162 lines
7.1 KiB
R
162 lines
7.1 KiB
R
library(tidyverse)
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c1_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case1/062725_c1_cleaned_phab.csv"
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c1_input_df <- read.csv(c1_count , header = TRUE)
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c2_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/070125_c2_title_cleaned.csv"
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c2_input_df <- read.csv(c2_count , header = TRUE)
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c3_count <-"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case3/070125_c3_title_cleaned.csv"
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c3_input_df <- read.csv(c3_count , header = TRUE)
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#getting the relative weeks to the publication date
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relative_week <- function(date, ref_date) {
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as.integer(as.numeric(difftime(date, ref_date, units = "days")) %/% 7)
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}
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#phase of feature deployments
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# pre opt-in (0)
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# opt-in beta (1)
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# post-announcement pre-deployment (2)
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# post-deployment opt-out (3)
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# c1 key dates
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# opt-in = as.Date("2012-12-11)
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# deployment announcement = as.Date("2013-06-06")
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# deployment_date <- as.Date("2013-07-01")
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library(dplyr)
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c1_input_df <- c1_input_df |>
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mutate(date_created = as.numeric(as.POSIXct(date_created, tz = "UTC"))) |>
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mutate(source = "c1") |>
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mutate(phase = case_when(
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date_created < as.numeric(as.POSIXct("2012-12-11", tz = "UTC")) ~ 0, # pre opt-in
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date_created >= as.numeric(as.POSIXct("2012-12-11", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2013-06-06", tz = "UTC")) ~ 1, # opt-in beta
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date_created >= as.numeric(as.POSIXct("2013-06-06", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2013-07-01", tz = "UTC")) ~ 2, # post-announcement pre-deployment
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date_created >= as.numeric(as.POSIXct("2013-07-01", tz = "UTC"))~ 3 # post-deployment opt-out
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)) |>
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mutate(author_closer = AuthorPHID %in% CloserPHID,
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same_author = AuthorPHID == CloserPHID) |>
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mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2013-10-01", tz = "UTC"))) |>
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mutate(week_index = relative_week(date_created, as.Date("2013-07-01")))
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# c2 key dates
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# opt-in = as.Date("2011-10-03)
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# deployment announcement = as.Date("2013-08-01")
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# deployment_date <- as.Date("2013-08-28")
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c2_input_df <- c2_input_df |>
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mutate(date_created = as.numeric(as.POSIXct(date_created, tz = "UTC"))) |>
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mutate(source = "c2") |>
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mutate(phase = case_when(
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date_created < as.numeric(as.POSIXct("2011-10-03", tz = "UTC")) ~ 0, # pre opt-in
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date_created >= as.numeric(as.POSIXct("2011-10-03", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2013-08-01", tz = "UTC")) ~ 1, # opt-in beta
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date_created >= as.numeric(as.POSIXct("2013-08-01", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2013-08-28", tz = "UTC")) ~ 2, # post-announcement pre-deployment
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date_created >= as.numeric(as.POSIXct("2013-08-28", tz = "UTC"))~ 3 # post-deployment opt-out
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)) |>
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mutate(author_closer = AuthorPHID %in% CloserPHID,
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same_author = AuthorPHID == CloserPHID) |>
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mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2013-11-27", tz = "UTC"))) |>
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mutate(week_index = relative_week(date_created, as.Date("2013-08-28")))
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# c3 key dates
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# opt-in = as.Date("2013-08-01)
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# deployment announcement = as.Date("2015-06-12")
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# deployment_date <- as.Date("2015-07-02")
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c3_input_df <- c3_input_df %>%
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mutate(date_created = as.numeric(as.POSIXct(date_created, tz = "UTC"))) |>
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mutate(source = "c3") |>
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mutate(phase = case_when(
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date_created < as.numeric(as.POSIXct("2013-08-01", tz = "UTC")) ~ 0, # pre opt-in
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date_created >= as.numeric(as.POSIXct("2013-08-01", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2015-06-12", tz = "UTC")) ~ 1, # opt-in beta
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date_created >= as.numeric(as.POSIXct("2015-06-12", tz = "UTC")) & date_created < as.numeric(as.POSIXct("2015-07-02", tz = "UTC")) ~ 2, # post-announcement pre-deployment
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date_created >= as.numeric(as.POSIXct("2015-07-02", tz = "UTC"))~ 3 # post-deployment opt-out
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)) |>
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mutate(author_closer = AuthorPHID %in% CloserPHID,
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same_author = AuthorPHID == CloserPHID) |>
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mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2015-10-02", tz = "UTC"))) |>
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mutate(week_index = relative_week(date_created, as.Date("2015-07-02")))
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# Combine the dataframes into one
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combined_df <- bind_rows(c1_input_df, c2_input_df, c3_input_df)
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#write.csv(combined_df, "~/p2/071425_master_discussion_data.csv", row.names = FALSE)
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modal_verbs <- c("can", "could", "may", "might", "must", "shall", "should", "will", "would", "ought")
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modal_subset <- c('should', 'ought', 'must')
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whatever_subset <- c('user')
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combined_df <- combined_df %>%
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group_by(AuthorPHID, source) %>%
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arrange(date_created, .by_group = TRUE) %>%
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mutate(
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task_index_prev = cumsum(comment_type == "task_description") - (comment_type == "task_description"),
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comment_index_prev = cumsum(comment_type == "task_subcomment") - (comment_type == "task_subcomment"),
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author_prior_phab_contrib = task_index_prev + comment_index_prev
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) %>%
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ungroup() |>
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rowwise() %>%
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mutate(
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modal_verb_count = sum(str_detect(
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str_to_lower(comment_text),
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paste0("\\b", modal_verbs, "\\b", collapse = "|")
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)),
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modal_subset_count = sum(str_detect(
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str_to_lower(comment_text),
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paste0("\\b", modal_subset, "\\b", collapse = "|")
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)),
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user_count = sum(str_detect(
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str_to_lower(comment_text),
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paste0("\\b", whatever_subset, "\\b", collapse = "|")
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))
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) %>%
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ungroup() |>
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filter(week_index <= 13)
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combined_task_df <- combined_df %>%
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add_count(TaskPHID, name = "task_event_comment_count") |>
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filter(comment_type == "task_description") |>
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mutate(time_to_close = date_closed - date_created,
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time_to_close_hours = as.numeric(difftime(date_closed, date_created, units = "hours"))
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) |>
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group_by(AuthorPHID, source) %>%
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arrange(date_created, .by_group = TRUE) %>% # recommended: order by date_created
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mutate(author_task_index = row_number()) %>%
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ungroup()
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library(dplyr)
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combined_task_df <- combined_task_df |>
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group_by(source) %>%
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mutate(
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time_to_close_percentile = 1- percent_rank(time_to_close_hours),
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comment_count_percentile = percent_rank(task_event_comment_count),
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author_task_percentile = percent_rank(task_index_prev)
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# inverting it so that higher percentile is faster
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) %>%
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ungroup()
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ggplot(combined_task_df, aes(x = author_task_percentile, y =priority_score, color = source)) +
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geom_point(alpha = 0.6) + # Points, with some transparency
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geom_smooth(method = "loess", se = TRUE) + # LOESS curve, no confidence band
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theme_minimal() +
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facet_grid(source ~ author_closer)
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library(ggdist)
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ggplot(combined_task_df, aes(x=phase, y=comment_count_percentile)) +
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stat_slabinterval() +
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theme_minimal()+
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facet_grid(source ~ AuthorWMFAffil)
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closed_combined_task_df <- combined_task_df |>
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filter(!is.na(closed_relevance))
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ggplot(combined_task_df, aes(x=time_to_close_percentile, y=priority_score)) +
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geom_point(alpha = 0.6) +
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geom_smooth(method = "loess", se = TRUE) + # LOESS curve, no confidence band# Points, with some transparency
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theme_minimal()+
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facet_grid(source ~ author_closer)
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