updating with new EDA

mgaughan-rstudio-server_27419348.out

@@ -0,0 +1,18 @@
+1. SSH tunnel from your workstation using the following command:
+
+   ssh -N -L 8787:n3439:50819 mjilg@klone.hyak.uw.edu
+
+   and point your web browser to http://localhost:8787
+
+2. log in to RStudio Server using the following credentials:
+
+   user: mjilg
+   password: lM83HdgeT310p2tkyoCk
+
+When done using RStudio Server, terminate the job by:
+
+1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
+2. Issue the following command on the login node:
+
+      scancel -f 27419348
+slurmstepd: error: *** JOB 27419348 ON n3439 CANCELLED AT 2025-07-07T13:08:38 ***

p2_EDA/phab_weekly_bins.R

@@ -34,6 +34,9 @@ c1_input_df <- c1_input_df |>
     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
     date_created >= as.numeric(as.POSIXct("2013-07-01", tz = "UTC"))~ 3                             # post-deployment opt-out
   )) |>
+  mutate(author_closer = AuthorPHID %in% CloserPHID,
+         same_author = AuthorPHID == CloserPHID) |>
+  mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2013-10-01", tz = "UTC"))) |>
   mutate(week_index = relative_week(date_created, as.Date("2013-07-01")))
 
 
@@ -51,6 +54,9 @@ c2_input_df <- c2_input_df |>
     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
     date_created >= as.numeric(as.POSIXct("2013-08-28", tz = "UTC"))~ 3                             # post-deployment opt-out
   )) |>
+  mutate(author_closer = AuthorPHID %in% CloserPHID,
+         same_author = AuthorPHID == CloserPHID) |>
+  mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2013-11-27", tz = "UTC"))) |>
   mutate(week_index = relative_week(date_created, as.Date("2013-08-28")))
 
 # c3 key dates 
@@ -66,6 +72,9 @@ c3_input_df <- c3_input_df %>%
     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
     date_created >= as.numeric(as.POSIXct("2015-07-02", tz = "UTC"))~ 3                             # post-deployment opt-out
   )) |>
+  mutate(author_closer = AuthorPHID %in% CloserPHID,
+         same_author = AuthorPHID == CloserPHID) |>
+  mutate(closed_relevance = date_closed <= as.numeric(as.POSIXct("2015-10-02", tz = "UTC"))) |>
   mutate(week_index = relative_week(date_created, as.Date("2015-07-02")))
 
 # Combine the dataframes into one
@@ -80,7 +89,8 @@ combined_df <- combined_df %>%
   arrange(date_created, .by_group = TRUE) %>%
   mutate(
     task_index_prev = cumsum(comment_type == "task_description") - (comment_type == "task_description"),
-    comment_index_prev = cumsum(comment_type == "task_subcomment") - (comment_type == "task_subcomment")
+    comment_index_prev = cumsum(comment_type == "task_subcomment") - (comment_type == "task_subcomment"),
+    author_prior_phab_contrib = task_index_prev + comment_index_prev
   ) %>%
   ungroup() |> 
   rowwise() %>%
@@ -103,52 +113,47 @@ combined_df <- combined_df %>%
 
 
 combined_task_df <- combined_df %>% 
-  add_count(TaskPHID, name = "TaskPHID_count") |>
+  add_count(TaskPHID, name = "task_event_comment_count") |>
   filter(comment_type == "task_description") |>
   mutate(time_to_close = date_closed - date_created,
          time_to_close_hours = as.numeric(difftime(date_closed, date_created, units = "hours"))
   ) |>
   group_by(AuthorPHID, source) %>%
   arrange(date_created, .by_group = TRUE) %>% # recommended: order by date_created
-  mutate(task_index = row_number()) %>%
+  mutate(author_task_index = row_number()) %>%
   ungroup()
 
-ggplot(combined_task_df, aes(x = week_index, y = priority_score, color = source)) +
-  geom_point(alpha = 0.6) +                # Points, with some transparency
-  geom_smooth(method = "loess", se = TRUE) + # LOESS curve, no confidence band
-  theme_minimal()        
+library(dplyr)
 
-library(stringr)
-
-# 1. Count modal verbs in each task comment_text
-combined_task_df <- combined_task_df %>%
-  rowwise() %>%
+combined_task_df <- combined_task_df |>
+  group_by(source) %>%
   mutate(
-    modal_verb_count = sum(str_detect(
-      str_to_lower(comment_text),
-      paste0("\\b", modal_verbs, "\\b", collapse = "|")
-    )),
-    modal_subset_count = sum(str_detect(
-      str_to_lower(comment_text),
-      paste0("\\b", modal_subset, "\\b", collapse = "|")
-    )),
-    user_count = sum(str_detect(
-      str_to_lower(comment_text),
-      paste0("\\b", whatever_subset, "\\b", collapse = "|")
-    ))
+    time_to_close_percentile = 1- percent_rank(time_to_close_hours),
+    comment_count_percentile = percent_rank(task_event_comment_count),
+    author_task_percentile = percent_rank(task_index_prev)
+    # inverting it so that higher percentile is faster
   ) %>%
   ungroup()
+
+ggplot(combined_task_df, aes(x = author_task_percentile, y =priority_score, color = source)) +
+  geom_point(alpha = 0.6) +                # Points, with some transparency
+  geom_smooth(method = "loess", se = TRUE) + # LOESS curve, no confidence band
+  theme_minimal() +
+  facet_grid(source ~ author_closer)
+
 library(ggdist)
-ggplot(combined_df, aes(x = week_index, y = modal_subset_count, color = source, linetype=AuthorWMFAffil)) +
-  geom_point(alpha=0.1) +             # Points, with some transparency
-  geom_smooth(method = "loess", se = FALSE) + 
-  theme_minimal()      
+
+ggplot(combined_task_df, aes(x=phase, y=comment_count_percentile)) +
+  stat_slabinterval() +
+  theme_minimal()+
+  facet_grid(source ~ AuthorWMFAffil)
 
 
+closed_combined_task_df <- combined_task_df |>
+  filter(!is.na(closed_relevance))
 
-combined_task_df_subset <- subset(combined_task_df, time_to_close_hours < 1000)
-
-ggplot(combined_task_df_subset, aes(x = TaskPHID_count, y = task_index, color = source)) +
-  geom_smooth(method = "loess", se = TRUE) + 
-  geom_point(alpha=0.1) +   
-  theme_minimal()
+ggplot(combined_task_df, aes(x=time_to_close_percentile, y=priority_score)) + 
+  geom_point(alpha = 0.6) + 
+  geom_smooth(method = "loess", se = TRUE) + # LOESS curve, no confidence band# Points, with some transparency
+  theme_minimal()+
+  facet_grid(source ~ author_closer)