513 lines
28 KiB
R
513 lines
28 KiB
R
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
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theme_bw()
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p
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
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expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
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}
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windowed_sample_data <- expanded_sample_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
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theme_bw()
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p
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (week_offset|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
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theme_bw()
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p
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs
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mrg_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=mrg_actions_data, REML=FALSE)
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summary(mrg_model)
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summary(all_model)
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)||upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
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expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
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}
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windowed_sample_data <- expanded_sample_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=mean(predict(test_model)), show.legend = FALSE)) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model), show.legend = FALSE)) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=mean(predict(test_model))), show.legend = FALSE) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=median(predict(test_model))), show.legend = FALSE) +
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theme_bw()
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p
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset) + scaled_project_age|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 22), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
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expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
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}
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windowed_sample_data <- expanded_sample_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
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expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
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}
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windowed_sample_data <- expanded_sample_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
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expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
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}
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windowed_sample_data <- expanded_sample_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
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windowed_sample_data$week_offset <- windowed_sample_data$week - 27
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all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
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#test model
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test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
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summary(test_model)
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#plot results
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p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
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geom_point(size=3, show.legend = FALSE) +
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geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
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theme_bw()
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p
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summary(all_model)
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all_residuals <- residuals(all_model)
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qqnorm(all_residuals)
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mrg_residuals <- residuals(mrg_model)
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qqnorm(mrg_residuals)
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summary(all_model)
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##end of the model testing and plotting section
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all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=TRUE)
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summary(all_model)
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##end of the model testing and plotting section
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all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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summary(all_model)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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#scale the age numbers
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windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
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windowed_data$week_offset <- windowed_data$week - 27
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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#find some EDA to identify which types of models might be the best for this
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hist(all_actions_data$count)
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#find some EDA to identify which types of models might be the best for this
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hist(log1p(all_actions_data$count))
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#find some EDA to identify which types of models might be the best for this
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hist(log(all_actions_data$count))
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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##end of the model testing and plotting section
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all_model <- lmer(log(count) ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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all_actions_data$logged_count <- log(all_actions_data$count)
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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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##end of the model testing and plotting section
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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summary(all_model)
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all_residuals <- residuals(all_model)
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qqnorm(all_residuals)
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##end of the model testing and plotting section
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all_model <- lmer(logged_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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##end of the model testing and plotting section
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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##end of the model testing and plotting section
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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##end of the model testing and plotting section
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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#making some random data
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sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
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expanded_sample_data <- expand_timeseries(sampled_data[1,])
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for (i in 2:nrow(sampled_data)){
|
|
expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
|
|
}
|
|
windowed_sample_data <- expanded_sample_data |>
|
|
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
|
mutate(D = ifelse(week > 27, 1, 0))
|
|
windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
|
|
windowed_sample_data$week_offset <- windowed_sample_data$week - 27
|
|
all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
|
|
#test model
|
|
test_model <- lmer(log1p_count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
|
|
all_actions_sample_data$log1p_count <- log1p(all_actions_sample_data$count)
|
|
#test model
|
|
test_model <- lmer(log1p_count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link), data=all_actions_sample_data, REML=FALSE)
|
|
summary(test_model)
|
|
#plot results
|
|
p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
|
|
geom_point(size=3, show.legend = FALSE) +
|
|
geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
|
|
theme_bw()
|
|
p
|
|
#plot results
|
|
p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
|
|
geom_point(size=3, show.legend = FALSE) +
|
|
geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
|
|
theme_bw()
|
|
p
|
|
library(merTools)
|
|
ICC(outcome="count", group="week", data=all_actions_data)
|
|
ICC(outcome="count", group="upstream_vcs_link", data=all_actions_data)
|
|
ICC(outcome="count", group="week", data=all_actions_data)
|
|
# this is the file with the lmer multi-level rddAnalysis
|
|
library(tidyverse)
|
|
library(plyr)
|
|
# 0 loading the readme data in
|
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
|
# 1 preprocessing
|
|
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
|
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
|
readme_df <- readme_df[,col_order]
|
|
readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
|
|
readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
|
|
readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
|
|
readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
|
|
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
|
readme_df = readme_df[,!(names(readme_df) %in% drop)]
|
|
# 2 some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
|
|
longer <- project_row |>
|
|
pivot_longer(cols = starts_with("ct"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count)
|
|
longer$observation_type <- gsub("^.*_", "", longer$window)
|
|
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
|
longer$count <- as.numeric(longer$count)
|
|
#longer <- longer[which(longer$observation_type == "all"),]
|
|
return(longer)
|
|
}
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
for (i in 2:nrow(readme_df)){
|
|
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
|
|
}
|
|
#filter out the windows of time that we're looking at
|
|
window_num <- 8
|
|
windowed_data <- expanded_data |>
|
|
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
|
mutate(D = ifelse(week > 27, 1, 0))
|
|
#scale the age numbers
|
|
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
|
|
windowed_data$week_offset <- windowed_data$week - 27
|
|
#separate out the cleaning d
|
|
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
|
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
|
all_actions_data$logged_count <- log(all_actions_data$count)
|
|
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
|
# 3 rdd in lmer analysis
|
|
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
|
|
library(lme4)
|
|
##end of the model testing and plotting section
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
|
summary(all_model)
|
|
(
|
|
##end of the model testing and plotting section
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer ="Nelder_Mead"))
|
|
##end of the model testing and plotting section
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer ="Nelder_Mead"))
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer="optimx",
|
|
optCtrl=list(method="nlminb")))
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(all_model)
|
|
all0_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
|
summary(all0_model)
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
all_model.ranef <- ranef(all_model)
|
|
str(all_model.ranef)
|
|
head(all_model.ranef)
|
|
all_actions_data$D_effect_quart <- ntile(all_model.ranef$D, 4)
|
|
head(all_model.ranef)
|
|
all_model.ranef <- random.effects(all_model)
|
|
head(as.data.frame(all_model.ranef))
|
|
head(all_model_ranef)
|
|
all_model_ranef <- as.data.frame(ranef(all_model))
|
|
head(all_model_ranef)
|
|
d_effect_ranef_all <- subset(all_model_ranef, term="D")
|
|
d_effect_ranef_all <- all_model_ranef[all_model_ranef$term="D",]
|
|
d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
|
|
View(d_effect_ranef_all)
|
|
d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
|
|
View(d_effect_ranef_all)
|
|
# mrg behavior for this
|
|
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(mrg_model)
|
|
library(tidyverse)
|
|
library(plyr)
|
|
#get the contrib data instead
|
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
|
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
|
|
#some preprocessing and expansion
|
|
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
|
contrib_df <- contrib_df[,col_order]
|
|
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
|
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
|
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
|
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
|
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
|
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
|
|
# 2 some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
|
|
longer <- project_row |>
|
|
pivot_longer(cols = starts_with("ct"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count)
|
|
longer$observation_type <- gsub("^.*_", "", longer$window)
|
|
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
|
longer$count <- as.numeric(longer$count)
|
|
#longer <- longer[which(longer$observation_type == "all"),]
|
|
return(longer)
|
|
}
|
|
expanded_data <- expand_timeseries(contrib_df[1,])
|
|
for (i in 2:nrow(contrib_df)){
|
|
expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
|
|
}
|
|
#filter out the windows of time that we're looking at
|
|
window_num <- 8
|
|
windowed_data <- expanded_data |>
|
|
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
|
mutate(D = ifelse(week > 27, 1, 0))
|
|
#scale the age numbers
|
|
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
|
|
windowed_data$week_offset <- windowed_data$week - 27
|
|
#separate out the cleaning d
|
|
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
|
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
|
|
#EDA?
|
|
#TKTK ---------------------
|
|
#imports for models
|
|
library(lme4)
|
|
library(optimx)
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
#EDA?
|
|
hist(log(all_actions_data$count))
|
|
all_actions_data$logged_count <- log(all_actions_data$count)
|
|
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(logged_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(all_model)
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(all_model)
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
|
|
week_offset
|
|
#models -- TKTK need to be fixed
|
|
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(all_model)
|
|
# mrg behavior for this
|
|
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
# mrg behavior for this
|
|
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(mrg_model)
|
|
# mrg behavior for this
|
|
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
# now for merge
|
|
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
|
|
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
|
|
# mrg behavior for this
|
|
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
|
|
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
|
|
summary(mrg_model)
|