191 lines
5.6 KiB
R
Executable File
191 lines
5.6 KiB
R
Executable File
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source('r/library.r')
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source('r/functions.r')
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source('r/data.r')
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source('r/graph.r')
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dataMaster <- data.frame( age=c(15:70) )
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##########
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##########
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########## Load Data
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##########
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##########
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#--------[ Data - Salary ]--------#
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data100 <- read.csv('data/SmartAsset-Salary.csv')
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data100$sa_salary <- normalize(data100$sa_salary)
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dataMaster <- combineMaster(dataMaster, data100)
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#--------[ Data - Female Fertility ]--------#
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data200 <- read.csv('data/BlitzResults-Fertility-female.csv')
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data200$blitz_infertility <- 1 - normalize(data200$blitz_infertility)
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data200$blitz_preg_per_year <- normalize(data200$blitz_preg_per_year)
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dataMaster <- combineMaster(dataMaster, data200)
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#--------[ Data - Advanced Maternal Age ]--------#
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data300 <- read.csv('data/DSHealth-Risk-Female.csv')
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data300$dsh_fetus <- 1 - normalize(data300$dsh_fetus)
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data300$dsh_birth <- 1 - normalize(data300$dsh_birth)
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dataMaster <- combineMaster(dataMaster, data300)
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data310 <- read.csv('data/OGMag-AMA-Complications.csv')
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data310$ama_ectopic <- 1 -normalize(data310$ama_ectopic)
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data310$ama_miscarriage <- 1- normalize(data310$ama_miscarriage)
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dataMaster <- combineMaster(dataMaster, data310)
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#--------[ Data - Male Fertility ]--------#
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data400 <- read.csv('data/NCBI-SpermQuality.csv')
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data400$sperm_volume <- normalize(data400$sperm_volume)
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data400$sperm_concentration <- normalize(data400$sperm_concentration)
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data400$sperm_motility <- normalize(data400$sperm_motility)
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data400$sperm_count <- normalize(data400$sperm_count)
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data400$sperm_motility_tot <- normalize(data400$sperm_motility_tot)
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dataMaster <- combineMaster(dataMaster, data400)
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#--------[ Data - Advanced Paternal Age ]--------#
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data500 <- read.csv('data/APA-Bipolar.csv')
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data500$apa_bpd <- 1 - normalize(data500$apa_bpd)
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dataMaster <- combineMaster(dataMaster, data500)
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data501 <- read.csv('data/APA-Autism.csv')
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data501$apa_autism <- 1 - normalize(data501$apa_autism)
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dataMaster <- combineMaster(dataMaster, data501)
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data502 <- read.csv('data/APA-Schizophrenia.csv')
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data502$apa_schizophrenia <- 1 - normalize(data502$apa_schizophrenia)
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dataMaster <- combineMaster(dataMaster, data502)
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data503 <- read.csv('data/APA-Intelligence.csv')
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data503$apa_iq <- normalize(data503$apa_iq)
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dataMaster <- combineMaster(dataMaster, data503)
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#--------[ Data - OKCupid Looks By Age ]--------#
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data700 <- read.csv('data/OKCupid-Aging-Both.csv')
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data700['okc_male_dist'] = abs(data700$age-data700$okc_female)
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data700['okc_female_dist'] = abs(data700$age-data700$okc_male)
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data700$okc_male <- normalize(data700$okc_male)
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data700$okc_female <- normalize(data700$okc_female)
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data700$okc_female_dist <- 1 - normalize(data700$okc_female_dist)
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data700$okc_male_dist <- 1 - normalize(data700$okc_male_dist)
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dataMaster <- combineMaster(dataMaster, data700)
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##########
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##########
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########## Build Model
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##########
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##########
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dataMaster <- cleanMaster(dataMaster)
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#--------[ Intermediate - Advanced Maternal Age (Average) ]--------#
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dataMaster$ama_average <- (
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dataMaster$dsh_fetus +
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dataMaster$ama_ectopic +
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dataMaster$ama_miscarriage
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)/3
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dataMaster$ama_average <- normalize(dataMaster$ama_average)
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#--------[ Intermediate - Advanced Maternal Age (Average) ]--------#
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dataMaster$apa_average <- (
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dataMaster$apa_bpd +
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dataMaster$apa_autism +
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dataMaster$apa_schizophrenia +
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dataMaster$apa_iq
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)/4
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dataMaster$apa_average <- normalize(dataMaster$apa_average)
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#--------[ Final - Male Sexual Market Value ]--------#
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dataMaster$smv_male <-(
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dataMaster$sa_salary +
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dataMaster$sperm_motility_tot +
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dataMaster$apa_average +
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dataMaster$okc_male_dist
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)/4
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dataMaster$smv_male <- normalize(dataMaster$smv_male)
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#--------[ Final - Male Sexual Market Value ]--------#
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dataMaster$smv_female <-(
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dataMaster$blitz_preg_per_year +
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dataMaster$blitz_infertility +
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dataMaster$ama_average +
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dataMaster$okc_female_dist
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)/4
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dataMaster$smv_female <- normalize(dataMaster$smv_female)
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#--------[ Final - Gender Advantage ]--------#
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dataMaster$advantage <- dataMaster$smv_male - dataMaster$smv_female
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##########
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##########
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########## Draw Graph
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##########
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##########
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#--------[ Graph - MetaData ]--------#
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metaData <- c(
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source='Combined data from various sources',
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file='NA',
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set='000-SMV-Model',
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id='000-SMV-Model',
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title='Sexual Marketplace Value: Final Model',
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xtitle='Age (years)',
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ytitle='Sexual Marketplace Value',
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ltitle='Data',
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aspect=c(16,9),
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lpos='mr'
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)
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#--------[ Graph - Draw Graph ]--------#
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tmpGraph <- createGraph(dataMaster, metaData)
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'sa_salary', 'Average Salary')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'blitz_preg_per_year', 'Female Fertility - Chances of Getting Pregnant Within One Year')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'blitz_infertility', 'Female Fertility - Likelyhood to be Infertile')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'ama_average', 'Advanced Maternal Age Complications (Average)')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'sperm_motility_tot', 'Male Fertility - Total Sperm Motility')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'apa_average', 'Advanced Paternal Age Complications (Average)')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'okc_male_dist', 'OKCupid - Male looks by age (Distance)')
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tmpGraph <- drawGraphAlpha(tmpGraph, dataMaster, 'okc_female_dist', 'OKCupid - Female looks by age (Distance)')
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tmpGraph <- drawGraphMaster(tmpGraph, dataMaster, 'smv_male', 'Male Sexual Market Value')
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tmpGraph <- drawGraphMaster(tmpGraph, dataMaster, 'smv_female', 'Female Sexual Market Value')
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tmpGraph <- tmpGraph + geom_vline(aes(xintercept=34), linetype=2, alpha=0.5)
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saveGraph(tmpGraph, metaData)
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