add sth
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# analysis based on mixed-effect regression model, where user login is the random effect
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library(lmerTest)
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data = read.csv(file="data_4_filtered_users.csv")
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data = data[!(data[['index']]==0),]
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data[['intervention']] = as.factor(data[['intervention']])
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m = lmer(
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formula=log(commit_num+0.5)~
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log(user_age+0.5) +
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log(history_commit_num+0.5) +
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log(history_pr_num+0.5) +
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log(history_issue_num+0.5) +
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log(history_issue_comment_num+0.5) +
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log(history_pr_review_num+0.5) +
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time +
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intervention +
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time_after_intervention +
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(1|login),
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verbose=TRUE,
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data=data
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)
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car::Anova(m, type=II)
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@ -71,6 +71,7 @@ gap_df['pr_review_num'] = -1
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min_start_at = min(gap_df['start_at'])
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for row in gap_df.itertuples():
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login = getattr(row, 'login')
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# 1.
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cur.execute("select created_at from github_user where login=%s", (login,))
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@ -80,7 +81,11 @@ for row in gap_df.itertuples():
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# 2.
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cur.execute("select sum(contribution_count) as history_commit_num from github_user_commits_per_day where date<%s and login=%s", (start_at,login))
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history_commit_num = int(cur.fetchone()['history_commit_num'])
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history_commit_num = cur.fetchone()['history_commit_num']
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if history_commit_num is None:
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history_commit_num = 0
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else:
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history_commit_num = int(history_commit_num)
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# 3.
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cur.execute("select count(*) as history_pr_num from github_user_pr where created_at<%s and login=%s", (start_at, login))
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@ -126,7 +131,11 @@ for row in gap_df.itertuples():
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# 10.
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end_at = getattr(row, 'end_at')
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cur.execute("select sum(contribution_count) as commit_num from github_user_commits_per_day where date>%s and date<=%s and login=%s", (start_at, end_at, login))
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commit_num = int(cur.fetchone()['commit_num'])
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commit_num = cur.fetchone()['commit_num']
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if commit_num is None:
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commit_num = 0
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else:
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commit_num = int(commit_num)
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# 11.
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cur.execute("select count(*) as pr_num from github_user_pr where created_at>%s and created_at<=%s and login=%s", (start_at, end_at, login))
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