---
title: "Newcomers Plots"
---
```{r}
library(here)
source(here("code/survival.R"))
plots <- NULL
plots$survival <- plot_km
```
```{r}
#| label: fig-newcomer-timeline
account_timeline_plot <- function() {
jm <- arrow::read_feather(here("data/newcomers/scratch/joinmastodon.feather"))
moved_to <- arrow::read_feather(here("data/newcomers/scratch/individual_moved_accounts.feather"))
accounts_unfilt <- arrow::read_feather(
here("data/newcomers/scratch/all_accounts.feather"),
col_select=c(
"server", "username", "created_at", "last_status_at",
"statuses_count", "has_moved", "bot", "suspended",
"following_count", "followers_count", "locked",
"noindex", "group", "discoverable"
))
accounts <- accounts_unfilt %>%
filter(!bot) %>%
# TODO: what's going on here?
filter(!is.na(last_status_at)) %>%
mutate(suspended = replace_na(suspended, FALSE)) %>%
# sanity check
filter(created_at >= "2020-10-01") %>%
#filter(created_at < "2024-01-01") %>%
filter(created_at < "2023-08-15") %>%
# We don't want accounts that were created and then immediately stopped being active
filter(statuses_count >= 1) %>%
filter(last_status_at >= created_at) %>%
mutate(active = last_status_at >= "2024-01-01") %>%
mutate(last_status_at = ifelse(active, lubridate::ymd_hms("2024-01-01 00:00:00", tz = "UTC"), last_status_at)) %>%
mutate(active_time = difftime(last_status_at, created_at, units="days")) #%>%
#filter(!has_moved)
acc_data <- accounts %>%
#filter(!has_moved) %>%
mutate(created_month = format(created_at, "%Y-%m")) %>%
mutate(created_week = floor_date(created_at, unit = "week")) %>%
mutate(active_now = active) %>%
mutate(active = active_time >= 91) %>%
mutate("Is mastodon.social" = server == "mastodon.social") %>%
mutate(jm = server %in% jm$domain) %>%
group_by(created_week) %>%
summarize(
`JoinMastodon Server` = sum(jm) / n(),
`Is mastodon.social` = sum(`Is mastodon.social`)/n(),
Suspended = sum(suspended)/n(),
Active = (sum(active)-sum(has_moved)-sum(suspended))/(n()-sum(has_moved)-sum(suspended)),
active_now = (sum(active_now)-sum(has_moved)-sum(suspended))/(n()-sum(has_moved)-sum(suspended)),
Moved=sum(has_moved)/n(),
count=n()) %>%
pivot_longer(cols=c("JoinMastodon Server", "Active", "Moved", "Is mastodon.social"), names_to="Measure", values_to="value") # "Suspended"
p1 <- acc_data %>%
ggplot(aes(x=as.Date(created_week), group=1)) +
geom_line(aes(y=value, group=Measure, color=Measure)) +
geom_point(aes(y=value, color=Measure), size=0.7) +
scale_y_continuous(limits = c(0, 1.0)) +
labs(y="Proportion") + scale_x_date(labels=date_format("%Y-%U"), breaks = "8 week") +
theme_bw_small_labels() +
theme(axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())
p2 <- acc_data %>%
distinct(created_week, count) %>%
ggplot(aes(x=as.Date(created_week), y=count)) +
geom_bar(stat="identity", fill="black") +
geom_vline(
aes(xintercept = as.numeric(as.Date("2022-10-27"))),
linetype="dashed", color = "black") +
geom_vline(
aes(xintercept = as.numeric(as.Date("2022-04-14"))),
linetype="dashed", color = "black") +
# https://twitter.com/elonmusk/status/1675187969420828672
geom_vline(
aes(xintercept = as.numeric(as.Date("2022-12-15"))),
linetype="dashed", color = "black") +
geom_vline(
aes(xintercept = as.numeric(as.Date("2023-07-01"))),
linetype="dashed", color = "black") +
#scale_y_continuous(limits = c(0, max(acc_data$count) + 100000)) +
scale_y_continuous(labels = scales::comma) +
labs(y="Count", x="Created Week") +
theme_bw_small_labels() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_x_date(labels=date_format("%Y-%U"), breaks = "8 week")
return(p1 + p2 + plot_layout(ncol = 1, guides = "collect"))
}
plots$timeline <- account_timeline_plot() |>
print()
```
```{r}
#| label: tbl-ergm
#| tbl-cap: ERGM model output
library(ergm)
library(tinytable)
library(modelsummary)
load(file = here("data/newcomers/scratch/ergm-model-early.rda"))
load(file = here("data/newcomers/scratch/ergm-model-late.rda"))
plots$ergm.table <- modelsummary::modelsummary(
list("Coef." = model.early, "Std.Error" = model.early, "Coef." = model.late, "Std.Error" = model.late),
estimate = c("{estimate}", "{stars}{std.error}", "{estimate}", "{stars}{std.error}"),
statistic = NULL,
gof_omit = ".*",
coef_rename = c(
"sum" = "(Sum)",
"diff.sum0.h-t.accounts" = "Smaller server",
"nodeocov.sum.accounts" = "Server size (outgoing)",
"nodeifactor.sum.registrations.TRUE" = "Open registrations (incoming)",
"nodematch.sum.language" = "Languages match"
),
align="lrrrr",
stars = c('*' = .05, '**' = 0.01, '***' = .001),
) %>%
tinytable::group_tt(j = list("Model A" = 2:3, "Model B" = 4:5))
plots$ergm.table
```
```{r}
#| label: tbl-coxme
#| fig-cap: "Cox Proportional Hazard Model with Mixed Effects. The model includes a random effect for the server."
library(ehahelper)
library(broom)
cxme_table <- tidy(cxme) %>%
mutate(conf.low = exp(conf.low), conf.high=exp(conf.high)) %>%
mutate(term = case_when(
term == "factor(group)1" ~ "Join Mastodon",
term == "factor(group)2" ~ "General Servers",
term == "small_serverTRUE" ~ "Small Server",
TRUE ~ term
)) %>%
#mutate(exp.coef = paste("(", round(conf.low, 2), ", ", round(conf.high, 2), ")", sep="")) %>%
select(term, estimate, conf.low, conf.high, p.value) |>
rename("Estimate" = "estimate", "Low" = "conf.low", "High" = "conf.high", "p-value" = "p.value", "Term" = "term")
plots$coxme.table <- cxme_table |>
tinytable::tt() |>
tinytable::style_tt(align = "ldddd") |>
tinytable::format_tt(i = 1:3, j = 2:4, digits = 3, num_fmt = "significant", num_suffix = F) |>
tinytable::format_tt(i = 1:3, j = 5, digits = 1, num_fmt = "significant", num_suffix = F) |>
print()
```
```{r}
#| label: save-data
saveRDS(plots, here::here("data/processed/newcomers_plots.rds"))
```
