216 lines
7.3 KiB
R
216 lines
7.3 KiB
R
library(tidyverse)
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library(dplyr)
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library(tidyr)
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dsl_csv <-"~/dsl/121625_DSL_frame.csv"
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dsl_df <- read.csv(dsl_csv, header = TRUE)
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#4.1
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weekly_summary <- dsl_df |>
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group_by(week_index, source, isAuthorWMF)|>
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summarise(
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tasks_made = sum(!is.na(resolution_outcome)),
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count_resolution_outcome = sum(dsl_score),
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author_closer_sum = sum(author_closer == TRUE),
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median_olmo_EP_prop_adac = median(olmo_EP_prop_adac),
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median_olmo_TSOL_prop_adac = median(olmo_TSOL_prop_adac),
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median_olmo_RK_prop_adac = median(olmo_RK_prop_adac),
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median_comments_before_resolution = median(n_comments_before)
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) |>
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mutate(isAuthorWMF = factor(isAuthorWMF, levels = c("FALSE", "BzImport", "TRUE")))
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tasks_created <- ggplot(
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weekly_summary,
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aes(
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x=week_index,
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y=tasks_made,
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fill=isAuthorWMF
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)
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) +
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facet_grid(source ~ .,
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scales = "free_y",
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labeller = labeller(source = c("c1" = "VisualEditor",
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"c2" = "HTTPS-login",
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"c3" = "HTTP-deprecation"))) +
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geom_col(position = position_dodge(width = 0.9), width = 0.8) +
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geom_vline(data = weekly_summary |> 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 = weekly_summary |> 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 = weekly_summary |> 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(data = weekly_summary |> filter(source == "c2"),
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aes(xintercept = -99),
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linetype = "dotted", color = "black", linewidth = 0.5) +
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geom_vline(data = weekly_summary |> filter(source == "c2"),
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aes(xintercept = -4),
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linetype = "3313", color = "black", linewidth = 0.5) +
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geom_vline(data = weekly_summary |> filter(source == "c3"),
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aes(xintercept = -97),
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linetype = "dotted", color = "black", linewidth = 0.5) +
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geom_vline(data = weekly_summary |> filter(source == "c3"),
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aes(xintercept = -3),
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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(weekly_summary, source == "c1" & week_index ==10),
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aes(x=week_index, y=120, label='Opt-out deployment'),
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size = 3) +
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geom_text(
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data = subset(weekly_summary, source == "c1" & week_index == -21),
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aes(x=week_index, y=120, label='Opt-in Testing'),
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size = 3) +
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geom_text(
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data = subset(weekly_summary, source == "c2" & week_index == -18),
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aes(x=week_index, y=20, 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("Nonaffiliate", "WMF-affiliate", "BzImport")
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) +
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labs(
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x = "Weeks from Feature Deployment",
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y = "Count of Tasks Created",
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fill = "Task Author"
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) +
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theme(legend.position = "top")
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tasks_created
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ggsave(
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filename = "011025_tasks_created.png",
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plot = tasks_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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#4.2 plot comparing the TTR for different things
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ttr_trajectory <- dsl_df |>
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mutate(ttr_weeks = TTR_hours / 168) |>
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mutate(isTriaged = if_else(priority == 'Needs Triage',
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"Not Triaged",
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"Triaged")) |>
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group_by(week_index, isTriaged, source) |>
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summarise(
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count = n(),
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mean_ttr = mean(ttr_weeks, na.rm = TRUE),
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sd_ttr = sd(ttr_weeks, na.rm = TRUE)
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)
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ttr_trajectory_plot <- ttr_trajectory |>
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filter(week_index >= -13) |>
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filter(isTriaged == "Not Triaged") |>
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ggplot(aes(x = week_index)) +
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# Line for mean TTR
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geom_line(aes(y = mean_ttr, color = "Mean TTR"), linewidth = 1) +
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# Ribbon for standard deviation
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geom_ribbon(aes(ymin = mean_ttr - sd_ttr, ymax = mean_ttr + sd_ttr),
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fill = "lightblue", alpha = 0.4) +
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# Line for count of tasks
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geom_point(aes(y = count,
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color = "Count of New Tasks"), linewidth = 1, linetype = "dashed") +
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# Facet the plot by source and triaged status
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facet_wrap(source ~ isTriaged, scales = "free_y") +
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labs(
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title = "TTR by Source and Triage Status (TODO)",
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x = "Week Index",
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y = "Mean TTR (in weeks)",
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color = "Metrics"
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) +
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scale_color_manual(values = c("Mean TTR" = "blue", "Count of New Tasks" = "red")) +
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theme_minimal()
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ttr_trajectory_plot
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ttr_boxplot <- dsl_df |>
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filter(priority == "Needs Triage" |
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priority == "Unbreak Now!" |
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priority == "High") |>
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filter(week_index >= -13) |>
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ggplot(
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aes(
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x=as.factor(week_index),
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y= TTR_hours/168,
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color=priority,
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)
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) +
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facet_grid(source ~ .,
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scales = "free_y",
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labeller = labeller(source = c("c1" = "VisualEditor",
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"c2" = "HTTPS-login",
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"c3" = "HTTP-deprecation"))) +
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geom_boxplot(outlier.shape = NA) +
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theme_minimal() +
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coord_cartesian(ylim = c(0, 112)) +
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geom_text(
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data = subset(dsl_df |>
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filter(priority == "Needs Triage" |
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priority == "Unbreak Now!" |
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priority == "High"), source == "c1" & week_index == 12),
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aes(x=week_index, y=80, label='Opt-in Testing'),
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color = "black",
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size = 3) +
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geom_vline(xintercept =14, linetype = "dashed", color = "black", linewidth = 0.5) +
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scale_color_viridis_d(option='turbo') +
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labs(x = "Weeks from Release",
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y = "Time to Resolution (weeks)",
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color = "Priority Tag") +
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theme(legend.position = "top")
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ttr_boxplot
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ggsave(
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filename = "011025_ttr_boxplot.png",
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plot = ttr_boxplot,
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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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#4.3 plot comparing machine labels of information type
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dsl_df <- dsl_df |>
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filter(isAuthorWMF != "BzImport")
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dsl_df_long <- dsl_df %>%
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pivot_longer(
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cols = c(olmo_EP_prop_adac, olmo_RK_prop_adac, olmo_TSOL_prop_adac),
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names_to = "tag",
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values_to = "proportion"
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) %>%
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mutate(tag = gsub("olmo_|_prop_adac", "", tag),
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tag = case_when(
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tag == "EP" ~ "Existent Problem",
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tag == "RK" ~ "Record Keeping",
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tag =="TSOL" ~ "Solutions"
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))
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olmo_comparison <- ggplot(
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dsl_df_long,
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aes(
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x = tag,
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y = proportion,
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fill = isAuthorWMF,
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)
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) +
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facet_grid(source ~ .,
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scales = "free_y",
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labeller = labeller(source = c("c1" = "VisualEditor",
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"c2" = "HTTPS-login",
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"c3" = "HTTP-deprecation"))) +
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geom_boxplot() +
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theme_minimal() +
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scale_fill_viridis_d() +
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labs(
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x = "Issue Information Type Category",
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y = "% of sentences machine-labeled",
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color = "Is Author WMF?",
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fill = "Is Author WMF?"
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) +
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theme(legend.position = "top")
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olmo_comparison
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ggsave(
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filename = "011025_machine_label_comparison.png",
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plot = olmo_comparison,
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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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