42 lines
1.3 KiB
R
42 lines
1.3 KiB
R
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library(dplyr)
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df<-read.csv('~/Research/kkex_repo/power_data_110923.csv')
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df1 <- read.csv('/data_ext/users/kcz1100/kaylea_dissertation/collabnetXS/raw_data/inst_all_packages_full_results.csv')
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hist(df$age/365) #there's a big bump at 9 years, why?
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hist(df$contributors) #skewed
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hist(log(df$contributors)) #better
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hist(df$collaborators) #skewed
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hist(log(df$collaborators)) #better
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hist(df$milestones)
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#one of many ways to generate a dichotomous variable
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df$uses_milestones <- case_when(df$milestones != 0 ~ TRUE,
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.default = FALSE)
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table(df$uses_milestones)
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#playing around
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cor.test(df$contributors, as.numeric(df$uses_milestones))
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cor.test(df$collaborators, as.numeric(df$uses_milestones))
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cor.test(df$contributors / df$collaborators, df$age / 365)
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t.test(df$age)
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#95 percent confidence interval:
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# 2793.638 3066.417
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t.test(df$contributors)
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#95 percent confidence interval:
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# 27.6519 154.6866
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t.test(df$collaborators)
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#95 percent confidence interval:
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# 50.01884 96.77090
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t.test(df1$up.fac.mean)
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#95 percent confidence interval:
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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$collaborators)
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t.test(df$old_mmt)
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# 95 percent confidence interval:
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# 1.610638 1.684438
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# |