99 lines
3.3 KiB
R
99 lines
3.3 KiB
R
library(here)
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library(survival)
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library(tidyverse)
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library(ggsurvfit)
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library(coxme)
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library(jsonlite)
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source(here("codebase/R/helpers.R"))
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options(arrow.skip_nul = TRUE)
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active_period <- 91
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a <- load_accounts() %>%
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filter(!has_moved) %>%
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filter(locked == FALSE) %>%
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anti_join(., arrow::read_feather(here("data/scratch/individual_moved_accounts.feather")), by=c("username"="moved_acct", "server"="moved_server")) %>%
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inner_join(arrow::read_feather(here("data/scratch/metadata.feather")), by="server") %>%
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filter(created_at > "2023-04-30") %>%
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filter(created_at <= "2023-06-30") %>%
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filter(created_at < last_status_at) %>%
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mutate(jm = server %in% as_tibble(jsonlite::fromJSON(here("data/joinmastodon-2023-08-25.json")))$domain) %>%
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mutate(active_time = difftime(last_status_at, created_at, units="days")) %>%
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mutate(active_time_censored = ifelse(active_time > active_period, active_period, active_time)) %>%
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#mutate(active_time = difftime(last_status_at, created_at, units="days")) %>%
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#0=alive, 1=dead.
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mutate(status = ifelse(active_time > active_period, 0, 1))# %>% filter(followers_count > 0) %>% filter(following_count > 0)
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a %>% select(status, created_at, last_status_at, active_time_censored)
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activity <- arrow::read_feather(
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here("data/scratch/activity.feather"),
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col_select = c("server", "logins")
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) %>%
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arrange(desc(logins))
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server_summary <- a %>%
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group_by(server) %>%
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summarize(cohort_size = n(), .groups = "drop")
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general_servers <- c(
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"mastodon.social",
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"mstdn.social",
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"mastodon.world",
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"mas.to",
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"mastodon.online",
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"mastodonapp.uk",
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"universeodon.com",
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"masto.ai",
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"c.im",
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"social.vivaldi.net",
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"mstdn.party",
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"ohai.social"
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) # > 100 cohort size + strength > .75
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server_centrality <- arrow::read_ipc_file(here("data/scratch/server_centrality.feather")) %>%
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rename(generality = strength)
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sel_a <- a %>%
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mutate(is_ms = server == "mastodon.social") %>%
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mutate(general = server %in% general_servers) %>%
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ungroup() %>%
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inner_join(server_summary, by = "server") %>%
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left_join(server_centrality, by = "server") %>%
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mutate(small_server = user_count <= 100) %>%
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mutate(large_server = user_count >= 1000) %>%
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#filter(jm) %>%
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mutate(group = as.integer(jm) + as.integer(general)) %>%
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#mutate(huge_server = user_count >= 3162.28) %>%
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#filter(statuses_count >= 10) %>%
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filter(!noindex)# %>% filter(cohort_size >= 2) #%>% filter(user_count > 100)
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cx <- sel_a %>% coxph(Surv(active_time_censored, status) ~ general, data = ., x=TRUE, robust = T, cluster=server)
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cxme <- sel_a %>% coxme(Surv(active_time_censored, status) ~ factor(group) + small_server + (1|server), data = .)
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cz <- cox.zph(cx)
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plot_km <- sel_a %>%
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mutate(id = paste(username, server, sep = "@")) %>%
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survfit2(
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Surv(active_time_censored, status) ~ general,
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data = ., id = id,
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cluster = server,
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robust = TRUE
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) %>%
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ggsurvfit() +
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add_confidence_interval() +
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labs(
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y = "Overall survival probability",
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x = "Time (days)",
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) +
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scale_fill_discrete(name = "Group", labels = c("Other JoinMastodon", "General")) +
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scale_color_discrete(name = "Group", labels = c("Other JoinMastodon", "General")) +
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theme_bw_small_labels() +
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theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="bottom")
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# logistic regression on generality
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logit <- sel_a %>%
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glm(active ~ generality, data = ., family = "binomial") |