expanding matching for data
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R/EDA.R
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R/EDA.R
@ -36,7 +36,7 @@ t.test(df1$up.fac.mean)
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# -0.1961401 -0.1647757
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df$mmt <- (df$contributors + (2 * df$collaborators)) / (df$contributors + df$collaborators)
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df$old_mmt <- (df$contributors) / (df$contributors + df$collaborators)
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t.test(df$mmt)
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t.test(df$old_mmt)
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# 95 percent confidence interval:
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# 1.610638 1.684438
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#
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@ -24,17 +24,20 @@ data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data2 <- read_csv('../inst_all_packages_full_results.csv')
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#d$nd <- to_logical(d$not.damaging, custom_true=c("Y"))
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#levels(d$source) <- c("IP-based Editors", "New Editors", "Registered Editors", "Tor-based Editors")
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data1$up.fac.mean <- as.numeric(data2$up.fac.mean[match(data1$pkg, data2$pkg)])
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data1$milestones <- as.numeric(data1$milestones > 0) + 1
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python_labeled <- as.numeric(data2$up.fac.mean[match(paste('python',tolower(data1$pkg), sep = "-"), data2$pkg)])
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same_labeled <- as.numeric(data2$up.fac.mean[match(tolower(data1$pkg), data2$pkg)])
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data1$up.fac.mean <- pmin(python_labeled, same_labeled, na.rm=TRUE)
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data1$milestones <- as.numeric(data1$milestones > 0)
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# (2) - Run the model on the pilot data
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data1$formal.score <- data1$mmt / (data1$milestones/data1$age)
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table(data1$milestones)
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hist(data1$mmt) #inequality of participation
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hist(data1$old_mmt) #inequality of participation
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hist(data1$formal.score)
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hist(data1$age/365)
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kmodel1 <- lm(up.fac.mean ~ mmt, data=data1)
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summary(kmodel1)
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kmodel1 <- lm(up.fac.mean ~ old_mmt, data=data1)
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summary(kmodel1)
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kmodel1 <- lm(up.fac.mean ~ formal.score, data=data1)
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summary(kmodel1)
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hist(data1$formal.score)
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@ -48,7 +51,7 @@ g <- ggplot(data1, aes(x=formal.score, y=up.fac.mean)) +
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geom_smooth()
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g
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data2 <- subset(data1, (data1$age / 365) < 9 )
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data2 <- subset(data1, (data1$age / 365) < 14 )
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hist(data2$age)
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g <- ggplot(data2, aes(x=formal.score, y=up.fac.mean)) +
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geom_point() +
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