Tidy Active User Workflows

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

library(sensortowerR)
library(dplyr)
library(tidyr)

Overview

st_active_users() is a focused, tidy wrapper that returns DAU/WAU/MAU in long format, built on the same underlying machinery as st_metrics().

This makes downstream analysis intuitive with standard tidyverse verbs.

Basic Pipeline

active_users <- st_active_users(
  os = "unified",
  app_list = c("5ba4585f539ce75b97db6bcb", "67ec0bf3e540b65904256cc4"),
  metrics = c("dau", "mau"),
  date_range = list(start_date = "2025-01-01", end_date = "2025-12-31"),
  countries = c("US", "GB"),
  granularity = "monthly"
)

active_summary <- active_users %>%
  group_by(original_id, app_name, metric, date, country) %>%
  summarise(total_active_users = sum(value, na.rm = TRUE), .groups = "drop")

Wide Reporting Table

active_wide <- active_users %>%
  group_by(original_id, app_name, date, country, metric) %>%
  summarise(value = sum(value, na.rm = TRUE), .groups = "drop") %>%
  pivot_wider(names_from = metric, values_from = value)

Join With Sales Data

sales <- st_metrics(
  app_id      = c("5ba4585f539ce75b97db6bcb", "67ec0bf3e540b65904256cc4"),
  os          = "unified",
  metrics     = c("revenue", "downloads"),
  countries   = c("US", "GB"),
  date_from   = "2025-01-01",
  date_to     = "2025-12-31",
  granularity = "monthly"
)

sales_wide <- sales %>%
  group_by(app_id, date, country, metric) %>%
  summarise(value = sum(value, na.rm = TRUE), .groups = "drop") %>%
  pivot_wider(names_from = metric, values_from = value)

combined <- active_wide %>%
  left_join(sales_wide, by = c("original_id" = "app_id", "date", "country"))

App-Level KPI Aggregation

app_kpis <- combined %>%
  mutate(
    rev_per_dau = if_else(!is.na(dau) & dau > 0, revenue / dau, NA_real_),
    rev_per_mau = if_else(!is.na(mau) & mau > 0, revenue / mau, NA_real_)
  ) %>%
  group_by(original_id, app_name, country) %>%
  summarise(
    avg_dau = mean(dau, na.rm = TRUE),
    avg_mau = mean(mau, na.rm = TRUE),
    total_revenue = sum(revenue, na.rm = TRUE),
    avg_rev_per_dau = mean(rev_per_dau, na.rm = TRUE),
    avg_rev_per_mau = mean(rev_per_mau, na.rm = TRUE),
    .groups = "drop"
  )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.