SensorTowerR

SensorTowerR is an R client for Sensor Tower's API with a tidyverse-first workflow: search apps and publishers, resolve IDs, fetch sales and active-user metrics, and chain everything with dplyr/tidyr.

Installation

remotes::install_github("econosopher/SensorTowerR")

Authentication

Store your Sensor Tower API token as an environment variable:

usethis::edit_r_environ()
# SENSORTOWER_AUTH_TOKEN="YOUR_SECRET_TOKEN_HERE"

Restart your R session after updating .Renviron.

Tidyverse Quick Start

library(SensorTowerR)
library(dplyr)
library(tidyr)

# 1) Find the app once, keep a canonical ID
app <- st_app_info("Royal Match") %>%
  slice(1)

app_ids <- st_app_lookup(app$unified_app_id)

# 2) Pull active users (long format, ready for tidy pipelines)
active <- st_active_users(
  os = "unified",
  app_list = app$unified_app_id,
  metrics = c("dau", "mau"),
  date_range = list(start_date = "2025-01-01", end_date = "2025-12-31"),
  countries = "US",
  granularity = "monthly"
)

# 3) Pull sales for the same window
sales <- st_batch_metrics(
  os = "unified",
  app_list = app$unified_app_id,
  metrics = c("revenue", "downloads"),
  date_range = list(start_date = "2025-01-01", end_date = "2025-12-31"),
  countries = "US",
  granularity = "monthly"
)

# 4) Join and compute KPIs
active_wide <- active %>%
  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)

sales_wide <- sales %>%
  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)

kpis <- active_wide %>%
  left_join(sales_wide, by = c("original_id", "app_name", "date", "country")) %>%
  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_)
  )

Endpoint and ID Management

The package is designed so users can think in terms of analysis, not raw endpoints.

If you need...	Use...	Behavior
DAU/WAU/MAU for one or many apps	st_active_users() or st_batch_metrics()	Handles active-user retrieval and returns tidy long metrics for pipelines
Revenue/downloads for platform-specific reporting	st_sales_report()	Use os = "ios" or os = "android"
Revenue/downloads where IDs may vary (unified, iOS, Android)	st_metrics()	Resolves IDs and fetches by the requested os
Cross-app workflows with mixed metrics	st_batch_metrics()	Batches active users + sales and standardizes shape
Market rankings	st_top_charts(), st_top_publishers()	Top-and-trending style analysis by region/category

Important behavior:

• st_sales_report() is platform-specific and does not accept os = "unified".
• st_metrics() is the easiest way to request unified-style sales/download workflows.
• Authentication and endpoint path construction are centralized internally, so API-facing functions now behave more consistently.

App and Publisher Workflows

App-first (recommended for specific apps)

app <- st_app_info("Candy Crush Saga") %>%
  slice(1)

ids <- st_app_lookup(app$unified_app_id)

ios_sales <- st_sales_report(
  os = "ios",
  ios_app_id = ids$ios_app_id,
  countries = "US",
  start_date = "2025-01-01",
  end_date = "2025-01-31",
  date_granularity = "daily"
)

Publisher portfolio in one call

portfolio <- st_publisher_portfolio(
  publisher = "Supercell",
  metrics = c("revenue", "downloads", "mau"),
  start_date = "2023-01-01",
  countries = "WW"
)

Top charts + dashboard

top_games <- st_top_charts(
  measure = "revenue",
  os = "unified",
  category = 6014,
  regions = "US",
  time_range = "month"
)

st_gt_dashboard(top_games, title = "Top Games - US")

Core Functions

Function	Purpose
st_app_info()	Search unified apps or publishers by term
st_app_lookup()	Resolve iOS/Android/unified IDs from any input ID
st_metrics()	Flexible single-app metric retrieval with ID resolution
st_sales_report()	Platform-specific sales/download estimates
st_unified_sales_report()	Unified aggregation across related regional SKUs
st_active_users()	Tidy DAU/WAU/MAU wrapper
st_batch_metrics()	Batch retrieval across apps and metrics
st_publisher_portfolio()	End-to-end publisher analysis
st_top_charts()	Top app rankings by metric
st_top_publishers()	Publisher rankings
st_category_rankings()	Official app store ranking pulls
st_gt_dashboard()	Formatted dashboard-style output

Data Notes

Data Type	Primary Function	Time Series	Coverage
Revenue/downloads (platform)	st_sales_report()	Yes	Country-level
Revenue/downloads (unified aggregation)	st_unified_sales_report()	Yes	Country-level
Revenue/downloads (ID-flexible)	st_metrics()	Yes	Country-level
DAU/WAU/MAU	st_active_users(), st_batch_metrics()	Yes	Country-level
Retention and demographics snapshots	st_app_enriched()	Snapshot	Primarily US/WW depending on metric

ID Cache Utilities

st_cache_info()
save_id_cache()
load_id_cache()
st_clear_id_cache()

Cache location is CRAN-compliant via tools::R_user_dir("SensorTowerR", "cache").

Learn More

• Vignette: vignette("tidy-active-users", package = "SensorTowerR")
• Changelog: NEWS.md

License

MIT (LICENSE file).