119 lines
4.4 KiB
R
119 lines
4.4 KiB
R
library(tidyverse)
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library(dplyr)
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library(lubridate)
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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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core_csv <-"~/121325_work/121225_vd_data/core_2010-01-01_to_2024-12-31.csv"
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core_df <- read.csv(core_csv, header = TRUE)
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core_df <- core_df |> mutate(repo = "core")
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ve_csv <-"~/121325_work/121225_vd_data/extension_VisualEditor_2000-01-01_to_2016-12-31.csv"
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ve_df <- read.csv(ve_csv, header = TRUE)
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ve_df <- ve_df |> mutate(repo = "ve")
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joint_df <- rbind(core_df, ve_df)
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top_four_emails <- ve_df %>%
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filter(commit_date >= as.Date("2012-11-11") & commit_date <= as.Date("2013-09-29") ) |>
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group_by(author_email) %>%
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summarise(email_count = n(), .groups = "drop") %>%
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arrange(desc(email_count)) %>%
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slice_head(n = 7)
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known_affil_emails <- c("krinkle@fastmail.com", "roan.kattouw@gmail.com",
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"trevorparscal@gmail.com", "krinklemail@gmail.com", "moriel@gmail.com")
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active_names<- c("Timo Tijhof", "Krinkle", "Roan Kattouw", "Catrope",
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"Trevor Parscal", "Ed Sanders", "Moriel Schottlender", "Gabriel Wicke", "C. Scott Ananian")
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#jforrester@wikimedia.org
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#(author_name %in% active_names) ~ "ActiveEmails",
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joint_df <- joint_df |>
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mutate(commit_date = ymd_hms(commit_date)) |>
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mutate(isAuthorWMF = case_when(
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(author_email %in% known_affil_emails) ~ "TRUE",
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grepl("l10n-bot@translatewiki\\.net", author_email, ignore.case = TRUE) ~ "localization",
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grepl("@wikimedia\\.org", author_email, ignore.case = TRUE) ~ "TRUE",
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grepl("@wikimedia\\.de", author_email, ignore.case = TRUE) ~ "TRUE",
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grepl("@gerrit\\.wikimedia\\.org", author_email, ignore.case = TRUE) ~ "Gerrit",
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TRUE ~ "FALSE"
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))
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library(tidyr)
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authors_in_both <- joint_df |>
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group_by(author_email, repo) |>
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summarise(commit_count = n(), .groups = "drop") |>
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pivot_wider(names_from = repo, values_from = commit_count, values_fill = 0) |>
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filter(core > 0 & ve > 0) |>
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pull(author_email)
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joint_df_filtered <- joint_df |>
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filter(author_email %in% authors_in_both)
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c1_event_date <- as.Date("2013-07-01")
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c1_weekly <- joint_df |>
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mutate(week_index = relative_week(commit_date, c1_event_date)) |>
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group_by(week_index, isAuthorWMF, repo)|>
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summarise(count = n(), .groups = 'drop')|>
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filter(week_index >= -33 & week_index <= 13) |>
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mutate(source = 'c1')
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counts <- c1_weekly |>
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filter(isAuthorWMF != "Gerrit")|>
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filter(isAuthorWMF != "localization")|>
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filter(source == 'c1')
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#counts <- c1_core_weekly |>
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# filter(isAuthorWMF == "ActiveEmails") |>
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# filter(source == 'c1')
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commits_created <-
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ggplot(
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counts,
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aes(
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x=week_index,
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y=count,
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fill=isAuthorWMF,
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)
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) +
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facet_grid(repo ~ .,
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labeller = labeller(repo = c("ve" = "extensions/visualeditor",
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"core" = "mediawiki/core"))) +
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geom_col(position = position_dodge(width = 0.9), width = 0.8) +
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geom_vline(data = counts |> filter(source == "c1"),
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aes(xintercept = -29),
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linetype = "dotted", color = "black", linewidth = 0.5) +
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geom_vline(data = counts |> filter(source == "c1"),
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aes(xintercept = -9),
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linetype = "dotted", color = "black", linewidth = 0.5) +
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geom_vline(data = counts |> filter(source == "c1"),
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aes(xintercept = -4),
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linetype = "3313", color = "black", linewidth = 0.5) +
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geom_vline(xintercept = 0, linetype = "dashed", color = "black", linewidth = 0.5) +
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geom_text(
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data = subset(counts, repo == "core" & week_index == 4),
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aes(x=week_index, y=75, label='Opt-out deployment'),
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size = 3) +
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geom_text(
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data = subset(counts, repo == "core" & week_index == -27),
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aes(x=week_index, y=75, label='Opt-in Testing'),
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size = 3) +
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geom_text(
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data = subset(counts, repo == "core" & week_index == -8),
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aes(x=week_index, y=75, label='Deployment Announcement'),
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size = 3) +
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theme_minimal() +
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scale_fill_viridis_d(
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breaks = c("FALSE", "TRUE", "BzImport"),
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labels = c("External Contributor", "WMF-affiliate", "BzImport")
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) +
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labs(
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x = "Weeks from VisualEditor Deployment on Wikipedia",
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y = "Count of New Commits",
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fill = "Commit Author"
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) +
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theme(legend.position = "top")
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commits_created
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ggsave(
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filename = "011925_ve_commits_created.png",
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plot = commits_created,
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width = 8, # inches
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height = 4, # inches
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dpi = 800 # high resolution
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)
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