library(IRdisplay)

display_html(
'<script>  
code_show=true; 
function code_toggle() {
  if (code_show){
    $(\'div.input\').hide();
  } else {
    $(\'div.input\').show();
  }
  code_show = !code_show
}  
$( document ).ready(code_toggle);
</script>
  <form action="javascript:code_toggle()">
    <input type="submit" value="Click here to toggle on/off the raw code.">
 </form>'
)

shhh <- function(expr) suppressPackageStartupMessages(suppressWarnings(suppressMessages(expr)))
shhh({
    library(tidyverse)
   # Modeling 
    library(brms)
    library(lme4)
    library(tidybayes)
    set.seed(5)
    # Tables:
    library(gt)
    library(gtsummary)
})

options(repr.plot.width = 15, repr.plot.height = 10)

# collect all new topic tool attempts and saves
# Add a join with talk_page_edit to ensure all saved edits are included due to bug https://phabricator.wikimedia.org/T305541
query <-
"
--find all edit attempts
WITH edit_attempts AS (
    SELECT
      wiki AS wiki,
      event.user_id as user_id,
      event.editing_session_id as edit_attempt_id,
      event.bucket AS experiment_group,
      event.is_oversample AS is_oversample,
      event.integration AS editing_method,
      If(event.integration == 'discussiontools', 1, 0) AS new_topic_tool_used,
      CASE
           WHEN event.init_type = 'section' AND event.integration == 'discussiontools' THEN 'new_topic_tool'
           WHEN event.init_type = 'section' AND event.integration == 'page' AND event.init_mechanism IN ('url-new', 'new') THEN 'new_section_link'
          ELSE 'NA' -- check to make sure all edit types accounted for in above list
          END AS section_edit_type,
          event.user_editcount AS experience_level
    FROM event.editattemptstep
    WHERE
-- only in participating wikis
    wiki IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
    'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
    'ukwiki', 'viwiki')
-- since deployment
    AND year = 2022
    AND ((month = 01 and day >= 27) OR (month = 02) OR
     (month = 03 and day <= 25))
  -- remove bots
    AND useragent.is_bot = false
-- look at only desktop events
    AND event.platform = 'desktop'
-- review all talk namespaces
    AND event.page_ns % 2 = 1
-- only users in AB test
    AND event.bucket IN ('test', 'control')
-- only registered user
    AND event.user_id != 0
    AND event.action = 'init'
-- discard VE/Wikieditor edits to create new page or reply tool edits
    AND NOT (
 -- not a reply tool edit
  (event.init_type = 'page' AND event.integration = 'discussiontools') OR
 -- not an wikitext edit to create a new page
   (event.init_type = 'page' AND event.init_mechanism IN ('url-new', 'new') AND event.integration = 'page') OR
 -- not a corrective edit to an existing section
   (event.init_type = 'section' AND event.init_mechanism IN ('click', 'url') AND event.integration == 'page') OR
-- not a full page edit
   (event.init_type = 'page' AND event.init_mechanism IN ('click', 'url') AND event.integration = 'page')
 )),

-- find all published comments
published_dt_new_topics AS (
    SELECT
    performer.user_id AS user_id,
    session_id AS edit_save_id,
    `database` AS wiki
    FROM event.mediawiki_talk_page_edit
    WHERE
    year = 2022
   AND ((month = 01 and day >= 27) OR (month = 02) OR
 (month = 03 and day <= 25))
   -- only in participating wikis
    AND `database` IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
    'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
    'ukwiki', 'viwiki')
),

published_section_link_new_topics AS (
SELECT
     event.user_id as user_id,
     event.editing_session_id AS edit_save_id,
     wiki AS wiki
     FROM event.editattemptstep
     WHERE
 -- only in participating wikis
     wiki IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
     'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
     'ukwiki', 'viwiki')
     AND year = 2022
      AND ((month = 01 and day >= 27) OR (month = 02) OR
 (month = 03 and day <= 25))
   AND event.action = 'saveSuccess'
)


-- main query
SELECT
    eas.wiki,
    eas.user_id,
    edit_attempt_id,
    experiment_group,
    is_oversample,
    editing_method,
    new_topic_tool_used,
    section_edit_type,
-- was saved in either talk page edit or editattemptstep
    IF ((section_edit_type = 'new_topic_tool' AND (tpe_save.edit_save_id IS NOT NULL OR eas_save.edit_save_id IS NOT NULL))
        OR (section_edit_type = 'new_section_link' AND (tpe_save.edit_save_id IS NOT NULL OR eas_save.edit_save_id IS NOT NULL)), 1, 0) AS edit_success,
     experience_level
FROM edit_attempts eas
LEFT JOIN published_dt_new_topics tpe_save ON
    eas.edit_attempt_id = tpe_save.edit_save_id AND
    eas.wiki = tpe_save.wiki
LEFT JOIN published_section_link_new_topics eas_save ON
    eas.edit_attempt_id = eas_save.edit_save_id AND
    eas.wiki = eas_save.wiki
"


new_topic_attempts <-  wmfdata::query_hive(query)

# data reformatting and cleanup

#set factor levels with correct baselines
new_topic_attempts$section_edit_type <-
  factor(
    new_topic_attempts$section_edit_type,
    levels = c("NA", "full_page_edits", "new_section_link", "new_topic_tool"),
    labels = c("NA", "Full page edits", "Existing add new section link", "New topic tool")
  )

new_topic_attempts$edit_success <-
  factor(
    new_topic_attempts$edit_success,
    levels = c(0, 1),
    labels = c("Not Complete", "Complete")
  )


# reformat user-id and adjust to include wiki to account for duplicate user id instances.
# Users do not have the smae user_id on different wikis
new_topic_attempts$user_id <-
  as.character(paste(new_topic_attempts$user_id, new_topic_attempts$wiki, sep ="-"))

#clarfiy wiki names
new_topic_attempts <- new_topic_attempts %>%
  mutate(
    wiki = case_when(
      #clarfiy participating project names
      wiki == 'amwiki' ~ "Amharic Wikipedia",
      wiki == 'bnwiki' ~ "Bengali Wikipedia",
      wiki == 'zhwiki' ~ "Chinese Wikipedia",
      wiki == 'nlwiki' ~ 'Dutch Wikipedia',
      wiki == 'arzwiki' ~ 'Egyptian Wikipedia',
      wiki == 'frwiki' ~ 'French Wikipedia',
      wiki == 'hewiki' ~ 'Hebrew Wikipedia',
      wiki == 'hiwiki' ~ 'Hindi Wikipedia',
      wiki == 'idwiki' ~ 'Indonesian Wikipedia',
      wiki == 'itwiki' ~ 'Italian Wikipedia',  
      wiki == 'jawiki' ~ 'Japanese Wikipedia',   
      wiki == 'kowiki' ~ 'Korean Wikipedia',
      wiki == 'omwiki' ~ 'Oromo Wikipedia', 
      wiki == 'fawiki' ~ 'Persian Wikipedia',  
      wiki == 'plwiki' ~ 'Polish Wikipedia',   
      wiki == 'ptwiki' ~ 'Portuguese Wikipedia',
      wiki == 'eswiki' ~ 'Spanish Wikipedia',
      wiki == 'thwiki' ~ 'Thai Wikipedia',  
      wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',
      wiki == 'viwiki' ~ 'Vietnamese Wikipedia'    
    )
  ) 

# Create new column to identify Junior and Non-Junior Contributors

new_topic_attempts <- new_topic_attempts %>%
  mutate(
    is_junior = case_when(
      #clarfiy participating project names
      experience_level < 100 ~ "Junior Contributor",
      experience_level >=  100 ~ "Non-Junior Contributor"   
    ),
    is_junior = factor(is_junior,
         levels = c("Non-Junior Contributor", "Junior Contributor")
   ))


new_topic_attempts_bygroup <- new_topic_attempts %>%
   filter(is_oversample == 'false') %>% #All Discussion Tool events are oversampled - removing to check balance.
  group_by(experiment_group) %>%
  summarise(users = n_distinct(user_id),
        attempts = n_distinct(edit_attempt_id), .groups = 'drop')

new_topic_attempts_bygroup

# Completion Rate By Session

new_topic_attempts_jc_bysession <- new_topic_attempts %>%
    filter(is_junior == 'Junior Contributor')  %>%  #only jc 
    group_by (wiki, section_edit_type) %>%
    summarise(n_attempts = n_distinct(edit_attempt_id),
              n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),
              #n_completions = sum(edit_success == 'Complete'),
           new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),
    .groups = 'drop') 


# By session

new_topic_attempts_jc_bysession_all <- new_topic_attempts_jc_bysession %>%
    group_by(section_edit_type) %>%
    summarise(n_attempts = sum(n_attempts), 
              n_attempts_completed = sum(n_completions), 
              completion_rate = paste0(round(n_attempts_completed / n_attempts  *100, 1), "%"),
              .groups = 'drop'
  ) 
new_topic_attempts_jc_bysession_all

# Completion Rate By Junior Contributors
new_topic_attempts_jc <- new_topic_attempts %>%
    filter(is_junior == 'Junior Contributor')  %>%  #only jc 
    group_by (wiki, section_edit_type, user_id) %>%
    summarise(n_attempts = n_distinct(edit_attempt_id),
              n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),
              edit_success = ifelse(sum(n_completions >= 1), 'Complete', 'Not Complete'),  #redefine edit success as user completed at least 1 edit attempt
           new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),
    .groups = 'drop') 


# By Contributor
# Review edit completion rate by editing method
new_topic_attempts_jc_all <- new_topic_attempts_jc %>%
    group_by(section_edit_type) %>%
    summarise(n_users = n_distinct(user_id), 
              n_users_completed = sum(n_completions >= 1), #user completed at least 1 edit
              completion_rate = paste0(round(n_users_completed / n_users  *100, 1), "%"),
              .groups = 'drop'
  ) 

# Create table of completion rate
new_topic_attempts_jc_all_table <- new_topic_attempts_jc_all %>%
  gt() %>%
  tab_header(
    title = "Junior contributors new topic completion rate",
    subtitle = "across all participating Wikipedias"
  )  %>%
  cols_label(
    section_edit_type = "Editing method",
    n_users = "Number of users attempted",
    n_users_completed = "Number of users completed",
    completion_rate = "New topic completion rate"
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of contributors that attempted and published at least 1 new topic",
    locations = cells_column_labels(
      columns = 'completion_rate'
    )
  ) %>%
  tab_footnote(
    footnote = "Sampling rate for Non-New Topic Tool events is 6.25%",
    locations = cells_body(
      columns = 'section_edit_type', rows = 1)
    ) %>%
  tab_footnote(
    footnote = "Sampling rate for New Topic Tool events is 100%",
    locations = cells_body(
      columns = 'section_edit_type', rows = 2)) %>%
    gtsave(
    "new_topic_attempts_jc_all_table.html", inline_css = TRUE)

IRdisplay::display_html(data = new_topic_attempts_jc_all_table, file = "new_topic_attempts_jc_all_table.html")


p <- new_topic_attempts_jc_all  %>%
    ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = "white") +
    scale_y_continuous(labels = scales::percent) +
    scale_x_discrete(labels = c("Existing add new section link", "New topic tool")) +
    labs (y = "Percent of junior contributors ",
          x = "Editing method",
          title = "Junior contributors new topic completion rate \n across all participating Wikipedias",
         caption = "Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic")  +
    scale_fill_manual(values= c("#999999", "steelblue2")) +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position= "none",
        axis.line = element_line(colour = "black")) 
 

p
ggsave("Figures/new_topic_attempts_jc_all.png", p, width = 16, height = 8, units = "in", dpi = 300)


# Review edit attempts by editing method and wiki
new_topic_attempts_jc_bywiki <- new_topic_attempts_jc %>%
    group_by(wiki, section_edit_type) %>%
    summarise(n_users = n_distinct(user_id), 
              n_users_completed = sum(n_completions >=1), #user completed at least 1 edit
              completion_rate = paste0(round(n_users_completed / n_users  *100, 1), "%"),
             .groups = 'drop') %>%
     #determine credible intervals
    cbind(as.data.frame(binom:::binom.bayes(x = .$n_users_completed, n = .$n_users, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))
        


new_topic_attempts_jc_bywiki_tbl <- new_topic_attempts_jc_bywiki   %>%
  select(c(1,2,3,4,5,12,13))  %>% #remove unneeded rows
  gt() %>%
  tab_header(
    title = "Junior contributors new topic completion rate by participating Wikipedia"
  )  %>%
  cols_label(
    wiki = "Wikipedia",
    section_edit_type= "Editing method",
    n_users = "Number of users attempted",
    n_users_completed = "Number of users completed",
    completion_rate = "Completion rate",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic",
    locations = cells_column_labels(
      columns = 'completion_rate'
    )
  ) %>%
  tab_footnote(
    footnote = "Sampling rate for Non-New Topic Tool events is 6.25%",
     locations = cells_column_labels(
      columns = 'section_edit_type'
    ) 
    ) %>%
  tab_footnote(
    footnote = "Sampling rate for New Topic Tool events is 100%",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )) %>%
    gtsave(
    "new_topic_attempts_jc_bywiki_tbl .html", inline_css = TRUE)

IRdisplay::display_html(data = new_topic_attempts_jc_bywiki_tbl, file = "new_topic_attempts_jc_bywiki_tbl .html")

new_topic_attempts_jc_bywiki_tbl 

# Plot edit completion rates for each wiki  

dodge <- position_dodge(width=0.9)

p <- new_topic_attempts_jc_bywiki  %>%
    filter(!(wiki %in% c('Amharic Wikipedia', 'Egyptian Wikipedia', 'Thai Wikipedia'))) %>% # remove wikis where there are under 10 events as we 
    ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +
    geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 5, color = "white") +
    facet_wrap(~ wiki) +
    scale_y_continuous(labels = scales::percent) +
    labs (y = "Percent of junior contributors ",
          title = "Junior contributors new topic completion rate by participating Wikipedia",
         caption = "Amharic, Egyptian, Thai, and Oromo Wikipedias removed from analysis due to insufficient events \n
         Red error bars: Reflect 95% credible intervals")  +
    scale_fill_manual(values= c("#999999", "steelblue2"), name = "Editing Method", labels = c("Existing add new section link", "New topic tool"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 

      
p
ggsave("Figures/new_topic_attempts_jc_bywiki_plot.png", p, width = 16, height = 8, units = "in", dpi = 300)

#redefine edit success as factor for use in the model
new_topic_attempts_jc$edit_success <-
  factor(
    new_topic_attempts_jc$edit_success,
    levels = c("Not Complete", "Complete")
  )

priors <- c(
  set_prior(prior = "std_normal()", class = "b"),
  set_prior("cauchy(0, 5)", class = "sd")
)


fit_jc <- brm(
  edit_success ~ section_edit_type + (1 | wiki/user_id),
  family = bernoulli(link = "logit"),
  data = new_topic_attempts_jc,
  prior = priors,
  chains = 4, cores = 4
)

fit_jc_tbl <- fit_jc  %>%
  spread_draws(b_section_edit_typeNewtopictool, b_Intercept) %>%
  mutate(
    exp_b = exp(b_section_edit_typeNewtopictool),
    b4 = b_section_edit_typeNewtopictool/ 4,
    avg_lift =  plogis(b_Intercept + b_section_edit_typeNewtopictool) - plogis(b_Intercept)
  ) %>%
  pivot_longer(
    b_section_edit_typeNewtopictool:avg_lift,
    names_to = "param",
    values_to = "val"
  ) %>%
  group_by(param) %>%
  summarize(
    ps = c(0.025, 0.5, 0.975),
    qs = quantile(val, probs = ps),
    .groups = "drop"
  ) %>%
  mutate(
    quantity = ifelse(
      param %in% c("b_Intercept", "b_section_edit_typeNewtopictool"),
      "Parameter", "Function of parameter(s)"
    ),
    param = factor(
      param,
      c("b_Intercept", "b_section_edit_typeNewtopictool", "exp_b", "b4", "avg_lift"),
      c("(Intercept)", "Using new topic tool", "Multiplicative effect on odds", "Maximum Lift", "Average lift")
    ),
    ps = factor(ps, c(0.025, 0.5, 0.975), c("lower", "median", "upper")),
  ) %>%
  pivot_wider(names_from = "ps", values_from = "qs") %>%
  arrange(quantity, param)


fit_jc_tbl%>%
  gt(rowname_col = "param", groupname_col = "quantity") %>%
  row_group_order(c("Parameter", "Function of parameter(s)")) %>%
  fmt_number(vars(lower, median, upper), decimals = 3) %>%
  fmt_percent(columns = vars(median, lower, upper), rows = 2:3, decimals = 1) %>%
  cols_align("center", vars(median, lower, upper)) %>%
  cols_merge(vars(lower, upper), pattern = "({1}, {2})") %>%
  cols_move_to_end(vars(lower)) %>%
  cols_label(median = "Point Estimate", lower = "95% CI") %>%
  tab_style(cell_text(weight = "bold"), cells_row_groups()) %>%
  tab_footnote("CI: Credible Interval", cells_column_labels(vars(lower))) %>%
  tab_footnote(
    html("Average lift = Pr(Success|New Topic Tool) - Pr(Success|New Section Link Editing) = logit<sup>-1</sup>(&beta;<sub>0</sub> + &beta;<sub>1</sub>) - logit<sup>-1</sup>(&beta;<sub>0</sub>)"),
    cells_body(vars(median), 3)
  ) %>%
  tab_footnote(
    html("Maximum lift calculated using the divide-by-4-rule"),
    cells_body(vars(median), 2)
  ) %>%
  tab_header("Junior Contributor Completion Rate: Posterior summary of model parameters") %>%
      gtsave(
    "fit_jc_tbl.html", inline_css = TRUE)


IRdisplay::display_html(file = "fit_jc_tbl.html")

# Create histogram

exp_histogram <- new_topic_attempts %>%
    filter(experience_level < 5000)  %>%  #remove significant outliers to help more clearly see distribution of majority
    group_by(user_id)  %>%
    summarize(experience_level = min(experience_level), .groups = 'drop') %>% #experience level at first recorded attempt in AB test
    ggplot(aes(x=experience_level)) + 
    geom_histogram(binwidth = 100, fill = 'steelblue2') +
    scale_x_continuous(labels = scales::comma, breaks=seq(0,5000,500)) +
    labs (title = "Distribution of AB Test Users Edit Count ",
          y = "Number of users")   + 
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.line = element_line(colour = "black")) 

exp_histogram 

# divide experiene level groups
new_topic_attempts_exp <- new_topic_attempts %>%
    mutate(experience_group = cut(as.numeric(experience_level), 
                                         breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,
                                                    Inf), 
                                         labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',
                                                    'over 3000 edits'), include.lowest = TRUE))



#aggregate data to show contributors that completed at least 1 edit by experience level
new_topic_attempts_byexp <- new_topic_attempts_exp %>%
  group_by (wiki, section_edit_type, user_id, experience_group) %>%
  summarise(n_attempts = n_distinct(edit_attempt_id),
            n_completions = sum(edit_success == 'Complete'),
            edit_success = ifelse(sum(n_completions >= 1), 'Complete', 'Not Complete'),  #redefine edit success as user completed at least 1 edit
            new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),
    .groups = 'drop') 


 #convert edit success as factor for use in the model
new_topic_attempts_byexp$edit_success <- factor(
    new_topic_attempts_byexp$edit_success,
    levels = c("Not Complete", "Complete"),
    labels = c("Not Complete", "Complete")
  )

# Review edit completion rate by section edit type across all experience levels
new_topic_completes_allexp <- new_topic_attempts_byexp %>%
    group_by(section_edit_type) %>%
    summarise(n_users = n_distinct(user_id), 
              n_users_completed = sum(n_completions >= 1), #user completed at least 1 edit
              completion_rate = paste0(round(n_users_completed / n_users  *100, 1), "%"),
              .groups = 'drop'
  ) 

new_topic_completes_allexp_table <- new_topic_completes_allexp %>%
  gt() %>%
  tab_header(
    title = "Contributors new topic completion rate",
    subtitle = "Across all experience levels and participating Wikipedias"
  )  %>%
  cols_label(
    section_edit_type = "Editing method",
    n_users = "Number of users attempted",
    n_users_completed = "Number of users completed",
    completion_rate = "Completion rate"
  )   %>%
  tab_footnote(
    footnote = "Defined as percent of contributors that make a new topic attempt and publish at least 1 comment.",
    locations = cells_column_labels(
      columns = 'completion_rate'
    )
  ) %>%
  tab_footnote(
    footnote = "Sampling rate for Non-New Topic Tool events is 6.25%",
    locations = cells_body(
      columns = 'section_edit_type', rows = 1)
    ) %>%
  tab_footnote(
    footnote = "Sampling rate for New Topic Tool events is 100%",
    locations = cells_body(
      columns = 'section_edit_type', rows = 2)
    ) %>%
    gtsave(
    "new_topic_completes_allexp_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_completes_allexp_table.html")

# Plot edit completion rates across all wikis and experience levels 

p <- new_topic_completes_allexp %>%
    ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = "white") +
    scale_y_continuous(labels = scales::percent) +
    scale_x_discrete(labels = c("Existing add new section link", "New topic tool")) +
    labs (y = "Percent of contributors ",
          x = "Editing method",
          title = "Contributors comment completion rate \n across all experience levels and participating Wikipedias",
         caption = "Defined as percent of contributors that make a new topic attempt and publish at least 1 new topic")  +
    scale_fill_manual(values= c("#999999", "steelblue2")) +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position= "none",
        axis.line = element_line(colour = "black")) 


p
ggsave("Figures/new_topic_completes_allexp.png", p, width = 16, height = 8, units = "in", dpi = 300)

# Completion Rate By Session

new_topic_attempts_byexp_bysession <- new_topic_attempts_exp %>%
    group_by (experience_group, section_edit_type) %>%
    summarise(n_attempts = n_distinct(edit_attempt_id),
              n_completions = n_distinct(edit_attempt_id[edit_success == 'Complete']),
              completion_rate = paste0(round(n_completions / n_attempts  *100, 1), "%"),
           new_topic_tool_used = as.integer(ifelse(sum(section_edit_type== 'New topic tool'), 1, 0)),
    .groups = 'drop') 

new_topic_attempts_byexp_bysession

# Review edit completion rate by contributors and experience level
new_topic_completes_byexp <- new_topic_attempts_byexp %>%
    group_by(experience_group, section_edit_type) %>%
    summarise(n_users = n_distinct(user_id), 
              n_users_completed = n_distinct(user_id[n_completions >= 1]), #user completed at least 1 edit
              completion_rate = paste0(round(n_users_completed / n_users  *100, 1), "%"),
              .groups = 'drop'
  )   %>%  #determine credible intervals
    cbind(as.data.frame(binom:::binom.bayes(x = .$n_users_completed, n = .$n_users, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))


new_topic_completes_byexp_table <- new_topic_completes_byexp %>%
 select(c(1,2,3,4,5,12,13))  %>% #remove unneeded rows
  gt() %>%
  tab_header(
    title = "Contributors new topic completion rate by experience level",
    subtitle = "Across all participating Wikipedias"
  )  %>%
  cols_label(
    experience_group = "Experience level group",
    section_edit_type = "Editing experience",
    n_users = "Number of users attempted",
    n_users_completed = "Number of users completed",
    completion_rate = "Completion rate",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  )   %>%
  tab_footnote(
    footnote = "Defined as percent of contributors that made a new topic attempt and publish at least 1 comment.",
    locations = cells_column_labels(
      columns = 'completion_rate'
    )) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )) %>%
    gtsave(
    "new_topic_completes_byexp_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_completes_byexp_table.html")
new_topic_completes_byexp_table 

# Plot edit completion rates for each user on each wiki  

p <- new_topic_completes_byexp %>%
    ggplot(aes(x= section_edit_type, y = n_users_completed / n_users, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_text(aes(label = paste(completion_rate), fontface=2), vjust=1.2, size = 8, color = "white") +
    geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +
    facet_wrap(~ experience_group) +
    scale_y_continuous(labels = scales::percent) +
    labs (y = "Percent of contributors ",
          title = "Contributors new topic completion rate by experience level \n across all participating Wikipedias",
          caption = "Red error bars: 95% credible intervals"
          )+
      scale_fill_manual(values= c("#999999", "steelblue2"), name = "Editing Method", labels = c("Existing add new section link", "New topic tool"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 

p 
ggsave("Figures/new_topic_completes_byexp.png", p, width = 16, height = 8, units = "in", dpi = 300)



fit_all <- brm(
  edit_success ~ section_edit_type * is_junior + (1 | wiki/user_id), 
  family = bernoulli(link = "logit"),
  data = new_topic_attempts_byexp, 
  prior = priors,
  chains = 4, cores = 4
)

conditional_effects <- conditional_effects(fit_all, effects = "is_junior:section_edit_type")
p <-  plot(conditional_effects, plot = FALSE)[[1]] +
  labs (y = "Probability of new topic completion",
          title = "Effects of experience level and editor type \n on new topic completion probability")  +
    theme_bw() +
   scale_color_manual(values= c("#999999", "steelblue2"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.line = element_line(colour = "black")) 
p
ggsave("Figures/conditional_effects_exp_editor.png", p, width = 16, height = 8, units = "in", dpi = 300) 

new_topic_reverts <-
  read.csv(
    file = 'Data/new_topic_reverts.csv',
    header = TRUE,
    sep = ",",
    stringsAsFactors = FALSE
  ) # loads all revert data

#clarfiy levels and lables for factor variables
new_topic_reverts$section_edit_type <-
  factor(
    new_topic_reverts$section_edit_type,
    levels = c("non-new-topic-tool", "new-topic-tool"),
    labels = c("Existing add new section link", "New topic tool")
  )

new_topic_reverts$is_reverted <-
  factor(new_topic_reverts$is_reverted,
         levels = c("reverted", "not-reverted"),
         labels = c("Reverted", "Not reverted"))

#reformat user-id and adjust to include wiki to account for duplicate user id instances.
# Users do not have the smae user_id on different wikis
new_topic_reverts$user_id <-
  as.character(paste(new_topic_reverts$user_id,new_topic_reverts$wiki,sep ="-" ))

#clarfiy wiki names
new_topic_reverts <- new_topic_reverts %>%
 mutate(
    wiki = case_when(
      #clarfiy participating project names
      wiki == 'amwiki' ~ "Amharic Wikipedia",
      wiki == 'bnwiki' ~ "Bengali Wikipedia",
      wiki == 'zhwiki' ~ "Chinese Wikipedia",
      wiki == 'nlwiki' ~ 'Dutch Wikipedia',
      wiki == 'arzwiki' ~ 'Egyptian Wikipedia',
      wiki == 'frwiki' ~ 'French Wikipedia',
      wiki == 'hewiki' ~ 'Hebrew Wikipedia',
      wiki == 'hiwiki' ~ 'Hindi Wikipedia',
      wiki == 'idwiki' ~ 'Indonesian Wikipedia',
      wiki == 'itwiki' ~ 'Italian Wikipedia',  
      wiki == 'jawiki' ~ 'Japanese Wikipedia',   
      wiki == 'kowiki' ~ 'Korean Wikipedia',
      wiki == 'omwiki' ~ 'Oromo Wikipedia', 
      wiki == 'fawiki' ~ 'Persian Wikipedia',  
      wiki == 'plwiki' ~ 'Polish Wikipedia',   
      wiki == 'ptwiki' ~ 'Portuguese Wikipedia',
      wiki == 'eswiki' ~ 'Spanish Wikipedia',
      wiki == 'thwiki' ~ 'Thai Wikipedia',  
      wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',
      wiki == 'viwiki' ~ 'Vietnamese Wikipedia',    
    )
  ) 

# Create new column to identify Junior and Non-Junior Contributors

new_topic_reverts <- new_topic_reverts %>%
  mutate(
    is_junior = case_when(
      #clarfiy participating project names
      experience_level < 100 ~ "Junior Contributor",
      experience_level >=  100 ~ "Non-Junior Contributor"   
    ),
    is_junior = factor(is_junior,
         levels = c("Non-Junior Contributor", "Junior Contributor")
   ))


# filter date to only look at junior contributor edits reverted
new_topic_reverts_jc <- new_topic_reverts %>%
    filter(is_junior == 'Junior Contributor') 

# aggregrate based on editing experience type
new_topic_reverts_jc_all <- new_topic_reverts_jc %>%
    group_by(section_edit_type) %>%
    summarise(total_reverts = n_distinct(revision_id[is_reverted == "Reverted"]),
              total_comments = n_distinct(revision_id),
              revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop')  %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))

new_topic_reverts_jc_all_table <- new_topic_reverts_jc_all %>%
  select(c(1,2,3,4,11,12))  %>% #remove unneeded rows
  gt()  %>%
  tab_header(
    title = "Junior contributors new topic revert rate across all participating Wikipedias",
    subtitle = "Across all participating Wikipedias"
  )  %>%
  cols_label(
    section_edit_type = "Editing experience",
    total_reverts = "Number of new topics reverted",
    total_comments = "Number of new topics published",
    revert_rate = "Revert rate",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of new topics reverted within 48 hours.",
    locations = cells_column_labels(
      columns = 'revert_rate'
    )
  )  %>%
  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_reverts_jc_all_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_reverts_jc_all_table.html")



# Plot edit completion rates for each user on each wiki  

dodge <- position_dodge(width=0.9)

p <- new_topic_reverts_jc_all %>%
    ggplot(aes(x= section_edit_type, y = total_reverts/ total_comments, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', size = 1, alpha = 0.5, position = dodge, width = 0.25) +
    geom_text(aes(label = paste(revert_rate), fontface=2), vjust=1.2, size = 8, color = "white") +
    scale_y_continuous(labels = scales::percent) +
    scale_x_discrete(labels = c("Existing add new section link", "New topic tool")) +
    labs (y = "Percent of new topics reverted ",
          x = "Editing Method",
          title = "Junior contributors new topic revert rate across \n all participating Wikipedias",
         caption = "Revert rate defined as percent of published new topics reverted within 48 hours \n
            Red error bars: 95% credible intervals")  +
    scale_fill_manual(values= c("#999999", "steelblue2"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
         plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position= "none",
        axis.line = element_line(colour = "black"))
p


ggsave("Figures/new_topic_reverts_jc_all .png", p, width = 16, height = 8, units = "in", dpi = 300)

# aggregrate data by wiki and editing interface
new_topic_reverts_jc_bywiki <- new_topic_reverts_jc %>%
    group_by(wiki, section_edit_type) %>%
    summarise(total_reverts = n_distinct(revision_id[is_reverted == "Reverted"]),
              total_comments = n_distinct(revision_id),
              revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop') %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))

new_topic_reverts_jc_bywiki_table <- new_topic_reverts_jc_bywiki   %>%
 select(c(1,2,3,4,5,12,13))  %>% #remove unneeded rows
  gt()  %>%
  tab_header(
    title = "Junior Contributors new topic revert rate by participating Wikipedia"
  )  %>%
  cols_label(
    wiki = "Wikipedia",
    section_edit_type = "Editing method",
    total_reverts = "Number of new topics reverted",
    total_comments = "Number of new topics published",
    revert_rate = "Revert rate",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of new topics reverted within 48 hours.",
    locations = cells_column_labels(
      columns = 'revert_rate'
    )
  ) %>%
  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_reverts_jc_bywiki_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_reverts_jc_bywiki_table.html")

# divide experience level groups
new_topic_reverts_exp <- new_topic_reverts %>%
    mutate(experience_group = cut(as.numeric(experience_level), 
                                         breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,
                                                    Inf), 
                                         labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',
                                                    'over 3000 edits'), include.lowest = TRUE))



# aggregate data based on editor and experience level
new_topic_reverts_byexp <- new_topic_reverts_exp %>%
    group_by(experience_group, section_edit_type) %>%
    summarise(total_reverts = n_distinct(revision_id[is_reverted == "Reverted"]),
              total_comments = n_distinct(revision_id),
              revert_rate =paste(round(total_reverts/total_comments * 100, 2), '%'), .groups = 'drop')   %>%
    ungroup()  %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_reverts, n = .$total_comments, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))

new_topic_reverts_byexp_table <- new_topic_reverts_byexp %>%
  select(c(1,2,3,4,5,12,13))  %>% #remove unneeded rows
  gt()  %>%
  tab_header(
    title = "Contributors new topic revert rate by experience level"
  )  %>%
  cols_label(
    experience_group = "Experience level",
    section_edit_type = "Editing method",
    total_reverts = "Number of new topics reverted",
    total_comments = "Number of new topics published",
    revert_rate = "Revert rate",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of new topics reverted within 48 hours.",
    locations = cells_column_labels(
      columns = 'revert_rate'
    )
  )%>%
  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "Junior contributor defined as having under cumulative 100 edits. Non-Junior Contributor is defined as having over 100 cumulative edits",
    locations = cells_column_labels(
      columns = 'experience_group'
    )
  ) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_reverts_byexp_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_reverts_byexp_table.html")

# plot reverts by experience

p <- new_topic_reverts_byexp  %>%
  ggplot(aes(x= section_edit_type , y = total_reverts/total_comments, fill = section_edit_type)) +
  geom_col(position = 'dodge') +
  geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', alpha = 0.5, size = 1, position = dodge, width = 0.25) +
  geom_text(aes(label = paste(revert_rate),fontface=2), vjust=1.2, size = 8, color = "white") +
  scale_y_continuous(labels = scales::percent) +
  facet_wrap(~ experience_group, scale = "free_y") +
  labs (y = "Percent of new topics reverted",
        title = "Contributor new topic revert rate by experience level \n across all participating Wikipedias",
       caption = "Red error bars: 95% credible intervals")  +
    scale_fill_manual(values= c("#999999", "steelblue2"), name = "Editing Method", labels = c("Existing add new section link", "New topic tool"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 
p

ggsave("Figures/new_topic_reverts_byexp.png", p, width = 16, height = 8, units = "in", dpi = 300)

new_topic_blocks <-
  read.csv(
    file = 'Data/new_topic_blocks.csv',
    header = TRUE,
    sep = ",",
    stringsAsFactors = FALSE
  ) # loads all revert data

#clarfiy levels and lables for factor variables
new_topic_blocks$section_edit_type <-
  factor(
    new_topic_blocks$section_edit_type,
    levels = c("non-new-topic-tool", "new-topic-tool"),
    labels = c("Existing add new section link", "New topic tool")
  )

#clarfiy wiki names
new_topic_blocks<- new_topic_blocks%>%
   mutate(
    wiki = case_when(
      #clarfiy participating project names
      wiki == 'amwiki' ~ "Amharic Wikipedia",
      wiki == 'bnwiki' ~ "Bengali Wikipedia",
      wiki == 'zhwiki' ~ "Chinese Wikipedia",
      wiki == 'nlwiki' ~ 'Dutch Wikipedia',
      wiki == 'arzwiki' ~ 'Egyptian Wikipedia',
      wiki == 'frwiki' ~ 'French Wikipedia',
      wiki == 'hewiki' ~ 'Hebrew Wikipedia',
      wiki == 'hiwiki' ~ 'Hindi Wikipedia',
      wiki == 'idwiki' ~ 'Indonesian Wikipedia',
      wiki == 'itwiki' ~ 'Italian Wikipedia',  
      wiki == 'jawiki' ~ 'Japanese Wikipedia',   
      wiki == 'kowiki' ~ 'Korean Wikipedia',
      wiki == 'omwiki' ~ 'Oromo Wikipedia', 
      wiki == 'fawiki' ~ 'Persian Wikipedia',  
      wiki == 'plwiki' ~ 'Polish Wikipedia',   
      wiki == 'ptwiki' ~ 'Portuguese Wikipedia',
      wiki == 'eswiki' ~ 'Spanish Wikipedia',
      wiki == 'thwiki' ~ 'Thai Wikipedia',  
      wiki == 'ukwiki' ~ 'Ukrainian Wikipedia',
      wiki == 'viwiki' ~ 'Vietnamese Wikipedia',    
    )
  ) 

# Create new column to identify Junior and Non-Junior Contributors

new_topic_blocks <- new_topic_blocks %>%
  mutate(
    is_junior = case_when(
      #clarfiy participating project names
      experience_level < 100 ~ "Junior Contributor",
      experience_level >=  100 ~ "Non-Junior Contributor"   
    ),
    is_junior = factor(is_junior,
         levels = c("Non-Junior Contributor", "Junior Contributor")
   ))


# filter date to only look at junior contributors blocked
new_topic_blocks_jc <- new_topic_blocks %>%
    filter(is_junior == 'Junior Contributor') 

# blocks for JCs across all wikis
new_topic_blocks_jc_all <- new_topic_blocks_jc  %>%
    group_by(section_edit_type) %>%
    summarise(total_blocked_users = sum(blocked_user),
              total_users = sum(all_users),
              pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), "%"), 
              .groups = 'drop')  %>%
    ungroup()  %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))

new_topic_blocks_jc_all_table <- new_topic_blocks_jc_all  %>%
  select(1,2,3,4, 11, 12) %>%
  gt() %>%
  tab_header(
    title = "Junior contributors blocked after publishing a new topic "
  )  %>%
  cols_label(
    section_edit_type = "Editing method",
    total_blocked_users = "Number of users blocked",
    total_users = "Number of users that published a new topic",
    pct_blocked = "Percent of users blocked",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%
  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "Percent of junior contributors blocked after posting a new topic during the AB test",
    locations = cells_column_labels(
      columns = 'pct_blocked'
    )
  ) %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_blocks_jc_all_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_blocks_jc_all_table.html")


# blocked users by wiki
new_topic_blocks_jc_bywiki <- new_topic_blocks_jc %>%
    group_by(wiki, section_edit_type) %>%
    summarise(total_blocked_users = sum(blocked_user),
              total_users = sum(all_users),
              pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), "%"), 
              .groups = 'drop')  %>%
    ungroup()  %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))

new_topic_blocks_jc_bywiki_table <- new_topic_blocks_jc_bywiki  %>%
  select(1,2,3,4,5,12,13) %>%
  gt() %>%
  tab_header(
    title = "Junior Contributors blocked after publishing a new topic by participating Wikipedia "
  )  %>%
  cols_label(
    wiki = "Wikipedia",
    section_edit_type= "Editing method",
    total_blocked_users = "Number of users blocked",
    total_users = "Number of users that published a new topic",
    pct_blocked = "Percent of users blocked",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%

  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool events",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "Percent of junior contributors blocked after posting a comment during the AB test",
    locations = cells_column_labels(
      columns = 'pct_blocked'
    )
  )  %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_blocks_jc_bywiki_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_blocks_jc_bywiki_table.html")



# divide experience level groups
new_topic_blocks_exp <- new_topic_blocks %>%
    mutate(experience_group = cut(as.numeric(experience_level), 
                                         breaks = c(0, 100, 500, 1000, 1500, 2000, 2500, 3000,
                                                    Inf), 
                                         labels = c('0-100 edits', '101-500 edits', '501-1000 edits', '1001-1500 edits', '1501-2000 edits', '2001-2500 edits', '2501-3000 edits',
                                                    'over 3000 edits'), include.lowest = TRUE))


new_topic_blocks_byexp <- new_topic_blocks_exp %>%
    group_by(section_edit_type, experience_group) %>%
    summarise(total_blocked_users = sum(blocked_user),
              total_users = sum(all_users),
              pct_blocked = paste(round(total_blocked_users/total_users * 100, 2), "%")
              , .groups = 'drop')  %>%
    ungroup()  %>%
    cbind(as.data.frame(binom:::binom.bayes(x = .$total_blocked_users, n = .$total_users, conf.level = 0.95, tol = 1e-10))) %>%
    mutate(lower = round(lower,2), 
           upper = round(upper, 2))


new_topic_blocks_byexp_table <- new_topic_blocks_byexp  %>%
  select(1,2,3,4,5, 12,13)  %>%
  gt() %>%
  tab_header(
    title = "Contributors blocked after publishing a new topic by experience level",
  )   %>%
  cols_label(
    section_edit_type = "Editing method",
    experience_group = "Experience level",
    total_blocked_users = "Number of users blocked",
    total_users = "Number of users that saved a new topic",
    pct_blocked = "Percent of users blocked",
    lower = "CI (Lower Bound)",
    upper = "CI (Upper Bound)"
  ) %>%

  tab_footnote(
    footnote = "Sampling rate is 100% for new topic tool events and 6.25% for non-new topic tool event",
    locations = cells_column_labels(
      columns = 'section_edit_type'
    )
  ) %>%
  tab_footnote(
    footnote = "Percent of contributors blocked after posting a new topic during the AB test",
    locations = cells_column_labels(
      columns = 'pct_blocked'
    )
  )  %>%
  tab_footnote(
    footnote = "95% credible intervals. There is a 95% probability that the parameter lies in this interval",
    locations = cells_column_labels(
      columns = c('lower', 'upper')
    )
  ) %>%
    gtsave(
    "new_topic_blocks_byexp_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "new_topic_blocks_byexp_table.html")


# plot proportion of blocks by experience levels

p <- new_topic_blocks_byexp  %>%
  ggplot(aes(x= section_edit_type, y = total_blocked_users/total_users, fill = section_edit_type,)) +
  geom_col(position = 'dodge') +
  geom_errorbar(aes(ymin = lower, ymax = upper), color = 'red', alpha = 0.5, size = 1, position = dodge, width = 0.25) +
  geom_text(aes(label = paste(pct_blocked), fontface=2), vjust=1.2, size = 8, color = "white") +
  scale_y_continuous(labels = scales::percent) +
  facet_wrap(~ experience_group, scale = "free_y") +
  labs (y = "Percent of blocked users",
        x = "Editing experience",
        title = "Contributors blocked after posting a comment by experience level",
       caption = "Red error bars: 95% credible intervals")  +
  scale_fill_manual(values= c("#999999", "steelblue2"), name = "Editing Method", labels = c("Existing add new section link", "New topic tool"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 

p

ggsave("Figures/new_topic_blocks_byexp.png", p, width = 16, height = 8, units = "in", dpi = 300)


num_jc_editors <- new_topic_attempts %>%
    filter(is_junior == 'Junior Contributor',
          is_oversample == 'false' ) %>% #remove oversampled events
    group_by(experiment_group, section_edit_type)  %>%
    summarise(total_users_attempt = n_distinct(user_id),
              total_users_complete = n_distinct(user_id[edit_success == 'Complete']), .groups = 'drop') 

num_jc_editors_table <- num_jc_editors %>%
  gt() %>%
  tab_header(
    title = "Number of Junior Contributors that made a new topic attempt during the AB test by test group and section edit type"
  )  %>%
  cols_label(
    experiment_group = "Test group",
    section_edit_type = "Editing method",
    total_users_attempt = "Number of users that attempted a new topic",
    total_users_complete = "Number of users that published a new topic"
  ) %>% 
  tab_row_group(
    rows = experiment_group == 'control'
  ) %>%
tab_row_group(
    rows = experiment_group == 'test'
  ) %>%
  tab_footnote(
    footnote = "Based on a sampling rate of 6.25% for all events. Any oversampled events were removed so data for the two editor types could be directly compared",
    locations = cells_title(
      )
  ) %>%
    gtsave(
    "num_jc_editors_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "num_jc_editors_table.html")



p <-num_jc_editors %>%
  group_by(experiment_group) %>%
  summarise(total_user_complete = sum(total_users_complete), .groups = 'drop') %>%
  ggplot(aes(x= experiment_group, y = total_user_complete, fill = experiment_group)) +
  geom_col(position = 'dodge') +
  geom_text(aes(label = paste(total_user_complete),fontface=2), vjust=1.2, size = 8, color = "white") +
  labs (y = "Number of Junior Contributors",
        x = "Experiment group",
        title = "Number of Junior Contributors that completed a new topic by test group \n across all participating Wikipedias")  +
  scale_fill_manual(values= c("#999999", "steelblue2"), name = "Test Group", labels = c("Control", "Test"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 

p

ggsave("Figures/num_jc_editors_bygroup.png", p, width = 16, height = 8, units = "in", dpi = 300)

query <-
"
WITH first_edits AS (
-- users that made an attempt during the AB Test
    SELECT
        event_user_text as user_name,
        wiki_db AS wiki,
        min(event_timestamp) as first_edit_time,
        CASE
        WHEN min(event_user_revision_count) is NULL THEN 'undefined'
        WHEN min(event_user_revision_count) < 100 THEN 'junior'
        ELSE 'non-junior'
        END AS experience_level,
        IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type
    FROM wmf.mediawiki_history mwh
    JOIN event.mediawiki_talk_page_edit tpe
ON  mwh.revision_id = tpe.revision_id
AND mwh.wiki_db = tpe.`database`
    WHERE
          snapshot = '2022-03'
        AND event_timestamp >= '2022-01-27' 
        AND event_timestamp <= '2022-03-04'
        AND ((month = 01 and day >= 27) OR (month = 02) OR
         (month = 03 and day <= 25))
        AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
        'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
         'ukwiki', 'viwiki')
    -- do not include new discussion tool talk page edits
        AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
        -- first edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
    GROUP BY event_user_text,
    IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),
    wiki_db
)
 
SELECT
    first_edits.experience_level,
    first_edits.section_edit_type,
    (count(first_week.user_name)/count(*)) as first_week_retention_rate
FROM first_edits
LEFT JOIN
(
    SELECT event_user_text as user_name,
    first_edits.first_edit_time,
    min(event_timestamp) as return_time
    FROM wmf.mediawiki_history mh
    INNER JOIN first_edits
    ON mh.event_user_text = first_edits.user_name
    WHERE
        snapshot = '2022-03'
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
         -- on all participating wikis
        AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',
    'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',
    'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')
    -- return edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
        AND first_edits.first_edit_time >=  '2022-01-27' 
        AND first_edits.first_edit_time  <= '2022-03-04'
        -- second revision is between two and 8 days
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (2*24*60*60)) 
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (8*24*60*60))
    GROUP BY event_user_text, 
        first_edits.first_edit_time 
) AS first_week
ON 
(first_edits.user_name = first_week.user_name and
first_edits.first_edit_time = first_week.first_edit_time 
)
GROUP BY
    first_edits.experience_level,
    first_edits.section_edit_type;
"

week_one_retention <-  wmfdata::query_hive(query)

# find week 2 retention

query <-
"
WITH first_edits AS (
-- users that made an attempt during the AB Test
    SELECT
        event_user_text as user_name,
        wiki_db AS wiki,
        min(event_timestamp) as first_edit_time,
        CASE
        WHEN min(event_user_revision_count) is NULL THEN 'undefined'
        WHEN min(event_user_revision_count) < 100 THEN 'junior'
        ELSE 'non-junior'
        END AS experience_level,
        IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type
    FROM wmf.mediawiki_history mwh
    JOIN event.mediawiki_talk_page_edit tpe
ON  mwh.revision_id = tpe.revision_id
AND mwh.wiki_db = tpe.`database`
    WHERE
          snapshot = '2022-03'
        AND event_timestamp >= '2022-01-27' 
        AND event_timestamp <= '2022-03-04'
        AND ((month = 01 and day >= 27) OR (month = 02) OR
         (month = 03 and day <= 25))
        AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
        'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
         'ukwiki', 'viwiki')
    -- do not include new discussion tool talk page edits
        AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
        -- first edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
    GROUP BY event_user_text,
    IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),
    wiki_db
)
 
SELECT
    first_edits.experience_level,
    first_edits.section_edit_type,
    (count(first_week.user_name)/count(*)) as two_week_retention_rate
FROM first_edits
LEFT JOIN
(
    SELECT event_user_text as user_name,
    first_edits.first_edit_time,
    min(event_timestamp) as return_time
    FROM wmf.mediawiki_history mh
    INNER JOIN first_edits
    ON mh.event_user_text = first_edits.user_name
    WHERE
        snapshot = '2022-03'
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
         -- on all participating wikis
        AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',
    'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',
    'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')
    -- return edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
        AND first_edits.first_edit_time >=  '2022-01-27' 
        AND first_edits.first_edit_time  <= '2022-03-04'
        -- second revision is between two and 8 days
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (9*24*60*60)) 
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (15*24*60*60))
    GROUP BY event_user_text, 
        first_edits.first_edit_time 
) AS first_week
ON 
(first_edits.user_name = first_week.user_name and
first_edits.first_edit_time = first_week.first_edit_time 
)
GROUP BY
    first_edits.experience_level,
    first_edits.section_edit_type;
"

week_two_retention <-  wmfdata::query_hive(query)

week_two_retention

# find week 3 retention

query <-
"
WITH first_edits AS (
-- users that made an attempt during the AB Test
    SELECT
        event_user_text as user_name,
        wiki_db AS wiki,
        min(event_timestamp) as first_edit_time,
        CASE
        WHEN min(event_user_revision_count) is NULL THEN 'undefined'
        WHEN min(event_user_revision_count) < 100 THEN 'junior'
        ELSE 'non-junior'
        END AS experience_level,
        IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool') AS section_edit_type
    FROM wmf.mediawiki_history mwh
    JOIN event.mediawiki_talk_page_edit tpe
ON  mwh.revision_id = tpe.revision_id
AND mwh.wiki_db = tpe.`database`
    WHERE
          snapshot = '2022-03'
        AND event_timestamp >= '2022-01-27' 
        AND event_timestamp <= '2022-03-04'
        AND ((month = 01 and day >= 27) OR (month = 02) OR
         (month = 03 and day <= 25))
        AND wiki_db IN ('amwiki', 'bnwiki', 'zhwiki', 'nlwiki', 'arzwiki', 'frwiki', 'hewiki', 'hiwiki',
        'idwiki', 'itwiki', 'jawiki', 'kowiki', 'omwiki', 'fawiki', 'plwiki', 'ptwiki', 'eswiki', 'thwiki',
         'ukwiki', 'viwiki')
    -- do not include new discussion tool talk page edits
        AND NOT (ARRAY_CONTAINS(revision_tags, 'discussiontools-reply'))
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
        -- first edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
    GROUP BY event_user_text,
    IF(ARRAY_CONTAINS(revision_tags, 'discussiontools-newtopic'), 'new-topic-tool', 'non-newtopic-tool'),
    wiki_db
)
 
SELECT
    first_edits.experience_level,
    first_edits.section_edit_type,
    (count(first_week.user_name)/count(*)) as third_week_retention_rate
FROM first_edits
LEFT JOIN
(
    SELECT event_user_text as user_name,
    first_edits.first_edit_time,
    min(event_timestamp) as return_time
    FROM wmf.mediawiki_history mh
    INNER JOIN first_edits
    ON mh.event_user_text = first_edits.user_name
    WHERE
        snapshot = '2022-03'
    -- include only desktop edits
        AND NOT array_contains(revision_tags, 'iOS')
        AND NOT array_contains(revision_tags, 'Android')
        AND NOT array_contains(revision_tags, 'Mobile Web')
     -- find all edits on talk pages
        AND page_namespace_historical % 2 = 1
        AND event_entity = 'revision'
        AND event_type = 'create'
         -- on all participating wikis
        AND wiki_db IN ('frwiki', 'eswiki', 'itwiki', 'jawiki', 'fawiki', 'plwiki', 'hewiki', 'nlwiki',
    'hiwiki', 'kowiki', 'viwiki', 'thwiki', 'ptwiki', 'bnwiki', 'arzwiki', 'swwiki', 'zhwiki',
    'ukwiki', 'idwiki', 'amwiki', 'omwiki', 'afwiki')
    -- return edit not reverted within 48 hours
        AND NOT (revision_is_identity_reverted AND 
            revision_seconds_to_identity_revert <= 172800)  -- 48 hours
    -- user is not a bot and not anonymous
         AND SIZE(event_user_is_bot_by_historical) = 0 
         AND SIZE(event_user_is_bot_by) = 0
        AND event_user_is_anonymous = FALSE
        AND first_edits.first_edit_time >=  '2022-01-27' 
        AND first_edits.first_edit_time  <= '2022-03-04'
        -- second revision is between two and 8 days
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') >=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (16*24*60*60)) 
        AND unix_timestamp(event_timestamp, 'yyyy-MM-dd HH:mm:ss.0') <=
            (unix_timestamp(first_edits.first_edit_time, 'yyyy-MM-dd HH:mm:ss.0') + (22*24*60*60))
    GROUP BY event_user_text, 
        first_edits.first_edit_time 
) AS first_week
ON 
(first_edits.user_name = first_week.user_name and
first_edits.first_edit_time = first_week.first_edit_time 
)
GROUP BY
    first_edits.experience_level,
    first_edits.section_edit_type;
"

week_three_retention <-  wmfdata::query_hive(query)

# Join all the data

retention_rates_all <- merge(week_one_retention, week_two_retention) %>%
              merge(week_three_retention)


#clarify levels and labels for factor variables
retention_rates_all$section_edit_type <-
  factor(
    retention_rates_all$section_edit_type,
    levels = c("non-newtopic-tool", "new-topic-tool"),
    labels = c("Previous add new section link", "New topic tool")
  )

retention_rates_all$experience_level <-
  factor(retention_rates_all$experience_level,
         levels = c( "non-junior", "junior"),
    labels = c("Non-Junior Contributor", "Junior Contributor")
  )

# Restrict to just Junior Contributors
retention_rates_jc_table <- retention_rates_all %>%
    filter(experience_level == 'Junior Contributor') %>%
    group_by(section_edit_type) %>%
   summarise(week_1 = paste(round((first_week_retention_rate * 100), 2), "%"),
          week_2= paste(round((two_week_retention_rate * 100), 2), "%"),
            week_3 = paste(round((third_week_retention_rate * 100), 2), "%"))  %>%
  gt() %>%
  tab_header(
    title = "Junior contributors retention rate"
  )  %>%
  cols_label(
    section_edit_type = "Editing method",
    week_1 = "Week 1 (2-8 days)",
    week_2 = "Week 2 (9-15 days)",
    week_3 = "Week 3 (16-22 days)",  
  ) %>%
  tab_footnote(
    footnote = "Defined as percent of Junior contributors that completed a new topic edit during the AB test and returned to make another edit",
    locations = cells_title(
    )
  )   %>%
  tab_footnote(
    footnote = "Defined as days since first new topic edit during the AB test.",
    locations = cells_column_labels(
      columns = c('week_1', 'week_2', 'week_3'))
    )   %>%
    gtsave(
    "retention_rates_jc_table.html", inline_css = TRUE)

IRdisplay::display_html(file = "retention_rates_jc_table.html")

# Plot retention rates 
p <- retention_rates_all %>%
    filter(experience_level == 'Junior Contributor') %>%
    gather("week", "retention_rate", 3:5) %>%
    mutate(week = factor(week,
                         levels = c("first_week_retention_rate", "two_week_retention_rate", "third_week_retention_rate"),
                         labels = c("week 1", "week 2", "week_3")))  %>%
    ggplot(aes(x= section_edit_type, y = retention_rate*100, fill = section_edit_type)) +
    geom_col(position = 'dodge') +
    geom_text(aes(label = paste(round(retention_rate*100, 2), "%"), fontface=2), vjust=1.2, size = 8, color = "white") +
    facet_wrap(~ week) +
    scale_y_continuous() +
    labs (y = "Percent of Junior Contributors ",
          title = "Junior contributors new topic tool retention rate",
         caption = "Defined as the percent of Junior contributors that made at least one new topic edit \n during the time of the AB test and returned to made another edit on a talk namespace.")  +
    theme_bw() +
   scale_fill_manual(values= c("#999999", "#000099"), name = "Editing Method", labels = c("Previous add new section link", "New topic tool"))  +
    theme(
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        plot.title = element_text(hjust = 0.5),
        text = element_text(size=16),
        legend.position="bottom",
        axis.text.x = element_blank(),
        axis.title.x=element_blank(),
        axis.line = element_line(colour = "black")) 

p
ggsave("Figures/jc_retention_rate.png", p, width = 16, height = 8, units = "in", dpi = 300)