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{{Project|Has project output=Data,How-to|Has sponsor=McNair ProjectsCenter
|Has title=VentureXpert Data
|Has owner=Augi Liebster,
|Has project status=Active
}}
The successors to this project include:
*[[VCDB24]], which is the most recent iteration.
*[[VCDB23]]
*[[VCDB20Q3]]
*[[VCDB20H1]]
*[[VCDB4]]
==Relevant Former Projects==
==Location==
My scripts for SDC pulls are located in the E drive in the location:
E:\McNair\Projects\VentureXpertDatabase\ScriptsForSDCExtract
My successfully pulled and normalized files are stored in the location:
E:\McNair\Projects\VentureXpertDatabase\ExtractedDataQ2
My script scripts for loading tables and data is are in one big text file in the location: E:\McNair\Projects\VentureXpertDatabase\vcdb3\LoadingScripts
There are a variety of SQL files in there with self explanatory names. The file that has all of the loading scripts is called LoadingScriptsV1. The folder vcdb2 is there for reference to see what people before had done. ExtractedData is there because I pulled data before July 1st, and Ed asked me to repull the data.
SELECT A.*, B.latitude AS BOLatitude, B.longitude AS BOLongitude FROM firmgeocore AS A LEFT JOIN bogeocore1 AS B ON A.firmname=B.firmname;
--15437
==Creating People Tables==
We pulled data on executives in both portcos and funds. I describe the process below. If any of the explanations don't make sense, I also describe most tables in the section called Marcos's Code.
===Company People===
DROP TABLE titlelookup;
CREATE TABLE titlelookup(
fulltitle varchar(150),
charman int,
ceo int,
cfo int,
coo int,
cio int,
cto int,
otherclvl int,
boardmember int,
president int,
vp int,
founder int,
director int
);
\COPY titlelookup FROM 'Important Titles in Women2017 dataset.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--628
This table lists various titles one can have and identifies where they fall under traditional executive titles.
DROP TABLE copeople;
CREATE TABLE copeople(
datefirstinv date,
cname varchar(150),
statecode varchar(2),
prefix varchar(5),
firstname varchar(50),
lastname varchar(50),
jobtitle varchar(150),
nonmanaging varchar(1),
prevpos varchar(255)
);
\COPY copeople FROM 'Executives-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--194359
This table gets various executives from portcos. This is loaded from SDC. Next we have to identify which traditional executive level job the listed job title corresponds to. It also identifies whether a prefix identifies an executive as male or female. I made a stupid mistake of writing cname instead of coname when loading in the data. If you want to save yourself work, write coname.
DROP TABLE copeoplebase;
CREATE TABLE copeoplebase AS
SELECT copeople.*,
CASE WHEN prefix='Ms' THEN 1::int
WHEN prefix='Mr' THEN 0::int
ELSE Null::int END AS titlefemale,
CASE WHEN prefix='Ms' THEN 0::int
WHEN prefix='Mr' THEN 1::int
ELSE Null::int END AS titlemale,
CASE WHEN prefix='Dr' THEN 1::int
ELSE 0::int END AS doctor,
CASE WHEN prefix IS NULL THEN 0::int
ELSE 1::int END AS hastitle,
CASE WHEN prefix IS NULL AND firstname IS NULL AND lastname IS NULL THEN 0::int
ELSE 1::int END AS hasperson,
CASE WHEN fulltitle IS NOT NULL THEN 1::int ELSE 0::int END AS hastitlelookup,
CASE WHEN charman IS NOT NULL THEN charman ELSE 0::int END AS chairman,
CASE WHEN ceo IS NOT NULL THEN ceo ELSE 0::int END AS ceo,
CASE WHEN cfo IS NOT NULL THEN cfo ELSE 0::int END AS cfo,
CASE WHEN coo IS NOT NULL THEN coo ELSE 0::int END AS coo,
CASE WHEN cio IS NOT NULL THEN cio ELSE 0::int END AS cio,
CASE WHEN cto IS NOT NULL THEN cto ELSE 0::int END AS cto,
CASE WHEN otherclvl IS NOT NULL THEN otherclvl ELSE 0::int END AS otherclvl,
CASE WHEN boardmember IS NOT NULL THEN boardmember ELSE 0::int END AS boardmember,
CASE WHEN president IS NOT NULL THEN president ELSE 0::int END AS president,
CASE WHEN vp IS NOT NULL THEN vp ELSE 0::int END AS vp,
CASE WHEN founder IS NOT NULL THEN founder ELSE 0::int END AS founder,
CASE WHEN director IS NOT NULL THEN director ELSE 0::int END AS director
FROM copeople
LEFT JOIN titlelookup ON copeople.jobtitle=titlelookup.fulltitle;
--194359
Next we will try to identify whether an executive is male or female based on their names.
DROP TABLE namegender;
CREATE TABLE namegender AS
SELECT firstname,
CASE WHEN countfemale > 0 AND countmale=0 THEN 1::int ELSE 0::int END AS exclusivelyfemale,
CASE WHEN countmale > 0 AND countfemale=0 THEN 1::int ELSE 0::int END AS exclusivelymale
FROM
(SELECT firstname, COALESCE(sum(titlefemale),0) as countfemale, COALESCE(sum(titlemale),0) as countmale
FROM copeoplebase WHERE doctor=0
GROUP BY firstname) As T
WHERE NOT (countfemale > 0 AND countmale>0);
--12736
The next table expands CoPeopleBase to include information on executive gender and executive position.
DROP TABLE CoPeopleFull;
CREATE TABLE CoPeopleFull AS
SELECT copeoplebase.*,
CASE WHEN titlefemale=1 THEN 1::int
WHEN exclusivelyfemale=1 THEN 1::int ELSE 0::int END AS female,
CASE WHEN titlemale=1 THEN 1::int
WHEN exclusivelymale=1 THEN 1::int ELSE 0::int END AS male,
CASE WHEN (titlefemale=1 OR titlemale=1 OR exclusivelymale=1 OR exclusivelyfemale=1) THEN 0::int ELSE 1::int END AS unknowngender,
CASE WHEN (ceo=1 OR president=1) THEN 1::int ELSE 0::int END AS ceopres,
CASE WHEN (chairman=1 OR ceo=1 OR cfo=1 OR coo=1 OR cio=1 OR cto=1 OR otherclvl=1 OR president=1) THEN 1::int ELSE 0::int END AS CLevel,
CASE WHEN (chairman=1 OR ceo=1 OR cfo=1 OR coo=1 OR cio=1 OR cto=1 OR otherclvl=1 OR president=1 OR director=1 OR boardmember=1) THEN 1::int ELSE 0::int END AS board,
CASE WHEN (chairman=1 OR ceo=1 OR cfo=1 OR coo=1 OR cio=1 OR cto=1 OR otherclvl=1 OR president=1 OR director=1 OR boardmember=1 OR vp=1 OR founder=1) THEN 1::int ELSE
0::int END AS vpandabove
FROM copeoplebase
LEFT JOIN namegender ON namegender.firstname=copeoplebase.firstname
WHERE hasperson=1;
--177547
The next table only keeps executive listings that have a valid portco primary key associated with them.
DROP TABLE CoPeopleKey;
CREATE TABLE CoPeopleKey AS
SELECT A.*
FROM CoPeopleFull AS A
JOIN (SELECT firstname, lastname, cname, datefirstinv, statecode, count(*) FROM CoPeopleFull
WHERE firstname IS NOT NULL AND lastname IS NOT NULL AND cname IS NOT NULL AND datefirstinv IS NOT NULL AND statecode IS NOT NULL
GROUP BY firstname, lastname, cname, datefirstinv, statecode HAVING COUNT(*)=1) AS B
ON A.firstname=B.firstname AND A.lastname=B.lastname AND A.datefirstinv=B.datefirstinv AND A.cname=B.cname AND A.statecode=B.statecode;
--176251
The next table identifies whether a person previously held executive positions.
CREATE TABLE CoPeopleSerial AS
SELECT firstname, lastname, cname, datefirstinv, statecode,
COALESCE(sum(hasperson),0) as prev,
COALESCE(sum(ceo),0) as prevceo,
COALESCE(sum(ceopres),0) as prevceopres,
COALESCE(sum(founder),0) as prevfounder,
COALESCE(sum(clevel),0) as prevclevel,
COALESCE(sum(board),0) as prevboard,
COALESCE(sum(vpandabove),0) as prevvpandabove,
CASE WHEN sum(hasperson) >=1 THEN 1::int ELSE 0::int END AS serial,
CASE WHEN sum(ceo) >=1 THEN 1::int ELSE 0::int END AS serialceo,
CASE WHEN sum(ceopres) >=1 THEN 1::int ELSE 0::int END AS serialceopres,
CASE WHEN sum(founder) >=1 THEN 1::int ELSE 0::int END AS serialfounder,
CASE WHEN sum(clevel) >=1 THEN 1::int ELSE 0::int END AS serialclevel,
CASE WHEN sum(board) >=1 THEN 1::int ELSE 0::int END AS serialboard,
CASE WHEN sum(vpandabove) >=1 THEN 1::int ELSE 0::int END AS serialvpandabove
FROM (
SELECT A.prefix, A.firstname, A.lastname, A.cname, A.datefirstinv, A.statecode,
B.cname as prevcname, B.datefirstinv as prevdatefirstinv, B.statecode as prevstatecode, B.ceo, B.ceopres, B.founder, B.clevel, B.board, B.vpandabove, B.hasperson
FROM CoPeopleKey AS A
LEFT JOIN CoPeopleKey AS B ON A.firstname=B.firstname AND A.lastname=B.lastname AND A.datefirstinv > B.datefirstinv
) AS T
GROUP BY firstname, lastname, cname, datefirstinv, statecode;
--176251
The last table aggregates a ton of information on executives for each company. There is too much information to explain it all.
DROP TABLE copeopleagg;
CREATE TABLE copeopleagg AS
SELECT A.cname, A.datefirstinv, A.statecode,
sum(hasperson) as numperson,
sum(hastitle) as numtitled,
CASE WHEN sum(ceopres) >=1 THEN 1::int ELSE 0::int END AS hasceopres,
CASE WHEN sum(founder) >=1 THEN 1::int ELSE 0::int END AS hasfounder,
CASE WHEN sum(clevel) >=1 THEN 1::int ELSE 0::int END AS hasclevel,
CASE WHEN sum(board) >=1 THEN 1::int ELSE 0::int END AS hasboard,
CASE WHEN sum(vpandabove) >=1 THEN 1::int ELSE 0::int END AS hasvpandabove,
sum(female) as females,
sum(male) as males,
sum(unknowngender) as ugs,
sum(doctor*female) as femaledoctors,
sum(doctor*male) as maledoctors,
sum(doctor*unknowngender) as ugdoctors,
sum(ceopres*female) as femaleceos,
sum(ceopres*male) as maleceos,
sum(ceopres*unknowngender) as ugceos,
sum(ceopres*female*doctor) as femaledoctorceos,
sum(ceopres*male*doctor) as maledoctorceos,
sum(ceopres*unknowngender*doctor) as ugdoctorceos,
sum(founder*female) as femalefounders,
sum(founder*male) as malefounders,
sum(founder*unknowngender) as ugfounders,
sum(founder*female*doctor) as femaledoctorfounders,
sum(founder*male*doctor) as maledoctorfounders,
sum(founder*unknowngender*doctor) as ugdoctorfounders,
sum(clevel*female) as femaleclevels,
sum(clevel*male) as maleclevels,
sum(clevel*unknowngender) as ugclevels,
sum(clevel*female*doctor) as femaledoctorclevels,
sum(clevel*male*doctor) as maledoctorclevels,
sum(clevel*unknowngender*doctor) as ugdoctorclevels,
sum(board*female) as femaleboards,
sum(board*male) as maleboards,
sum(board*unknowngender) as ugboards,
sum(board*female*doctor) as femaledoctorboards,
sum(board*male*doctor) as maledoctorboards,
sum(board*unknowngender*doctor) as ugdoctorboards,
sum(vpandabove*female) as femaleabovevps,
sum(vpandabove*male) as maleabovevps,
sum(vpandabove*unknowngender) as ugabovevps,
sum(vpandabove*female*doctor) as femaledoctorabovevps,
sum(vpandabove*male*doctor) as maledoctorabovevps,
sum(vpandabove*unknowngender*doctor) as ugdoctorabovevps,
sum(prev*female) as femaleprevs,
sum(prev*male) as maleprevs,
sum(prev*unknowngender) as ugprevs,
sum(prevceopres*female) as femaleprevceopres,
sum(prevceopres*male) as maleprevceopres,
sum(prevceopres*unknowngender) as ugprevceopres,
sum(prevfounder*female) as femaleprevfounder,
sum(prevfounder*male) as maleprevfounder,
sum(prevfounder*unknowngender) as ugprevfounder,
sum(prevclevel*female) as femaleprevclevel,
sum(prevclevel*male) as maleprevclevel,
sum(prevclevel*unknowngender) as ugprevclevel,
sum(prevboard*female) as femaleprevboard,
sum(prevboard*male) as maleprevboard,
sum(prevboard*unknowngender) as ugprevboard,
sum(prevvpandabove*female) as femaleprevvpandabove,
sum(prevvpandabove*male) as maleprevvpandabove,
sum(prevvpandabove*unknowngender) as ugprevvpandabove,
sum(serial*female) as femaleserials,
sum(serial*male) as maleserials,
sum(serial*unknowngender) as ugserials,
sum(serialceopres*female) as femaleserialceopres,
sum(serialceopres*male) as maleserialceopres,
sum(serialceopres*unknowngender) as ugserialceopres,
sum(serialfounder*female) as femaleserialfounder,
sum(serialfounder*male) as maleserialfounder,
sum(serialfounder*unknowngender) as ugserialfounder,
sum(serialclevel*female) as femaleserialclevel,
sum(serialclevel*male) as maleserialclevel,
sum(serialclevel*unknowngender) as ugserialclevel,
sum(serialboard*female) as femaleserialboard,
sum(serialboard*male) as maleserialboard,
sum(serialboard*unknowngender) as ugserialboard,
sum(serialvpandabove*female) as femaleserialvpandabove,
sum(serialvpandabove*male) as maleserialvpandabove,
sum(serialvpandabove*unknowngender) as ugserialvpandabove,
sum(ceopres*serialceopres*female) as femaleceopresserialceopres,
sum(ceopres*serialceopres*male) as maleceopresserialceopres,
sum(ceopres*serialceopres*unknowngender) as ugceopresserialceopres,
sum(founder*serialfounder*female) as femalefounderserialfounder,
sum(founder*serialfounder*male) as malefounderserialfounder,
sum(founder*serialfounder*unknowngender) as ugfounderserialfounder
FROM CoPeoplekey AS A
JOIN CoPeopleSerial AS B
ON A.firstname=B.firstname AND A.lastname=B.lastname AND A.datefirstinv=B.datefirstinv AND A.cname=B.cname AND A.statecode=B.statecode
GROUP BY A.cname, A.datefirstinv, A.statecode;
--30413
Since this table is so big, it is a good idea to have a smaller, more manageable table to work with.
DROP TABLE copeopleaggsimple;
CREATE TABLE copeopleaggsimple AS
SELECT cname, datefirstinv, statecode, numperson, females, males, ugs, ugdoctors, maleserials+femaleserials+ugserials AS serials
FROM copeopleagg;
--30413
===Fund People===
Luckily, this process is much easier than the company people process. First we must simply load the data into the db.
DROP TABLE fundpeople;
CREATE TABLE fundpeople(
fundname varchar(255),
fundyear int,
prefix varchar(5),
firstname varchar(50),
lastname varchar(50),
jobtitle varchar(150),
prevpos varchar(255)
);
\COPY fundpeople FROM 'Executives-Funds-NoFoot-normal.txt' WITH DELIMITER AS E'\t' HEADER NULL AS '' CSV
--328994
The next table identifies degree and sex information about the executives of the fund.
DROP TABLE fundpeoplebase;
CREATE TABLE fundpeoplebase AS
SELECT fundpeople.*,
CASE WHEN prefix='Ms' THEN 1::int
WHEN prefix='Mr' THEN 0::int
ELSE Null::int END AS titlefemale,
CASE WHEN prefix='Ms' THEN 0::int
WHEN prefix='Mr' THEN 1::int
ELSE Null::int END AS titlemale,
CASE WHEN prefix='Dr' THEN 1::int
ELSE 0::int END AS doctor,
CASE WHEN prefix IS NULL THEN 0::int
ELSE 1::int END AS hastitle,
CASE WHEN prefix IS NULL AND firstname IS NULL AND lastname IS NULL THEN 0::int
ELSE 1::int END AS hasperson
FROM fundpeople;
--328994
The next table tries to identify the sex of the executive using the above defined namegender table. It only selects rows where a person is actually listed.
DROP TABLE FundPeopleFull;
CREATE TABLE FundPeopleFull AS
SELECT fundpeoplebase.*, exclusivelyfemale, exclusivelymale,
CASE WHEN titlefemale=1 THEN 1::int
WHEN exclusivelyfemale=1 AND exclusivelymale=0 AND (titlemale=0 OR titlemale IS NULL) THEN 1::int ELSE 0::int END AS female,
CASE WHEN titlemale=1 THEN 1::int
WHEN exclusivelymale=1 AND exclusivelyfemale=0 AND (titlefemale =0 OR titlefemale IS NULL) THEN 1::int ELSE 0::int END AS male,
CASE WHEN (titlefemale=1 OR titlemale=1 OR exclusivelymale=1 OR exclusivelyfemale=1) THEN 0::int ELSE 1::int END AS unknowngender
FROM fundpeoplebase
LEFT JOIN namegender ON namegender.firstname=fundpeoplebase.firstname
WHERE hasperson=1;
--320915
The next table gives you information on executives aggregated by fund.
DROP TABLE FundPeopleAgg;
CREATE TABLE FundPeopleAgg AS
SELECT fundname,
sum(female) as numfemale,
sum(male) as nummale,
sum(unknowngender) as numunknowngender,
sum(doctor) as numdoctor,
sum(female*doctor) as numfemaledoctor,
sum(male*doctor) as nummaledoctor,
sum(unknowngender*doctor) as numunknowngenderdoctor,
sum(hastitle) as numtitled,
sum(hasperson) as numpeople,
CASE WHEN sum(hasperson) > 0 THEN sum(female)/sum(hasperson) ELSE NULL END as fracfemale,
CASE WHEN sum(male) > 0 THEN sum(female)/sum(male) ELSE NULL END as ratiofemale
FROM FundPeopleFull
GROUP BY fundname;
--21536
It is also good to have this information on firms. We do not pull firm people information from SDC. However, we have enough information to create it from preexisting tables.
DROP TABLE firmpeopleagg;
CREATE TABLE firmpeopleagg AS
SELECT _firmname as firmname, sum(numfemale) as firmwomen, sum(nummale) as firmmen, sum(numunknowngender) as firmugs,
sum(numdoctor) as firmdoctors, sum(numpeople) as firmpeople,
CASE WHEN sum(numpeople) > 0 THEN (sum(numfemale)/sum(numpeople))::real ELSE NULL END as firmfracwomen,
CASE WHEN sum(nummale) > 0 THEN (sum(numfemale)/sum(nummale))::real ELSE NULL END as firmratiowomen
FROM roundlineaggfunds AS A
JOIN fundpeopleagg AS B ON A._fundname=B.fundname
GROUP BY _firmname;
--5273
==Marcos's Code==
This is code that a Rice student, Marcos Lee, wrote. I cleaned it and ran it. I have described the tables that I built and where they come from below. My code is located in:
E:McNair\Projects\VentureXpert Database\vcdb3\LoadingScripts\MatchingEntrepsV3
If you have issues understanding my explanation, go to this location and read the query. Most of them are straight forward.
===Describing Stacks Created in Code===
CoPeopleBase:
-Builds from copeople and titlelookup
-Identifies what roles people played in their companies
namegender:
-built from copeoplebase
-identifies male/female/unknown
CoPeopleFull:
-built from copeoplebase and namegender
-builds more extensive information on executive including speficially what level of executive they are
CoPeopleKey:
-built from CoPeopleFull
-creates table where only executives with full primary keys are kept
CoPeopleSerial:
-built from copeoplekey
-keeps track of executives previous jobs at executive level
CoPoepleAgg:
-built from copeoplekey and copeopleserial
-gets extensive information on executives for each company
FundPeopleBae:
-built from fundpeople
-identifies male/female/doctor
-hasperson column slightly weird because we can only have the lastname without prefix or first name and still have a 1 in column. Seems to be of little use/too broad
FundPeopleFull:
-built from fundpeoplebase, namegender
-adds in male/female
Fundpeopleagg:
-built from fundpeoplefull
-has aggregations of gender info for each fund
RoundLineJoinerLeanffWlistno:
-built from rounlinejoinerleanff
-adds listno to funds
RoundLineAggFunds:
-built from roundlinejoinerleanffwlistno and rounlineaggfirms
-if there are two funds from one firm that invest in same portco, we choose only one and leave the others behind
RoundLineAggWExit:
-built from roundlineaggfirms, portcoexitupdated, roundlineaggfunds
-adds in exit information for each company in roundlineaggfirms
FirmPerf:
-built from roundlineaggwexit
-adds in various performance measures for a given firm
PortCoFundDemo:
-built from roundlinejoinerleanffclean and fundpeopleagg
-gives information on executives of funds who invested in the portcos
PortCoPeopleMaster:
-built from PortCoMaster, PortCoIndustry, PortCoPatent, PortCoSBIR, copeoplagg, PortCoFundDemo, CPI, statelookupint
-huge amount of data about companies and their executives
RoundAggDistBase:
-built from portcogeo, firmbogeo, roundlineaggwexit
-creates geographic points using long, lat from geocoding
RoundAggDist:
-Built from roundaggdistbase
-gets actual distances between portcos and firms. if branch office exists and distance is less than distance to firm chooses that also generates random number
FirmPeopleAgg:
-built from roundlineaggfunds, fundpeopleagg
-finds information on executives from different firms
PortCoMatchmaster:
-built from portcopatent, porcoindustry, portcosbir, copeopleaggsimple, portcoid
-gets all information together about portcos
FirmMatchMaster:
-built from firmperf, firmvars, firmpeopleagg, firmid
-gets all information together about firms
RoundLineMasterBase:
-built from portcomatchmaster, firmmatchmaster, roundaggdist, roundlineaggwexit
-builds large amount of information about portcos and firms spceifically info about exits and distances
MatchMostNumerous:
-built from roundlinemasterbase
-finds max number of portcos invested in by a firm that also invested in the company grouping by
MatchHighestRandom:
-built from matchmostnumerous
-if two firms that invested in one company had the same number of max port cos this randomly chooses one company
FirmActiveYearsCode20:
-built from roundlinejoinerleanffclean, porcoindustry
-adds firmname to industry code not exactly sure why distinct is used in query
RealMatchesCode20:
-built from MatchHighestRandom, PortCoIndustry
-real matches between portcos and firms that invested in them including the code20
SyntheticFirmSetBaseCode20:
-built from realmatchescode20, firmactiveyarscode20
-crossproduct of firms and portcos. finds firms that invested in same year as portco received first inv, firms invested in same type of company, and makes sure matches are unique
AllMatchKeys:
-built from SyntheticFirmSetBaseCode20, RealMatchesCode20
-combines synthetic and real matches
SynthRoundAggDistBaseCode20:
-built from allmatchkeys, portcogeo, firmbogeo
-builds points for all portco, firm listings in allmatch keys
SynthRoundAddDistCode20:
-built from synthroundaggdistvasecode20
-finds actual distance between portcos and firms using installed extensions chooses branch offices if distance between portco and bo less than firm
SynthFirmnameInduBlowoutCode20:
-built from allmatchkeys, roundlinemasterbase
-gets every firm combination and checks whehter the companies that those firms invested in are in the same general industry
SynthFirmNameroundInduHistCode20:
-built from SynthFirmnameInduBlowoutcode20
-gets information by portco, firmname match about what the firms past investment patterns are
MasterWithSynthBaseCode20Portco:
-built from Allmatchkeys, matchhighestrandom, synthroundaggdistcode20, sythnfirmnameroundinduhistcode20, synthfirmnameroundindutotalcode20, firmvars, copeopleaggsimple, portcomaster
-builds a bunch of information about synthetic and real matches
SynthFirmnameRoundInduTotalCode20:
-built from allmatchkeys, roundlinemasterbase
-finds number of portcos in certain industries by firmnames
MasterWithSynthCode20Firms:
-built with firmmatchmaster, allmatchkeys
-matching a bunch of information to all firms
MasterWithSynthcode20:
-built from masterwithsynthbasecode20portco, masterwithsynthcode20firms
-gets a huge amount of info together on real and synthetic matches about firms and companies
MasterReals:
-built from masterwithsynthcode20
-gets just real matches from code
MasterOneSynth:
-built from masterwithsynthcode20
-gets just one randomly chosen synthetic match between companies and firms
MasterRealOneSynth:
-built from masteronesynth, masterreals
-combines the real and one synth table
==Ranking Tables and Graphs==
This is a slight detour from the creation of VCDB3. However, this is a cool process because you actually get to use the data you've been working with. This process is extensive, but the queries are easy to understand. If you wish to have deeper understanding of the process, read the code. It is located in:
E:McNair\Projects\VentureXpert Database\vcdb3\LoadingScripts\RoundRanking.SQL
First you must create a table that has aggregate round information grouped by cities and round year. Since this is a little difficult to picture, I will attach the code.
DROP TABLE roundleveloutput;
CREATE TABLE roundleveloutput AS SELECT
city, statecode, roundyear AS year,
SUM(rndamtestm*seedflag) AS seedamnt,
SUM(rndamtestm*earlyflag) AS earlyamnt,
SUM(rndamtestm*laterflag) AS lateramnt,
SUM(rndamtestm*growthflag) AS selamnt,
SUM(growthflag*dealflag) AS numseldeals
FROM round GROUP BY city, statecode, roundyear;
--30028
Next create a table that lists the all time SEL amount by city. Keep including the state code since this will ensure that you have the right city. City names are often repeated in different states. Next, create a table which lists unique city, state for every year since 1980. Then, build a table which matches portcos to the city, state, year blowout table for each year they were alive. This table should be relatively large since it lists companies once for every year they were alive up until the present. Then create a table that displays the number of companies alive in a city every year since 1980. Then add in a table that lists all of the information you have built in tables previously based on city, state, year. Also add in population. Then you can run the ranking queries.
For states follow the same general process but group by states not cities and states.
If this explanation was not enough for you (it was not meant to be in depth) go to the location defined above and read the actual code. With the description I have given, you should be able to piece together what each query does.
==Master Tables==
Throughout the creation of the database, there are inevitably some tables that are vital to create a solid foundation. The following tables are the master tables with a quick explanation:
* '''Companybasecore'''- The base table for portcos. This is data that was drawn directly from SDC and was not changed other than for cleaning purposes. Count: 48001
* '''BranchOfficeCore'''- The base table for branch offices. This is data drawn directly from SDC. Here only branch offices with distinct firm names are included. Count: 10032
* '''FirmBaseCore'''- The base table for firms. This is also data taken directly from SDC and was not changed other than for cleaning purposes. Count: 15437
* '''FundBaseCore'''- The base table for funds. This is also data taken directly from SDC and was not changed other than for cleaning purposes. Count: 28833
* '''IPOCleanNoDups''' - This is the clean table of IPOs after being run through the matcher against portcos. It was cleaned manually and had duplicates removed. Count: 2136
* '''IPONoDups'''- This is the table before the cleaning process of matching to portcos. There could be problems with this table as we used an aggregate function here. Be careful using this table. Count: 11149
* '''MACleanNoDups'''- This is the clean table of MAs after being run through the matcher against portcos. It was cleaned manually and had duplicates removed. Count: 7171
* '''MANoDups'''- This is the table before the cleaning process of matching to portcos. There could be problems with this table as we used an aggregate function here as well. Be careful using this table. Count: 119374
* '''Round'''- This is the master round table. It has SEL flags attached to it and has the most round info. RoundBaseClean is also a decent table but has less information. This table is your best bet for round information. Count: 151323
* '''RoundLineJoinerLeanFFClean'''- This is the master round table for joining purposes. It was cleaned and used for widespread joining purposes. Count: 163157
* '''CoPeople'''- This is the base table for PortCo people information. It was pulled directly from SDC. Count: 194359
* '''FirmBoGeo'''- This is the base table for firm/branch office geocoding. This table was cleaned and contains lat/long readings for firms and branch offices where the information was available. Count: 15437
* '''PortCoGeo'''- This is the base table for portco geocoding. Table was cleaned and contains lat/long reading for portcos where the Google API returned a valid reading. Count: 48001
* '''FirmPerf'''- This is a wide reaching table about the performance of firms. It was mainly used later in the project but is extremely useful. Count: 8336
* '''FundPeople'''- This is the base table for fund people information. It was pulled directly from SDC. Count: 328994.
* '''PortCoExitUpdated'''- This is the master exit table for portcos. The difference between this and PortCoExit is that Updated has two columns marking MAs and IPOs while the other has one column MAvsIPO. Use which ever one is more convenient. Count: 48001
* '''PortCoMaster'''- This table is great. There's a ton of information on PortCos including SEL flags, round amounts, and industry classifications. Count: 48001
