116 lines
3.8 KiB
R
116 lines
3.8 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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library(dplyr)
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# Add a column to each dataframe to label them
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c1_input_df <- c1_input_df |> mutate(source = "c1")
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c2_input_df <- c2_input_df %>% mutate(source = "c2")
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c3_input_df <- c3_input_df %>% mutate(source = "c3")
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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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combined_task_df <- combined_df %>%
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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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ggplot(combined_task_df, aes(x = source, y = time_to_close_hours, fill = AuthorWMFAffil)) +
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ggdist::stat_halfeye(
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adjust = 0.5,
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width = 1.5, # increase width
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scale = 8.8, # new: increase scale for fatter density
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.width = 0,
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justification = 0,
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point_colour = NA
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) +
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facet_wrap(~ AuthorWMFAffil) +
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labs(
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title = "Distribution Plot: Time to Close by AuthorWMFAffil and Source",
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x = "Source",
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y = "Time to Close (hours)"
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) +
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theme_minimal()
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# Calculate proportions of status within each (AuthorWMFAffil, source) group
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prop_df <- combined_task_df %>%
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group_by(AuthorWMFAffil, source, status) %>%
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summarize(n = n(), .groups = "drop") %>%
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group_by(AuthorWMFAffil, source) %>%
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mutate(prop = n / sum(n))
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# Plot: filled bar plot (proportion)
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ggplot(prop_df, aes(x = source, y = prop, fill = status)) +
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geom_col(position = "fill") +
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facet_wrap(~ AuthorWMFAffil) +
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scale_y_continuous(labels = scales::percent) +
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labs(
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title = "Proportion of Phabricator Task Status by Affiliation and Case",
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x = "Source",
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y = "Proportion",
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fill = "Status"
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) +
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theme_minimal()
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library(stringr)
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# modal verbs
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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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# 1. Count modal verbs in each comment_text
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combined_task_df <- combined_task_df %>%
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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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whatever_subset_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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# 3. Plot (e.g., bar plot of mean modal verbs per group)
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ggplot(combined_task_df, aes(x = source, y = modal_verb_count, fill = AuthorWMFAffil)) +
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geom_violin(trim = FALSE, position = position_dodge(width = 0.8), alpha = 0.6) +
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stat_summary(
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fun = mean,
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geom = "point",
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shape = 23,
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size = 3,
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color = "black",
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fill = "yellow",
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position = position_dodge(width = 0.8)
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) +
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facet_wrap(~ AuthorWMFAffil) +
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labs(
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title = "Distribution and Mean of 'user' by Affiliation and Source",
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x = "Source",
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y = "Count"
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) +
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theme_minimal()
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binned_task_df <- combined_task_df |>
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mutate(description_length = nchar(comment_text))
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ggplot(binned_task_df, aes(x = time_to_close_hours, 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() |