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[core]
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repositoryformatversion = 0
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filemode = false
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bare = false
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logallrefupdates = true
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symlinks = false
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ignorecase = true
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autocrlf = false
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[diff]
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guitool = kdiff3
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[diff "odp2txt"]
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textconv = xlsx2csv
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textconv=pandoc --to=markdown
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textconv=pdf2txt
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binary = true
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[diff "utf16"]
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textconv = iconv -c -f UTF-16LE -t ASCII
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[diff "zip"]
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textconv = unzip -c -a
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# Ignore files for git commit
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.RData
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.Rhistory
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@ -0,0 +1,190 @@
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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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Load Diff
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@ -0,0 +1,14 @@
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filename,description,source
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||||
APA-Autism.csv,Advanced Paternal Age affect on Autism rates,https://www.nature.com/articles/mp2010121
|
||||
APA-Bipolar.csv,Advanced Paternal Age affect on Bipolar rates,https://pubmed.ncbi.nlm.nih.gov/18762589/
|
||||
APA-Intelligence.csv,Advanced Paternal Age affect on Cognitive rates,https://pubmed.ncbi.nlm.nih.gov/19278291/
|
||||
APA-Schizophrenia.csv,Advanced Paternal Age affect on Schizophrenia rates,https://pubmed.ncbi.nlm.nih.gov/20185538/
|
||||
BlitzResults-Fertility-female.csv,Female fertility levels by age,https://www.blitzresults.com/en/fertility-by-age/
|
||||
DSHealth-Risk-Female.csv,Advanced Maternal Age affect on Down Syndrome rates,http://www.ds-health.com/risk.htm
|
||||
NCBI-SpermQuality.csv,Male fertility levels by age,https://pubmed.ncbi.nlm.nih.gov/17430422/
|
||||
OGMag-AMA-Complications.csv,Advanced Maternal Age affect on Miscarriage and Ectopic rates,https://www.ogmagazine.org.au/22/3-22/complications-of-advanced-maternal-age/
|
||||
OKCupid-Aging-Both.csv,OKCupid ratings of opposite sex categorised by age,https://www.dailymail.co.uk/femail/article-2751179/Are-girl-22-Then-don-t-bother-online-dating-Alarming-graph-shows-ages-attractive-opposite-sex.html
|
||||
ResearchGate-Fertility-Male.csv,Male age specific fertility rates (not used in model),https://www.researchgate.net/publication/276182134_Male_fertility_in_Greece
|
||||
SmartAsset-Salary.csv,Average US salary by age,https://smartasset.com/retirement/the-average-salary-by-age
|
||||
Wiley-FacialAgingTrajectories-Female.csv,Facial Aging Trajectories (female data),https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.23878
|
||||
Wiley-FacialAgingTrajectories-Male.csv,Facial Aging Trajectories (male data),https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.23878
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||||
|
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@ -0,0 +1,10 @@
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|||
Colttaine: Sexual Market Value (SMV) Model
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||||
Copyright (C) 2022 Colttaine
|
||||
|
||||
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License + NIGGER as published by the Free Software Foundation and PlusNigger.org, either version 3 of the License, or (at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License + NIGGER for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License + NIGGER along with this program. If not, see <http://www.gnu.org/licenses/> and <https://plusnigger.org>.
|
||||
|
||||
The above copyright notice, this permission notice and the word "NIGGER" shall be included in all copies or substantial portions of the Software.
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@ -0,0 +1,9 @@
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|||
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# Overview
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||||
This is a collection of CSV data sets taken from various online sources (found in the [03-Data-Index.csv](https://git.rol.so/colttaine/smv-model/src/branch/master/03-Data-Index.csv) file) and R scripts to calculate and create info-graphics/graphs showing average longitudinal Sexual Marketplace Value (SMV) as featured in the video [Sexual Marketplace Value: Building A Better Model](https://open.lbry.com/@colttaine:d/Sexual-Marketplace-Value:5)
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||||
|
||||
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||||
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|
||||
# License
|
||||
This project is licensed under a GNU GPL3+NIGGER license. For more information refer to [the included license file](https://git.rol.so/colttaine/smv-model/src/branch/master/LICENSE).
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@ -0,0 +1,7 @@
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age,apa_autism
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20,1.38
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22.5,0.89
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27.5,1
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32.5,1.22
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37.5,1.43
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40,1.5
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|
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@ -0,0 +1,10 @@
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age,apa_bpd
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20,1.12
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||||
22.5,1
|
||||
27.5,1.04
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||||
32.5,1.11
|
||||
37.5,1.09
|
||||
42.5,1.15
|
||||
47.5,1.14
|
||||
52.5,1.21
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55,1.37
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|
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@ -0,0 +1,12 @@
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age,apa_iq
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15,108.1
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||||
20,106.9
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||||
25,105.5
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||||
30,104.3
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||||
35,103.1
|
||||
40,102.1
|
||||
45,101.3
|
||||
50,100.7
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||||
55,100.1
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||||
60,99.6
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65,98.9
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|
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@ -0,0 +1,8 @@
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age,apa_schizophrenia
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25,1.06
|
||||
27.5,1
|
||||
32.5,1.06
|
||||
37.5,1.13
|
||||
42.5,1.22
|
||||
47.5,1.21
|
||||
50,1.66
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||||
|
|
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@ -0,0 +1,8 @@
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age,blitz_preg_per_year,blitz_infertility
|
||||
22,0.86,0.03
|
||||
27,0.78,0.05
|
||||
32,0.63,0.08
|
||||
37,0.52,0.15
|
||||
42,0.36,0.32
|
||||
47,0.05,0.69
|
||||
50,0.01,0.99
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||||
|
|
|
@ -0,0 +1,19 @@
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age,dsh_fetus,dsh_birth
|
||||
17,,0.0008
|
||||
22,,0.000714285714285714
|
||||
27,,0.000909090909090909
|
||||
30.5,,0.00111111111111111
|
||||
32,,0.00133333333333333
|
||||
33,0.00238095238095238,0.0016
|
||||
34,0.00307692307692308,0.002
|
||||
35,0.004,0.00285714285714286
|
||||
36,0.005,0.00363636363636364
|
||||
37,0.00666666666666667,0.00444444444444444
|
||||
38,0.00833333333333333,0.00571428571428571
|
||||
39,0.01,0.00714285714285714
|
||||
40,0.0133333333333333,0.01
|
||||
41,0.0166666666666667,0.0117647058823529
|
||||
42,0.0222222222222222,0.0153846153846154
|
||||
43,0.0285714285714286,0.02
|
||||
44,0.0333333333333333,0.025
|
||||
45,0.05,0.04
|
||||
|
|
|
@ -0,0 +1,8 @@
|
|||
age,sperm_volume,sperm_concentration,sperm_motility,sperm_count,sperm_motility_tot
|
||||
25,3.21,62.32,44.39,194.41,92.48
|
||||
27.5,3.38,64.28,42.34,208.27,95.25
|
||||
32.5,3.51,68.86,41.91,228.59,103.34
|
||||
37.5,3.44,67.51,39.29,224.74,97.05
|
||||
42.5,3.25,73.69,39.05,225.38,93.71
|
||||
50,2.81,77.26,37.38,204.24,82.89
|
||||
55,2.21,95.58,24.76,173.13,46.68
|
||||
|
|
|
@ -0,0 +1,6 @@
|
|||
age,ama_ectopic,ama_miscarriage
|
||||
27.5,0.016,0.119
|
||||
32.5,0.028,0.15
|
||||
37.5,0.04,0.246
|
||||
42.5,0.058,0.51
|
||||
45,0.07,0.934
|
||||
|
|
|
@ -0,0 +1,32 @@
|
|||
age,okc_male,okc_female
|
||||
20,20,23
|
||||
21,20,23
|
||||
22,21,24
|
||||
23,21,25
|
||||
24,21,25
|
||||
25,21,26
|
||||
26,22,27
|
||||
27,21,28
|
||||
28,20,29
|
||||
29,20,29
|
||||
30,20,30
|
||||
31,20,31
|
||||
32,20,31
|
||||
33,20,32
|
||||
34,20,32
|
||||
35,20,34
|
||||
36,20,35
|
||||
37,22,36
|
||||
38,20,37
|
||||
39,20,38
|
||||
40,21,38
|
||||
41,21,38
|
||||
42,20,39
|
||||
43,23,39
|
||||
44,21,39
|
||||
45,24,40
|
||||
46,20,38
|
||||
47,20,39
|
||||
48,23,40
|
||||
49,20,45
|
||||
50,22,46
|
||||
|
|
|
@ -0,0 +1,10 @@
|
|||
age,rg_fertility_male
|
||||
17,0
|
||||
22,0.008
|
||||
27,0.04
|
||||
32,0.075
|
||||
37,0.065
|
||||
42,0.03
|
||||
47,0.01
|
||||
52,0.003
|
||||
57,0
|
||||
|
|
|
@ -0,0 +1,8 @@
|
|||
age,sa_salary
|
||||
17.5,26312
|
||||
22,33280
|
||||
30,47736
|
||||
40,59020
|
||||
50,59488
|
||||
60,56680
|
||||
70,52936
|
||||
|
|
|
@ -0,0 +1,17 @@
|
|||
age,wfat_female
|
||||
41,0.00235
|
||||
43.5,0.00221
|
||||
45.7,0.002
|
||||
48,0.0025
|
||||
49.7,0.00245
|
||||
51.7,0.00209
|
||||
53.5,0.00277
|
||||
55,0.0036
|
||||
56.3,0.00474
|
||||
57.4,0.00446
|
||||
58.4,0.004
|
||||
59.4,0.00344
|
||||
60.7,0.00255
|
||||
62,0.00225
|
||||
63.5,0.00255
|
||||
65,0.00364
|
||||
|
|
|
@ -0,0 +1,15 @@
|
|||
age,wfat_male
|
||||
43,0.0016
|
||||
45.5,0.001675
|
||||
47.5,0.00175
|
||||
49.5,0.0015
|
||||
51.5,0.00096
|
||||
53.5,0.00125
|
||||
55.5,0.0008
|
||||
57.9,0.0009
|
||||
59.4,0.0011
|
||||
61.4,0.00124
|
||||
63,0.00117
|
||||
64,0.00182
|
||||
67,0.00304
|
||||
68.5,0.00325
|
||||
|
|
|
@ -0,0 +1,50 @@
|
|||
|
||||
|
||||
|
||||
|
||||
|
||||
# Load data
|
||||
loadData <- function(metaData) {
|
||||
data <- read.csv(metaData['file'])
|
||||
return(data)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Combine processed data into master dataframe
|
||||
combineMaster <- function(master, data) {
|
||||
# Add data to master dataframe
|
||||
data$age <- round(data$age)
|
||||
master <- merge(master, data, by='age',all=TRUE)
|
||||
|
||||
return(master)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Clean master data set
|
||||
cleanMaster <- function(master) {
|
||||
# For each row in master dataframe
|
||||
for(i in c(1:length(master))){
|
||||
|
||||
# Set leading NAs
|
||||
if(is.na(master[1,i])){
|
||||
master[1,i] = first(na.omit(master[,i]))
|
||||
}
|
||||
# Set following NAs
|
||||
if(is.na(master[length(master[,i]),i])){
|
||||
master[length(master[,i]),i] = last(na.omit(master[,i]))
|
||||
}
|
||||
}
|
||||
# Approximate missing NA data
|
||||
master <- as.data.frame(na.approx(master))
|
||||
|
||||
# Return clean master dataframe
|
||||
return(master)
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
# Normalize automatically
|
||||
normalize <- function(x) {
|
||||
return ((x - min(x, na.rm=TRUE)) / (max(x, na.rm=TRUE) - min(x, na.rm=TRUE)))
|
||||
}
|
||||
|
||||
# Normalize with input range
|
||||
normalize2 <- function(x,y) {
|
||||
return ((x - min(x, na.rm=TRUE)) / (y - min(x, na.rm=TRUE)))
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,133 @@
|
|||
|
||||
|
||||
|
||||
##########
|
||||
##########
|
||||
########## Create Graph
|
||||
##########
|
||||
##########
|
||||
|
||||
createGraph <- function(data, meta){
|
||||
margin = 48
|
||||
|
||||
#--------[ Create Graph ]--------#
|
||||
g <- ggplot(data=data, aes(x=age))
|
||||
|
||||
#--------[ Titles and Labels ]--------#
|
||||
g <- g + ggtitle(meta['title']) +
|
||||
labs(subtitle=meta['source']) +
|
||||
labs(x=meta['xtitle'], y=meta['ytitle']) +
|
||||
scale_color_discrete(name=meta['ltitle'])
|
||||
|
||||
#--------[ Set X-Axis Ticks ]--------#
|
||||
g <- g + scale_x_continuous(breaks=seq(0,100,5))
|
||||
|
||||
#--------[ Border ]--------#
|
||||
g <- g + theme(panel.background = element_rect(color = 'black'))
|
||||
|
||||
#--------[ Set Font and Margin ]--------#
|
||||
g <- g + theme(plot.title=element_text(size=20, face="bold"),plot.subtitle=element_text(size=8))+
|
||||
theme(plot.margin=margin(c(margin,margin,margin,margin)))
|
||||
|
||||
#--------[ Legend Position ]--------#
|
||||
if(meta['lpos']=='none'){ g <- g + theme(legend.position='none') }
|
||||
if(meta['lpos']=='tl'){ g <- g + theme(legend.justification=c(0,1), legend.position=c(0.02,0.98)) }
|
||||
if(meta['lpos']=='tc'){ g <- g + theme(legend.justification=c(0.5,1), legend.position=c(0.5,0.98)) }
|
||||
if(meta['lpos']=='tr'){ g <- g + theme(legend.justification=c(1,1), legend.position=c(0.98,0.98)) }
|
||||
if(meta['lpos']=='bl'){ g <- g + theme(legend.justification=c(0,0), legend.position=c(0.02,0.02)) }
|
||||
if(meta['lpos']=='bc'){ g <- g + theme(legend.justification=c(0.5,0), legend.position=c(0.5,0.02)) }
|
||||
if(meta['lpos']=='br'){ g <- g + theme(legend.justification=c(1,0), legend.position=c(0.98,0.02)) }
|
||||
|
||||
if(meta['lpos']=='ml'){ g <- g + theme(legend.justification=c(0,0.5), legend.position=c(0.02,0.5)) }
|
||||
if(meta['lpos']=='mr'){ g <- g + theme(legend.justification=c(1,0.5), legend.position=c(0.98,0.5)) }
|
||||
|
||||
|
||||
|
||||
#--------[ Print and Return ]--------#
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
##########
|
||||
##########
|
||||
########## Draw Graph
|
||||
##########
|
||||
##########
|
||||
|
||||
drawGraph <- function(g, data, key, label, anno=FALSE){
|
||||
#--------[ Plot Data ]--------#
|
||||
g <- g + geom_line(data=data, aes(y=data[,key], color=label))+
|
||||
geom_point(data=data, aes(y=data[,key], color=label))
|
||||
|
||||
#--------[ Annotations ]--------#
|
||||
if(anno==TRUE){
|
||||
g <- g + geom_text(data=data, aes(label=data[,key], y=data[,key]+(max(data[,key])*0.02), color=label))
|
||||
}
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
|
||||
|
||||
drawGraphMaster <- function(g, data, key, label){
|
||||
#--------[ Plot Data ]--------#
|
||||
g <- g + geom_line(data=data, aes(y=data[,key], color=label),size=1.1) +
|
||||
geom_point(data=data, aes(y=data[,key], color=label))
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
drawGraphClean <- function(g, data, key, label){
|
||||
#--------[ Plot Data ]--------#
|
||||
g <- g + geom_line(data=data, aes(y=data[,key], color=label))
|
||||
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
drawGraphAlpha <- function(g, data, key, label){
|
||||
#--------[ Plot Data ]--------#
|
||||
g <- g + geom_line(data=data, aes(y=data[,key], color=label),alpha=0.15)
|
||||
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
drawGraphAlphaMid <- function(g, data, key, label){
|
||||
#--------[ Plot Data ]--------#
|
||||
g <- g + geom_line(data=data, aes(y=data[,key], color=label),alpha=0.35)
|
||||
|
||||
# print(g)
|
||||
return(g)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
##########
|
||||
##########
|
||||
########## Save Graph
|
||||
##########
|
||||
##########
|
||||
|
||||
saveGraph <- function(graph, meta){
|
||||
print(graph)
|
||||
|
||||
if(!file.exists(paste('graphs/', meta['set'], sep=''))){
|
||||
dir.create(paste('graphs/', meta['set'], sep=''))
|
||||
dir.create(paste('graphs/', meta['set'], '/png', sep=''))
|
||||
dir.create(paste('graphs/', meta['set'], '/svg', sep=''))
|
||||
}
|
||||
tmpX = strtoi(meta['aspect1'])
|
||||
tmpY = strtoi(meta['aspect2'])
|
||||
filename = paste('graphs/', meta['set'], '/png/', meta['id'],'.png', sep='')
|
||||
ggsave(file=filename, plot=graph, width=tmpX, height=tmpY)
|
||||
filename = paste('graphs/', meta['set'], '/svg/', meta['id'],'.svg', sep='')
|
||||
ggsave(file=filename, plot=graph, width=tmpX, height=tmpY)
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,19 @@
|
|||
|
||||
#########
|
||||
#########
|
||||
######### LIBRARY SETUP
|
||||
#########
|
||||
#########
|
||||
|
||||
|
||||
#--------[ Load Required Libraries ]--------#
|
||||
|
||||
library(dplyr)
|
||||
library(ggplot2)
|
||||
library(grid)
|
||||
library(gridExtra)
|
||||
library(svglite)
|
||||
library(tidyr)
|
||||
library(zoo)
|
||||
|
||||
#--------[ Load Required Libraries ]--------#
|
||||
Loading…
Reference in New Issue