consider, for example, if we had two tables of contributors and we wanted to find contributors who appeared in both tables. Even without an explicit reference defined via a foreign key, we could still do a rough join on a combination of other columns, such as first and last names: -
SELECT a.last_name, a.first_name, a.amount, b.amount FROM a, b WHERE a.last_name = b.last_name AND a.first_name = b.first_name;
+Occasionally, it's useful to perform a query that joins a table to itself. Consider, for example, if we had two tables of contributors (contributors_a and contributors_b), and we wanted to find contributors who appeared in both tables. Even without an explicit reference defined via a foreign key, we could still do a rough join on a combination of other columns, such as first and last names: +
SELECT a.last_name, a.first_name, a.amount, b.amount FROM contributors_a a, contributors_b b WHERE a.last_name = b.last_name AND a.first_name = b.first_name;
Obviously, there are some limitations here (what if two people have the same first and last names? What if someone appears twice in one of the tables?) But it would still give us a rough and ready picture of the overlap between the two tables. From 1a4bc9d412afe363ad615538de756731e886a46a Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 09:49:36 -0600 Subject: [PATCH 10/25] Adding NICAR 2016 --- NICAR2016/data/candidates.txt | 15 ++ NICAR2016/data/contributions.txt | 208 ++++++++++++++++++++++ NICAR2016/data/scripts/Gemfile | 3 + NICAR2016/data/scripts/filter_contribs.rb | 48 +++++ NICAR2016/data/scripts/fix_names.rb | 14 ++ NICAR2016/images/customize_menu.png | Bin 0 -> 214300 bytes NICAR2016/part1_steps.textile | 130 ++++++++++++++ 7 files changed, 418 insertions(+) create mode 100644 NICAR2016/data/candidates.txt create mode 100644 NICAR2016/data/contributions.txt create mode 100644 NICAR2016/data/scripts/Gemfile create mode 100755 NICAR2016/data/scripts/filter_contribs.rb create mode 100755 NICAR2016/data/scripts/fix_names.rb create mode 100644 NICAR2016/images/customize_menu.png create mode 100644 NICAR2016/part1_steps.textile diff --git a/NICAR2016/data/candidates.txt b/NICAR2016/data/candidates.txt new file mode 100644 index 0000000..01349f7 --- /dev/null +++ b/NICAR2016/data/candidates.txt @@ -0,0 +1,15 @@ +id|committee_id|first_name|last_name|middle_name|party +C00580100|TRUMP|DONALD|J|REP +C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP +C00458844|RUBIO|MARCO||REP +C00581876|KASICH|JOHN|R|REP +C00573519|CARSON|BENJAMIN|S|REP +C00577130|SANDERS|BERNARD||DEM +C00575795|CLINTON|HILLARY|RODHAM|DEM +C00578658|O'MALLEY|MARTIN|JOSEPH|DEM +C00579458|BUSH|JEB||REP +C00580399|CHRISTIE|CHRISTOPHER|J|REP +C00575449|PAUL|RAND||REP +C00577312|FIORINA|CARLY||REP +C00578757|GRAHAM|LINDSEY|O|REP +C00577981|HUCKABEE|MIKE||REP diff --git a/NICAR2016/data/contributions.txt b/NICAR2016/data/contributions.txt new file mode 100644 index 0000000..a587473 --- /dev/null +++ b/NICAR2016/data/contributions.txt @@ -0,0 +1,208 @@ +id|last_name|first_name|middle_name|city|state|zip|employer|occupation|date|amount|candidate_id +|ST. 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100755 index 0000000..e2f05f9 --- /dev/null +++ b/NICAR2016/data/scripts/filter_contribs.rb @@ -0,0 +1,48 @@ +#!/usr/bin/env ruby + +require 'csv' + +committee_ids = [] +CSV.foreach('candidates.txt', :headers => true, :col_sep => '|', :quote_char => "\x00") do |row| + committee_ids << row[0] +end + +skips = ['C00577981', 'C00578757', 'C00577312', 'C00575449', 'C00578658'] +used = committee_ids - skips + +included = {} +used.each {|id| included[id] = 0} + +CSV.foreach('itcont.txt', :col_sep => '|', :quote_char => "\x00") do |row| + #skip if not a contrib + next if row[5] != '15' + committee_id = row[0] + if used.include? committee_id + if included[committee_id] < 25 + puts row.to_csv(:col_sep => '|') + included[committee_id] += 1 + else + used.delete committee_id + break if used.length == 0 + end + end +end + + +exit +##### +## Check the number of contributors per candidate +##### +committee_counts = {} +committee_ids.each do |id| + committee_counts[id] = 0 +end + + +committee_ids.each do |id| + CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| + committee_counts[id] += 1 if row[0] == id + end +end + +puts committee_counts diff --git a/NICAR2016/data/scripts/fix_names.rb b/NICAR2016/data/scripts/fix_names.rb new file mode 100755 index 0000000..488cbf2 --- /dev/null +++ b/NICAR2016/data/scripts/fix_names.rb @@ -0,0 +1,14 @@ +#!/usr/bin/env ruby + +require 'csv' +require 'people' + +np = People::NameParser.new(:case_mode => 'upper') +CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| + no_title = row[7].gsub(/ (MR.|MRS.|DR.|MS.)$/, '') + name = np.parse(no_title) + next if name[:last].empty? + date_parts = row[13].match(/(\d\d)(\d\d)(\d\d\d\d)/) + date_string = "#{date_parts[3]}-#{date_parts[1]}-#{date_parts[2]}" + 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") +end diff --git 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A Quick Introduction to SQL Using SQLite (Just the Steps) + +1. Open Firefox and find the SQLite Manager Plugin: + +!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/customize_menu.png! + +

+ +2. Create a new database called "campaign_finance": +!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! + +
+ +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: +!https://github.com/tthibo/SQL-Tutorial/raw/master/NICAR2015/images/create_contributions_table.png! + +
+ +4. Insert a row of data: +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! + +

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

+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);
+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);
+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');
+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');
+
+ + +6. Select everything from the contributions table: +
SELECT * FROM contributions;
+ +7. Choose some fields to select: +
SELECT city, state FROM contributions;
+ +8. Get a distinct set of cities and states: +
SELECT DISTINCT city, state FROM contributions;
+ +h3. THE WHERE CLAUSE + +9. Add some conditions to the SELECT: +
SELECT * from contributions WHERE state='IL';
+Now try playing around with some of the operators: + +|*operator*|*description*| +|=|Equal| +|<>|Not equal*| +|>|Greater than| +|<|Less than| +|>=|Greater than or equal| +|<=|Less than or equal| + +Here are some examples: +
SELECT * from contributions WHERE amount > 500;
+
SELECT * from contributions WHERE amount >= 500;
+
SELECT last_name from contributions WHERE state <> 'IL';
+ +10. Combine conditions with AND or OR: + +Find only the big spenders from Illinois +
SELECT * FROM contributions WHERE state = 'IL' AND amount > 2000;
+ +Find any big spender and anyone from Illinois +
SELECT * FROM contributions WHERE state = 'IL' OR amount > 2000;
+ +11. But watch out for operator precedence + +Looking for big spenders from Nevada and Chicago +
SELECT * FROM contributions WHERE city = 'CHICAGO' OR state = 'NV' AND amount > 2000;
+ +How did Oprah sneak in there? + +Try that again: +
SELECT * FROM contributions WHERE (city = 'CHICAGO' OR state = 'NV') AND amount > 2000;
+ +h3. Sorting the results + +12. Use ORDER BY to sort: +
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount;
+And reverse the order: +
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount DESC;
+And order by more than one column: +
SELECT last_name, state, amount FROM contributions ORDER BY state, amount DESC;
+ +h3. Limiting the number of rows returned + +13. Return only the top two contributions: +
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+ + +h3. Changing values with UPDATE + +14. Check the list of state names again: +
SELECT state FROM contributions;
+ +Let's correct Nebraska: +
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
+ +And check again: +
SELECT DISTINCT state FROM contributions;
+ +h3. DELETING rows + +15. Let's get rid of a row: +
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
+ +h3. Importing data from a flat file + +14. Import a delimited file: +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] +b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). + +c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). + +* Using the **Select File** button, browse for the **contributions.txt** file you just saved. +* Check the "First row contains column names" check box. +* Select **Pipe(|)** for "Fields separated by" +* For everything else, you can keep the default selections. +* You should have 1006 rows of data. + + +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. \ No newline at end of file From cefad625f56d3135d49ae4bb99711d0b5aadcb03 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 11:21:31 -0600 Subject: [PATCH 11/25] Update the INSERT queries for new data --- NICAR2016/data/contributions.txt | 408 +++++++++++++++---------------- NICAR2016/part1_steps.textile | 13 +- 2 files changed, 206 insertions(+), 215 deletions(-) diff --git a/NICAR2016/data/contributions.txt b/NICAR2016/data/contributions.txt index a587473..1468335 100644 --- a/NICAR2016/data/contributions.txt +++ b/NICAR2016/data/contributions.txt @@ -1,208 +1,200 @@ -id|last_name|first_name|middle_name|city|state|zip|employer|occupation|date|amount|candidate_id -|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-14|200|C00573519 -|ST. 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A Quick Introduction to SQL Using SQLite (Just the Steps) - 1. Open Firefox and find the SQLite Manager Plugin: !https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/customize_menu.png! @@ -20,17 +19,17 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 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! 

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

-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);
-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);
-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');
-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');
+INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00494740','IND','WINFREY','OPRAH','','CHICAGO','IL','60607','HARPO INC.','CHAIRMAN/CEO','2015-03-03',500);
+INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount)  VALUES ('C00494740','IND','PRITZKER','PENNY','S','CHICAGO','IL','60614','HYATT','EXEC','2015-03-14',2500);
+INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2015-10-10','2500');
+INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','2015-11-19','587');
@@ -115,7 +114,7 @@ h3. DELETING rows h3. Importing data from a flat file 14. Import a delimited file: -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] +a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt] b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). From 86b3a4248a1847303cd7aa1ecbe7d052dbfd4d25 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 11:30:25 -0600 Subject: [PATCH 12/25] Fix image of customize --- NICAR2016/part1_steps.textile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NICAR2016/part1_steps.textile b/NICAR2016/part1_steps.textile index 70a3b1c..5992170 100644 --- a/NICAR2016/part1_steps.textile +++ b/NICAR2016/part1_steps.textile @@ -1,7 +1,7 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) 1. Open Firefox and find the SQLite Manager Plugin: -!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/customize_menu.png! +!https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/images/customize_menu.png!
From 07fce70e839dc9c04b305901f91a36cc54288e37 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 11:48:36 -0600 Subject: [PATCH 13/25] Including HTML version --- NICAR2016/part1_steps.textile | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/NICAR2016/part1_steps.textile b/NICAR2016/part1_steps.textile index 5992170..5c879cd 100644 --- a/NICAR2016/part1_steps.textile +++ b/NICAR2016/part1_steps.textile @@ -1,7 +1,9 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) + + 1. Open Firefox and find the SQLite Manager Plugin: -!https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/images/customize_menu.png! +!./images/customize_menu.png!
From e26de5ab4caf04c35c1bfe2dfb863a69930bc573 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 19:27:32 -0600 Subject: [PATCH 14/25] Part one edits complete --- .../images/create_contributions_table.png | Bin 0 -> 117246 bytes ...{customize_menu.png => customize-menu.png} | Bin NICAR2016/images/execute_sql.png | Bin 0 -> 14768 bytes NICAR2016/images/ff-menu.png | Bin 0 -> 102635 bytes NICAR2016/images/import.png | Bin 0 -> 63710 bytes NICAR2016/images/import_icon.png | Bin 0 -> 7015 bytes NICAR2016/images/new_database.png | Bin 0 -> 1100 bytes NICAR2016/images/new_table.png | Bin 0 -> 1001 bytes NICAR2016/part1/data/.#famous_donors.txt | 1 + NICAR2016/{ => part1}/data/candidates.txt | 0 NICAR2016/{ => part1}/data/contributions.txt | 0 NICAR2016/part1/data/famous_donors.txt | 3 + .../data/scripts/utility}/Gemfile | 0 .../data/scripts/utility}/filter_contribs.rb | 0 .../data/scripts/utility}/fix_names.rb | 0 NICAR2016/part1/part1_steps.html | 129 +++++++++++ NICAR2016/part1/part1_steps.textile | 175 +++++++++++++++ NICAR2016/part1_steps.textile | 131 ----------- NICAR2016/part3/contributions.csv | 205 ++++++++++++++++++ NICAR2016/part3/create_contributions.sql | 15 ++ 20 files changed, 528 insertions(+), 131 deletions(-) create mode 100644 NICAR2016/images/create_contributions_table.png rename NICAR2016/images/{customize_menu.png => customize-menu.png} (100%) create mode 100644 NICAR2016/images/execute_sql.png create mode 100644 NICAR2016/images/ff-menu.png create mode 100644 NICAR2016/images/import.png create mode 100644 NICAR2016/images/import_icon.png create mode 100644 NICAR2016/images/new_database.png create mode 100644 NICAR2016/images/new_table.png create mode 120000 NICAR2016/part1/data/.#famous_donors.txt rename NICAR2016/{ => part1}/data/candidates.txt (100%) rename NICAR2016/{ => part1}/data/contributions.txt (100%) create mode 100644 NICAR2016/part1/data/famous_donors.txt rename NICAR2016/{data/scripts => part1/data/scripts/utility}/Gemfile (100%) rename NICAR2016/{data/scripts => part1/data/scripts/utility}/filter_contribs.rb (100%) rename NICAR2016/{data/scripts => part1/data/scripts/utility}/fix_names.rb (100%) create mode 100644 NICAR2016/part1/part1_steps.html create mode 100644 NICAR2016/part1/part1_steps.textile delete mode 100644 NICAR2016/part1_steps.textile create mode 100644 NICAR2016/part3/contributions.csv create mode 100644 NICAR2016/part3/create_contributions.sql diff --git a/NICAR2016/images/create_contributions_table.png b/NICAR2016/images/create_contributions_table.png new file mode 100644 index 0000000000000000000000000000000000000000..d3aa81bb5b7e483cad4f8de7092e00960027c5b5 GIT binary patch literal 117246 zcmZs>b3i4)`!Aer+qP}nZnL&JVXMt&N+j}GxN;z znNTGKNq86>7$6`Zcxfpy6(Ar`F(4oi4k(C^7W*GkN-SzmabD}S=jcJ$&vOgv!zkpFB+$fRc~s~q=-N8* zDw8xG@nUgGZiKR4KJVwxK9%?G>L&m712L4g=C$yr#?`e1~#T-jD9 zSEL44dsLw~p_F_Hk^F!*3LnJ9ah8^sec!7+&p{`S_E{g*4c=?l-Wy|z>Zk;Ph&;fa 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A Quick Introduction to SQL Using SQLite (Just the Steps)

+

1. Open Firefox and click on the menu icon:

+

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

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+

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

+
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2. Create a new database called campaign_finance:

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

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

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4. Insert a row of data.

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

+

And paste or type the following statement:

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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);
+
+

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

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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);
+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);
+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);
+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);
+
+

The SELECT statement

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6. Select everything from the contributions table:

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SELECT * FROM contributions;
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Note the wildcard * character.

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7. Choose some fields to select:

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SELECT city, state FROM contributions;
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Note the duplicates; why are they there?

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+

8. Get a distinct set of cities and states:

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SELECT DISTINCT city, state FROM contributions;
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+

THE WHERE CLAUSE

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9. Add some conditions to the SELECT:

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SELECT * from contributions WHERE state='NY';
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Now try playing around with some of the operators:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
operatordescription
=Equal
<>Not equal*
>Greater than
<Less than
>=Greater than or equal
<=Less than or equal
+

Here are some examples:

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SELECT * from contributions WHERE amount > 500;
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SELECT * from contributions WHERE amount >= 500;
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SELECT last_name from contributions WHERE state <> 'NY';
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+

10. Combine conditions with AND or OR:

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Find only the big spenders from New York

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SELECT * FROM contributions WHERE state = 'NY' AND amount > 2000;
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Find any big spender and anyone from New York

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SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
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+

11. But watch out for operator precedence

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Looking for big spenders from Las Vegas and New York City

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SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
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How did Mark Ruffalo sneak in there?

+

Try that again:

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

Sorting the results

+

12. Use ORDER BY to sort:

+
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
+

And reverse the order:

+
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
+

And order by more than one column:

+
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
+
+

Limiting the number of rows returned

+

13. Return only the top two contributions:

+
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+
+

Changing values with UPDATE

+

14. Check the list of state names again:

+
SELECT state FROM contributions;
+

Let's correct Nebraska:

+
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
+

And check again:

+
SELECT DISTINCT state FROM contributions;
+
+

DELETING rows

+

15. Let's get rid of a row:

+
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
+
+

Importing data from a flat file

+

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

+

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

+

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

+
    +
  • Using the Select File button, browse for the contributions.txt file you just saved.
    • +
  • Check the "First row contains column names" check box.
    • +
  • Select Pipe(|) for "Fields separated by"
    • +
  • For everything else, you can keep the default selections.
    • +
  • You should have 204 rows of data.
    • +
diff --git a/NICAR2016/part1/part1_steps.textile b/NICAR2016/part1/part1_steps.textile new file mode 100644 index 0000000..beb4838 --- /dev/null +++ b/NICAR2016/part1/part1_steps.textile @@ -0,0 +1,175 @@ +h2. A Quick Introduction to SQL Using SQLite (Just the Steps) + + +1. Open Firefox and click on the menu icon: + +!./images/ff-menu.png! + +Use the "customize" option to find the SQLite Manager Plugin: + +!./images/customize-menu.png! + +You can add it to your menu by dragging or right-click to add it to the toolbar. + +
+ +2. Create a new database called campaign_finance: + +!./images/new_database.png! + +
+ +3. Create a new table called contributions (!./images/new_table.png!) and give it the following field definitions: + +!./images/create_contributions_table.png! + +
+ +h3. Inserting data manually (Don't worry, you won't do this often.) + +4. Insert a row of data. + +Use the "Execute SQL" tab (it's in the second menu row, under the icons) +!./images/execute_sql.png! + +And paste or type the following statement: +

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

+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);
+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);
+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);
+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);
+
+ + +
+ +h3. The SELECT statement + +6. Select everything from the contributions table: + +
SELECT * FROM contributions;
+ +Note the wildcard * character. + +
+ +7. Choose some fields to select: + +
SELECT city, state FROM contributions;
+ +Note the duplicates; why are they there? + +
+ +8. Get a distinct set of cities and states: + +
SELECT DISTINCT city, state FROM contributions;
+ +
+ +h3. THE WHERE CLAUSE + +9. Add some conditions to the SELECT: +
SELECT * from contributions WHERE state='NY';
+Now try playing around with some of the operators: + +|*operator*|*description*| +|=|Equal| +|<>|Not equal*| +|>|Greater than| +|<|Less than| +|>=|Greater than or equal| +|<=|Less than or equal| + +Here are some examples: +
SELECT * from contributions WHERE amount > 500;
+
SELECT * from contributions WHERE amount >= 500;
+
SELECT last_name from contributions WHERE state <> 'NY';
+ +
+ +10. Combine conditions with AND or OR: + +Find only the big spenders from New York +
SELECT * FROM contributions WHERE state = 'NY' AND amount > 2000;
+ +Find any big spender and anyone from New York +
SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
+ +
+ +11. But watch out for operator precedence + +Looking for big spenders from Las Vegas and New York City +
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
+ +How did Mark Ruffalo sneak in there? + +Try that again: +
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
+ +
+ +h3. Sorting the results + +12. Use ORDER BY to sort: +
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
+And reverse the order: +
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
+And order by more than one column: +
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
+ +
+ +h3. Limiting the number of rows returned + +13. Return only the top two contributions: +
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+ +
+ +h3. Changing values with UPDATE + +14. Check the list of state names again: +
SELECT state FROM contributions;
+ +Let's correct Nebraska: +
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
+ +And check again: +
SELECT DISTINCT state FROM contributions;
+ + +
+ +h3. DELETING rows + +15. Let's get rid of a row: +
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
+ + +
+ +h3. Importing data from a flat file + +16. Import a delimited file: +a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt] (Or grab it from the conference computer.) + +b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). + +c. Click the SQLite Manager Import Wizard icon (!./images/import_icon.png!). + +* Using the **Select File** button, browse for the **contributions.txt** file you just saved. +* Check the "First row contains column names" check box. +* Select **Pipe(|)** for "Fields separated by" +* For everything else, you can keep the default selections. +* You should have 204 rows of data. + diff --git a/NICAR2016/part1_steps.textile b/NICAR2016/part1_steps.textile deleted file mode 100644 index 5c879cd..0000000 --- a/NICAR2016/part1_steps.textile +++ /dev/null @@ -1,131 +0,0 @@ -h2. A Quick Introduction to SQL Using SQLite (Just the Steps) - - -1. Open Firefox and find the SQLite Manager Plugin: - -!./images/customize_menu.png! - -
- -2. Create a new database called "campaign_finance": -!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/new_database.png! - -
- -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: -!https://github.com/tthibo/SQL-Tutorial/raw/master/NICAR2015/images/create_contributions_table.png! - -
- -4. Insert a row of data: -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! - -

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

-INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00494740','IND','WINFREY','OPRAH','','CHICAGO','IL','60607','HARPO INC.','CHAIRMAN/CEO','2015-03-03',500);
-INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount)  VALUES ('C00494740','IND','PRITZKER','PENNY','S','CHICAGO','IL','60614','HYATT','EXEC','2015-03-14',2500);
-INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00431171','IND','ADELSON','SHELDON','','LAS VEGAS','NV','89109','VENETIAN HOTEL','OWNER','2015-10-10','2500');
-INSERT INTO contributions (committee_id, entity_type, last_name, first_name, city, state, zip_code, employer, occupation, date, amount) VALUES ('C00431171','IND','MARRIOTT','BILL MR. JR.','','BETHESDA','MD','20817','MARRIOTT INTERNATINAL INC.','CHAIRMAN & C.E.O.','2015-11-19','587');
-
- - -6. Select everything from the contributions table: -
SELECT * FROM contributions;
- -7. Choose some fields to select: -
SELECT city, state FROM contributions;
- -8. Get a distinct set of cities and states: -
SELECT DISTINCT city, state FROM contributions;
- -h3. THE WHERE CLAUSE - -9. Add some conditions to the SELECT: -
SELECT * from contributions WHERE state='IL';
-Now try playing around with some of the operators: - -|*operator*|*description*| -|=|Equal| -|<>|Not equal*| -|>|Greater than| -|<|Less than| -|>=|Greater than or equal| -|<=|Less than or equal| - -Here are some examples: -
SELECT * from contributions WHERE amount > 500;
-
SELECT * from contributions WHERE amount >= 500;
-
SELECT last_name from contributions WHERE state <> 'IL';
- -10. Combine conditions with AND or OR: - -Find only the big spenders from Illinois -
SELECT * FROM contributions WHERE state = 'IL' AND amount > 2000;
- -Find any big spender and anyone from Illinois -
SELECT * FROM contributions WHERE state = 'IL' OR amount > 2000;
- -11. But watch out for operator precedence - -Looking for big spenders from Nevada and Chicago -
SELECT * FROM contributions WHERE city = 'CHICAGO' OR state = 'NV' AND amount > 2000;
- -How did Oprah sneak in there? - -Try that again: -
SELECT * FROM contributions WHERE (city = 'CHICAGO' OR state = 'NV') AND amount > 2000;
- -h3. Sorting the results - -12. Use ORDER BY to sort: -
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount;
-And reverse the order: -
SELECT last_name, amount FROM contributions WHERE amount > 200 ORDER BY amount DESC;
-And order by more than one column: -
SELECT last_name, state, amount FROM contributions ORDER BY state, amount DESC;
- -h3. Limiting the number of rows returned - -13. Return only the top two contributions: -
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
- - -h3. Changing values with UPDATE - -14. Check the list of state names again: -
SELECT state FROM contributions;
- -Let's correct Nebraska: -
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
- -And check again: -
SELECT DISTINCT state FROM contributions;
- -h3. DELETING rows - -15. Let's get rid of a row: -
SELECT * FROM contributions WHERE id = 1;
-
DELETE FROM contributions WHERE id = 1;
- -h3. Importing data from a flat file - -14. Import a delimited file: -a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt] -b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). - -c. Click the SQLite Manager Import Wizard icon (!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/import_icon.png!). - -* Using the **Select File** button, browse for the **contributions.txt** file you just saved. -* Check the "First row contains column names" check box. -* Select **Pipe(|)** for "Fields separated by" -* For everything else, you can keep the default selections. -* You should have 1006 rows of data. - - -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. \ No newline at end of file diff --git a/NICAR2016/part3/contributions.csv b/NICAR2016/part3/contributions.csv new file mode 100644 index 0000000..1883be9 --- /dev/null +++ b/NICAR2016/part3/contributions.csv @@ -0,0 +1,205 @@ +id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount +"6","C00575795","IND","BUFFETT","WARREN","","OMAHA","Nebraska","68131","BERKSHIRE HATHAWAY","CHAIRMAN","2015-04-15","2700" +"7","C00577130","IND","RUFFALO","MARK","","NEW YORK","NY","|100031526","SELF-EMPLOYED","ACTOR","2015-06-02","250" +"8","C00458844","IND","ADELSON","SHELDON","","LAS VEGAS","NV","89145","","","2015-06-12","2600" +"9","C00579458","IND","MANNING","PEYTON MR.","","CLEVELAND","OH","441141717","DENVER BRONCOS","PROFESSIONAL ATHLETE","2015-08-04","2700" +"10","C00575795","IND","COPPOLA","SOFIA","","NEW YORK","NY","100130778","SELF-EMPLOYED","DIRECTOR/WRITER/ACTOR","2016-01-22","2700" +"11","C00573519","IND","ST. CLAIR","ANNE","K","MECHANICSVILLE","VA","231112600","RETIRED","RETIRED","2015-03-14","200" +"12","C00573519","IND","ST. CLAIR","ANNE","K","MECHANICSVILLE","VA","231112600","RETIRED","RETIRED","2015-03-28","250" +"13","C00573519","IND","STANFIELD","AMY",,"FRANKLIN","TN","370675672","SELF-EMPLOYED","ARTIST","2015-03-31","250" +"14","C00573519","IND","STARK","CHERYL","ANN","ENGLEWOOD","FL","342245434","PATHWAY LIGHTING PRODUCTS INC.","MARKETING RESEARCH","2015-03-11","500" +"15","C00573519","IND","STARK","FRED",,"ENGLEWOOD","FL","342245434","PATHWAY LIGHTING PRODUCTS INC.","CEO","2015-03-19","1000" +"16","C00573519","IND","STEBBENS","KIM",,"EVERETT","WA","982011557","WAYA GROUP INC","EXECUTIVE","2015-03-14","250" +"17","C00573519","IND","STEGEMANN","BELINDA",,"CLINT","TX","798365308","RETIRED","RETIRED","2015-03-28","500" +"18","C00573519","IND","STEIGERWALD","DONALD",,"ESCONDIDO","CA","920263942","STEIGERWALD DOUGHERTY INC","BUILDING CONTRACTOR","2015-03-04","250" +"19","C00573519","IND","STEINKAMP","JEFFREY",,"ROCHESTER","VT","057670098","RETIRED","RETIRED","2015-03-31","1500" +"20","C00573519","IND","STENGEL","LINDA",,"SIOUX FALLS","SD","571058110","RETIRED","RETIRED","2015-03-30","250" +"21","C00573519","IND","STENSETHER","BARBARA","L","TURLOCK","CA","953821415","RETIRED","RETIRED","2015-03-11","500" +"22","C00573519","IND","STENSETHER","JOHN",,"TURLOCK","CA","953821415","RETIRED","RETIRED","2015-03-11","500" +"23","C00573519","IND","STEWARDSON","MELINDA",,"BUFFALO GAP","TX","795082140","WYLIE INDEPENDENT SCHOOL DISTRICT","READING TEACHER","2015-03-31","250" +"24","C00573519","IND","STOCKSTILL","JAMES",,"PICAYUNE","MS","394665552","RETIRED","RETIRED","2015-03-04","250" +"25","C00573519","IND","STODDARD","CHERYL",,"FREDERICKSBURG","VA","224052180","ANTHEM","REGIONAL MANAGER","2015-03-31","250" +"26","C00573519","IND","STOKER","RICHARD",,"MIAMI BEACH","FL","331417401","RETIRED","RETIRED","2015-03-24","300" +"27","C00573519","IND","THORNTON","JOE",,"DALLAS","TX","752254816","HFF","REAL ESTATE CAPITAL MARKETS","2015-03-05","2700" +"28","C00573519","IND","TIMBLE","JAMES",,"FRANKLIN GROVE","IL","610319555","RETIRED","RETIRED","2015-03-28","250" +"29","C00573519","IND","TOBB","RONALD",,"BROOKEVILLE","MD","208333208","BOWLES FLUIDICS","OPERATIONS VP","2015-03-03","250" +"30","C00573519","IND","TOLLAKSON","DARLENE","M","SOUTH WAYNE","WI","535879761",,,"2015-03-30","400" +"31","C00573519","IND","TOMANN","DON",,"BUFFALO","MN","553134253","UMC INC.","BUSINESS OWNER/PROFESSIONAL","2015-03-03","250" +"32","C00573519","IND","TORRENCE","DARREN",,"PENSACOLA","FL","325075234","SELF-EMPLOYED","OWNER","2015-03-18","250" +"33","C00573519","IND","TORRE","THOMAS",,"CUMMING","GA","300411205","AT&T","ENGINEER","2015-03-23","250" +"34","C00573519","IND","TRAN","VINCENT",,"CRESCENT CITY","CA","955319576","STATE OF CALIFORNIA","DENTIST","2015-03-04","1000" +"35","C00574624","IND","CHOATE","CHARLIE",,"BURLESON","TX","760282217","SELF EMPLOYED","SMALL BUSINESS OWNER","2015-03-31","250" +"36","C00574624","IND","CHOR","MARLENE",,"ANACONDA","MT","597112904",,,"2015-03-31","500" +"37","C00574624","IND","MACDONALD","LENNA","R","DANIEL ISLAND","SC","294927523","BVCC LLC","ENTREPRENEUR","2015-03-25","1000" +"38","C00574624","IND","MACH","STEVEN","P","HOUSTON","TX","772190630","MACH INDUSTRIAL GROUP LP","VP FINANCE","2015-03-23","2700" +"39","C00574624","IND","MACKEY","WILLIAM",,"MIAMI","FL","331566023",,,"2015-03-31","500" +"40","C00574624","IND","MADDOX","MATTHEW","MATT","HOUSTON","TX","770083017","BROTHERS ALVARADO; 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INC.","INVESTMENT MANAGER","2015-06-18","400" +"77","C00577130","IND","SMITH","PRENTISS","CHARLES","BRATTLEBORO","VT","053014232","PRENTISS SMITH & CO,. 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+"id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE , +"committee_id" VARCHAR, +"entity_type" VARCHAR, +"last_name" VARCHAR, +"first_name" VARCHAR, +"middle_name" VARCHAR, +"city" VARCHAR, +"state" VARCHAR, +"zip_code" VARCHAR, +"employer" VARCHAR, +"occupation" VARCHAR, +"date" DATETIME, +"amount" INTEGER +); \ No newline at end of file From 488fe7ff01e591a609826527e3b4ae58569a63ec Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Wed, 9 Mar 2016 19:29:39 -0600 Subject: [PATCH 15/25] Move image dir --- .../images/create_contributions_table.png | Bin 117246 -> 0 bytes NICAR2016/images/customize-menu.png | Bin 214300 -> 0 bytes NICAR2016/images/execute_sql.png | Bin 14768 -> 0 bytes NICAR2016/images/ff-menu.png | Bin 102635 -> 0 bytes NICAR2016/images/import.png | Bin 63710 -> 0 bytes NICAR2016/images/import_icon.png | Bin 7015 -> 0 bytes NICAR2016/images/new_database.png | Bin 1100 -> 0 bytes NICAR2016/images/new_table.png | Bin 1001 -> 0 bytes 8 files changed, 0 insertions(+), 0 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++-- 7 files changed, 263 insertions(+), 233 deletions(-) delete mode 100644 NICAR2016/part1/data/candidates.txt create mode 100644 NICAR2016/part3/candidates.txt delete mode 100644 NICAR2016/part3/contributions.csv create mode 100644 NICAR2016/part3/contributions.txt create mode 100644 NICAR2016/part3/create_candidates.sql diff --git a/NICAR2016/part1/data/candidates.txt b/NICAR2016/part1/data/candidates.txt deleted file mode 100644 index 01349f7..0000000 --- a/NICAR2016/part1/data/candidates.txt +++ /dev/null @@ -1,15 +0,0 @@ -id|committee_id|first_name|last_name|middle_name|party -C00580100|TRUMP|DONALD|J|REP -C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP -C00458844|RUBIO|MARCO||REP -C00581876|KASICH|JOHN|R|REP -C00573519|CARSON|BENJAMIN|S|REP -C00577130|SANDERS|BERNARD||DEM -C00575795|CLINTON|HILLARY|RODHAM|DEM -C00578658|O'MALLEY|MARTIN|JOSEPH|DEM -C00579458|BUSH|JEB||REP -C00580399|CHRISTIE|CHRISTOPHER|J|REP -C00575449|PAUL|RAND||REP -C00577312|FIORINA|CARLY||REP -C00578757|GRAHAM|LINDSEY|O|REP -C00577981|HUCKABEE|MIKE||REP diff --git a/NICAR2016/part1/part1_steps.textile b/NICAR2016/part1/part1_steps.textile index beb4838..bdf9545 100644 --- a/NICAR2016/part1/part1_steps.textile +++ b/NICAR2016/part1/part1_steps.textile @@ -95,6 +95,24 @@ Here are some examples:

+h3. Pick One: Using BETWEEN and IN (NOT IN) + +Often you'll want to get a value from within a range. The BETWEEN operator can do exactly that. Let's see which of our contributors has given between 500 and 1000 dollars: +
SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
+(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) + +At other times, you may need to match values from within a set of choices. This is where the IN operator comes in handy. Let's find all contributors from a few southern states: +
SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
+The choices are surrounded by parentheses and separated by commas. And don't forget the quote marks around literal strings. here's the result: +!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/in.png! +(Again, you could have used a compound statement with state = 'AL' OR state = 'GA' OR state = 'FL' to achieve the same result, but the IN syntax makes things much clearer, and it's easier to write.) + +You can also use NOT IN to find results where a value is not included in the given set: +
SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
+But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above. + +
+ 10. Combine conditions with AND or OR: Find only the big spenders from New York @@ -158,6 +176,8 @@ h3. DELETING rows
+ + h3. Importing data from a flat file 16. Import a delimited file: diff --git a/NICAR2016/part3/candidates.txt b/NICAR2016/part3/candidates.txt new file mode 100644 index 0000000..1f60fc9 --- /dev/null +++ b/NICAR2016/part3/candidates.txt @@ -0,0 +1,15 @@ +id|committee_id|last_name|first_name|middle_name|party +1|C00580100|TRUMP|DONALD|J|REP +2|C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP +3|C00458844|RUBIO|MARCO||REP +4|C00581876|KASICH|JOHN|R|REP +5|C00573519|CARSON|BENJAMIN|S|REP +6|C00577130|SANDERS|BERNARD||DEM +7|C00575795|CLINTON|HILLARY|RODHAM|DEM +8|C00578658|O'MALLEY|MARTIN|JOSEPH|DEM +9|C00579458|BUSH|JEB||REP +10|C00580399|CHRISTIE|CHRISTOPHER|J|REP +11|C00575449|PAUL|RAND||REP +12|C00577312|FIORINA|CARLY||REP +13|C00578757|GRAHAM|LINDSEY|O|REP +14|C00577981|HUCKABEE|MIKE||REP diff --git a/NICAR2016/part3/contributions.csv b/NICAR2016/part3/contributions.csv deleted file mode 100644 index 1883be9..0000000 --- a/NICAR2016/part3/contributions.csv +++ /dev/null @@ -1,205 +0,0 @@ -id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount -"6","C00575795","IND","BUFFETT","WARREN","","OMAHA","Nebraska","68131","BERKSHIRE HATHAWAY","CHAIRMAN","2015-04-15","2700" -"7","C00577130","IND","RUFFALO","MARK","","NEW YORK","NY","|100031526","SELF-EMPLOYED","ACTOR","2015-06-02","250" -"8","C00458844","IND","ADELSON","SHELDON","","LAS VEGAS","NV","89145","","","2015-06-12","2600" -"9","C00579458","IND","MANNING","PEYTON MR.","","CLEVELAND","OH","441141717","DENVER BRONCOS","PROFESSIONAL ATHLETE","2015-08-04","2700" -"10","C00575795","IND","COPPOLA","SOFIA","","NEW YORK","NY","100130778","SELF-EMPLOYED","DIRECTOR/WRITER/ACTOR","2016-01-22","2700" -"11","C00573519","IND","ST. 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P.C.","ATTORNEY","2015-03-30","1000" -"41","C00574624","IND","MADSON","RYAN",,"EL DORADO HILLS","CA","957629665","NA","N/A","2015-03-28","1000" -"42","C00574624","IND","MAGGLOS","LINDA",,"MALIBU","CA","902653746","HOMEMAKER","HOMEMAKER","2015-03-24","1000" -"43","C00574624","IND","MAGNESS","SOPHIE",,"HOUSTON","TX","770571814","MAGNESS ORTHODONTICS","DIRECTOR OF MARKETING","2015-03-23","500" -"44","C00574624","IND","MALONEY","THOMAS",,"HAMPTON BAYS","NY","119462826","SHINNECOCK HARDWARE","OWNER","2015-03-25","1000" -"45","C00574624","IND","MANN","JODI",,"STUDIO CITY","CA","916043855","GJ SULLIVAN","BROKER","2015-03-24","1000" -"46","C00574624","IND","MANN","KEN",,"STUDIO CITY","CA","916043855","COVERX","INSURANCE","2015-03-24","1000" -"47","C00574624","IND","MANN","RICHARD","F","PONTE VEDRA BEACH","FL","320824609","RETIRED","RETIRED","2015-03-31","200" -"48","C00574624","IND","MANNING","JOHN","OWEN","AUSTIN","TX","787315848","GREAT POINT CAPITAL","TRADER","2015-03-31","5400" -"49","C00574624","IND","HARDY","LONNIE",,"SHREVEPORT","LA","711192503","SELF EMPLOYED","CPA","2015-03-23","1000" -"50","C00574624","IND","MARACCHINI","CHRIS",,"CEDAR HILL","TX","751041003","AMERICAN AIRLINES","PILOT","2015-03-24","1000" -"51","C00574624","IND","MARDIGIAN","DAVID",,"BLOOMFIELD HILLS","MI","483040934","MCM MANAGEMENT CORPORATION","CEO","2015-03-31","5400" -"52","C00574624","IND","HARDAWAY","MIKE",,"MCKINNEY","TX","750706221","RAYTHEON COMPANY","TECH WRITER/EDITOR","2015-03-24","500" -"53","C00574624","IND","HARIPRASAD","SATISH",,"EAST STROUDSBURG","PA","183027984","SELF EMPLOYED","PRINTER","2015-03-24","500" -"54","C00574624","IND","HARPER","JIM",,"BIRMINGHAM","MI","480091311","RETIRED","RETIRED","2015-03-24","1000" -"55","C00574624","IND","HARRELL","KEITH",,"MCKINNEY","TX","750717390","BUSINESS OWNER","COUNSELOR/RESTAURANT OWNER","2015-03-25","5400" -"56","C00574624","IND","JANES","RALPH",,"MARBLE FALLS","TX","786544071","RE JANES GRAVEL COMPNAY","CONSULTANT","2015-03-31","5400" -"57","C00574624","IND","JANES","RALPH",,"MARBLE FALLS","TX","786544071","RE JANES GRAVEL COMPNAY","CONSULTANT","2015-03-31","-2700" -"58","C00574624","IND","JANES","RALPH",,"MARBLE FALLS","TX","786544071","RE JANES GRAVEL COMPNAY","CONSULTANT","2015-03-31","2700" -"59","C00458844","IND","IRIZARRY","STEVEN",,"WASHINGTON","DC","200041008","ROBERTI+WHITE LLC","CONSULTANT","2014-12-22","2500" -"60","C00458844","IND","SIMMONS","ANNETTE","C","DALLAS","TX","752402620","HOMEMAKER","HOMEMAKER","2014-12-17","5000" -"61","C00458844","IND","MANTENA","RAMA","R","JUPITER","FL","334774202","P4 HEALTHCARE","CEO","2014-11-19","5000" -"62","C00458844","IND","REUS","SANDRA",,"DORAL","FL","331783787","SUNSHINE GASOLINE","EXECUTIVE","2014-12-19","9600" -"63","C00458844","IND","PATTEN","CAROLYN","A","PORT SAINT LUCIE","FL","349863252","UNIVERSITY OF FLORIDA",,"2015-02-17","250" -"64","C00458844","IND","PETHOKOUKIS","JOHN","MR","WESTERN SPRINGS","IL","605582013","MORGAN STANLEY","FINANCIAL ADVISOR","2015-02-25","1000" -"65","C00458844","IND","PETHOKOUKIS","SARA",,"WESTERN SPRINGS","IL","605582013","HOMEMAKER","HOMEMAKER","2015-02-25","1000" -"66","C00458844","IND","PLOMARITIS","TITUS","MR","PELHAM","NH","030765003","RETIRED","RETIRED","2015-03-31","1000" -"67","C00458844","IND","REPLOGLE","STEPHEN",,"WASHINGTON","DC","200172912","COVE STRATEGIES","CONSULTANT","2015-02-09","5000" -"68","C00458844","IND","REPLOGLE","STEPHEN",,"WASHINGTON","DC","200172912","COVE STRATEGIES","CONSULTANT","2015-02-09","-2300" -"69","C00458844","IND","REPLOGLE","STEPHEN",,"WASHINGTON","DC","200172912","COVE STRATEGIES","CONSULTANT","2015-02-09","2300" -"70","C00577130","IND","HOLZHAUER","FRED",,"NOVATO","CA","949471909","METHOD PRODUCTS, PBC","CHEMIST","2015-06-30","250" -"71","C00577130","IND","HONG","PENELOPE",,"ROCKPORT","TX","783823610","NOT EMPLOYED","RETIRED","2015-06-30","250" -"72","C00577130","IND","WYATT","EDNA",,"SANTA FE","NM","875012242","SELF-EMPLOYED","CARPENTER","2015-05-20","300" -"73","C00577130","IND","YOUNKIN","RANDY",,"YOUNGSTOWN","OH","445052563",,,"2015-05-20","500" -"74","C00577130","IND","SHEWFELT","MATTHEW",,"LOS ANGELES","CA","900263322","WILSHIRE ESCROW COMPANY","ESCROW OFFICER","2015-06-18","250" -"75","C00577130","IND","ROTHNER","GLENN",,"PASADENA","CA","911052720","ROTHNER, SEGALL & GREENSTONE","ATTORNEY","2015-06-18","250" -"76","C00577130","IND","SMITH","PRENTISS","CHARLES","BRATTLEBORO","VT","053014232","PRENTISS SMITH & CO,. INC.","INVESTMENT MANAGER","2015-06-18","400" -"77","C00577130","IND","SMITH","PRENTISS","CHARLES","BRATTLEBORO","VT","053014232","PRENTISS SMITH & CO,. INC.","INVESTMENT MANAGER","2015-06-18","400" -"78","C00577130","IND","STRATTON","RICHARD","G","BUFFALO","NY","142163417","NICHOLS SCHOOL","TEACHER","2015-06-18","300" -"79","C00577130","IND","STRATTON","RICHARD","G","BUFFALO","NY","142163417","NICHOLS SCHOOL","TEACHER","2015-06-18","300" -"80","C00577130","IND","KAST","RICHARD",,"BURLINGTON","VT","054082544","SELF-EMPLOYED","PHYSICIAN","2015-06-18","212" -"81","C00577130","IND","KUSNERZ","PEGGY","ANN","ANN ARBOR","MI","481044303",,,"2015-06-18","200" -"82","C00577130","IND","KUSNERZ","PEGGY","ANN","ANN ARBOR","MI","481044303",,,"2015-06-18","200" -"83","C00577130","IND","MARSH","PAUL","G","HAMILTON","OH","450139476",,,"2015-06-18","250" -"84","C00577130","IND","MARSH","PAUL","G","HAMILTON","OH","450139476",,,"2015-06-18","250" -"85","C00577130","IND","MCDANIEL","MICHAEL","J","SAN DIEGO","CA","921082806","SELF","MD","2015-06-18","200" -"86","C00577130","IND","ALLEN","BRUCE",,"LAUGHLIN","NV","890280673","SELF-EMPLOYED","CLINICAL 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\ No newline at end of file diff --git a/NICAR2016/part3/create_contributions.sql b/NICAR2016/part3/create_contributions.sql index d3254c3..7b5fc68 100644 --- a/NICAR2016/part3/create_contributions.sql +++ b/NICAR2016/part3/create_contributions.sql @@ -1,15 +1,17 @@ CREATE TABLE "contributions" ( -"id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE , -"committee_id" VARCHAR, -"entity_type" VARCHAR, -"last_name" VARCHAR, -"first_name" VARCHAR, -"middle_name" VARCHAR, -"city" VARCHAR, -"state" VARCHAR, -"zip_code" VARCHAR, -"employer" VARCHAR, -"occupation" VARCHAR, -"date" DATETIME, -"amount" INTEGER + "id" INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL UNIQUE , + "committee_id" VARCHAR, + "entity_type" VARCHAR, + "last_name" VARCHAR, + "first_name" VARCHAR, + "middle_name" VARCHAR, + "city" VARCHAR, + "state" VARCHAR, + "zip_code" VARCHAR, + "employer" VARCHAR, + "occupation" VARCHAR, + "date" DATETIME, + "amount" INTEGER, + "candidate_id" INTEGER NOT NULL, + FOREIGN KEY(candidate_id) REFERENCES candidates(id) ); \ No newline at end of file From 172aca7346e64c5f6c4d522054bd628926eb75f1 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Thu, 10 Mar 2016 00:49:34 -0600 Subject: [PATCH 18/25] Steps for part 3 --- NICAR2016/part1/part1_steps.html | 24 ++- NICAR2016/part1/part1_steps.textile | 52 +++--- NICAR2016/part3/{ => data}/candidates.txt | 0 NICAR2016/part3/{ => data}/contributions.txt | 1 - NICAR2016/part3/images/ff-menu.png | Bin 0 -> 102635 bytes NICAR2016/part3/images/import_icon.png | Bin 0 -> 7015 bytes NICAR2016/part3/images/new_database.png | Bin 0 -> 1100 bytes NICAR2016/part3/part3_steps.html | 102 +++++++++++ NICAR2016/part3/part3_steps.textile | 169 ++++++++++++++++++ .../{ => sql_scripts}/create_candidates.sql | 0 .../create_contributions.sql | 0 11 files changed, 318 insertions(+), 30 deletions(-) rename NICAR2016/part3/{ => data}/candidates.txt (100%) rename NICAR2016/part3/{ => data}/contributions.txt (99%) create mode 100644 NICAR2016/part3/images/ff-menu.png create mode 100644 NICAR2016/part3/images/import_icon.png create mode 100644 NICAR2016/part3/images/new_database.png create mode 100644 NICAR2016/part3/part3_steps.html create mode 100644 NICAR2016/part3/part3_steps.textile rename NICAR2016/part3/{ => sql_scripts}/create_candidates.sql (100%) rename NICAR2016/part3/{ => sql_scripts}/create_contributions.sql (100%) diff --git a/NICAR2016/part1/part1_steps.html b/NICAR2016/part1/part1_steps.html index c04fb12..b215775 100644 --- a/NICAR2016/part1/part1_steps.html +++ b/NICAR2016/part1/part1_steps.html @@ -90,8 +90,22 @@

THE WHERE CLAUSE

Try that again:

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

Pick One: Using BETWEEN and IN (NOT IN)

+

11. Find all contributions between $500 and $1000

+
SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
+

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

+
+

12. Find all contributors from a few southern states:

+
SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
+

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

+
+

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

+
SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
+

But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above.

+

Sorting the results

-

12. Use ORDER BY to sort:

+

14. Use ORDER BY to sort:

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

And reverse the order:

SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
@@ -99,11 +113,11 @@

Sorting the results

SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;

Limiting the number of rows returned

-

13. Return only the top two contributions:

+

15. Return only the top two contributions:

SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;

Changing values with UPDATE

-

14. Check the list of state names again:

+

16. Check the list of state names again:

SELECT state FROM contributions;

Let's correct Nebraska:

UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
@@ -111,12 +125,12 @@

Changing values with UPDATE

SELECT DISTINCT state FROM contributions;

DELETING rows

-

15. Let's get rid of a row:

+

17. Let's get rid of a row:

SELECT * FROM contributions WHERE id = 1;
DELETE FROM contributions WHERE id = 1;

Importing data from a flat file

-

16. Import a delimited file:
+

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

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

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

diff --git a/NICAR2016/part1/part1_steps.textile b/NICAR2016/part1/part1_steps.textile index bdf9545..374b7af 100644 --- a/NICAR2016/part1/part1_steps.textile +++ b/NICAR2016/part1/part1_steps.textile @@ -1,6 +1,5 @@ h2. A Quick Introduction to SQL Using SQLite (Just the Steps) - 1. Open Firefox and click on the menu icon: !./images/ff-menu.png! @@ -95,24 +94,6 @@ Here are some examples:
-h3. Pick One: Using BETWEEN and IN (NOT IN) - -Often you'll want to get a value from within a range. The BETWEEN operator can do exactly that. Let's see which of our contributors has given between 500 and 1000 dollars: -
SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
-(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) - -At other times, you may need to match values from within a set of choices. This is where the IN operator comes in handy. Let's find all contributors from a few southern states: -
SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
-The choices are surrounded by parentheses and separated by commas. And don't forget the quote marks around literal strings. here's the result: -!https://github.com/tthibo/SQL-Tutorial/raw/master/tutorial_files/images/in.png! -(Again, you could have used a compound statement with state = 'AL' OR state = 'GA' OR state = 'FL' to achieve the same result, but the IN syntax makes things much clearer, and it's easier to write.) - -You can also use NOT IN to find results where a value is not included in the given set: -
SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
-But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above. - -
- 10. Combine conditions with AND or OR: Find only the big spenders from New York @@ -135,9 +116,32 @@ Try that again:
+h3. Pick One: Using BETWEEN and IN (NOT IN) + +11. Find all contributions between $500 and $1000 +
SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
+(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) + +
+ +12. Find all contributors from a few southern states: +
SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
+ +(Note: A compound statement could accomplish this same goal: state = 'AL' OR state = 'GA' OR state = 'FL' +But the IN syntax makes things much clearer, and it's easier to write.) + +
+ +13. Use NOT IN to find results where a value is not included in the given set: +
SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
+But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above. + +
+ + h3. Sorting the results -12. Use ORDER BY to sort: +14. Use ORDER BY to sort: 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
And reverse the order: 
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
@@ -148,14 +152,14 @@ And order by more than one column: h3. Limiting the number of rows returned -13. Return only the top two contributions: +15. Return only the top two contributions: 
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
h3. Changing values with UPDATE -14. Check the list of state names again: +16. Check the list of state names again: 
SELECT state FROM contributions;
Let's correct Nebraska: @@ -169,7 +173,7 @@ And check again: h3. DELETING rows -15. Let's get rid of a row: +17. Let's get rid of a row: 
SELECT * FROM contributions WHERE id = 1;
DELETE FROM contributions WHERE id = 1;
@@ -180,7 +184,7 @@ h3. DELETING rows h3. Importing data from a flat file -16. Import a delimited file: +18. Import a delimited file: a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt] (Or grab it from the conference computer.) b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). diff --git a/NICAR2016/part3/candidates.txt b/NICAR2016/part3/data/candidates.txt similarity index 100% rename from NICAR2016/part3/candidates.txt rename to NICAR2016/part3/data/candidates.txt diff --git a/NICAR2016/part3/contributions.txt b/NICAR2016/part3/data/contributions.txt similarity index 99% rename from NICAR2016/part3/contributions.txt rename to NICAR2016/part3/data/contributions.txt index 9f4026e..f398415 100644 --- a/NICAR2016/part3/contributions.txt +++ b/NICAR2016/part3/data/contributions.txt @@ -55,7 +55,6 @@ id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code |C00458844|IND|IRIZARRY|STEVEN||WASHINGTON|DC|200041008|ROBERTI+WHITE LLC|CONSULTANT|2014-12-22|2500|3 |C00458844|IND|SIMMONS|ANNETTE|C|DALLAS|TX|752402620|HOMEMAKER|HOMEMAKER|2014-12-17|5000|3 |C00458844|IND|MANTENA|RAMA|R|JUPITER|FL|334774202|P4 HEALTHCARE|CEO|2014-11-19|5000|3 -|C00458844|IND|REUS|SANDRA||DORAL|FL|331783787|SUNSHINE GASOLINE|EXECUTIVE|2014-12-19|9600|3 |C00458844|IND|PATTEN|CAROLYN|A|PORT SAINT LUCIE|FL|349863252|UNIVERSITY OF FLORIDA||2015-02-17|250|3 |C00458844|IND|PETHOKOUKIS|JOHN|MR|WESTERN SPRINGS|IL|605582013|MORGAN STANLEY|FINANCIAL ADVISOR|2015-02-25|1000|3 |C00458844|IND|PETHOKOUKIS|SARA||WESTERN SPRINGS|IL|605582013|HOMEMAKER|HOMEMAKER|2015-02-25|1000|3 diff --git a/NICAR2016/part3/images/ff-menu.png b/NICAR2016/part3/images/ff-menu.png new file mode 100644 index 0000000000000000000000000000000000000000..03f2cb518b93c80de17d55bf3edcdc857469c85e GIT binary patch literal 102635 zcmZU4by%D+voB6@D^T2_=;F@eP+W?;yZho++={ywN^ytc?pEBrxGc6fm!9w3bI>ZJpSw;9!9oG> zY1r964j_wYd2z1KtbG|?@B8|J=Yw(}8D> z##?j3&xX&!P~<*vPpU=Fy_mjeP@M6G)Ix+%<2Vv5g!m+_QC$@2IdPb9P|=11&?o7R zxKCB4Ke$LNrA^_opaSVsKi@z>rE9*R3_QY<`=?WV=WcScQRXeo3^Nky5<$j=%Jqzwr{k`5>XhK3tl19;K44tIyj~KQhv*Tm#D*oYK%HS?hks5cAm0 zQi-4O+-#fZ{A}HQ~N1Q4*>rSo=Y@=RtbAqlK}t3)CTtnSr!V)P|*2qOx_^d)i# 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1. Open Firefox and the SQLite Manager Plugin
+

+
+

2. Create a new database called contributions_candidates

+

+
+

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

+

+CREATE TABLE "candidates" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "first_name" VARCHAR, 
+    "last_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "party" VARCHAR
+);
+
+
+

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

+

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

+ +

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

+
CREATE TABLE "contributions" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "entity_type" VARCHAR, 
+    "last_name" VARCHAR, 
+    "first_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "city" VARCHAR, 
+    "state" VARCHAR, 
+    "zip_code" VARCHAR, 
+    "employer" VARCHAR, 
+    "occupation" VARCHAR, 
+    "date" DATETIME, 
+    "amount" INTEGER,
+    "candidate_id" INTEGER NOT NULL,
+    FOREIGN KEY(candidate_id) REFERENCES candidates(id)
+);
+
+

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

+
+

Using Subqueries

+

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

+

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

+

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

+

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

+

Nope.

+

Hardcode the amount?

+
SELECT * FROM contributions WHERE amount = 5400;
+

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

+

Subqueries to the rescue:

+
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
+
+

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

+
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
+
+

Using a subquery on another table

+

9. Find all of the contributors to Hillary Clinton

+
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');
+
+

Joins: Queries across tables

+

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

+
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
+
+

11. Use table name aliases to save typing:

+
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
+

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
+like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand.

+
+

Explicit JOIN syntax

+

12. Rewrite the above query using explicit JOIN syntax:

+
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
+
+

13. Use aliases work with JOIN syntax:

+
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
+
+

14. Get the number of contributions for each candidate

+
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;
+

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

+
SELECT DISTINCT id, last_name FROM candidates;
+

Definitely missing candidates. What happened?

+

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

+

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

+

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

+

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:

+
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;
+

The results will be the same.

+
+

OUTER JOINS

+
    +
  • 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.
    • +
  • 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.
    • +
  • A FULL OUTER JOIN includes all rows from both tables.
    • +
+

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

+

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

+
SELECT count(contributions.id), candidates.id, candidates.last_name FROM candidates LEFT OUTER JOIN contributoins ON candidates.id = contributions.candidate_id GROUP BY candidates.id, candidates.last_name;
+

(Note: Aliases would work here as well.)

diff --git a/NICAR2016/part3/part3_steps.textile b/NICAR2016/part3/part3_steps.textile new file mode 100644 index 0000000..20c87da --- /dev/null +++ b/NICAR2016/part3/part3_steps.textile @@ -0,0 +1,169 @@ +h2. SQLite III: Subqueries and Joins in SQLite + +1. Open Firefox and the SQLite Manager Plugin +!./images/ff-menu.png! + +
+ +2. Create a new database called contributions_candidates + +!./images/new_database.png! + +
+ +3. Use a CREATE TABLE statement to create a new table for candidate data: +
+
+CREATE TABLE "candidates" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "first_name" VARCHAR, 
+    "last_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "party" VARCHAR
+);
+
+
+ +
+ +4. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import candidates.txt + +Remember to select the pipe delimiter ("|") and to check the box indicating that the first row contains headers. + + + +5. Use a CREATE TABLE statement to create a new contributions table: + +

+CREATE TABLE "contributions" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "entity_type" VARCHAR, 
+    "last_name" VARCHAR, 
+    "first_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "city" VARCHAR, 
+    "state" VARCHAR, 
+    "zip_code" VARCHAR, 
+    "employer" VARCHAR, 
+    "occupation" VARCHAR, 
+    "date" DATETIME, 
+    "amount" INTEGER,
+    "candidate_id" INTEGER NOT NULL,
+    FOREIGN KEY(candidate_id) REFERENCES candidates(id)
+);
+
+
+ + +6. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import contributions.txt: + +
+ +h3. Using Subqueries + +7. Find out the maxiumum contribution amount in the data set and see which contributors have given this amount. + +First, determine the maximum amount contriubuted: +SELECT MAX(amount) FROM contributions; + +Then SELECT all fields for any contribution at that amount . . . but how? + +Like this? +SELECT * FROM contributions WHERE amount = MAX(amount); + +Nope. + +Hardcode the amount? +
SELECT * FROM contributions WHERE amount = 5400;
+ +But what if next month the max changes? How can we make this dynamic? + +Subqueries to the rescue: +
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
+ +
+ + +8. Write a query that gets the sum amount of the top 20 contributions + +
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
+ +
+ + +h3. Using a subquery on another table + +9. Find all of the contributors to Hillary Clinton + +
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');
+ +
+ + +h3. Joins: Queries across tables + +10. It is possible to do an *implicit* join simply by defining the relationship between the two tables in the WHERE clause: + +
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
+ +
+ +11. Use table name aliases to save typing: + +
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
+ +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 +like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand. + +
+ +h4. Explicit JOIN syntax + +12. Rewrite the above query using explicit JOIN syntax: +
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
+ +
+ +13. Use aliases work with JOIN syntax: +
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
+ +
+ +14. Get the number of contributions for each candidate +
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;
+ +Our list of candidates seems to be coming up short. Let's check it: + +
SELECT DISTINCT id, last_name FROM candidates;
+ +Definitely missing candidates. What happened? + +Performing the JOIN returns the same results as the query with this clause: WHERE contributions.candidate_id = candidates.id. + +What if a candidate has no contributions? Then that candidate is not returned by the query. + +The JOIN acts just like the WHERE clause and filters out any rows that don't match the condition defined. + +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: + +
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;
+ +The results will be the same. +
+ + +h3. OUTER JOINS + +* 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. +* 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. +* A FULL OUTER JOIN includes all rows from both tables. + +Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well. + +15. Get the number of contributions for each candidate, including those with no contributions: +
SELECT count(contributions.id), candidates.id, candidates.last_name FROM candidates LEFT OUTER JOIN contributoins ON candidates.id = contributions.candidate_id GROUP BY candidates.id, candidates.last_name;
+ +(Note: Aliases would work here as well.) + diff --git a/NICAR2016/part3/create_candidates.sql b/NICAR2016/part3/sql_scripts/create_candidates.sql similarity index 100% rename from NICAR2016/part3/create_candidates.sql rename to NICAR2016/part3/sql_scripts/create_candidates.sql diff --git a/NICAR2016/part3/create_contributions.sql b/NICAR2016/part3/sql_scripts/create_contributions.sql similarity index 100% rename from NICAR2016/part3/create_contributions.sql rename to NICAR2016/part3/sql_scripts/create_contributions.sql From e141b61e5463fde756c2d092f205e6ec57972484 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Thu, 10 Mar 2016 08:48:43 -0600 Subject: [PATCH 19/25] Fix URL and update a couple queries --- NICAR2016/part1/part1_steps.html | 17 ++++++++--------- NICAR2016/part1/part1_steps.textile | 18 +++++++++--------- 2 files changed, 17 insertions(+), 18 deletions(-) diff --git a/NICAR2016/part1/part1_steps.html b/NICAR2016/part1/part1_steps.html index b215775..2aa4a26 100644 --- a/NICAR2016/part1/part1_steps.html +++ b/NICAR2016/part1/part1_steps.html @@ -79,7 +79,7 @@

THE WHERE CLAUSE

10. Combine conditions with AND or OR:

Find only the big spenders from New York

-
SELECT * FROM contributions WHERE state = 'NY' AND amount > 2000;
+
SELECT * FROM contributions WHERE city = 'NY' AND amount > 2000;

Find any big spender and anyone from New York

SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
@@ -92,17 +92,16 @@

THE WHERE CLAUSE

Pick One: Using BETWEEN and IN (NOT IN)

11. Find all contributions between $500 and $1000

-
SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
+
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;

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

-

12. Find all contributors from a few southern states:

-
SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
-

(Note: A compound statement could accomplish this same goal: state = 'AL' OR state = 'GA' OR state = 'FL'
+

12. Find all contributors from a list of states:

+
SELECT * from contributions WHERE state IN ('OH', 'NY');
+

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

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

-
SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
-

But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above.

+
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');

Sorting the results

14. Use ORDER BY to sort:

@@ -114,7 +113,7 @@

Sorting the results

Limiting the number of rows returned

15. Return only the top two contributions:

-
SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
+
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;

Changing values with UPDATE

16. Check the list of state names again:

@@ -131,7 +130,7 @@

DELETING rows

Importing data from a flat file

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

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

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

    diff --git a/NICAR2016/part1/part1_steps.textile b/NICAR2016/part1/part1_steps.textile index 374b7af..49f145e 100644 --- a/NICAR2016/part1/part1_steps.textile +++ b/NICAR2016/part1/part1_steps.textile @@ -97,7 +97,7 @@ Here are some examples: 10. Combine conditions with AND or OR: Find only the big spenders from New York -
    SELECT * FROM contributions WHERE state = 'NY' AND amount > 2000;
    +
    SELECT * FROM contributions WHERE city = 'NY' AND amount > 2000;
    Find any big spender and anyone from New York 
    SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
    @@ -119,22 +119,22 @@ Try that again: h3. Pick One: Using BETWEEN and IN (NOT IN) 11. Find all contributions between $500 and $1000 -
    SELECT * FROM contributors WHERE amount BETWEEN 500 AND 1000;
    +
    SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
    (Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.)
    -12. Find all contributors from a few southern states: -
    SELECT * FROM contributors WHERE state IN ('AL', 'GA', 'FL');
    +12. Find all contributors from a list of states: +
    SELECT * from contributions WHERE state IN ('OH', 'NY');
    -(Note: A compound statement could accomplish this same goal: state = 'AL' OR state = 'GA' OR state = 'FL' +(Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY' But the IN syntax makes things much clearer, and it's easier to write.)
    13. Use NOT IN to find results where a value is not included in the given set: -
    SELECT * FROM contributors WHERE state NOT IN ('CA', 'OR', 'AZ');
    -But beware that NOT IN won't work with null fields. So, if one of the rows has a null value for state, it would not be returned by the query above. +
    SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
    +
    @@ -153,7 +153,7 @@ And order by more than one column: h3. Limiting the number of rows returned 15. Return only the top two contributions: -
    SELECT * FROM CONTRIBUTIONS ORDER BY amount DESC LIMIT 2;
    +
    SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
    @@ -185,7 +185,7 @@ h3. DELETING rows h3. Importing data from a flat file 18. Import a delimited file: -a. Go to ["https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt":https://raw.githubusercontent.com/tthibo/SQL-Tutorial/master/NICAR2016/contributions.txt] (Or grab it from the conference computer.) +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.) b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). From ab752ddeeb29171457ac5cb14ce5e7c93027303a Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Thu, 10 Mar 2016 09:33:38 -0600 Subject: [PATCH 20/25] Fix queries in part3 --- NICAR2016/part3/data/contributions.txt | 24 ++++++++++++------------ NICAR2016/part3/part3_steps.html | 8 ++++++-- NICAR2016/part3/part3_steps.textile | 10 ++++++++-- 3 files changed, 26 insertions(+), 16 deletions(-) diff --git a/NICAR2016/part3/data/contributions.txt b/NICAR2016/part3/data/contributions.txt index f398415..cb7354c 100644 --- a/NICAR2016/part3/data/contributions.txt +++ b/NICAR2016/part3/data/contributions.txt @@ -64,29 +64,29 @@ id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code |C00458844|IND|REPLOGLE|STEPHEN||WASHINGTON|DC|200172912|COVE STRATEGIES|CONSULTANT|2015-02-09|2300|3 |C00577130|IND|HOLZHAUER|FRED||NOVATO|CA|949471909|METHOD PRODUCTS, PBC|CHEMIST|2015-06-30|250|6 |C00577130|IND|HONG|PENELOPE||ROCKPORT|TX|783823610|NOT EMPLOYED|RETIRED|2015-06-30|250|6 -|C00577130|IND|WYATT|EDNA||SANTA FE|NM|875012242|SELF-EMPLOYED|CARPENTER|2015-05-20|300 +|C00577130|IND|WYATT|EDNA||SANTA FE|NM|875012242|SELF-EMPLOYED|CARPENTER|2015-05-20|300|6 |C00577130|IND|YOUNKIN|RANDY||YOUNGSTOWN|OH|445052563|||2015-05-20|500|6 -|C00577130|IND|SHEWFELT|MATTHEW||LOS ANGELES|CA|900263322|WILSHIRE ESCROW COMPANY|ESCROW OFFICER|2015-06-18|250 +|C00577130|IND|SHEWFELT|MATTHEW||LOS ANGELES|CA|900263322|WILSHIRE ESCROW COMPANY|ESCROW OFFICER|2015-06-18|250|6 |C00577130|IND|ROTHNER|GLENN||PASADENA|CA|911052720|ROTHNER, SEGALL & GREENSTONE|ATTORNEY|2015-06-18|250|6 -|C00577130|IND|SMITH|PRENTISS|CHARLES|BRATTLEBORO|VT|053014232|PRENTISS SMITH & CO,. INC.|INVESTMENT MANAGER|2015-06-18|400 |C00577130|IND|SMITH|PRENTISS|CHARLES|BRATTLEBORO|VT|053014232|PRENTISS SMITH & CO,. INC.|INVESTMENT MANAGER|2015-06-18|400|6 -|C00577130|IND|STRATTON|RICHARD|G|BUFFALO|NY|142163417|NICHOLS SCHOOL|TEACHER|2015-06-18|300 +|C00577130|IND|SMITH|PRENTISS|CHARLES|BRATTLEBORO|VT|053014232|PRENTISS SMITH & CO,. INC.|INVESTMENT MANAGER|2015-06-18|400|6 +|C00577130|IND|STRATTON|RICHARD|G|BUFFALO|NY|142163417|NICHOLS SCHOOL|TEACHER|2015-06-18|300|6 |C00577130|IND|STRATTON|RICHARD|G|BUFFALO|NY|142163417|NICHOLS SCHOOL|TEACHER|2015-06-18|300|6 -|C00577130|IND|KAST|RICHARD||BURLINGTON|VT|054082544|SELF-EMPLOYED|PHYSICIAN|2015-06-18|212 +|C00577130|IND|KAST|RICHARD||BURLINGTON|VT|054082544|SELF-EMPLOYED|PHYSICIAN|2015-06-18|212|6 +|C00577130|IND|KUSNERZ|PEGGY|ANN|ANN ARBOR|MI|481044303|||2015-06-18|200|6 |C00577130|IND|KUSNERZ|PEGGY|ANN|ANN ARBOR|MI|481044303|||2015-06-18|200|6 -|C00577130|IND|KUSNERZ|PEGGY|ANN|ANN ARBOR|MI|481044303|||2015-06-18|200 |C00577130|IND|MARSH|PAUL|G|HAMILTON|OH|450139476|||2015-06-18|250|6 -|C00577130|IND|MARSH|PAUL|G|HAMILTON|OH|450139476|||2015-06-18|250 +|C00577130|IND|MARSH|PAUL|G|HAMILTON|OH|450139476|||2015-06-18|250|6 |C00577130|IND|MCDANIEL|MICHAEL|J|SAN DIEGO|CA|921082806|SELF|MD|2015-06-18|200|6 -|C00577130|IND|ALLEN|BRUCE||LAUGHLIN|NV|890280673|SELF-EMPLOYED|CLINICAL PSYCHOLOGIST|2015-06-02|2700 +|C00577130|IND|ALLEN|BRUCE||LAUGHLIN|NV|890280673|SELF-EMPLOYED|CLINICAL PSYCHOLOGIST|2015-06-02|2700|6 |C00577130|IND|ALLEN|YOKO||LAUGHLIN|NV|890280673|N/A|HOMEMAKER|2015-06-02|2700|6 -|C00577130|IND|ROBERTSON|JOAN|R|MILWAUKEE|WI|532174340|||2015-06-10|1000 +|C00577130|IND|ROBERTSON|JOAN|R|MILWAUKEE|WI|532174340|||2015-06-10|1000|6 |C00577130|IND|MURRAY|BRIAN||LOS ANGELES|CA|900344551|SHEPPARD MULLIN LLP|ATTORNEY|2015-06-18|250|6 -|C00577130|IND|OWENS|BONNIE|L|NAPERVILLE|IL|605648987|RETIRED|FINANCE DIRECTOR, LOCAL GOVERNMENT|2015-06-18|500 |C00577130|IND|OWENS|BONNIE|L|NAPERVILLE|IL|605648987|RETIRED|FINANCE DIRECTOR, LOCAL GOVERNMENT|2015-06-18|500|6 -|C00577130|IND|PAUL|JANE||LOS ANGELES|CA|900662840|ANTIOCH UNIVERSITY|TEACHER|2015-06-18|250 +|C00577130|IND|OWENS|BONNIE|L|NAPERVILLE|IL|605648987|RETIRED|FINANCE DIRECTOR, LOCAL GOVERNMENT|2015-06-18|500|6 +|C00577130|IND|PAUL|JANE||LOS ANGELES|CA|900662840|ANTIOCH UNIVERSITY|TEACHER|2015-06-18|250|6 |C00577130|IND|PRUITT|GRETA||LA CRESCENTA|CA|912141541|NOT EMPLOYED|RETIRED|2015-06-18|250|6 -|C00577130|IND|PULICE|MICHAEL||LOS ANGELES|CA|900683128|HOUSEBLEND MEDIA|PRODUCTION ASSISTANT|2015-06-18|250 +|C00577130|IND|PULICE|MICHAEL||LOS ANGELES|CA|900683128|HOUSEBLEND MEDIA|PRODUCTION ASSISTANT|2015-06-18|250|6 |C00580100|IND|ANDALORO|JIM||MARBLEHEAD|MA|01945|METAL TRONICS, INC.|OWNER|2015-06-30|500|1 |C00580100|IND|ANTON|FRANCINE||SPRING LAKE|MI|49456|RETIRED|RETIRED|2015-06-24|250|1 |C00580100|IND|ATKINS|LORI||SAN ANTONIO|TX|78231|TEXAS SINUS CENTER|PHYSICIAN ASSISTANT|2015-06-20|250|1 diff --git a/NICAR2016/part3/part3_steps.html b/NICAR2016/part3/part3_steps.html index fa859e7..b81ab91 100644 --- a/NICAR2016/part3/part3_steps.html +++ b/NICAR2016/part3/part3_steps.html @@ -59,7 +59,7 @@

    Using Subqueries

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

    Using a subquery on another table

    -

    9. Find all of the contributors to Hillary Clinton

    +

    9. Find all of the contributions to Hillary Clinton

    SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');

    Joins: Queries across tables

    @@ -98,5 +98,9 @@

    OUTER JOINS

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

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

-
SELECT count(contributions.id), candidates.id, candidates.last_name FROM candidates LEFT OUTER JOIN contributoins ON candidates.id = contributions.candidate_id GROUP BY candidates.id, candidates.last_name;
+
SELECT candidates.last_name, count(contributions.id)
+FROM candidates LEFT OUTER JOIN contributions 
+ON candidates.id = contributions.candidate_id 
+GROUP BY candidates.id, candidates.last_name 
+ORDER BY count(contributions.id);

(Note: Aliases would work here as well.)

diff --git a/NICAR2016/part3/part3_steps.textile b/NICAR2016/part3/part3_steps.textile index 20c87da..34e1c7a 100644 --- a/NICAR2016/part3/part3_steps.textile +++ b/NICAR2016/part3/part3_steps.textile @@ -95,7 +95,7 @@ Subqueries to the rescue: h3. Using a subquery on another table -9. Find all of the contributors to Hillary Clinton +9. Find all of the contributions to Hillary Clinton 
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');
@@ -163,7 +163,13 @@ h3. OUTER JOINS Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well. 15. Get the number of contributions for each candidate, including those with no contributions: -
SELECT count(contributions.id), candidates.id, candidates.last_name FROM candidates LEFT OUTER JOIN contributoins ON candidates.id = contributions.candidate_id GROUP BY candidates.id, candidates.last_name;
+

+SELECT candidates.last_name, count(contributions.id)
+FROM candidates LEFT OUTER JOIN contributions 
+ON candidates.id = contributions.candidate_id 
+GROUP BY candidates.id, candidates.last_name 
+ORDER BY count(contributions.id);
+
(Note: Aliases would work here as well.) From 692b94f5b49d8211b010d095d1a252d3f144b46e Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Thu, 10 Mar 2016 12:19:26 -0600 Subject: [PATCH 21/25] Final edits --- NICAR2016/part1/part1_steps.html | 4 ++-- NICAR2016/part1/part1_steps.textile | 4 ++-- NICAR2016/part3/part3_steps.html | 2 +- NICAR2016/part3/part3_steps.textile | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/NICAR2016/part1/part1_steps.html b/NICAR2016/part1/part1_steps.html index 2aa4a26..d547e87 100644 --- a/NICAR2016/part1/part1_steps.html +++ b/NICAR2016/part1/part1_steps.html @@ -79,9 +79,9 @@

THE WHERE CLAUSE

10. Combine conditions with AND or OR:

Find only the big spenders from New York

-
SELECT * FROM contributions WHERE city = 'NY' AND amount > 2000;
+
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;

Find any big spender and anyone from New York

-
SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
+
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;

11. But watch out for operator precedence

Looking for big spenders from Las Vegas and New York City

diff --git a/NICAR2016/part1/part1_steps.textile b/NICAR2016/part1/part1_steps.textile index 49f145e..c8a8eaf 100644 --- a/NICAR2016/part1/part1_steps.textile +++ b/NICAR2016/part1/part1_steps.textile @@ -97,10 +97,10 @@ Here are some examples: 10. Combine conditions with AND or OR: Find only the big spenders from New York -
SELECT * FROM contributions WHERE city = 'NY' AND amount > 2000;
+
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
Find any big spender and anyone from New York -
SELECT * FROM contributions WHERE state = 'NY' OR amount > 2000;
+
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
diff --git a/NICAR2016/part3/part3_steps.html b/NICAR2016/part3/part3_steps.html index b81ab91..da221ab 100644 --- a/NICAR2016/part3/part3_steps.html +++ b/NICAR2016/part3/part3_steps.html @@ -60,7 +60,7 @@

Using Subqueries

Using a subquery on another table

9. Find all of the contributions to Hillary Clinton

-
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');
+
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');

Joins: Queries across tables

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

diff --git a/NICAR2016/part3/part3_steps.textile b/NICAR2016/part3/part3_steps.textile index 34e1c7a..274149b 100644 --- a/NICAR2016/part3/part3_steps.textile +++ b/NICAR2016/part3/part3_steps.textile @@ -97,7 +97,7 @@ h3. Using a subquery on another table 9. Find all of the contributions to Hillary Clinton -
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'Clinton' AND first_name = 'Hillary');
+
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');
From 9fe9a3d19f6c4c99c5a5523df57b656040bd6513 Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Sat, 18 Jun 2016 21:34:14 -0500 Subject: [PATCH 22/25] 2-session version for IRE --- IRE2016/part1/data/contributions.txt | 200 +++++++++++++++++ IRE2016/part1/data/famous_donors.txt | 3 + IRE2016/part1/data/scripts/utility/Gemfile | 3 + .../data/scripts/utility/filter_contribs.rb | 48 ++++ .../part1/data/scripts/utility/fix_names.rb | 14 ++ .../images/create_contributions_table.png | Bin 0 -> 117246 bytes IRE2016/part1/images/customize-menu.png | Bin 0 -> 214300 bytes IRE2016/part1/images/execute_sql.png | Bin 0 -> 14768 bytes IRE2016/part1/images/ff-menu.png | Bin 0 -> 102635 bytes IRE2016/part1/images/import.png | Bin 0 -> 63710 bytes IRE2016/part1/images/import_icon.png | Bin 0 -> 7015 bytes IRE2016/part1/images/new_database.png | Bin 0 -> 1100 bytes IRE2016/part1/images/new_table.png | Bin 0 -> 1001 bytes IRE2016/part1/part1_steps.html | 142 ++++++++++++ IRE2016/part1/part1_steps.textile | 199 +++++++++++++++++ IRE2016/part2/data/candidates.txt | 15 ++ IRE2016/part2/data/contributions.txt | 204 +++++++++++++++++ IRE2016/part2/images/ff-menu.png | Bin 0 -> 102635 bytes IRE2016/part2/images/import_icon.png | Bin 0 -> 7015 bytes IRE2016/part2/images/new_database.png | Bin 0 -> 1100 bytes IRE2016/part2/part2.textile | 208 ++++++++++++++++++ 21 files changed, 1036 insertions(+) create mode 100644 IRE2016/part1/data/contributions.txt create mode 100644 IRE2016/part1/data/famous_donors.txt create mode 100644 IRE2016/part1/data/scripts/utility/Gemfile create mode 100755 IRE2016/part1/data/scripts/utility/filter_contribs.rb create mode 100755 IRE2016/part1/data/scripts/utility/fix_names.rb create mode 100644 IRE2016/part1/images/create_contributions_table.png create mode 100644 IRE2016/part1/images/customize-menu.png create mode 100644 IRE2016/part1/images/execute_sql.png create mode 100644 IRE2016/part1/images/ff-menu.png create mode 100644 IRE2016/part1/images/import.png create mode 100644 IRE2016/part1/images/import_icon.png create mode 100644 IRE2016/part1/images/new_database.png create mode 100644 IRE2016/part1/images/new_table.png create mode 100644 IRE2016/part1/part1_steps.html create mode 100644 IRE2016/part1/part1_steps.textile create mode 100644 IRE2016/part2/data/candidates.txt create mode 100644 IRE2016/part2/data/contributions.txt create mode 100644 IRE2016/part2/images/ff-menu.png create mode 100644 IRE2016/part2/images/import_icon.png create mode 100644 IRE2016/part2/images/new_database.png create mode 100644 IRE2016/part2/part2.textile diff --git a/IRE2016/part1/data/contributions.txt b/IRE2016/part1/data/contributions.txt new file mode 100644 index 0000000..1468335 --- /dev/null +++ b/IRE2016/part1/data/contributions.txt @@ -0,0 +1,200 @@ +id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount +|C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-14|200 +|C00573519|IND|ST. 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b/IRE2016/part1/data/famous_donors.txt new file mode 100644 index 0000000..c235fb4 --- /dev/null +++ b/IRE2016/part1/data/famous_donors.txt @@ -0,0 +1,3 @@ +C00575795|A|Q2|P|201509039001608655|15|IND|BUFFETT, WARREN|OMAHA|NE|681313311|BERKSHIRE HATHAWAY|CHAIRMAN|04152015|2700||C51337|1024052|||4090920151249626621 +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 +C00458844|A|Q2|G|201510159003030910|22Y|IND|ADELSON, SHELDON|LAS VEGAS|NV|89145|||06122015|2600||GENREF0042|1029436|||4102120151256390199 diff --git a/IRE2016/part1/data/scripts/utility/Gemfile b/IRE2016/part1/data/scripts/utility/Gemfile new file mode 100644 index 0000000..1f95933 --- /dev/null +++ b/IRE2016/part1/data/scripts/utility/Gemfile @@ -0,0 +1,3 @@ +source 'https://rubygems.org' + +gem 'people' \ No newline at end of file diff --git a/IRE2016/part1/data/scripts/utility/filter_contribs.rb b/IRE2016/part1/data/scripts/utility/filter_contribs.rb new file mode 100755 index 0000000..e2f05f9 --- /dev/null +++ b/IRE2016/part1/data/scripts/utility/filter_contribs.rb @@ -0,0 +1,48 @@ +#!/usr/bin/env ruby + +require 'csv' + +committee_ids = [] +CSV.foreach('candidates.txt', :headers => true, :col_sep => '|', :quote_char => "\x00") do |row| + committee_ids << row[0] +end + +skips = ['C00577981', 'C00578757', 'C00577312', 'C00575449', 'C00578658'] +used = committee_ids - skips + +included = {} +used.each {|id| included[id] = 0} + +CSV.foreach('itcont.txt', :col_sep => '|', :quote_char => "\x00") do |row| + #skip if not a contrib + next if row[5] != '15' + committee_id = row[0] + if used.include? committee_id + if included[committee_id] < 25 + puts row.to_csv(:col_sep => '|') + included[committee_id] += 1 + else + used.delete committee_id + break if used.length == 0 + end + end +end + + +exit +##### +## Check the number of contributors per candidate +##### +committee_counts = {} +committee_ids.each do |id| + committee_counts[id] = 0 +end + + +committee_ids.each do |id| + CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| + committee_counts[id] += 1 if row[0] == id + end +end + +puts committee_counts diff --git a/IRE2016/part1/data/scripts/utility/fix_names.rb b/IRE2016/part1/data/scripts/utility/fix_names.rb new file mode 100755 index 0000000..488cbf2 --- /dev/null +++ b/IRE2016/part1/data/scripts/utility/fix_names.rb @@ -0,0 +1,14 @@ +#!/usr/bin/env ruby + +require 'csv' +require 'people' + +np = People::NameParser.new(:case_mode => 'upper') +CSV.foreach('contributors.txt', :col_sep => '|', :quote_char => "\x00") do |row| + no_title = row[7].gsub(/ (MR.|MRS.|DR.|MS.)$/, '') + name = np.parse(no_title) + next if name[:last].empty? + date_parts = row[13].match(/(\d\d)(\d\d)(\d\d\d\d)/) + date_string = "#{date_parts[3]}-#{date_parts[1]}-#{date_parts[2]}" + 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") +end diff --git a/IRE2016/part1/images/create_contributions_table.png b/IRE2016/part1/images/create_contributions_table.png new file mode 100644 index 0000000000000000000000000000000000000000..d3aa81bb5b7e483cad4f8de7092e00960027c5b5 GIT binary patch literal 117246 zcmZs>b3i4)`!Aer+qP}nZnL&JVXMt&N+j}GxN;z znNTGKNq86>7$6`Zcxfpy6(Ar`F(4oi4k(C^7W*GkN-SzmabD}S=jcJ$&vOgv!zkpFB+$fRc~s~q=-N8* zDw8xG@nUgGZiKR4KJVwxK9%?G>L&m712L4g=C$yr#?`e1~#T-jD9 zSEL44dsLw~p_F_Hk^F!*3LnJ9ah8^sec!7+&p{`S_E{g*4c=?l-Wy|z>Zk;Ph&;fa zRdQc`BYDCDu|*q@@#6rEpo%fzVB$B2brL6ML?M9zg&Xt%A1B+PKUbLKv*TMxnt-JN z`BJJ--2ee4YrewvJ%JN>CzHl!DSXL^h-C&%2THM}>FdSm9@NU=G7`-2#!OE*LB6gS1-6&)c9kA>-`oQ$l?Ah;14RW-y=Y!ZhxRq7v9FEVOYW|kI#dLXD#9eO>P&s%vW&msO7w#Pc-kSu1w*cN=l zyTSmD-&rq6-^&mPxQZyYJ2D+VM+oNdCyWy0QWsD%2(2Mxx*z0)hy*{<58|@zF54Km 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A Quick Introduction to SQL Using SQLite (Just the Steps)

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1. Open Firefox and click on the menu icon:

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

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

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2. Create a new database called campaign_finance:

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

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

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4. Insert a row of data.

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Use the "Execute SQL" tab (it's in the second menu row, under the icons)
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And paste or type the following statement:

+
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);
+
+

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

+
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);
+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);
+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);
+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);
+
+

The SELECT statement

+

6. Select everything from the contributions table:

+
SELECT * FROM contributions;
+

Note the wildcard * character.

+
+

7. Choose some fields to select:

+
SELECT city, state FROM contributions;
+

Note the duplicates; why are they there?

+
+

8. Get a distinct set of cities and states:

+
SELECT DISTINCT city, state FROM contributions;
+
+

THE WHERE CLAUSE

+

9. Add some conditions to the SELECT:

+
SELECT * from contributions WHERE state='NY';
+

Now try playing around with some of the operators:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
operatordescription
=Equal
<>Not equal*
>Greater than
<Less than
>=Greater than or equal
<=Less than or equal
+

Here are some examples:

+
SELECT * from contributions WHERE amount > 500;
+
SELECT * from contributions WHERE amount >= 500;
+
SELECT last_name from contributions WHERE state <> 'NY';
+
+

10. Combine conditions with AND or OR:

+

Find only the big spenders from New York

+
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
+

Find any big spender and anyone from New York

+
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
+
+

11. But watch out for operator precedence

+

Looking for big spenders from Las Vegas and New York City

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

How did Mark Ruffalo sneak in there?

+

Try that again:

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

Pick One: Using BETWEEN and IN (NOT IN)

+

11. Find all contributions between $500 and $1000

+
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
+

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

+
+

12. Find all contributors from a list of states:

+
SELECT * from contributions WHERE state IN ('OH', 'NY');
+

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

+
+

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

+
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
+
+

Sorting the results

+

14. Use ORDER BY to sort:

+
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
+

And reverse the order:

+
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
+

And order by more than one column:

+
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
+
+

Limiting the number of rows returned

+

15. Return only the top two contributions:

+
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
+
+

Changing values with UPDATE

+

16. Check the list of state names again:

+
SELECT state FROM contributions;
+

Let's correct Nebraska:

+
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
+

And check again:

+
SELECT DISTINCT state FROM contributions;
+
+

DELETING rows

+

17. Let's get rid of a row:

+
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
+
+

Importing data from a flat file

+

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

+

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

+

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

+
    +
  • Using the Select File button, browse for the contributions.txt file you just saved.
    • +
  • Check the "First row contains column names" check box.
    • +
  • Select Pipe(|) for "Fields separated by"
    • +
  • For everything else, you can keep the default selections.
    • +
  • You should have 204 rows of data.
    • +
diff --git a/IRE2016/part1/part1_steps.textile b/IRE2016/part1/part1_steps.textile new file mode 100644 index 0000000..c8a8eaf --- /dev/null +++ b/IRE2016/part1/part1_steps.textile @@ -0,0 +1,199 @@ +h2. A Quick Introduction to SQL Using SQLite (Just the Steps) + +1. Open Firefox and click on the menu icon: + +!./images/ff-menu.png! + +Use the "customize" option to find the SQLite Manager Plugin: + +!./images/customize-menu.png! + +You can add it to your menu by dragging or right-click to add it to the toolbar. + +
+ +2. Create a new database called campaign_finance: + +!./images/new_database.png! + +
+ +3. Create a new table called contributions (!./images/new_table.png!) and give it the following field definitions: + +!./images/create_contributions_table.png! + +
+ +h3. Inserting data manually (Don't worry, you won't do this often.) + +4. Insert a row of data. + +Use the "Execute SQL" tab (it's in the second menu row, under the icons) +!./images/execute_sql.png! + +And paste or type the following statement: +

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

+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);
+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);
+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);
+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);
+
+ + +
+ +h3. The SELECT statement + +6. Select everything from the contributions table: + +
SELECT * FROM contributions;
+ +Note the wildcard * character. + +
+ +7. Choose some fields to select: + +
SELECT city, state FROM contributions;
+ +Note the duplicates; why are they there? + +
+ +8. Get a distinct set of cities and states: + +
SELECT DISTINCT city, state FROM contributions;
+ +
+ +h3. THE WHERE CLAUSE + +9. Add some conditions to the SELECT: +
SELECT * from contributions WHERE state='NY';
+Now try playing around with some of the operators: + +|*operator*|*description*| +|=|Equal| +|<>|Not equal*| +|>|Greater than| +|<|Less than| +|>=|Greater than or equal| +|<=|Less than or equal| + +Here are some examples: +
SELECT * from contributions WHERE amount > 500;
+
SELECT * from contributions WHERE amount >= 500;
+
SELECT last_name from contributions WHERE state <> 'NY';
+ +
+ +10. Combine conditions with AND or OR: + +Find only the big spenders from New York +
SELECT * FROM contributions WHERE city = 'NEW YORK' AND amount > 2000;
+ +Find any big spender and anyone from New York +
SELECT * FROM contributions WHERE city = 'NEW YORK' OR amount > 2000;
+ +
+ +11. But watch out for operator precedence + +Looking for big spenders from Las Vegas and New York City +
SELECT * FROM contributions WHERE city = 'NEW YORK' OR city = 'LAS VEGAS' AND amount > 2000;
+ +How did Mark Ruffalo sneak in there? + +Try that again: +
SELECT * FROM contributions WHERE (city = 'NEW YORK' OR city = 'LAS VEGAS') AND amount > 2000;
+ +
+ +h3. Pick One: Using BETWEEN and IN (NOT IN) + +11. Find all contributions between $500 and $1000 +
SELECT * FROM contributions WHERE amount BETWEEN 251 AND 2699;
+(Note: this query returns the same results as SELECT * FROM contributors WHERE amount >= 500 AND amount <= 1000; -- but it's much more readable.) + +
+ +12. Find all contributors from a list of states: +
SELECT * from contributions WHERE state IN ('OH', 'NY');
+ +(Note: A compound statement could accomplish this same goal: state = 'OH' OR state = 'NY' +But the IN syntax makes things much clearer, and it's easier to write.) + +
+ +13. Use NOT IN to find results where a value is not included in the given set: +
SELECT * FROM contributions WHERE state NOT IN ('NY', 'OH');
+ + +
+ + +h3. Sorting the results + +14. Use ORDER BY to sort: +
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount;
+And reverse the order: +
SELECT last_name, amount FROM contributions WHERE amount > 250 ORDER BY amount DESC;
+And order by more than one column: +
SELECT state, last_name, amount FROM contributions ORDER BY state, amount DESC;
+ +
+ +h3. Limiting the number of rows returned + +15. Return only the top two contributions: +
SELECT * FROM contributions ORDER BY amount DESC LIMIT 2;
+ +
+ +h3. Changing values with UPDATE + +16. Check the list of state names again: +
SELECT state FROM contributions;
+ +Let's correct Nebraska: +
UPDATE contributions SET state = 'NE' WHERE state = 'Nebraska';
+ +And check again: +
SELECT DISTINCT state FROM contributions;
+ + +
+ +h3. DELETING rows + +17. Let's get rid of a row: +
SELECT * FROM contributions WHERE id = 1;
+
DELETE FROM contributions WHERE id = 1;
+ + +
+ + + +h3. Importing data from a flat file + +18. Import a delimited file: +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.) + +b. Save the file as "contributions.txt" somewhere you can find it (your desktop is a good place). + +c. Click the SQLite Manager Import Wizard icon (!./images/import_icon.png!). + +* Using the **Select File** button, browse for the **contributions.txt** file you just saved. +* Check the "First row contains column names" check box. +* Select **Pipe(|)** for "Fields separated by" +* For everything else, you can keep the default selections. +* You should have 204 rows of data. + diff --git a/IRE2016/part2/data/candidates.txt b/IRE2016/part2/data/candidates.txt new file mode 100644 index 0000000..1f60fc9 --- /dev/null +++ b/IRE2016/part2/data/candidates.txt @@ -0,0 +1,15 @@ +id|committee_id|last_name|first_name|middle_name|party +1|C00580100|TRUMP|DONALD|J|REP +2|C00574624|CRUZ|RAFAEL "TED"|EDWARD|REP +3|C00458844|RUBIO|MARCO||REP +4|C00581876|KASICH|JOHN|R|REP +5|C00573519|CARSON|BENJAMIN|S|REP +6|C00577130|SANDERS|BERNARD||DEM +7|C00575795|CLINTON|HILLARY|RODHAM|DEM +8|C00578658|O'MALLEY|MARTIN|JOSEPH|DEM +9|C00579458|BUSH|JEB||REP +10|C00580399|CHRISTIE|CHRISTOPHER|J|REP +11|C00575449|PAUL|RAND||REP +12|C00577312|FIORINA|CARLY||REP +13|C00578757|GRAHAM|LINDSEY|O|REP +14|C00577981|HUCKABEE|MIKE||REP diff --git a/IRE2016/part2/data/contributions.txt b/IRE2016/part2/data/contributions.txt new file mode 100644 index 0000000..cb7354c --- /dev/null +++ b/IRE2016/part2/data/contributions.txt @@ -0,0 +1,204 @@ +id|committee_id|entity_type|last_name|first_name|middle_name|city|state|zip_code|employer|occupation|date|amount|candidate_id +|C00575795|IND|BUFFETT|WARREN||OMAHA|Nebraska|68131|BERKSHIRE HATHAWAY|CHAIRMAN|2015-04-15|2700|7 +|C00577130|IND|RUFFALO|MARK||NEW YORK|NY|100031526|SELF-EMPLOYED|ACTOR|2015-06-02|250|6 +|C00458844|IND|ADELSON|SHELDON||LAS VEGAS|NV|89145|||2015-06-12|2600|3 +|C00579458|IND|MANNING|PEYTON MR.||CLEVELAND|OH|441141717|DENVER BRONCOS|PROFESSIONAL ATHLETE|2015-08-04|2700|9 +|C00575795|IND|COPPOLA|SOFIA||NEW YORK|NY|100130778|SELF-EMPLOYED|DIRECTOR/WRITER/ACTOR|2016-01-22|2700|7 +|C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-14|200|5 +|C00573519|IND|ST. CLAIR|ANNE|K|MECHANICSVILLE|VA|231112600|RETIRED|RETIRED|2015-03-28|250|5 +|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|5 +|C00573519|IND|STARK|FRED||ENGLEWOOD|FL|342245434|PATHWAY LIGHTING PRODUCTS INC.|CEO|2015-03-19|1000|5 +|C00573519|IND|STEBBENS|KIM||EVERETT|WA|982011557|WAYA GROUP INC|EXECUTIVE|2015-03-14|250|5 +|C00573519|IND|STEGEMANN|BELINDA||CLINT|TX|798365308|RETIRED|RETIRED|2015-03-28|500|5 +|C00573519|IND|STEIGERWALD|DONALD||ESCONDIDO|CA|920263942|STEIGERWALD DOUGHERTY INC|BUILDING CONTRACTOR|2015-03-04|250|5 +|C00573519|IND|STEINKAMP|JEFFREY||ROCHESTER|VT|057670098|RETIRED|RETIRED|2015-03-31|1500|5 +|C00573519|IND|STENGEL|LINDA||SIOUX FALLS|SD|571058110|RETIRED|RETIRED|2015-03-30|250|5 +|C00573519|IND|STENSETHER|BARBARA|L|TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500|5 +|C00573519|IND|STENSETHER|JOHN||TURLOCK|CA|953821415|RETIRED|RETIRED|2015-03-11|500|5 +|C00573519|IND|STEWARDSON|MELINDA||BUFFALO GAP|TX|795082140|WYLIE INDEPENDENT SCHOOL DISTRICT|READING TEACHER|2015-03-31|250|5 +|C00573519|IND|STOCKSTILL|JAMES||PICAYUNE|MS|394665552|RETIRED|RETIRED|2015-03-04|250|5 +|C00573519|IND|STODDARD|CHERYL||FREDERICKSBURG|VA|224052180|ANTHEM|REGIONAL MANAGER|2015-03-31|250|5 +|C00573519|IND|STOKER|RICHARD||MIAMI BEACH|FL|331417401|RETIRED|RETIRED|2015-03-24|300|5 +|C00573519|IND|THORNTON|JOE||DALLAS|TX|752254816|HFF|REAL ESTATE CAPITAL MARKETS|2015-03-05|2700|5 +|C00573519|IND|TIMBLE|JAMES||FRANKLIN GROVE|IL|610319555|RETIRED|RETIRED|2015-03-28|250|5 +|C00573519|IND|TOBB|RONALD||BROOKEVILLE|MD|208333208|BOWLES FLUIDICS|OPERATIONS VP|2015-03-03|250|5 +|C00573519|IND|TOLLAKSON|DARLENE|M|SOUTH WAYNE|WI|535879761|||2015-03-30|400|5 +|C00573519|IND|TOMANN|DON||BUFFALO|MN|553134253|UMC INC.|BUSINESS OWNER/PROFESSIONAL|2015-03-03|250|5 +|C00573519|IND|TORRENCE|DARREN||PENSACOLA|FL|325075234|SELF-EMPLOYED|OWNER|2015-03-18|250|5 +|C00573519|IND|TORRE|THOMAS||CUMMING|GA|300411205|AT&T|ENGINEER|2015-03-23|250|5 +|C00573519|IND|TRAN|VINCENT||CRESCENT CITY|CA|955319576|STATE OF CALIFORNIA|DENTIST|2015-03-04|1000|5 +|C00574624|IND|CHOATE|CHARLIE||BURLESON|TX|760282217|SELF EMPLOYED|SMALL BUSINESS OWNER|2015-03-31|250|2 +|C00574624|IND|CHOR|MARLENE||ANACONDA|MT|597112904|||2015-03-31|500|2 +|C00574624|IND|MACDONALD|LENNA|R|DANIEL ISLAND|SC|294927523|BVCC LLC|ENTREPRENEUR|2015-03-25|1000|2 +|C00574624|IND|MACH|STEVEN|P|HOUSTON|TX|772190630|MACH INDUSTRIAL GROUP LP|VP FINANCE|2015-03-23|2700|2 +|C00574624|IND|MACKEY|WILLIAM||MIAMI|FL|331566023|||2015-03-31|500|2 +|C00574624|IND|MADDOX|MATTHEW|MATT|HOUSTON|TX|770083017|BROTHERS ALVARADO; 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Open Firefox and click on the menu icon: + +!./images/ff-menu.png! + +Use the "customize" option to find the SQLite Manager Plugin: + +!./images/customize-menu.png! + +
+ +2. Create a new database called contributions_candidates + +!./images/new_database.png! + +
+ +3. Use a CREATE TABLE statement to create a new contributions table: + +
+ +

+CREATE TABLE "contributions" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "entity_type" VARCHAR, 
+    "last_name" VARCHAR, 
+    "first_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "city" VARCHAR, 
+    "state" VARCHAR, 
+    "zip_code" VARCHAR, 
+    "employer" VARCHAR, 
+    "occupation" VARCHAR, 
+    "date" DATETIME, 
+    "amount" INTEGER,
+    "candidate_id" INTEGER NOT NULL,
+    FOREIGN KEY(candidate_id) REFERENCES candidates(id)
+);
+
+
+ +4. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import contributions.txt: + +
+ +h3. Aggregate Functions (count, max, min, sum, avg) + +5. Count the number of contributors from California: +
SELECT COUNT(id) FROM contributors WHERE state = 'CA';
+ +6. Select the minimum and maximum contributions: +
SELECT MAX(amount) FROM contributors;
+
SELECT MIN(amount) FROM contributors;
+ +7. Add up the contributions from Georgia: +
SELECT SUM(amount) FROM contributors WHERE state = 'GA';
+ +8. Find the average contribution: +
SELECT AVG(amount) FROM contributors;
+ +Use GROUP BY to create subtotals + +9. Find the total amount of contributions per state: +
SELECT state, SUM(amount) FROM contributors GROUP BY state;
+ +10. Find the total amount of contributions per city and state: +
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state;
+Now order cities by their total contributions: +
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state ORDER BY SUM(amount) DESC;
+ +h2. Subqueries and Joins + +11. Use a CREATE TABLE statement to create a new table for candidate data: +
+
+CREATE TABLE "candidates" (
+    "id" INTEGER PRIMARY KEY  AUTOINCREMENT  NOT NULL  UNIQUE , 
+    "committee_id" VARCHAR, 
+    "first_name" VARCHAR, 
+    "last_name" VARCHAR, 
+    "middle_name" VARCHAR, 
+    "party" VARCHAR
+);
+
+
+ +
+ +12. Use the SQLite Manager Import Wizard (!./images/import_icon.png!) to import candidates.txt + +Remember to select the pipe delimiter ("|") and to check the box indicating that the first row contains headers. + + + +h3. Using Subqueries + +13. Find out the maxiumum contribution amount in the data set and see which contributors have given this amount. + +First, determine the maximum amount contriubuted: +SELECT MAX(amount) FROM contributions; + +Then SELECT all fields for any contribution at that amount . . . but how? + +Like this? +SELECT * FROM contributions WHERE amount = MAX(amount); + +Nope. + +Hardcode the amount? +
SELECT * FROM contributions WHERE amount = 5400;
+ +But what if next month the max changes? How can we make this dynamic? + +Subqueries to the rescue: +
SELECT * FROM contributions WHERE amount = (SELECT MAX(amount) FROM contributions);
+ +
+ + +14. Write a query that gets the sum amount of the top 20 contributions + +
SELECT SUM(amount) FROM contributions WHERE id IN (SELECT id FROM contributions ORDER BY amount DESC LIMIT 20);
+ +
+ + +h3. Using a subquery on another table + +15. Find all of the contributions to Hillary Clinton + +
SELECT * FROM contributions WHERE candidate_id = (SELECT id from candidates WHERE last_name = 'CLINTON' AND first_name = 'HILLARY');
+ +
+ + +h3. Joins: Queries across tables + +16. It is possible to do an *implicit* join simply by defining the relationship between the two tables in the WHERE clause: + +
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions, candidates WHERE contributions.candidate_id = candidates.id;
+ +
+ +17. Use table name aliases to save typing: + +
SELECT a.last_name, a.first_name, b.last_name FROM contributions a, candidates b WHERE a.candidate_id = b.id;
+ +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 +like, but obviously shorter aliases will save more typing, while longer ones may make the intention of the query easier to understand. + +
+ +h4. Explicit JOIN syntax + +18. Rewrite the above query using explicit JOIN syntax: +
SELECT contributions.last_name, contributions.first_name, candidates.last_name FROM contributions JOIN candidates ON contributions.candidate_id = candidates.id;
+ +
+ +19. Use aliases work with JOIN syntax: +
SELECT a.last_name, a.first_name, b.last_name FROM contributions a JOIN candidates b ON a.candidate_id = b.id;
+ +
+ +20. Get the number of contributions for each candidate +
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;
+ +Our list of candidates seems to be coming up short. Let's check it: + +
SELECT DISTINCT id, last_name FROM candidates;
+ +Definitely missing candidates. What happened? + +Performing the JOIN returns the same results as the query with this clause: WHERE contributions.candidate_id = candidates.id. + +What if a candidate has no contributions? Then that candidate is not returned by the query. + +The JOIN acts just like the WHERE clause and filters out any rows that don't match the condition defined. + +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: + +
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;
+ +The results will be the same. +
+ + +h3. OUTER JOINS + +* 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. +* 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. +* A FULL OUTER JOIN includes all rows from both tables. + +Currently, SQLite only supports LEFT OUTER JOIN from the list above, but some other database management systems support the other two types as well. + +15. Get the number of contributions for each candidate, including those with no contributions: +

+SELECT candidates.last_name, count(contributions.id)
+FROM candidates LEFT OUTER JOIN contributions 
+ON candidates.id = contributions.candidate_id 
+GROUP BY candidates.id, candidates.last_name 
+ORDER BY count(contributions.id);
+
+ +(Note: Aliases would work here as well.) + From 3031c2f5a2240704eb1b75c363a71c82727ff77a Mon Sep 17 00:00:00 2001 From: tthibodeaux Date: Sat, 18 Jun 2016 22:17:07 -0500 Subject: [PATCH 23/25] Rename table in queries --- IRE2016/part2/part2.textile | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/IRE2016/part2/part2.textile b/IRE2016/part2/part2.textile index a187e27..f9f75a8 100644 --- a/IRE2016/part2/part2.textile +++ b/IRE2016/part2/part2.textile @@ -48,27 +48,27 @@ CREATE TABLE "contributions" ( h3. Aggregate Functions (count, max, min, sum, avg) 5. Count the number of contributors from California: -
SELECT COUNT(id) FROM contributors WHERE state = 'CA';
+
SELECT COUNT(id) FROM contributions WHERE state = 'CA';
6. Select the minimum and maximum contributions: -
SELECT MAX(amount) FROM contributors;
-
SELECT MIN(amount) FROM contributors;
+
SELECT MAX(amount) FROM contributions;
+
SELECT MIN(amount) FROM contributions;
7. Add up the contributions from Georgia: -
SELECT SUM(amount) FROM contributors WHERE state = 'GA';
+
SELECT SUM(amount) FROM contributions WHERE state = 'GA';
8. Find the average contribution: -
SELECT AVG(amount) FROM contributors;
+
SELECT AVG(amount) FROM contributions;
Use GROUP BY to create subtotals 9. Find the total amount of contributions per state: -
SELECT state, SUM(amount) FROM contributors GROUP BY state;
+
SELECT state, SUM(amount) FROM contributions GROUP BY state;
10. Find the total amount of contributions per city and state: -
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state;
+
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state;
Now order cities by their total contributions: -
SELECT city, state, SUM(amount) FROM contributors GROUP BY city, state ORDER BY SUM(amount) DESC;
+
SELECT city, state, SUM(amount) FROM contributions GROUP BY city, state ORDER BY SUM(amount) DESC;
h2. Subqueries and Joins From 694414e3cf25a5dc399ec8c5195d5980c3253038 Mon Sep 17 00:00:00 2001 From: Succhisingh <37015891+Succhisingh@users.noreply.github.com> Date: Sun, 22 Apr 2018 15:52:05 +0530 Subject: [PATCH 24/25] Added Curated SQL Resources Programming Community Curated Resources for learning SQL - https://hackr.io/tutorials/learn-sql --- tutorial_files/part1.textile | 1 + 1 file changed, 1 insertion(+) diff --git a/tutorial_files/part1.textile b/tutorial_files/part1.textile index e09aca2..c1d249e 100644 --- a/tutorial_files/part1.textile +++ b/tutorial_files/part1.textile @@ -278,5 +278,6 @@ h3. Further References http://www.w3schools.com/sql/sql_intro.asp http://www.firstsql.com/tutor.htm +https://hackr.io/tutorials/learn-sql 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. From 0efb78bee64373c0f96bfdcbfbae124dd7d1103e Mon Sep 17 00:00:00 2001 From: Troy Thibodeaux Date: Wed, 26 Dec 2018 14:15:08 -0600 Subject: [PATCH 25/25] Update readme.textile --- readme.textile | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/readme.textile b/readme.textile index 7984ed5..d5ff8b8 100644 --- a/readme.textile +++ b/readme.textile @@ -1,3 +1,7 @@ +h2. Note: This version of the tutorial is out of date: the SQLite Manager Firefox plugin referenced is obsolete. +Please check out the newer version of this tutorial being maintained by @zstumoren: +https://a-gentle-introduction-to-sql.readthedocs.io/en/latest/ + h2. A Gentle Introduction to SQL Using SQLite "Part I":https://github.com/tthibo/SQL-Tutorial/blob/master/tutorial_files/part1.textile

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

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