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Tibble pipelines

Discovery returns explicit identifiers. Pass that table to metadata or metrics; the package handles batching and preserves your columns.

st_publishers("Supercell") |>
  st_publisher_apps() |>
  mutate(cohort = "Supercell portfolio") |>
  st_metrics(date_from = "2026-01-01", date_to = "2026-03-31",
             countries = "US", granularity = "monthly")

Character IDs require os. Tables require app_id and os. Both native pipes and magrittr pipes work. To change ID namespaces, use explicit provider mappings:

st_apps("Clash of Clans") |>
  st_app(target_os = "ios") |>
  st_demographics(date_from = "2026-04-01", date_to = "2026-06-30")

There is no name-based merge. All mapped regional store IDs remain visible. The input_app_id and input_os columns trace each mapping to its input.

Empty data stays typed and needs no token:

tibble(app_id = character(), os = character(), cohort = character()) |>
  st_metrics(date_from = "2026-01-01", date_to = "2026-01-31")
#> # A tibble: 0 × 9
#> # ℹ 9 variables: app_id <chr>, os <chr>, cohort <chr>, country <chr>,
#> #   date <date>, metric <chr>, value <dbl>, unit <chr>, period <chr>

Long metrics have explicit units and periods. DAU, WAU and MAU use native windows; granularity changes sales aggregation only. Missing values are not zero. A failed endpoint aborts unless you explicitly request errors = "partial". In partial mode, inspect status, error and endpoint before analysis.

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.