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diff --git a/R/.Rhistory b/R/.Rhistory
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--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,512 +1,512 @@
-data1$new_milestones <- as.numeric(data1$milestones > 0) + 1
-# (2) - Run the model on the pilot data
-data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
-table(data1$milestones)
-table(data1$old_milestones)
-hist(data1$old_mmt, prob=TRUE) #inequality of participation
-hist(data1$formal.score)
-data1$new_mmt <- data1$mmt - 1
-hist(data1$new_mmt, prob=TRUE)
-data1$new.age <- as.numeric(cut(data1$age/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
-data1$new.formal.score <- data1$mmt / (data1$new_milestones/data1$new.age)
-hist(as.numeric(data1$new.age))
-hist(data1$formal.score)
-hist(data1$new.formal.score)
-fsmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
-summary(kmodel1)
-summary(fsmodel1)
-kmodel3 <- lm(up.fac.mean ~ formal.score, data=data1)
-# (2) - Run the model on the pilot data
-data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
-table(data1$formal.score)
-fsmodel1 <- lm(up.fac.mean ~ is.finite(formal.score), data=data1)
-summary(fsmodel1)
-fsmodel2 <- lm(up.fac.mean ~ new.formal.score, data=data1)
-summary(kmodel2)
-summary(fsmodel2)
-mmtmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
-summary(mmtmodel1)
-agemodel1 <- lm(up.fac.mean ~ new.age, data=data1)
-summary(agemodel1)
-msmodel1 <- lm(up.fac.mean ~ old_milestones, data=data1)
-summary(msmodel1)
-msmodel2 <- lm(up.fac.mean ~ new_milestones, data=data1)
-summary(msmodel2)
-texreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-install.packages(textref)
-install.packages(textreg)
-library(textreg)
-install.packages("textreg_0.1.tar.gz", repos = NULL, type="source")
-install.packages("textreg_0.1.tar.gz", repos = NULL, type="source")
-install.packages("textreg_0.1.5.tar.gz", repos = NULL, type="source")
-import.packages(tm)
-import.package(tm)
-import.package("tm")
-install.package("tm")
-install.packages("tm")
-install.packages("nlp")
-yes
-install.packages("textreg_0.1.5.tar.gz", repos = NULL, type="source")
-library(textreg)
-texreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-textreg(list(m1,m2,m3,m4), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: no lang/network measures', 'M2: No language measures', 'M3: No network measures', 'M4: Full model'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-textreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Uploader Count', 'Did maintainer change?', 'Team proportion', 'Eigenvector Centrality', 'Betweenness Centrality', 'Mean Language Age', 'Package Age : Mean Language Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-coeff(fsmodel1)
-coef(fsmodel1)
-textreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-coef(fsmodel2)
-textreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-textreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2), stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-install.packages(texreg)
-install.packages("texreg_1.39.3.tar.gz", repos = NULL, type="source")
-library(texreg)
-texreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score'),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test', ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score', 'M3: original milestones', 'M4: binomial milestones', 'M5: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test' ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M5: binomial milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'test', 'test', 'test', 'test', 'test' ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, msmodel2, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M5: binomial milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Package Age (years)', 'Relationship to Underproduction', 'test', 'test', 'test', 'test' ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'milestone usage relationship to underproduction', 'age group relationship to underproduction' ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'milestone usage relationship to underproduction', 'age group relationship to underproduction', 'test' ),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'Milestone usage relationship to underproduction', 'Age group relationship to underproduction'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-data5 <- subset(data1, is.finite(data1$formal.score))
-fsmodel1 <- lm(up.fac.mean ~ formal.score, data=data5)
-summary(fsmodel1)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: original formality score', 'M2: augmented formality score','M3: MMT', 'M4: original milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Original formality relationship to underproduction', 'Augmented formality relationship to Underproduction', 'MMT relationship to underproduction', 'Milestone usage relationship to underproduction', 'Age group relationship to underproduction'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: orig. formality', 'M2: augm. formality','M3: MMT', 'M4: milestones', 'M6: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Original formality/underproduction', 'Augmented formality/Underproduction', 'MMT/underproduction', 'Milestones / underproduction', 'Age/underproduction'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel1,fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c('M1: orig. formality', 'M2: augm. formality','M3: MMT', 'M4: milestones', 'M5: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Original formality', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones', 'M4: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Original formality', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones', 'M4: age (grouped)'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'Age'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-#Sample values
-powerCheck(300, 1000)
-source('powerAnalysis.R') #my little "lib"
-#Sample values
-powerCheck(300, 1000)
-powerCheck(200, 1000)
-powerCheck(250, 1000)
-powerCheck(275, 5000)
-powerCheck(275, 1000)
-source('powerAnalysis.R') #my little "lib"
-#Sample values
-powerCheck(300, 1000)
-powerCheck(275, 1000)
-powerCheck(500, 1000)
-powerCheck(700, 1000)
-powerCheck(7000, 1000)
-fsmodel2 <- lm(up.fac.mean ~ new.formal.score + as.factor(new.age), data=data1)
-summary(fsmodel2)
-mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
-summary(mmtmodel1)
-mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(age), data=data1)
-summary(mmtmodel1)
-mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
-summary(mmtmodel1)
-msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
-summary(msmodel1)
-fsmodel2 <- lm(up.fac.mean ~ new.formal.score + as.factor(new.age), data=data1)
-summary(fsmodel2)
-texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1, agemodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'test'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'test'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), omit.coef = 'factor', stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-source('powerAnalysis.R') #my little "lib"
-powerCheck(250, 1000)
-powerCheck(275, 1000)
-#Sample values
-powerCheck(300, 1000)
-summary(mmtmodel1)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'age 2', 'age 3', 'age 4'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-mmtmodel1 <- lm(up.fac.mean ~ mmt + as.factor(new.age), data=data1)
-msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
-fsmodel2 <- lm(up.fac.mean ~ new.formal.score, data=data1)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Milestones', 'age 2', 'age 3', 'age 4'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'age 2', 'age 3', 'age 4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-summary(msmodel1)
-texreg(list(fsmodel2, mmtmodel1, msmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
+scale_colour_manual(values=legend)
+g
 g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
 geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
+geom_abline(aes(intercept=1.65, slope=-1.38, colour = "0-9y"), size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31, colour = "9-12y"), size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78, colour = "12-15y"), size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23, colour = "15-16y"), size=1)+
+labs(x="MMT", y="Mean Underproduction Factor", colour="Project Age Group") +
+scale_colour_manual(values=legend)
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor", color="Project Age Group") +
+scale_colour_manual(values=legend)
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor", colour="Project Age Group") +
+scale_colour_manual(values=legend)
+g
+colors <- c("0-9y":"#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, colour=new.age.factor), size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+data2 <- subset(data1, (data1$age / 365) < 14 )
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.5, colour=new.age.factor), size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.5, colour=new.age.factor), size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.7, colour=new.age.factor), size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=2)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38),colour = "#E69F00", size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31), colour = "#56B4E9", size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78), colour = "#D55E00", size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23) , colour = "#CC79A7", size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(name="Project Age Group", values=c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7"))
+g
+colors <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(values=colors_legend)
+g
+#colors <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+colors_legend <- c("0-9y"="red","9-12y":"green", "12-15y":"blue","15-16y":"orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(values=colors_legend)
+#colors <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+colors_legend <- c("0-9y"="red","9-12y":"green", "12-15y":"blue","15-16y":"orange")
+#colors <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(values=colors_legend)
+g
+colors_legend <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(values=colors_legend)
+g
+colors_legend <- c("0-9y"="#E69F00","9-12y":"#56B4E9", "12-15y":"#D55E00","15-16y":"#CC79A7")
+colors_legend <- c("0-9y"="#E69F00","9-12y"="#56B4E9", "12-15y"="#D55E00","15-16y"="#CC79A7")
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor") +
+scale_colour_manual(values=colors_legend)
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend)
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend)
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, breaks = c("1", "2", "3", "4"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="0-9y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="9-12y"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="12-15y" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="15-16y") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, breaks=c("1", "2", "3", "4"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color=0), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color=1), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color=2 ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color=3) , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend)
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend)
+g
+colors_legend <- c("a"="#E69F00","b"="#56B4E9", "c"="#D55E00","d"="#CC79A7")
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend)
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y"))
+g
++ theme(legend.position='bottom')
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme(legend.position='bottom')
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme(legend.position=c(0.95, 0.95))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme(legend.position=c(1, -5))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme(legend.position=c(-5, 1))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme(legend.position="bottom")
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.justification = c("right", "top"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.95, 0.95), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.75, 0.95), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.35, 0.95), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.35, 0.25), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.35, 0.15), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.25, 0.15), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.15, 0.15), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.15, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.31, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.78, color="c" ), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-0.23, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=1.65, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.255, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom")) +
 theme_bw()
 g
-library(ggplot2)
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
 g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
 geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme_bw( legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
 g
-msmodel1 <- lm(up.fac.mean ~ old_milestones + as.factor(new.age), data=data1)
-summary(msmodel1)
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
 g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
 geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+theme_bw(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+geom_point() +
+geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
 theme_bw()+
-scale_color_brewer(palette="Dark2")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_brewer(palette="Dark2")
-g + scale_color_viridis_b()
-g + scale_color_viridis_d()
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b()
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b()
-g + scale_color_viridis_b() scale_fill_continuous(name = "New Legend Title")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b() scale_fill_continuous(name = "New Legend Title")
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b() + scale_fill_continuous(name = "New Legend Title")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b() + scale_fill_continuous(name = "New Legend Title")
-g + scale_color_viridis_b() + scale_color_continuous(name = "New Legend Title")
-g + scale_color_viridis_b() + scale_color_continuous(name = "Age Group")
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b() + scale_color_continuous(name = "Age Group")
-g
-g + scale_color_viridis_b(name = "Age Group")
-g + scale_color_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
-g
-g + scale_fill_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_fill_viridis_b(name = "Age Group", labels = c('1', '2', '3', '4'))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_fill_viridis_b(name = "Age Group")
-g
-g + scale_fill_viridis_b(name = "Age Group")
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_colour_viridis_b(name = "Age Group")
-g + scale_fill_continuous(name = "Age Group")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_fill_continuous(name = "Age Group")
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor")
-g + scale_fill_continuous(name = "Age Group")
-g + scale_fill_continuous(name = "Age Group", labels=c("Woman", "Man"))
-g + scale_color_continuous(name = "Age Group", labels=c("Woman", "Man"))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_continuous(name = "Age Group", labels=c("Woman", "Man"))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g + scale_color_continuous(type = "viridis", name = "Age Group", labels=c("Woman", "Man"))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels="0-9y", "9-12y", "12-15y", "15-17y")
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y"))
-g
-+ theme(legend.position = c(0.8, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.8, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.1))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-summary(mmtmodel1)
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=as.factor(new.age))+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_continuous(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=as.factor(new.age))+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_discrete(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=new.age)+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-aes(x=mmt, y=up.fac.mean, color=round(new.age))+
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-scale_color_identity(type = "viridis", name = "Age Group", labels=c("0-9y", "9-12y", "12-15y", "15-17y")) +
-theme(legend.position = c(0.1, 0.2))
-g
-g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_point() +
-geom_smooth() +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
+theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
 g
diff --git a/R/0119-final-mmt.png b/R/0119-final-mmt.png
new file mode 100644
index 0000000..3c4e81e
Binary files /dev/null and b/R/0119-final-mmt.png differ
diff --git a/R/630_0119_final.png b/R/630_0119_final.png
new file mode 100644
index 0000000..0980bb4
Binary files /dev/null and b/R/630_0119_final.png differ
diff --git a/R/calculatePower.R b/R/calculatePower.R
index e7dca57..c9df63b 100644
--- a/R/calculatePower.R
+++ b/R/calculatePower.R
@@ -34,8 +34,9 @@ data1$new_milestones <- as.numeric(data1$milestones > 0) + 1
 data1$formal.score <- data1$mmt / (data1$old_milestones/data1$age)
 table(data1$formal.score)
 hist(data1$old_mmt, prob=TRUE) #inequality of participation
-hist(data1$formal.score)
-hist(data1$age/365)
+median(data1$contributors)
+median(data1$collaborators)
+median(data1$age/365)
 data1$new_mmt <- data1$mmt - 1
 hist(data1$new_mmt, prob=TRUE)
 
@@ -67,15 +68,35 @@ cor.test(data1$mmt, data1$up.fac.mean)
 cor.test(data1$milestones, data1$up.fac.mean)
 cor.test(data1$age, data1$up.fac.mean)
 
+data1$new.age.factor <- as.factor(data1$new.age)
+#geom_abline(intercept=coef(mmtmodel1)[1], slope=coef(mmtmodel1)[2], colour = "orange")+
+
 g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
   geom_point() +
-  geom_smooth() +
+  #geom_smooth( method="lm", formula=(y~x), colour = "orange")+
+  geom_abline(intercept=coef(mmtmodel1)[1], slope=coef(mmtmodel1)[2], colour = "orange", size=1)+
+  geom_errorbar(aes(ymin=y-yerr, ymax=y+yerr), width=0.09)+
   xlab("MMT") +
   ylab("Underproduction Factor") +
   theme_bw() 
 g
 g
 
+colors_legend <- c("a"="#E69F00","b"="#56B4E9", "c"="#D55E00","d"="#CC79A7")
+#colors_legend <- c("0-9y"="red","9-12y"="green", "12-15y"="blue","15-16y"="orange")
+g <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
+  geom_point() +
+  geom_abline(aes(intercept=1.65, slope=-1.38, color="a"), size=1.5)+
+  geom_abline(aes(intercept=1.72, slope=-1.38, color="b"), size=1.5)+
+  geom_abline(aes(intercept=2.25, slope=-1.38, color="c" ), size=1.5)+
+  geom_abline(aes(intercept=2.8, slope=-1.38, color="d") , size=1.5)+
+  labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
+  scale_colour_manual(values=colors_legend, labels=c("0-9y", "9-12y", "12-15y","15-16y")) +
+  theme_bw()+
+  theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
+g
+
+
 data2 <- subset(data1, (data1$age / 365) < 14 )
 hist(floor(data2$age))
 g <- ggplot(data2, aes(x=mmt, y=up.fac.mean)) +
diff --git a/R/data_subset_agegroup.png b/R/data_subset_agegroup.png
new file mode 100644
index 0000000..7090f97
Binary files /dev/null and b/R/data_subset_agegroup.png differ
diff --git a/R/final-mmt-plot.png b/R/final-mmt-plot.png
new file mode 100644
index 0000000..816aa93
Binary files /dev/null and b/R/final-mmt-plot.png differ
diff --git a/R/final-mmt-underprod-final-last.png b/R/final-mmt-underprod-final-last.png
new file mode 100644
index 0000000..1e5e2f0
Binary files /dev/null and b/R/final-mmt-underprod-final-last.png differ
diff --git a/R/newAnalysis.R b/R/newAnalysis.R
new file mode 100644
index 0000000..ddf547c
--- /dev/null
+++ b/R/newAnalysis.R
@@ -0,0 +1,143 @@
+rm(list=ls())
+set.seed(424242) 
+
+library(readr)
+library(ggplot2)
+
+data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
+data2 <- read_csv('../kk_final_octo_data_total.csv',show_col_types = FALSE)
+data3 <- read_csv('../kk_final_doclist_roster.csv',show_col_types = FALSE)
+data4 <-read_csv('../kk_final_rosterslist.csv',show_col_types = FALSE)
+#getting data subset metadata
+
+head(data1)
+head(data2)
+head(data3)
+head(data4)
+
+length(which(data2$underproduction_low < 0))
+mean(data2$underproduction_mean)
+
+length(which(data1$underproduction_low < 0))
+mean(data1$underproduction_mean)
+
+length(which(data3$underproduction_low < 0))
+mean(data3$underproduction_mean)
+
+length(which(data4$underproduction_low < 0))
+mean(data4$underproduction_mean)
+
+data1$mmt <- (((data1$collaborators * 2)+ data1$contributors) / (data1$contributors + data1$collaborators)) - 1
+mean(data1$mmt)
+hist(data1$mmt, probability = TRUE)
+
+data1$new.age <- as.numeric(cut(data1$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
+table(data1$new.age)
+data1$new.age.factor <- as.factor(data1$new.age)
+hist(data1$new.age)
+age1 <- c(0.39369, 0.239271, 0.2096806, 0.1573584)
+
+d1label <- rep("Overall", length(data1$new.age.factor))
+d1per <- data1$new.age
+d1per[d1per==1] <- 39.37
+d1per[d1per==2] <- 23.93
+d1per[d1per==3] <- 20.97
+d1per[d1per==4] <- 15.74
+d1per.factor<- as.factor(d1per)
+
+data5 <- (d1label)
+
+data2$new.age <- as.numeric(cut(data2$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
+table(data2$new.age)
+data2$new.age.factor <- as.factor(data2$new.age)
+hist(data2$new.age)
+age2 <- c(0.5675676, 0.1981982, 0.1681682, 0.06606607)
+
+d2label <- rep("Expanded Contrib.", length(data2$new.age.factor))
+d2per <- data2$new.age
+d2per[d2per==1] <- 56.76
+d2per[d2per==2] <- 19.82
+d2per[d2per==3] <- 16.82
+d2per[d2per==4] <- 06.61
+d2per.factor <- as.factor(d2per)
+
+data3$new.age <- as.numeric(cut(data3$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
+table(data3$new.age)
+data3$new.age.factor <- as.factor(data3$new.age)
+hist(data3$new.age)
+age3 <-c(0.2556818, 0.2954545, 0.2405303, 0.2083333)
+d3label <- rep("Contrib. Files", length(data3$new.age.factor))
+d3per <- data3$new.age
+d3per[d3per==1] <- 25.57
+d3per[d3per==2] <- 29.55
+d3per[d3per==3] <- 24.05
+d3per[d3per==4] <- 20.83
+d3per.factor <- as.factor(d3per)
+
+data4$new.age <- as.numeric(cut(data4$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
+table(data4$new.age)
+data4$new.age.factor <- as.factor(data4$new.age)
+hist(data4$new.age)
+age4 <- c(0.5, 0.125, 0.125, 0.25)
+d4label <- rep("Contrib. Rosters", length(data4$new.age.factor))
+d4per <- data4$new.age
+d4per[d4per==1] <- 57.14
+d4per[d4per==2] <- 14.29
+d4per[d4per==3] <- 14.29
+d4per[d4per==4] <- 28.57
+d4per.factor <- as.factor(d4per)
+
+all_per <- c(d1per.factor, d2per.factor, d3per.factor, d4per.factor)
+all_persss <- c(d1per, d2per, d3per, d4per)
+all_labels <- c(d1label, d2label, d3label, d4label)
+all_age_groups <- c(data1$new.age.factor, data2$new.age.factor, data3$new.age.factor, data4$new.age.factor)
+
+d5 <- data.frame(labels = all_labels,
+                 age_groups = all_age_groups,
+                 per = all_per,
+                 persss = all_persss)
+d5 <- na.omit(d5)
+
+g <- ggplot(d5, aes(fill=forcats::fct_rev(age_groups), y = 1, x=forcats::fct_rev(labels))) + 
+  geom_bar(position="fill", stat="identity") + 
+  scale_fill_discrete(name = "Project Age Group", labels = c("15-16y", "12-15y", "9-12y", "0-9y"), guide = guide_legend(reverse = TRUE)) +
+  xlab("Dataset") +
+  ylab("Age Grouping Percentage") +
+  theme_bw()+ 
+  theme(axis.text.x = element_text(angle = 0), legend.position="top")
+g
+
+sdata1$new_milestones <- as.numeric(data1$milestone_count > 0) + 1
+data1$new.formal.score <- data1$mmt / (data1$new_milestones/data1$new.age)
+
+mmtmodel1 <- lm(underproduction_mean ~ mmt + as.factor(new.age), data=data1)
+summary(mmtmodel1)
+
+agemodel1 <- lm(mmt ~ age_of_project, data=data1)
+summary(agemodel1)
+
+fsmodel2 <- lm(underproduction_mean ~ new.formal.score, data=data1)
+summary(fsmodel2)
+
+g <- ggplot(data1, aes(x=mmt, y=underproduction_mean)) +
+  geom_point() +
+  geom_smooth(method='lm', formula= y~x) +
+  xlab("MMT") +
+  ylab("Underproduction Factor") +
+  theme_bw() 
+g
+
+#shows the cross-age downward slopes for all underproduction averages in the face of MMT
+g3 <- ggplot(data1, aes(x=mmt, y=underproduction_mean)) +
+  geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
+    method='lm', formula= y~x) +
+  xlab("MMT") +
+  ylab("Underproduction Factor") +
+  theme_bw() 
+g3
+
+cor.test(data1$mmt, data1$new.age)
+age_data <- subset(data1, !is.na(new.age))
+g2 <- ggplot(age_data, aes(x=factor(new.age), y=mmt))+
+  geom_boxplot()
+g2
diff --git a/R/new_mmt_underprod_plot.png b/R/new_mmt_underprod_plot.png
new file mode 100644
index 0000000..f1d5b39
Binary files /dev/null and b/R/new_mmt_underprod_plot.png differ
diff --git a/R/newmmt-underprod-plot.png b/R/newmmt-underprod-plot.png
index 84e9010..c183ab6 100644
Binary files a/R/newmmt-underprod-plot.png and b/R/newmmt-underprod-plot.png differ
diff --git a/R/plotting_age.R b/R/plotting_age.R
new file mode 100644
index 0000000..9ad939c
--- /dev/null
+++ b/R/plotting_age.R
@@ -0,0 +1,9 @@
+rm(list=ls())
+set.seed(424242) 
+
+library(readr)
+library(ggplot2)
+data1 <- read_csv('../age_percentages.csv',show_col_types = FALSE)
+
+head(data1)
+
diff --git a/R/temp-mmt-colors.png b/R/temp-mmt-colors.png
new file mode 100644
index 0000000..34159b0
Binary files /dev/null and b/R/temp-mmt-colors.png differ
diff --git a/R/temp-temp.png b/R/temp-temp.png
new file mode 100644
index 0000000..0daee90
Binary files /dev/null and b/R/temp-temp.png differ
diff --git a/age_percentages.csv b/age_percentages.csv
new file mode 100644
index 0000000..a1b9cc4
--- /dev/null
+++ b/age_percentages.csv
@@ -0,0 +1,5 @@
+setname,age1,age2,age3,age4
+data1,0.39369,0.239271,0.2096806,0.1573584
+data2,0.5675676,0.1981982,0.1681682,0.06606607
+data3,0.2556818,0.2954545,0.2405303,0.2083333
+data4,0.5714286,0.1428571,0.1428571,0.2857143
\ No newline at end of file
diff --git a/kkex-github-api-key.rtf b/kkex-github-api-key.rtf
deleted file mode 100644
index 699d182..0000000
--- a/kkex-github-api-key.rtf
+++ /dev/null
@@ -1,8 +0,0 @@
-{\rtf1\ansi\ansicpg1252\cocoartf2708
-\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
-{\colortbl;\red255\green255\blue255;}
-{\*\expandedcolortbl;;}
-\margl1440\margr1440\vieww11520\viewh8400\viewkind0
-\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
-
-\f0\fs24 \cf0 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}
\ No newline at end of file