moving to gitea
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mjgaughan
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R/.Rhistory
118
R/.Rhistory
@ -1,62 +1,3 @@
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longer <- longer %>%
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filter(week >= (26 - window_num) & week <= (26 + window_num))
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#testing out analysis below
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longer[which(longer$observation_type == "all"),] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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longer[which(longer$observation_type == "all"),] |>
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mutate(D = ifelse(week >= 26, 1, 0)) |>
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lm(formula = count ~ D * I(week - 26)) |>
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summary()
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longer[which(longer$observation_type == "all"),] |>
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select(count, week) |>
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mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
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ggplot(aes(x = week, y = count, color = D)) +
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geom_point() +
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geom_smooth(se = FALSE) +
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geom_vline(xintercept = 26)
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window_num <- 4
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longer <- longer %>%
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filter(week >= (26 - window_num) & week <= (26 + window_num))
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#testing out analysis below
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longer[which(longer$observation_type == "all"),] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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longer[which(longer$observation_type == "all"),] |>
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mutate(D = ifelse(week >= 26, 1, 0)) |>
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lm(formula = count ~ D * I(week - 26)) |>
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summary()
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longer[which(longer$observation_type == "all"),] |>
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select(count, week) |>
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mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
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ggplot(aes(x = week, y = count, color = D)) +
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geom_point() +
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geom_smooth(se = FALSE) +
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geom_vline(xintercept = 26)
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window_num <- 10
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longer <- longer %>%
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filter(week >= (26 - window_num) & week <= (26 + window_num))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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new_test <- readme_df[450,]
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longer <- new_test |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$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$count)
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window_num <- 10
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longer <- longer %>%
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filter(week >= (26 - window_num) & week <= (26 + window_num))
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@ -510,3 +451,62 @@ draft_model <- lmer(count ~ D * I(week - 26) + (1|as.factor(upstream_vcs_link)),
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draft_model <- lmer(count ~ D * I(week - 26) + (1|as.factor(upstream_vcs_link)), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_model)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$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", "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$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the timewindows
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window_num <- 8
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expanded_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week >= 26, 1, 0))
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# 3 rdd in lmer analysis
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library(lme4)
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draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_model)
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expanded_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week > 26, 1, 0))
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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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draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_model)
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View(expanded_data)
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draft_all_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_all_model)
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "mrg"),])
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summary(draft_mrg_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=TRUE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_all_model)
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summary(draft_all_model)
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